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string itself (properly escaping the `\*` sign), for example: `city.\\*:something`: [source,js] -------------------------------------------------- GET /\_search { "query": { "query\_string" : { "query" : "city.\\\*:(this AND that OR thus)" } } } -------------------------------------------------- // CONSOLE NOTE: Since `\` (backslash) is a special character in json strings, it needs to be escaped, hence the two backslashes in the above `query\_string`. When running the `query\_string` query against multiple fields, the following additional parameters are allowed: [cols="<,<",options="header",] |======================================================================= |Parameter |Description |`type` |How the fields should be combined to build the text query. See <> for a complete example. Defaults to `best\_fields` |======================================================================= [cols="<,<",options="header",] |======================================================================= |Parameter |Description |`tie\_breaker` |The disjunction max tie breaker for multi fields. Defaults to `0` |======================================================================= The fields parameter can also include pattern based field names, allowing to automatically expand to the relevant fields (dynamically introduced fields included). For example: [source,js] -------------------------------------------------- GET /\_search { "query": { "query\_string" : { "fields" : ["content", "name.\*^5"], "query" : "this AND that OR thus" } } } -------------------------------------------------- // CONSOLE [float] ==== Synonyms The `query\_string` query supports multi-terms synonym expansion with the <> token filter. When this filter is used, the parser creates a phrase query for each multi-terms synonyms. For example, the following synonym: `"ny, new york" would produce:` `(ny OR ("new york"))` It is also possible to match multi terms synonyms with conjunctions instead: [source,js] -------------------------------------------------- GET /\_search { "query": { "query\_string" : { "default\_field": "title", "query" : "ny city", "auto\_generate\_synonyms\_phrase\_query" : false } } } -------------------------------------------------- // CONSOLE The example above creates a boolean query: `(ny OR (new AND york)) city)` that matches documents with the term `ny` or the conjunction `new AND york`. By default the parameter `auto\_generate\_synonyms\_phrase\_query` is set to `true`. include::query-string-syntax.asciidoc[]
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/query-string-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.025949
|
[[query-dsl-span-containing-query]] === Span Containing Query Returns matches which enclose another span query. The span containing query maps to Lucene `SpanContainingQuery`. Here is an example: [source,js] -------------------------------------------------- GET /\_search { "query": { "span\_containing" : { "little" : { "span\_term" : { "field1" : "foo" } }, "big" : { "span\_near" : { "clauses" : [ { "span\_term" : { "field1" : "bar" } }, { "span\_term" : { "field1" : "baz" } } ], "slop" : 5, "in\_order" : true } } } } } -------------------------------------------------- // CONSOLE The `big` and `little` clauses can be any span type query. Matching spans from `big` that contain matches from `little` are returned.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/span-containing-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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|
[[query-dsl-simple-query-string-query]] === Simple Query String Query A query that uses the SimpleQueryParser to parse its context. Unlike the regular `query\_string` query, the `simple\_query\_string` query will never throw an exception, and discards invalid parts of the query. Here is an example: [source,js] -------------------------------------------------- GET /\_search { "query": { "simple\_query\_string" : { "query": "\"fried eggs\" +(eggplant | potato) -frittata", "fields": ["title^5", "body"], "default\_operator": "and" } } } -------------------------------------------------- // CONSOLE The `simple\_query\_string` top level parameters include: [cols="<,<",options="header",] |======================================================================= |Parameter |Description |`query` |The actual query to be parsed. See below for syntax. |`fields` |The fields to perform the parsed query against. Defaults to the `index.query.default\_field` index settings, which in turn defaults to `\*`. `\*` extracts all fields in the mapping that are eligible to term queries and filters the metadata fields. |`default\_operator` |The default operator used if no explicit operator is specified. For example, with a default operator of `OR`, the query `capital of Hungary` is translated to `capital OR of OR Hungary`, and with default operator of `AND`, the same query is translated to `capital AND of AND Hungary`. The default value is `OR`. |`analyzer` |Force the analyzer to use to analyze each term of the query when creating composite queries. |`flags` |Flags specifying which features of the `simple\_query\_string` to enable. Defaults to `ALL`. |`analyze\_wildcard` | Whether terms of prefix queries should be automatically analyzed or not. If `true` a best effort will be made to analyze the prefix. However, some analyzers will be not able to provide a meaningful results based just on the prefix of a term. Defaults to `false`. |`lenient` | If set to `true` will cause format based failures (like providing text to a numeric field) to be ignored. |`minimum\_should\_match` | The minimum number of clauses that must match for a document to be returned. See the <> documentation for the full list of options. |`quote\_field\_suffix` | A suffix to append to fields for quoted parts of the query string. This allows to use a field that has a different analysis chain for exact matching. Look <> for a comprehensive example. |`auto\_generate\_synonyms\_phrase\_query` |Whether phrase queries should be automatically generated for multi terms synonyms. Defaults to `true`. |`all\_fields` | deprecated[6.0.0, set `default\_field` to `\*` instead] Perform the query on all fields detected in the mapping that can be queried. Will be used by default when the `\_all` field is disabled and no `default\_field` is specified (either in the index settings or in the request body) and no `fields` are specified. |`fuzzy\_prefix\_length` |Set the prefix length for fuzzy queries. Default is `0`. |`fuzzy\_max\_expansions` |Controls the number of terms fuzzy queries will expand to. Defaults to `50` |`fuzzy\_transpositions` |Set to `false` to disable fuzzy transpositions (`ab` -> `ba`). Default is `true`. |======================================================================= [float] ===== Simple Query String Syntax The `simple\_query\_string` supports the following special characters: \* `+` signifies AND operation \* `|` signifies OR operation \* `-` negates a single token \* `"` wraps a number of tokens to signify a phrase for searching \* `\*` at the end of a term signifies a prefix query \* `(` and `)` signify precedence \* `~N` after a word signifies edit distance (fuzziness) \* `~N` after a phrase signifies slop amount In order to search for any of these special characters, they will need to be escaped with `\`. Be aware that this syntax may have a different behavior depending on the `default\_operator` value. For example, consider the following query: [source,js] -------------------------------------------------- GET /\_search { "query": { "simple\_query\_string" : { "fields" : ["content"], "query" : "foo bar -baz" } } } -------------------------------------------------- // CONSOLE You may expect that documents containing only "foo" or
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/simple-query-string-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | -0.035044
|
syntax may have a different behavior depending on the `default\_operator` value. For example, consider the following query: [source,js] -------------------------------------------------- GET /\_search { "query": { "simple\_query\_string" : { "fields" : ["content"], "query" : "foo bar -baz" } } } -------------------------------------------------- // CONSOLE You may expect that documents containing only "foo" or "bar" will be returned, as long as they do not contain "baz", however, due to the `default\_operator` being OR, this really means "match documents that contain "foo" or documents that contain "bar", or documents that don't contain "baz". If this is unintended then the query can be switched to `"foo bar +-baz"` which will not return documents that contain "baz". [float] ==== Default Field When not explicitly specifying the field to search on in the query string syntax, the `index.query.default\_field` will be used to derive which fields to search on. It defaults to `\*` and the query will automatically attempt to determine the existing fields in the index's mapping that are queryable, and perform the search on those fields. [float] ==== Multi Field The fields parameter can also include pattern based field names, allowing to automatically expand to the relevant fields (dynamically introduced fields included). For example: [source,js] -------------------------------------------------- GET /\_search { "query": { "simple\_query\_string" : { "fields" : ["content", "name.\*^5"], "query" : "foo bar baz" } } } -------------------------------------------------- // CONSOLE [float] ==== Flags `simple\_query\_string` support multiple flags to specify which parsing features should be enabled. It is specified as a `|`-delimited string with the `flags` parameter: [source,js] -------------------------------------------------- GET /\_search { "query": { "simple\_query\_string" : { "query" : "foo | bar + baz\*", "flags" : "OR|AND|PREFIX" } } } -------------------------------------------------- // CONSOLE The available flags are: `ALL`, `NONE`, `AND`, `OR`, `NOT`, `PREFIX`, `PHRASE`, `PRECEDENCE`, `ESCAPE`, `WHITESPACE`, `FUZZY`, `NEAR`, and `SLOP`. [float] ==== Synonyms The `simple\_query\_string` query supports multi-terms synonym expansion with the <> token filter. When this filter is used, the parser creates a phrase query for each multi-terms synonyms. For example, the following synonym: `"ny, new york" would produce:` `(ny OR ("new york"))` It is also possible to match multi terms synonyms with conjunctions instead: [source,js] -------------------------------------------------- GET /\_search { "query": { "simple\_query\_string" : { "query" : "ny city", "auto\_generate\_synonyms\_phrase\_query" : false } } } -------------------------------------------------- // CONSOLE The example above creates a boolean query: `(ny OR (new AND york)) city)` that matches documents with the term `ny` or the conjunction `new AND york`. By default the parameter `auto\_generate\_synonyms\_phrase\_query` is set to `true`.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/simple-query-string-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.015283
|
[[query-dsl-match-query-phrase]] === Match Phrase Query The `match\_phrase` query analyzes the text and creates a `phrase` query out of the analyzed text. For example: [source,js] -------------------------------------------------- GET /\_search { "query": { "match\_phrase" : { "message" : "this is a test" } } } -------------------------------------------------- // CONSOLE A phrase query matches terms up to a configurable `slop` (which defaults to 0) in any order. Transposed terms have a slop of 2. The `analyzer` can be set to control which analyzer will perform the analysis process on the text. It defaults to the field explicit mapping definition, or the default search analyzer, for example: [source,js] -------------------------------------------------- GET /\_search { "query": { "match\_phrase" : { "message" : { "query" : "this is a test", "analyzer" : "my\_analyzer" } } } } -------------------------------------------------- // CONSOLE
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/match-phrase-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.146147
|
[[query-dsl-wrapper-query]] === Wrapper Query A query that accepts any other query as base64 encoded string. [source,js] -------------------------------------------------- GET /\_search { "query" : { "wrapper": { "query" : "eyJ0ZXJtIiA6IHsgInVzZXIiIDogIktpbWNoeSIgfX0=" <1> } } } -------------------------------------------------- // CONSOLE <1> Base64 encoded string: `{"term" : { "user" : "Kimchy" }}` This query is more useful in the context of the Java high-level REST client or transport client to also accept queries as json formatted string. In these cases queries can be specified as a json or yaml formatted string or as a query builder (which is a available in the Java high-level REST client).
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/wrapper-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.048084
|
[[query-dsl-terms-set-query]] === Terms Set Query experimental[The terms\_set query is a new query and its syntax may change in the future] Returns any documents that match with at least one or more of the provided terms. The terms are not analyzed and thus must match exactly. The number of terms that must match varies per document and is either controlled by a minimum should match field or computed per document in a minimum should match script. The field that controls the number of required terms that must match must be a number field: [source,js] -------------------------------------------------- PUT /my-index { "mappings": { "\_doc": { "properties": { "required\_matches": { "type": "long" } } } } } PUT /my-index/\_doc/1?refresh { "codes": ["ghi", "jkl"], "required\_matches": 2 } PUT /my-index/\_doc/2?refresh { "codes": ["def", "ghi"], "required\_matches": 2 } -------------------------------------------------- // CONSOLE // TESTSETUP An example that uses the minimum should match field: [source,js] -------------------------------------------------- GET /my-index/\_search { "query": { "terms\_set": { "codes" : { "terms" : ["abc", "def", "ghi"], "minimum\_should\_match\_field": "required\_matches" } } } } -------------------------------------------------- // CONSOLE Response: [source,js] -------------------------------------------------- { "took": 13, "timed\_out": false, "\_shards": { "total": 5, "successful": 5, "skipped" : 0, "failed": 0 }, "hits": { "total": 1, "max\_score": 0.5753642, "hits": [ { "\_index": "my-index", "\_type": "\_doc", "\_id": "2", "\_score": 0.5753642, "\_source": { "codes": ["def", "ghi"], "required\_matches": 2 } } ] } } -------------------------------------------------- // TESTRESPONSE[s/"took": 13,/"took": "$body.took",/] Scripts can also be used to control how many terms are required to match in a more dynamic way. For example a create date or a popularity field can be used as basis for the number of required terms to match. Also the `params.num\_terms` parameter is available in the script to indicate the number of terms that have been specified. An example that always limits the number of required terms to match to never become larger than the number of terms specified: [source,js] -------------------------------------------------- GET /my-index/\_search { "query": { "terms\_set": { "codes" : { "terms" : ["abc", "def", "ghi"], "minimum\_should\_match\_script": { "source": "Math.min(params.num\_terms, doc['required\_matches'].value)" } } } } } -------------------------------------------------- // CONSOLE
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/terms-set-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.059735
|
[[geo-queries]] == Geo queries Elasticsearch supports two types of geo data: <> fields which support lat/lon pairs, and <> fields, which support points, lines, circles, polygons, multi-polygons etc. The queries in this group are: <> query:: Find document with geo-shapes which either intersect, are contained by, or do not intersect with the specified geo-shape. <> query:: Finds documents with geo-points that fall into the specified rectangle. <> query:: Finds document with geo-points within the specified distance of a central point. <> query:: Find documents with geo-points within the specified polygon. include::geo-shape-query.asciidoc[] include::geo-bounding-box-query.asciidoc[] include::geo-distance-query.asciidoc[] include::geo-polygon-query.asciidoc[]
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/geo-queries.asciidoc
|
6.2
|
elasticsearch-6-2
|
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|
[[query-dsl-minimum-should-match]] == Minimum Should Match The `minimum\_should\_match` parameter possible values: [cols="<,<,<",options="header",] |======================================================================= |Type |Example |Description |Integer |`3` |Indicates a fixed value regardless of the number of optional clauses. |Negative integer |`-2` |Indicates that the total number of optional clauses, minus this number should be mandatory. |Percentage |`75%` |Indicates that this percent of the total number of optional clauses are necessary. The number computed from the percentage is rounded down and used as the minimum. |Negative percentage |`-25%` |Indicates that this percent of the total number of optional clauses can be missing. The number computed from the percentage is rounded down, before being subtracted from the total to determine the minimum. |Combination |`3<90%` |A positive integer, followed by the less-than symbol, followed by any of the previously mentioned specifiers is a conditional specification. It indicates that if the number of optional clauses is equal to (or less than) the integer, they are all required, but if it's greater than the integer, the specification applies. In this example: if there are 1 to 3 clauses they are all required, but for 4 or more clauses only 90% are required. |Multiple combinations |`2<-25% 9<-3` |Multiple conditional specifications can be separated by spaces, each one only being valid for numbers greater than the one before it. In this example: if there are 1 or 2 clauses both are required, if there are 3-9 clauses all but 25% are required, and if there are more than 9 clauses, all but three are required. |======================================================================= \*NOTE:\* When dealing with percentages, negative values can be used to get different behavior in edge cases. 75% and -25% mean the same thing when dealing with 4 clauses, but when dealing with 5 clauses 75% means 3 are required, but -25% means 4 are required. If the calculations based on the specification determine that no optional clauses are needed, the usual rules about BooleanQueries still apply at search time (a BooleanQuery containing no required clauses must still match at least one optional clause) No matter what number the calculation arrives at, a value greater than the number of optional clauses, or a value less than 1 will never be used. (ie: no matter how low or how high the result of the calculation result is, the minimum number of required matches will never be lower than 1 or greater than the number of clauses.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/minimum-should-match.asciidoc
|
6.2
|
elasticsearch-6-2
|
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|
[[query-string-syntax]] ==== Query string syntax The query string ``mini-language'' is used by the <> and by the `q` query string parameter in the <>. The query string is parsed into a series of \_terms\_ and \_operators\_. A term can be a single word -- `quick` or `brown` -- or a phrase, surrounded by double quotes -- `"quick brown"` -- which searches for all the words in the phrase, in the same order. Operators allow you to customize the search -- the available options are explained below. ===== Field names As mentioned in <>, the `default\_field` is searched for the search terms, but it is possible to specify other fields in the query syntax: \* where the `status` field contains `active` status:active \* where the `title` field contains `quick` or `brown`. If you omit the OR operator the default operator will be used title:(quick OR brown) title:(quick brown) \* where the `author` field contains the exact phrase `"john smith"` author:"John Smith" \* where any of the fields `book.title`, `book.content` or `book.date` contains `quick` or `brown` (note how we need to escape the `\*` with a backslash): book.\\*:(quick brown) \* where the field `title` has any non-null value: \_exists\_:title ===== Wildcards Wildcard searches can be run on individual terms, using `?` to replace a single character, and `\*` to replace zero or more characters: qu?ck bro\* Be aware that wildcard queries can use an enormous amount of memory and perform very badly -- just think how many terms need to be queried to match the query string `"a\* b\* c\*"`. [WARNING] ======= Pure wildcards `\\*` are rewritten to <> queries for efficiency. As a consequence, the wildcard `"field:\*"` would match documents with an emtpy value like the following: ``` { "field": "" } ``` \... and would \*\*not\*\* match if the field is missing or set with an explicit null value like the following: ``` { "field": null } ``` ======= [WARNING] ======= Allowing a wildcard at the beginning of a word (eg `"\*ing"`) is particularly heavy, because all terms in the index need to be examined, just in case they match. Leading wildcards can be disabled by setting `allow\_leading\_wildcard` to `false`. ======= Only parts of the analysis chain that operate at the character level are applied. So for instance, if the analyzer performs both lowercasing and stemming, only the lowercasing will be applied: it would be wrong to perform stemming on a word that is missing some of its letters. By setting `analyze\_wildcard` to true, queries that end with a `\*` will be analyzed and a boolean query will be built out of the different tokens, by ensuring exact matches on the first N-1 tokens, and prefix match on the last token. ===== Regular expressions Regular expression patterns can be embedded in the query string by wrapping them in forward-slashes (`"/"`): name:/joh?n(ath[oa]n)/ The supported regular expression syntax is explained in <>. [WARNING] ======= The `allow\_leading\_wildcard` parameter does not have any control over regular expressions. A query string such as the following would force Elasticsearch to visit every term in the index: /.\*n/ Use with caution! ======= ===== Fuzziness We can search for terms that are similar to, but not exactly like our search terms, using the ``fuzzy'' operator: quikc~ brwn~ foks~ This uses the http://en.wikipedia.org/wiki/Damerau-Levenshtein\_distance[Damerau-Levenshtein distance] to find all terms with a maximum of two changes, where a change is the insertion, deletion or substitution of a single character, or transposition of two adjacent characters. The default \_edit distance\_ is `2`, but an edit distance of `1` should be sufficient to catch 80% of all human misspellings. It can be specified
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/query-string-syntax.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.076629
|
a maximum of two changes, where a change is the insertion, deletion or substitution of a single character, or transposition of two adjacent characters. The default \_edit distance\_ is `2`, but an edit distance of `1` should be sufficient to catch 80% of all human misspellings. It can be specified as: quikc~1 ===== Proximity searches While a phrase query (eg `"john smith"`) expects all of the terms in exactly the same order, a proximity query allows the specified words to be further apart or in a different order. In the same way that fuzzy queries can specify a maximum edit distance for characters in a word, a proximity search allows us to specify a maximum edit distance of words in a phrase: "fox quick"~5 The closer the text in a field is to the original order specified in the query string, the more relevant that document is considered to be. When compared to the above example query, the phrase `"quick fox"` would be considered more relevant than `"quick brown fox"`. ===== Ranges Ranges can be specified for date, numeric or string fields. Inclusive ranges are specified with square brackets `[min TO max]` and exclusive ranges with curly brackets `{min TO max}`. \* All days in 2012: date:[2012-01-01 TO 2012-12-31] \* Numbers 1..5 count:[1 TO 5] \* Tags between `alpha` and `omega`, excluding `alpha` and `omega`: tag:{alpha TO omega} \* Numbers from 10 upwards count:[10 TO \*] \* Dates before 2012 date:{\* TO 2012-01-01} Curly and square brackets can be combined: \* Numbers from 1 up to but not including 5 count:[1 TO 5} Ranges with one side unbounded can use the following syntax: age:>10 age:>=10 age:<10 age:<=10 [NOTE] ==================================================================== To combine an upper and lower bound with the simplified syntax, you would need to join two clauses with an `AND` operator: age:(>=10 AND <20) age:(+>=10 +<20) ==================================================================== The parsing of ranges in query strings can be complex and error prone. It is much more reliable to use an explicit <>. ===== Boosting Use the \_boost\_ operator `^` to make one term more relevant than another. For instance, if we want to find all documents about foxes, but we are especially interested in quick foxes: quick^2 fox The default `boost` value is 1, but can be any positive floating point number. Boosts between 0 and 1 reduce relevance. Boosts can also be applied to phrases or to groups: "john smith"^2 (foo bar)^4 ===== Boolean operators By default, all terms are optional, as long as one term matches. A search for `foo bar baz` will find any document that contains one or more of `foo` or `bar` or `baz`. We have already discussed the `default\_operator` above which allows you to force all terms to be required, but there are also \_boolean operators\_ which can be used in the query string itself to provide more control. The preferred operators are `+` (this term \*must\* be present) and `-` (this term \*must not\* be present). All other terms are optional. For example, this query: quick brown +fox -news states that: \* `fox` must be present \* `news` must not be present \* `quick` and `brown` are optional -- their presence increases the relevance The familiar operators `AND`, `OR` and `NOT` (also written `&&`, `||` and `!`) are also supported. However, the effects of these operators can be more complicated than is obvious at first glance. `NOT` takes precedence over `AND`, which takes precedence over `OR`. While the `+` and `-` only affect the term to the right of the operator, `AND` and `OR` can affect the terms to the left and right. \*\*\*\* Rewriting
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/query-string-syntax.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.069974
|
operators can be more complicated than is obvious at first glance. `NOT` takes precedence over `AND`, which takes precedence over `OR`. While the `+` and `-` only affect the term to the right of the operator, `AND` and `OR` can affect the terms to the left and right. \*\*\*\* Rewriting the above query using `AND`, `OR` and `NOT` demonstrates the complexity: `quick OR brown AND fox AND NOT news`:: This is incorrect, because `brown` is now a required term. `(quick OR brown) AND fox AND NOT news`:: This is incorrect because at least one of `quick` or `brown` is now required and the search for those terms would be scored differently from the original query. `((quick AND fox) OR (brown AND fox) OR fox) AND NOT news`:: This form now replicates the logic from the original query correctly, but the relevance scoring bears little resemblance to the original. In contrast, the same query rewritten using the <> would look like this: { "bool": { "must": { "match": "fox" }, "should": { "match": "quick brown" }, "must\_not": { "match": "news" } } } \*\*\*\* ===== Grouping Multiple terms or clauses can be grouped together with parentheses, to form sub-queries: (quick OR brown) AND fox Groups can be used to target a particular field, or to boost the result of a sub-query: status:(active OR pending) title:(full text search)^2 ===== Reserved characters If you need to use any of the characters which function as operators in your query itself (and not as operators), then you should escape them with a leading backslash. For instance, to search for `(1+1)=2`, you would need to write your query as `\(1\+1\)\=2`. The reserved characters are: `+ - = && || > < ! ( ) { } [ ] ^ " ~ \* ? : \ /` Failing to escape these special characters correctly could lead to a syntax error which prevents your query from running. NOTE: `<` and `>` can't be escaped at all. The only way to prevent them from attempting to create a range query is to remove them from the query string entirely. ===== Empty Query If the query string is empty or only contains whitespaces the query will yield an empty result set.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/query-string-syntax.asciidoc
|
6.2
|
elasticsearch-6-2
|
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|
[[query-dsl-range-query]] === Range Query Matches documents with fields that have terms within a certain range. The type of the Lucene query depends on the field type, for `string` fields, the `TermRangeQuery`, while for number/date fields, the query is a `NumericRangeQuery`. The following example returns all documents where `age` is between `10` and `20`: [source,js] -------------------------------------------------- GET \_search { "query": { "range" : { "age" : { "gte" : 10, "lte" : 20, "boost" : 2.0 } } } } -------------------------------------------------- // CONSOLE The `range` query accepts the following parameters: [horizontal] `gte`:: Greater-than or equal to `gt`:: Greater-than `lte`:: Less-than or equal to `lt`:: Less-than `boost`:: Sets the boost value of the query, defaults to `1.0` [[ranges-on-dates]] ==== Ranges on date fields When running `range` queries on fields of type <>, ranges can be specified using <>: [source,js] -------------------------------------------------- GET \_search { "query": { "range" : { "date" : { "gte" : "now-1d/d", "lt" : "now/d" } } } } -------------------------------------------------- // CONSOLE ===== Date math and rounding When using <> to round dates to the nearest day, month, hour, etc, the rounded dates depend on whether the ends of the ranges are inclusive or exclusive. Rounding up moves to the last millisecond of the rounding scope, and rounding down to the first millisecond of the rounding scope. For example: [horizontal] `gt`:: Greater than the date rounded up: `2014-11-18||/M` becomes `2014-11-30T23:59:59.999`, ie excluding the entire month. `gte`:: Greater than or equal to the date rounded down: `2014-11-18||/M` becomes `2014-11-01`, ie including the entire month. `lt`:: Less than the date rounded down: `2014-11-18||/M` becomes `2014-11-01`, ie excluding the entire month. `lte`:: Less than or equal to the date rounded up: `2014-11-18||/M` becomes `2014-11-30T23:59:59.999`, ie including the entire month. ===== Date format in range queries Formatted dates will be parsed using the <> specified on the <> field by default, but it can be overridden by passing the `format` parameter to the `range` query: [source,js] -------------------------------------------------- GET \_search { "query": { "range" : { "born" : { "gte": "01/01/2012", "lte": "2013", "format": "dd/MM/yyyy||yyyy" } } } } -------------------------------------------------- // CONSOLE Note that if the date misses some of the year, month and day coordinates, the missing parts are filled with the start of https://en.wikipedia.org/wiki/Unix\_time[unix time], which is January 1st, 1970. This means, that when e.g. specifying `dd` as the format, a value like `"gte" : 10` will translate to `1970-01-10T00:00:00.000Z`. ===== Time zone in range queries Dates can be converted from another timezone to UTC either by specifying the time zone in the date value itself (if the <> accepts it), or it can be specified as the `time\_zone` parameter: [source,js] -------------------------------------------------- GET \_search { "query": { "range" : { "timestamp" : { "gte": "2015-01-01 00:00:00", <1> "lte": "now", <2> "time\_zone": "+01:00" } } } } -------------------------------------------------- // CONSOLE <1> This date will be converted to `2014-12-31T23:00:00 UTC`. <2> `now` is not affected by the `time\_zone` parameter (dates must be stored as UTC).
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/range-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.010614
|
[[query-dsl-constant-score-query]] === Constant Score Query A query that wraps another query and simply returns a constant score equal to the query boost for every document in the filter. Maps to Lucene `ConstantScoreQuery`. [source,js] -------------------------------------------------- GET /\_search { "query": { "constant\_score" : { "filter" : { "term" : { "user" : "kimchy"} }, "boost" : 1.2 } } } -------------------------------------------------- // CONSOLE Filter clauses are executed in <>, meaning that scoring is ignored and clauses are considered for caching.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/constant-score-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.054309
|
[[query-filter-context]] == Query and filter context The behaviour of a query clause depends on whether it is used in \_query context\_ or in \_filter context\_: Query context:: + -- A query clause used in query context answers the question ``\_\_How well does this document match this query clause?\_\_'' Besides deciding whether or not the document matches, the query clause also calculates a `\_score` representing how well the document matches, relative to other documents. Query context is in effect whenever a query clause is passed to a `query` parameter, such as the `query` parameter in the <> API. -- Filter context:: + -- In \_filter\_ context, a query clause answers the question ``\_\_Does this document match this query clause?\_\_'' The answer is a simple Yes or No -- no scores are calculated. Filter context is mostly used for filtering structured data, e.g. \* \_\_Does this +timestamp+ fall into the range 2015 to 2016?\_\_ \* \_\_Is the +status+ field set to ++"published"++\_\_? Frequently used filters will be cached automatically by Elasticsearch, to speed up performance. Filter context is in effect whenever a query clause is passed to a `filter` parameter, such as the `filter` or `must\_not` parameters in the <> query, the `filter` parameter in the <> query, or the <> aggregation. -- Below is an example of query clauses being used in query and filter context in the `search` API. This query will match documents where all of the following conditions are met: \* The `title` field contains the word `search`. \* The `content` field contains the word `elasticsearch`. \* The `status` field contains the exact word `published`. \* The `publish\_date` field contains a date from 1 Jan 2015 onwards. [source,js] ------------------------------------ GET /\_search { "query": { <1> "bool": { <2> "must": [ { "match": { "title": "Search" }}, <2> { "match": { "content": "Elasticsearch" }} <2> ], "filter": [ <3> { "term": { "status": "published" }}, <4> { "range": { "publish\_date": { "gte": "2015-01-01" }}} <4> ] } } } ------------------------------------ // CONSOLE <1> The `query` parameter indicates query context. <2> The `bool` and two `match` clauses are used in query context, which means that they are used to score how well each document matches. <3> The `filter` parameter indicates filter context. <4> The `term` and `range` clauses are used in filter context. They will filter out documents which do not match, but they will not affect the score for matching documents. TIP: Use query clauses in query context for conditions which should affect the score of matching documents (i.e. how well does the document match), and use all other query clauses in filter context.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/query_filter_context.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.094074
|
[[query-dsl-multi-match-query]] === Multi Match Query The `multi\_match` query builds on the <> to allow multi-field queries: [source,js] -------------------------------------------------- GET /\_search { "query": { "multi\_match" : { "query": "this is a test", <1> "fields": [ "subject", "message" ] <2> } } } -------------------------------------------------- // CONSOLE <1> The query string. <2> The fields to be queried. [float] [[field-boost]] ==== `fields` and per-field boosting Fields can be specified with wildcards, eg: [source,js] -------------------------------------------------- GET /\_search { "query": { "multi\_match" : { "query": "Will Smith", "fields": [ "title", "\*\_name" ] <1> } } } -------------------------------------------------- // CONSOLE <1> Query the `title`, `first\_name` and `last\_name` fields. Individual fields can be boosted with the caret (`^`) notation: [source,js] -------------------------------------------------- GET /\_search { "query": { "multi\_match" : { "query" : "this is a test", "fields" : [ "subject^3", "message" ] <1> } } } -------------------------------------------------- // CONSOLE <1> The `subject` field is three times as important as the `message` field. If no `fields` are provided, the `multi\_match` query defaults to the `index.query.default\_field` index settings, which in turn defaults to `\*`. `\*` extracts all fields in the mapping that are eligible to term queries and filters the metadata fields. All extracted fields are then combined to build a query. WARNING: If you have a huge number of fields, the above auto expansion might lead to querying a large number of fields which might cause performance issues. In future versions (stating in 7.0) there will be a limit of no more than 1024 fields that can be queried at once. [[multi-match-types]] [float] ==== Types of `multi\_match` query: The way the `multi\_match` query is executed internally depends on the `type` parameter, which can be set to: [horizontal] `best\_fields`:: (\*default\*) Finds documents which match any field, but uses the `\_score` from the best field. See <>. `most\_fields`:: Finds documents which match any field and combines the `\_score` from each field. See <>. `cross\_fields`:: Treats fields with the same `analyzer` as though they were one big field. Looks for each word in \*any\* field. See <>. `phrase`:: Runs a `match\_phrase` query on each field and combines the `\_score` from each field. See <>. `phrase\_prefix`:: Runs a `match\_phrase\_prefix` query on each field and combines the `\_score` from each field. See <>. [[type-best-fields]] ==== `best\_fields` The `best\_fields` type is most useful when you are searching for multiple words best found in the same field. For instance ``brown fox'' in a single field is more meaningful than ``brown'' in one field and ``fox'' in the other. The `best\_fields` type generates a <> for each field and wraps them in a <> query, to find the single best matching field. For instance, this query: [source,js] -------------------------------------------------- GET /\_search { "query": { "multi\_match" : { "query": "brown fox", "type": "best\_fields", "fields": [ "subject", "message" ], "tie\_breaker": 0.3 } } } -------------------------------------------------- // CONSOLE would be executed as: [source,js] -------------------------------------------------- GET /\_search { "query": { "dis\_max": { "queries": [ { "match": { "subject": "brown fox" }}, { "match": { "message": "brown fox" }} ], "tie\_breaker": 0.3 } } } -------------------------------------------------- // CONSOLE Normally the `best\_fields` type uses the score of the \*single\* best matching field, but if `tie\_breaker` is specified, then it calculates the score as follows: \* the score from the best matching field \* plus `tie\_breaker \* \_score` for all other matching fields Also, accepts `analyzer`, `boost`, `operator`, `minimum\_should\_match`, `fuzziness`, `lenient`, `prefix\_length`, `max\_expansions`, `rewrite`, `zero\_terms\_query`, `cutoff\_frequency`, `auto\_generate\_synonyms\_phrase\_query` and `fuzzy\_transpositions`, as explained in <>. [IMPORTANT] [[operator-min]] .`operator` and `minimum\_should\_match` =================================================== The `best\_fields` and `most\_fields` types are \_field-centric\_ -- they generate a `match` query \*per field\*. This means that the `operator` and `minimum\_should\_match` parameters are applied to each
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/multi-match-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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0.057380761951208115,
-0.06450111418962479,
-0.017478356137871742,
0.062238678336143494
] | 0.012285
|
`boost`, `operator`, `minimum\_should\_match`, `fuzziness`, `lenient`, `prefix\_length`, `max\_expansions`, `rewrite`, `zero\_terms\_query`, `cutoff\_frequency`, `auto\_generate\_synonyms\_phrase\_query` and `fuzzy\_transpositions`, as explained in <>. [IMPORTANT] [[operator-min]] .`operator` and `minimum\_should\_match` =================================================== The `best\_fields` and `most\_fields` types are \_field-centric\_ -- they generate a `match` query \*per field\*. This means that the `operator` and `minimum\_should\_match` parameters are applied to each field individually, which is probably not what you want. Take this query for example: [source,js] -------------------------------------------------- GET /\_search { "query": { "multi\_match" : { "query": "Will Smith", "type": "best\_fields", "fields": [ "first\_name", "last\_name" ], "operator": "and" <1> } } } -------------------------------------------------- // CONSOLE <1> All terms must be present. This query is executed as: (+first\_name:will +first\_name:smith) | (+last\_name:will +last\_name:smith) In other words, \*all terms\* must be present \*in a single field\* for a document to match. See <> for a better solution. =================================================== [[type-most-fields]] ==== `most\_fields` The `most\_fields` type is most useful when querying multiple fields that contain the same text analyzed in different ways. For instance, the main field may contain synonyms, stemming and terms without diacritics. A second field may contain the original terms, and a third field might contain shingles. By combining scores from all three fields we can match as many documents as possible with the main field, but use the second and third fields to push the most similar results to the top of the list. This query: [source,js] -------------------------------------------------- GET /\_search { "query": { "multi\_match" : { "query": "quick brown fox", "type": "most\_fields", "fields": [ "title", "title.original", "title.shingles" ] } } } -------------------------------------------------- // CONSOLE would be executed as: [source,js] -------------------------------------------------- GET /\_search { "query": { "bool": { "should": [ { "match": { "title": "quick brown fox" }}, { "match": { "title.original": "quick brown fox" }}, { "match": { "title.shingles": "quick brown fox" }} ] } } } -------------------------------------------------- // CONSOLE The score from each `match` clause is added together, then divided by the number of `match` clauses. Also, accepts `analyzer`, `boost`, `operator`, `minimum\_should\_match`, `fuzziness`, `lenient`, `prefix\_length`, `max\_expansions`, `rewrite`, `zero\_terms\_query` and `cutoff\_frequency`, as explained in <>, but \*see <>\*. [[type-phrase]] ==== `phrase` and `phrase\_prefix` The `phrase` and `phrase\_prefix` types behave just like <>, but they use a `match\_phrase` or `match\_phrase\_prefix` query instead of a `match` query. This query: [source,js] -------------------------------------------------- GET /\_search { "query": { "multi\_match" : { "query": "quick brown f", "type": "phrase\_prefix", "fields": [ "subject", "message" ] } } } -------------------------------------------------- // CONSOLE would be executed as: [source,js] -------------------------------------------------- GET /\_search { "query": { "dis\_max": { "queries": [ { "match\_phrase\_prefix": { "subject": "quick brown f" }}, { "match\_phrase\_prefix": { "message": "quick brown f" }} ] } } } -------------------------------------------------- // CONSOLE Also, accepts `analyzer`, `boost`, `lenient`, `slop` and `zero\_terms\_query` as explained in <>. Type `phrase\_prefix` additionally accepts `max\_expansions`. [IMPORTANT] [[phrase-fuzziness]] .`phrase`, `phrase\_prefix` and `fuzziness` =================================================== The `fuzziness` parameter cannot be used with the `phrase` or `phrase\_prefix` type. =================================================== [[type-cross-fields]] ==== `cross\_fields` The `cross\_fields` type is particularly useful with structured documents where multiple fields \*should\* match. For instance, when querying the `first\_name` and `last\_name` fields for ``Will Smith'', the best match is likely to have ``Will'' in one field and ``Smith'' in the other. \*\*\*\* This sounds like a job for <> but there are two problems with that approach. The first problem is that `operator` and `minimum\_should\_match` are applied per-field, instead of per-term (see <>). The second problem is to do with relevance: the different term frequencies in the `first\_name` and `last\_name` fields can produce unexpected results. For instance, imagine we have two people: ``Will Smith'' and ``Smith Jones''. ``Smith'' as a last name is very common (and so is of low importance) but ``Smith'' as a first name is very uncommon
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/multi-match-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.075204
|
relevance: the different term frequencies in the `first\_name` and `last\_name` fields can produce unexpected results. For instance, imagine we have two people: ``Will Smith'' and ``Smith Jones''. ``Smith'' as a last name is very common (and so is of low importance) but ``Smith'' as a first name is very uncommon (and so is of great importance). If we do a search for ``Will Smith'', the ``Smith Jones'' document will probably appear above the better matching ``Will Smith'' because the score of `first\_name:smith` has trumped the combined scores of `first\_name:will` plus `last\_name:smith`. \*\*\*\* One way of dealing with these types of queries is simply to index the `first\_name` and `last\_name` fields into a single `full\_name` field. Of course, this can only be done at index time. The `cross\_field` type tries to solve these problems at query time by taking a \_term-centric\_ approach. It first analyzes the query string into individual terms, then looks for each term in any of the fields, as though they were one big field. A query like: [source,js] -------------------------------------------------- GET /\_search { "query": { "multi\_match" : { "query": "Will Smith", "type": "cross\_fields", "fields": [ "first\_name", "last\_name" ], "operator": "and" } } } -------------------------------------------------- // CONSOLE is executed as: +(first\_name:will last\_name:will) +(first\_name:smith last\_name:smith) In other words, \*all terms\* must be present \*in at least one field\* for a document to match. (Compare this to <>.) That solves one of the two problems. The problem of differing term frequencies is solved by \_blending\_ the term frequencies for all fields in order to even out the differences. In practice, `first\_name:smith` will be treated as though it has the same frequencies as `last\_name:smith`, plus one. This will make matches on `first\_name` and `last\_name` have comparable scores, with a tiny advantage for `last\_name` since it is the most likely field that contains `smith`. Note that `cross\_fields` is usually only useful on short string fields that all have a `boost` of `1`. Otherwise boosts, term freqs and length normalization contribute to the score in such a way that the blending of term statistics is not meaningful anymore. If you run the above query through the <>, it returns this explanation: +blended("will", fields: [first\_name, last\_name]) +blended("smith", fields: [first\_name, last\_name]) Also, accepts `analyzer`, `boost`, `operator`, `minimum\_should\_match`, `lenient`, `zero\_terms\_query` and `cutoff\_frequency`, as explained in <>. [[cross-field-analysis]] ===== `cross\_field` and analysis The `cross\_field` type can only work in term-centric mode on fields that have the same analyzer. Fields with the same analyzer are grouped together as in the example above. If there are multiple groups, they are combined with a `bool` query. For instance, if we have a `first` and `last` field which have the same analyzer, plus a `first.edge` and `last.edge` which both use an `edge\_ngram` analyzer, this query: [source,js] -------------------------------------------------- GET /\_search { "query": { "multi\_match" : { "query": "Jon", "type": "cross\_fields", "fields": [ "first", "first.edge", "last", "last.edge" ] } } } -------------------------------------------------- // CONSOLE would be executed as: blended("jon", fields: [first, last]) | ( blended("j", fields: [first.edge, last.edge]) blended("jo", fields: [first.edge, last.edge]) blended("jon", fields: [first.edge, last.edge]) ) In other words, `first` and `last` would be grouped together and treated as a single field, and `first.edge` and `last.edge` would be grouped together and treated as a single field. Having multiple groups is fine, but when combined with `operator` or `minimum\_should\_match`, it can suffer from the <> as `most\_fields` or `best\_fields`. You can easily rewrite this query yourself as two separate `cross\_fields` queries combined with a `bool` query, and apply the `minimum\_should\_match` parameter to just one of them: [source,js] -------------------------------------------------- GET /\_search { "query": { "bool": { "should": [ { "multi\_match" : { "query": "Will Smith",
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/multi-match-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.101149
|
<> as `most\_fields` or `best\_fields`. You can easily rewrite this query yourself as two separate `cross\_fields` queries combined with a `bool` query, and apply the `minimum\_should\_match` parameter to just one of them: [source,js] -------------------------------------------------- GET /\_search { "query": { "bool": { "should": [ { "multi\_match" : { "query": "Will Smith", "type": "cross\_fields", "fields": [ "first", "last" ], "minimum\_should\_match": "50%" <1> } }, { "multi\_match" : { "query": "Will Smith", "type": "cross\_fields", "fields": [ "\*.edge" ] } } ] } } } -------------------------------------------------- // CONSOLE <1> Either `will` or `smith` must be present in either of the `first` or `last` fields You can force all fields into the same group by specifying the `analyzer` parameter in the query. [source,js] -------------------------------------------------- GET /\_search { "query": { "multi\_match" : { "query": "Jon", "type": "cross\_fields", "analyzer": "standard", <1> "fields": [ "first", "last", "\*.edge" ] } } } -------------------------------------------------- // CONSOLE <1> Use the `standard` analyzer for all fields. which will be executed as: blended("will", fields: [first, first.edge, last.edge, last]) blended("smith", fields: [first, first.edge, last.edge, last]) [[tie-breaker]] ===== `tie\_breaker` By default, each per-term `blended` query will use the best score returned by any field in a group, then these scores are added together to give the final score. The `tie\_breaker` parameter can change the default behaviour of the per-term `blended` queries. It accepts: [horizontal] `0.0`:: Take the single best score out of (eg) `first\_name:will` and `last\_name:will` (\*default\*) `1.0`:: Add together the scores for (eg) `first\_name:will` and `last\_name:will` `0.0 < n < 1.0`:: Take the single best score plus +tie\_breaker+ multiplied by each of the scores from other matching fields. [IMPORTANT] [[crossfields-fuzziness]] .`cross\_fields` and `fuzziness` =================================================== The `fuzziness` parameter cannot be used with the `cross\_fields` type. ===================================================
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/multi-match-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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|
[[query-dsl-terms-query]] === Terms Query Filters documents that have fields that match any of the provided terms (\*not analyzed\*). For example: [source,js] -------------------------------------------------- GET /\_search { "query": { "terms" : { "user" : ["kimchy", "elasticsearch"]} } } -------------------------------------------------- // CONSOLE [float] [[query-dsl-terms-lookup]] ===== Terms lookup mechanism When it's needed to specify a `terms` filter with a lot of terms it can be beneficial to fetch those term values from a document in an index. A concrete example would be to filter tweets tweeted by your followers. Potentially the amount of user ids specified in the terms filter can be a lot. In this scenario it makes sense to use the terms filter's terms lookup mechanism. The terms lookup mechanism supports the following options: [horizontal] `index`:: The index to fetch the term values from. `type`:: The type to fetch the term values from. `id`:: The id of the document to fetch the term values from. `path`:: The field specified as path to fetch the actual values for the `terms` filter. `routing`:: A custom routing value to be used when retrieving the external terms doc. The values for the `terms` filter will be fetched from a field in a document with the specified id in the specified type and index. Internally a get request is executed to fetch the values from the specified path. At the moment for this feature to work the `\_source` needs to be stored. Also, consider using an index with a single shard and fully replicated across all nodes if the "reference" terms data is not large. The lookup terms filter will prefer to execute the get request on a local node if possible, reducing the need for networking. [WARNING] Executing a Terms Query request with a lot of terms can be quite slow, as each additional term demands extra processing and memory. To safeguard against this, the maximum number of terms that can be used in a Terms Query both directly or through lookup has been limited to `65536`. This default maximum can be changed for a particular index with the index setting `index.max\_terms\_count`. [float] ===== Terms lookup twitter example At first we index the information for user with id 2, specifically, its followers, then index a tweet from user with id 1. Finally we search on all the tweets that match the followers of user 2. [source,js] -------------------------------------------------- PUT /users/\_doc/2 { "followers" : ["1", "3"] } PUT /tweets/\_doc/1 { "user" : "1" } GET /tweets/\_search { "query" : { "terms" : { "user" : { "index" : "users", "type" : "\_doc", "id" : "2", "path" : "followers" } } } } -------------------------------------------------- // CONSOLE The structure of the external terms document can also include an array of inner objects, for example: [source,js] -------------------------------------------------- PUT /users/\_doc/2 { "followers" : [ { "id" : "1" }, { "id" : "2" } ] } -------------------------------------------------- // CONSOLE In which case, the lookup path will be `followers.id`.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/terms-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.0306
|
[[query-dsl-mlt-query]] === More Like This Query The More Like This Query finds documents that are "like" a given set of documents. In order to do so, MLT selects a set of representative terms of these input documents, forms a query using these terms, executes the query and returns the results. The user controls the input documents, how the terms should be selected and how the query is formed. The simplest use case consists of asking for documents that are similar to a provided piece of text. Here, we are asking for all movies that have some text similar to "Once upon a time" in their "title" and in their "description" fields, limiting the number of selected terms to 12. [source,js] -------------------------------------------------- GET /\_search { "query": { "more\_like\_this" : { "fields" : ["title", "description"], "like" : "Once upon a time", "min\_term\_freq" : 1, "max\_query\_terms" : 12 } } } -------------------------------------------------- // CONSOLE A more complicated use case consists of mixing texts with documents already existing in the index. In this case, the syntax to specify a document is similar to the one used in the <>. [source,js] -------------------------------------------------- GET /\_search { "query": { "more\_like\_this" : { "fields" : ["title", "description"], "like" : [ { "\_index" : "imdb", "\_type" : "movies", "\_id" : "1" }, { "\_index" : "imdb", "\_type" : "movies", "\_id" : "2" }, "and potentially some more text here as well" ], "min\_term\_freq" : 1, "max\_query\_terms" : 12 } } } -------------------------------------------------- // CONSOLE Finally, users can mix some texts, a chosen set of documents but also provide documents not necessarily present in the index. To provide documents not present in the index, the syntax is similar to <>. [source,js] -------------------------------------------------- GET /\_search { "query": { "more\_like\_this" : { "fields" : ["name.first", "name.last"], "like" : [ { "\_index" : "marvel", "\_type" : "quotes", "doc" : { "name": { "first": "Ben", "last": "Grimm" }, "\_doc": "You got no idea what I'd... what I'd give to be invisible." } }, { "\_index" : "marvel", "\_type" : "quotes", "\_id" : "2" } ], "min\_term\_freq" : 1, "max\_query\_terms" : 12 } } } -------------------------------------------------- // CONSOLE ==== How it Works Suppose we wanted to find all documents similar to a given input document. Obviously, the input document itself should be its best match for that type of query. And the reason would be mostly, according to link:https://lucene.apache.org/core/4\_9\_0/core/org/apache/lucene/search/similarities/TFIDFSimilarity.html[Lucene scoring formula], due to the terms with the highest tf-idf. Therefore, the terms of the input document that have the highest tf-idf are good representatives of that document, and could be used within a disjunctive query (or `OR`) to retrieve similar documents. The MLT query simply extracts the text from the input document, analyzes it, usually using the same analyzer at the field, then selects the top K terms with highest tf-idf to form a disjunctive query of these terms. IMPORTANT: The fields on which to perform MLT must be indexed and of type `text` or `keyword``. Additionally, when using `like` with documents, either `\_source` must be enabled or the fields must be `stored` or store `term\_vector`. In order to speed up analysis, it could help to store term vectors at index time. For example, if we wish to perform MLT on the "title" and "tags.raw" fields, we can explicitly store their `term\_vector` at index time. We can still perform MLT on the "description" and "tags" fields, as `\_source` is enabled by default, but there will be no speed up on analysis for these fields. [source,js] -------------------------------------------------- PUT /imdb { "mappings": { "movies": { "properties": { "title": { "type": "text", "term\_vector": "yes" }, "description": { "type":
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/mlt-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.085282
|
We can still perform MLT on the "description" and "tags" fields, as `\_source` is enabled by default, but there will be no speed up on analysis for these fields. [source,js] -------------------------------------------------- PUT /imdb { "mappings": { "movies": { "properties": { "title": { "type": "text", "term\_vector": "yes" }, "description": { "type": "text" }, "tags": { "type": "text", "fields" : { "raw": { "type" : "text", "analyzer": "keyword", "term\_vector" : "yes" } } } } } } } -------------------------------------------------- // CONSOLE ==== Parameters The only required parameter is `like`, all other parameters have sensible defaults. There are three types of parameters: one to specify the document input, the other one for term selection and for query formation. [float] ==== Document Input Parameters [horizontal] `like`:: The only \*required\* parameter of the MLT query is `like` and follows a versatile syntax, in which the user can specify free form text and/or a single or multiple documents (see examples above). The syntax to specify documents is similar to the one used by the <>. When specifying documents, the text is fetched from `fields` unless overridden in each document request. The text is analyzed by the analyzer at the field, but could also be overridden. The syntax to override the analyzer at the field follows a similar syntax to the `per\_field\_analyzer` parameter of the <>. Additionally, to provide documents not necessarily present in the index, <> are also supported. `unlike`:: The `unlike` parameter is used in conjunction with `like` in order not to select terms found in a chosen set of documents. In other words, we could ask for documents `like: "Apple"`, but `unlike: "cake crumble tree"`. The syntax is the same as `like`. `fields`:: A list of fields to fetch and analyze the text from. Defaults to the `\_all` field for free text and to all possible fields for document inputs. [float] [[mlt-query-term-selection]] ==== Term Selection Parameters [horizontal] `max\_query\_terms`:: The maximum number of query terms that will be selected. Increasing this value gives greater accuracy at the expense of query execution speed. Defaults to `25`. `min\_term\_freq`:: The minimum term frequency below which the terms will be ignored from the input document. Defaults to `2`. `min\_doc\_freq`:: The minimum document frequency below which the terms will be ignored from the input document. Defaults to `5`. `max\_doc\_freq`:: The maximum document frequency above which the terms will be ignored from the input document. This could be useful in order to ignore highly frequent words such as stop words. Defaults to unbounded (`0`). `min\_word\_length`:: The minimum word length below which the terms will be ignored. The old name `min\_word\_len` is deprecated. Defaults to `0`. `max\_word\_length`:: The maximum word length above which the terms will be ignored. The old name `max\_word\_len` is deprecated. Defaults to unbounded (`0`). `stop\_words`:: An array of stop words. Any word in this set is considered "uninteresting" and ignored. If the analyzer allows for stop words, you might want to tell MLT to explicitly ignore them, as for the purposes of document similarity it seems reasonable to assume that "a stop word is never interesting". `analyzer`:: The analyzer that is used to analyze the free form text. Defaults to the analyzer associated with the first field in `fields`. [float] ==== Query Formation Parameters [horizontal] `minimum\_should\_match`:: After the disjunctive query has been formed, this parameter controls the number of terms that must match. The syntax is the same as the <>. (Defaults to `"30%"`). `fail\_on\_unsupported\_field`:: Controls whether the query should fail (throw an exception) if any of the specified fields are not of the supported types (`text` or `keyword'). Set this to `false` to ignore the field and
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/mlt-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | -0.003031
|
of terms that must match. The syntax is the same as the <>. (Defaults to `"30%"`). `fail\_on\_unsupported\_field`:: Controls whether the query should fail (throw an exception) if any of the specified fields are not of the supported types (`text` or `keyword'). Set this to `false` to ignore the field and continue processing. Defaults to `true`. `boost\_terms`:: Each term in the formed query could be further boosted by their tf-idf score. This sets the boost factor to use when using this feature. Defaults to deactivated (`0`). Any other positive value activates terms boosting with the given boost factor. `include`:: Specifies whether the input documents should also be included in the search results returned. Defaults to `false`. `boost`:: Sets the boost value of the whole query. Defaults to `1.0`.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/mlt-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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|
[[query-dsl-match-all-query]] == Match All Query The most simple query, which matches all documents, giving them all a `\_score` of `1.0`. [source,js] -------------------------------------------------- GET /\_search { "query": { "match\_all": {} } } -------------------------------------------------- // CONSOLE The `\_score` can be changed with the `boost` parameter: [source,js] -------------------------------------------------- GET /\_search { "query": { "match\_all": { "boost" : 1.2 } } } -------------------------------------------------- // CONSOLE [[query-dsl-match-none-query]] [float] == Match None Query This is the inverse of the `match\_all` query, which matches no documents. [source,js] -------------------------------------------------- GET /\_search { "query": { "match\_none": {} } } -------------------------------------------------- // CONSOLE
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/match-all-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.025742
|
[[query-dsl-span-field-masking-query]] === Span Field Masking Query Wrapper to allow span queries to participate in composite single-field span queries by 'lying' about their search field. The span field masking query maps to Lucene's `SpanFieldMaskingQuery` This can be used to support queries like `span-near` or `span-or` across different fields, which is not ordinarily permitted. Span field masking query is invaluable in conjunction with \*multi-fields\* when same content is indexed with multiple analyzers. For instance we could index a field with the standard analyzer which breaks text up into words, and again with the english analyzer which stems words into their root form. Example: [source,js] -------------------------------------------------- GET /\_search { "query": { "span\_near": { "clauses": [ { "span\_term": { "text": "quick brown" } }, { "field\_masking\_span": { "query": { "span\_term": { "text.stems": "fox" } }, "field": "text" } } ], "slop": 5, "in\_order": false } } } -------------------------------------------------- // CONSOLE Note: as span field masking query returns the masked field, scoring will be done using the norms of the field name supplied. This may lead to unexpected scoring behaviour.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/span-field-masking-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.056887
|
[[query-dsl-has-parent-query]] === Has Parent Query The `has\_parent` query accepts a query and a parent type. The query is executed in the parent document space, which is specified by the parent type. This query returns child documents which associated parents have matched. For the rest `has\_parent` query has the same options and works in the same manner as the `has\_child` query. [source,js] -------------------------------------------------- GET /\_search { "query": { "has\_parent" : { "parent\_type" : "blog", "query" : { "term" : { "tag" : "something" } } } } } -------------------------------------------------- // CONSOLE Note that the `has\_parent` is a slow query compared to other queries in the query dsl due to the fact that it performs a join. The performance degrades as the number of matching parent documents increases. If you care about query performance you should not use this query. However if you do happen to use this query then use it as less as possible. Each `has\_parent` query that gets added to a search request can increase query time significantly. [float] ==== Scoring capabilities The `has\_parent` also has scoring support. The default is `false` which ignores the score from the parent document. The score is in this case equal to the boost on the `has\_parent` query (Defaults to 1). If the score is set to `true`, then the score of the matching parent document is aggregated into the child documents belonging to the matching parent document. The score mode can be specified with the `score` field inside the `has\_parent` query: [source,js] -------------------------------------------------- GET /\_search { "query": { "has\_parent" : { "parent\_type" : "blog", "score" : true, "query" : { "term" : { "tag" : "something" } } } } } -------------------------------------------------- // CONSOLE [float] ==== Ignore Unmapped When set to `true` the `ignore\_unmapped` option will ignore an unmapped `type` and will not match any documents for this query. This can be useful when querying multiple indexes which might have different mappings. When set to `false` (the default value) the query will throw an exception if the `type` is not mapped. [float] ==== Sorting Child documents can't be sorted by fields in matching parent documents via the regular sort options. If you need to sort child documents by field in the parent documents then you should use the `function\_score` query and then just sort by `\_score`. Sorting tags by parent document' `view\_count` field: [source,js] -------------------------------------------------- GET /\_search { "query": { "has\_parent" : { "parent\_type" : "blog", "score" : true, "query" : { "function\_score" : { "script\_score": { "script": "\_score \* doc['view\_count'].value" } } } } } } -------------------------------------------------- // CONSOLE
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/has-parent-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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|
[[query-dsl-nested-query]] === Nested Query Nested query allows to query nested objects / docs (see <>). The query is executed against the nested objects / docs as if they were indexed as separate docs (they are, internally) and resulting in the root parent doc (or parent nested mapping). Here is a sample mapping we will work with: [source,js] -------------------------------------------------- PUT /my\_index { "mappings": { "type1" : { "properties" : { "obj1" : { "type" : "nested" } } } } } -------------------------------------------------- // CONSOLE // TESTSETUP And here is a sample nested query usage: [source,js] -------------------------------------------------- GET /\_search { "query": { "nested" : { "path" : "obj1", "score\_mode" : "avg", "query" : { "bool" : { "must" : [ { "match" : {"obj1.name" : "blue"} }, { "range" : {"obj1.count" : {"gt" : 5}} } ] } } } } } -------------------------------------------------- // CONSOLE The query `path` points to the nested object path, and the `query` includes the query that will run on the nested docs matching the direct path, and joining with the root parent docs. Note that any fields referenced inside the query must use the complete path (fully qualified). The `score\_mode` allows to set how inner children matching affects scoring of parent. It defaults to `avg`, but can be `sum`, `min`, `max` and `none`. There is also an `ignore\_unmapped` option which, when set to `true` will ignore an unmapped `path` and will not match any documents for this query. This can be useful when querying multiple indexes which might have different mappings. When set to `false` (the default value) the query will throw an exception if the `path` is not mapped. Multi level nesting is automatically supported, and detected, resulting in an inner nested query to automatically match the relevant nesting level (and not root) if it exists within another nested query.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/nested-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.001507
|
[[query-dsl-script-query]] === Script Query A query allowing to define <> as queries. They are typically used in a filter context, for example: [source,js] ---------------------------------------------- GET /\_search { "query": { "bool" : { "must" : { "script" : { "script" : { "source": "doc['num1'].value > 1", "lang": "painless" } } } } } } ---------------------------------------------- // CONSOLE [float] ==== Custom Parameters Scripts are compiled and cached for faster execution. If the same script can be used, just with different parameters provider, it is preferable to use the ability to pass parameters to the script itself, for example: [source,js] ---------------------------------------------- GET /\_search { "query": { "bool" : { "must" : { "script" : { "script" : { "source" : "doc['num1'].value > params.param1", "lang" : "painless", "params" : { "param1" : 5 } } } } } } } ---------------------------------------------- // CONSOLE
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/script-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.066606
|
[[query-dsl-percolate-query]] === Percolate Query The `percolate` query can be used to match queries stored in an index. The `percolate` query itself contains the document that will be used as query to match with the stored queries. [float] === Sample Usage Create an index with two fields: [source,js] -------------------------------------------------- PUT /my-index { "mappings": { "\_doc": { "properties": { "message": { "type": "text" }, "query": { "type": "percolator" } } } } } -------------------------------------------------- // CONSOLE The `message` field is the field used to preprocess the document defined in the `percolator` query before it gets indexed into a temporary index. The `query` field is used for indexing the query documents. It will hold a json object that represents an actual Elasticsearch query. The `query` field has been configured to use the <>. This field type understands the query dsl and stores the query in such a way that it can be used later on to match documents defined on the `percolate` query. Register a query in the percolator: [source,js] -------------------------------------------------- PUT /my-index/\_doc/1?refresh { "query" : { "match" : { "message" : "bonsai tree" } } } -------------------------------------------------- // CONSOLE // TEST[continued] Match a document to the registered percolator queries: [source,js] -------------------------------------------------- GET /my-index/\_search { "query" : { "percolate" : { "field" : "query", "document" : { "message" : "A new bonsai tree in the office" } } } } -------------------------------------------------- // CONSOLE // TEST[continued] The above request will yield the following response: [source,js] -------------------------------------------------- { "took": 13, "timed\_out": false, "\_shards": { "total": 5, "successful": 5, "skipped" : 0, "failed": 0 }, "hits": { "total": 1, "max\_score": 0.5753642, "hits": [ { <1> "\_index": "my-index", "\_type": "\_doc", "\_id": "1", "\_score": 0.5753642, "\_source": { "query": { "match": { "message": "bonsai tree" } } }, "fields" : { "\_percolator\_document\_slot" : [0] <2> } } ] } } -------------------------------------------------- // TESTRESPONSE[s/"took": 13,/"took": "$body.took",/] <1> The query with id `1` matches our document. <2> The `\_percolator\_document\_slot` field indicates which document has matched with this query. Useful when percolating multiple document simultaneously. [float] ==== Parameters The following parameters are required when percolating a document: [horizontal] `field`:: The field of type `percolator` that holds the indexed queries. This is a required parameter. `name`:: The suffix to be used for the `\_percolator\_document\_slot` field in case multiple `percolate` queries have been specified. This is an optional parameter. `document`:: The source of the document being percolated. `documents`:: Like the `document` parameter, but accepts multiple documents via a json array. `document\_type`:: The type / mapping of the document being percolated. This setting is deprecated and only required for indices created before 6.0 Instead of specifying the source of the document being percolated, the source can also be retrieved from an already stored document. The `percolate` query will then internally execute a get request to fetch that document. In that case the `document` parameter can be substituted with the following parameters: [horizontal] `index`:: The index the document resides in. This is a required parameter. `type`:: The type of the document to fetch. This is a required parameter. `id`:: The id of the document to fetch. This is a required parameter. `routing`:: Optionally, routing to be used to fetch document to percolate. `preference`:: Optionally, preference to be used to fetch document to percolate. `version`:: Optionally, the expected version of the document to be fetched. [float] ==== Percolating in a filter context In case you are not interested in the score, better performance can be expected by wrapping the percolator query in a `bool` query's filter clause or in a `constant\_score` query: [source,js] -------------------------------------------------- GET /my-index/\_search { "query" : { "constant\_score": { "filter": { "percolate" : {
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/percolate-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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|
Percolating in a filter context In case you are not interested in the score, better performance can be expected by wrapping the percolator query in a `bool` query's filter clause or in a `constant\_score` query: [source,js] -------------------------------------------------- GET /my-index/\_search { "query" : { "constant\_score": { "filter": { "percolate" : { "field" : "query", "document" : { "message" : "A new bonsai tree in the office" } } } } } } -------------------------------------------------- // CONSOLE // TEST[continued] At index time terms are extracted from the percolator query and the percolator can often determine whether a query matches just by looking at those extracted terms. However, computing scores requires to deserialize each matching query and run it against the percolated document, which is a much more expensive operation. Hence if computing scores is not required the `percolate` query should be wrapped in a `constant\_score` query or a `bool` query's filter clause. Note that the `percolate` query never gets cached by the query cache. [float] ==== Percolating multiple documents The `percolate` query can match multiple documents simultaneously with the indexed percolator queries. Percolating multiple documents in a single request can improve performance as queries only need to be parsed and matched once instead of multiple times. The `\_percolator\_document\_slot` field that is being returned with each matched percolator query is important when percolating multiple documents simultaneously. It indicates which documents matched with a particular percolator query. The numbers correlate with the slot in the `documents` array specified in the `percolate` query. [source,js] -------------------------------------------------- GET /my-index/\_search { "query" : { "percolate" : { "field" : "query", "documents" : [ <1> { "message" : "bonsai tree" }, { "message" : "new tree" }, { "message" : "the office" }, { "message" : "office tree" } ] } } } -------------------------------------------------- // CONSOLE // TEST[continued] <1> The documents array contains 4 documents that are going to be percolated at the same time. [source,js] -------------------------------------------------- { "took": 13, "timed\_out": false, "\_shards": { "total": 5, "successful": 5, "skipped" : 0, "failed": 0 }, "hits": { "total": 1, "max\_score": 1.5606477, "hits": [ { "\_index": "my-index", "\_type": "\_doc", "\_id": "1", "\_score": 1.5606477, "\_source": { "query": { "match": { "message": "bonsai tree" } } }, "fields" : { "\_percolator\_document\_slot" : [0, 1, 3] <1> } } ] } } -------------------------------------------------- // TESTRESPONSE[s/"took": 13,/"took": "$body.took",/] <1> The `\_percolator\_document\_slot` indicates that the first, second and last documents specified in the `percolate` query are matching with this query. [float] ==== Percolating an Existing Document In order to percolate a newly indexed document, the `percolate` query can be used. Based on the response from an index request, the `\_id` and other meta information can be used to immediately percolate the newly added document. [float] ===== Example Based on the previous example. Index the document we want to percolate: [source,js] -------------------------------------------------- PUT /my-index/\_doc/2 { "message" : "A new bonsai tree in the office" } -------------------------------------------------- // CONSOLE // TEST[continued] Index response: [source,js] -------------------------------------------------- { "\_index": "my-index", "\_type": "\_doc", "\_id": "2", "\_version": 1, "\_shards": { "total": 2, "successful": 1, "failed": 0 }, "result": "created", "\_seq\_no" : 0, "\_primary\_term" : 1 } -------------------------------------------------- // TESTRESPONSE Percolating an existing document, using the index response as basis to build to new search request: [source,js] -------------------------------------------------- GET /my-index/\_search { "query" : { "percolate" : { "field": "query", "index" : "my-index", "type" : "\_doc", "id" : "2", "version" : 1 <1> } } } -------------------------------------------------- // CONSOLE // TEST[continued] <1> The version is optional, but useful in certain cases. We can ensure that we are trying to percolate the document we just have indexed. A change may be made after we have indexed,
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/percolate-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.056339
|
"\_doc", "id" : "2", "version" : 1 <1> } } } -------------------------------------------------- // CONSOLE // TEST[continued] <1> The version is optional, but useful in certain cases. We can ensure that we are trying to percolate the document we just have indexed. A change may be made after we have indexed, and if that is the case the search request would fail with a version conflict error. The search response returned is identical as in the previous example. [float] ==== Percolate query and highlighting The `percolate` query is handled in a special way when it comes to highlighting. The queries hits are used to highlight the document that is provided in the `percolate` query. Whereas with regular highlighting the query in the search request is used to highlight the hits. [float] ===== Example This example is based on the mapping of the first example. Save a query: [source,js] -------------------------------------------------- PUT /my-index/\_doc/3?refresh { "query" : { "match" : { "message" : "brown fox" } } } -------------------------------------------------- // CONSOLE // TEST[continued] Save another query: [source,js] -------------------------------------------------- PUT /my-index/\_doc/4?refresh { "query" : { "match" : { "message" : "lazy dog" } } } -------------------------------------------------- // CONSOLE // TEST[continued] Execute a search request with the `percolate` query and highlighting enabled: [source,js] -------------------------------------------------- GET /my-index/\_search { "query" : { "percolate" : { "field": "query", "document" : { "message" : "The quick brown fox jumps over the lazy dog" } } }, "highlight": { "fields": { "message": {} } } } -------------------------------------------------- // CONSOLE // TEST[continued] This will yield the following response. [source,js] -------------------------------------------------- { "took": 7, "timed\_out": false, "\_shards": { "total": 5, "successful": 5, "skipped" : 0, "failed": 0 }, "hits": { "total": 2, "max\_score": 0.5753642, "hits": [ { "\_index": "my-index", "\_type": "\_doc", "\_id": "4", "\_score": 0.5753642, "\_source": { "query": { "match": { "message": "lazy dog" } } }, "highlight": { "message": [ "The quick brown fox jumps over the *lazy* *dog*" <1> ] }, "fields" : { "\_percolator\_document\_slot" : [0] } }, { "\_index": "my-index", "\_type": "\_doc", "\_id": "3", "\_score": 0.5753642, "\_source": { "query": { "match": { "message": "brown fox" } } }, "highlight": { "message": [ "The quick *brown* *fox* jumps over the lazy dog" <1> ] }, "fields" : { "\_percolator\_document\_slot" : [0] } } ] } } -------------------------------------------------- // TESTRESPONSE[s/"took": 7,/"took": "$body.took",/] <1> The terms from each query have been highlighted in the document. Instead of the query in the search request highlighting the percolator hits, the percolator queries are highlighting the document defined in the `percolate` query. When percolating multiple documents at the same time like the request below then the highlight response is different: [source,js] -------------------------------------------------- GET /my-index/\_search { "query" : { "percolate" : { "field": "query", "documents" : [ { "message" : "bonsai tree" }, { "message" : "new tree" }, { "message" : "the office" }, { "message" : "office tree" } ] } }, "highlight": { "fields": { "message": {} } } } -------------------------------------------------- // CONSOLE // TEST[continued] The slightly different response: [source,js] -------------------------------------------------- { "took": 13, "timed\_out": false, "\_shards": { "total": 5, "successful": 5, "skipped" : 0, "failed": 0 }, "hits": { "total": 1, "max\_score": 1.5606477, "hits": [ { "\_index": "my-index", "\_type": "\_doc", "\_id": "1", "\_score": 1.5606477, "\_source": { "query": { "match": { "message": "bonsai tree" } } }, "fields" : { "\_percolator\_document\_slot" : [0, 1, 3] }, "highlight" : { <1> "0\_message" : [ "*bonsai* *tree*" ], "3\_message" : [ "office *tree*" ], "1\_message" : [ "new *tree*" ] } } ] } } -------------------------------------------------- // TESTRESPONSE[s/"took": 13,/"took": "$body.took",/] <1> The highlight fields have been prefixed with the document slot they belong to, in
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/percolate-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.020973
|
1, 3] }, "highlight" : { <1> "0\_message" : [ "*bonsai* *tree*" ], "3\_message" : [ "office *tree*" ], "1\_message" : [ "new *tree*" ] } } ] } } -------------------------------------------------- // TESTRESPONSE[s/"took": 13,/"took": "$body.took",/] <1> The highlight fields have been prefixed with the document slot they belong to, in order to know which highlight field belongs to what document. [float] ==== Specifying multiple percolate queries It is possible to specify multiple `percolate` queries in a single search request: [source,js] -------------------------------------------------- GET /my-index/\_search { "query" : { "bool" : { "should" : [ { "percolate" : { "field" : "query", "document" : { "message" : "bonsai tree" }, "name": "query1" <1> } }, { "percolate" : { "field" : "query", "document" : { "message" : "tulip flower" }, "name": "query2" <1> } } ] } } } -------------------------------------------------- // CONSOLE // TEST[continued] <1> The `name` parameter will be used to identify which percolator document slots belong to what `percolate` query. The `\_percolator\_document\_slot` field name will be suffixed with what is specified in the `\_name` parameter. If that isn't specified then the `field` parameter will be used, which in this case will result in ambiguity. The above search request returns a response similar to this: [source,js] -------------------------------------------------- { "took": 13, "timed\_out": false, "\_shards": { "total": 5, "successful": 5, "skipped" : 0, "failed": 0 }, "hits": { "total": 1, "max\_score": 0.5753642, "hits": [ { "\_index": "my-index", "\_type": "\_doc", "\_id": "1", "\_score": 0.5753642, "\_source": { "query": { "match": { "message": "bonsai tree" } } }, "fields" : { "\_percolator\_document\_slot\_query1" : [0] <1> } } ] } } -------------------------------------------------- // TESTRESPONSE[s/"took": 13,/"took": "$body.took",/] <1> The `\_percolator\_document\_slot\_query1` percolator slot field indicates that these matched slots are from the `percolate` query with `\_name` parameter set to `query1`. [float] ==== How it Works Under the Hood When indexing a document into an index that has the <> mapping configured, the query part of the document gets parsed into a Lucene query and is stored into the Lucene index. A binary representation of the query gets stored, but also the query's terms are analyzed and stored into an indexed field. At search time, the document specified in the request gets parsed into a Lucene document and is stored in a in-memory temporary Lucene index. This in-memory index can just hold this one document and it is optimized for that. After this a special query is built based on the terms in the in-memory index that select candidate percolator queries based on their indexed query terms. These queries are then evaluated by the in-memory index if they actually match. The selecting of candidate percolator queries matches is an important performance optimization during the execution of the `percolate` query as it can significantly reduce the number of candidate matches the in-memory index needs to evaluate. The reason the `percolate` query can do this is because during indexing of the percolator queries the query terms are being extracted and indexed with the percolator query. Unfortunately the percolator cannot extract terms from all queries (for example the `wildcard` or `geo\_shape` query) and as a result of that in certain cases the percolator can't do the selecting optimization (for example if an unsupported query is defined in a required clause of a boolean query or the unsupported query is the only query in the percolator document). These queries are marked by the percolator and can be found by running the following search: [source,js] --------------------------------------------------- GET /\_search { "query": { "term" : { "query.extraction\_result" : "failed" } } } --------------------------------------------------- // CONSOLE NOTE: The above example assumes that there is a `query` field of
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/percolate-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.043058
|
the percolator document). These queries are marked by the percolator and can be found by running the following search: [source,js] --------------------------------------------------- GET /\_search { "query": { "term" : { "query.extraction\_result" : "failed" } } } --------------------------------------------------- // CONSOLE NOTE: The above example assumes that there is a `query` field of type `percolator` in the mappings.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/percolate-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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|
[[query-dsl-function-score-query]] === Function Score Query The `function\_score` allows you to modify the score of documents that are retrieved by a query. This can be useful if, for example, a score function is computationally expensive and it is sufficient to compute the score on a filtered set of documents. To use `function\_score`, the user has to define a query and one or more functions, that compute a new score for each document returned by the query. `function\_score` can be used with only one function like this: [source,js] -------------------------------------------------- GET /\_search { "query": { "function\_score": { "query": { "match\_all": {} }, "boost": "5", "random\_score": {}, <1> "boost\_mode":"multiply" } } } -------------------------------------------------- // CONSOLE // TEST[setup:twitter] <1> See <> for a list of supported functions. Furthermore, several functions can be combined. In this case one can optionally choose to apply the function only if a document matches a given filtering query [source,js] -------------------------------------------------- GET /\_search { "query": { "function\_score": { "query": { "match\_all": {} }, "boost": "5", <1> "functions": [ { "filter": { "match": { "test": "bar" } }, "random\_score": {}, <2> "weight": 23 }, { "filter": { "match": { "test": "cat" } }, "weight": 42 } ], "max\_boost": 42, "score\_mode": "max", "boost\_mode": "multiply", "min\_score" : 42 } } } -------------------------------------------------- // CONSOLE // TEST[setup:twitter] <1> Boost for the whole query. <2> See <> for a list of supported functions. NOTE: The scores produced by the filtering query of each function do not matter. If no filter is given with a function this is equivalent to specifying `"match\_all": {}` First, each document is scored by the defined functions. The parameter `score\_mode` specifies how the computed scores are combined: [horizontal] `multiply`:: scores are multiplied (default) `sum`:: scores are summed `avg`:: scores are averaged `first`:: the first function that has a matching filter is applied `max`:: maximum score is used `min`:: minimum score is used Because scores can be on different scales (for example, between 0 and 1 for decay functions but arbitrary for `field\_value\_factor`) and also because sometimes a different impact of functions on the score is desirable, the score of each function can be adjusted with a user defined `weight`. The `weight` can be defined per function in the `functions` array (example above) and is multiplied with the score computed by the respective function. If weight is given without any other function declaration, `weight` acts as a function that simply returns the `weight`. In case `score\_mode` is set to `avg` the individual scores will be combined by a \*\*weighted\*\* average. For example, if two functions return score 1 and 2 and their respective weights are 3 and 4, then their scores will be combined as `(1\*3+2\*4)/(3+4)` and \*\*not\*\* `(1\*3+2\*4)/2`. The new score can be restricted to not exceed a certain limit by setting the `max\_boost` parameter. The default for `max\_boost` is FLT\_MAX. The newly computed score is combined with the score of the query. The parameter `boost\_mode` defines how: [horizontal] `multiply`:: query score and function score is multiplied (default) `replace`:: only function score is used, the query score is ignored `sum`:: query score and function score are added `avg`:: average `max`:: max of query score and function score `min`:: min of query score and function score By default, modifying the score does not change which documents match. To exclude documents that do not meet a certain score threshold the `min\_score` parameter can be set to the desired score threshold. NOTE: For `min\_score` to work, \*\*all\*\* documents returned by the query need to be scored and then filtered out one by one. [[score-functions]] The `function\_score` query provides several types of score functions. \* <>
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/function-score-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.058834
|
meet a certain score threshold the `min\_score` parameter can be set to the desired score threshold. NOTE: For `min\_score` to work, \*\*all\*\* documents returned by the query need to be scored and then filtered out one by one. [[score-functions]] The `function\_score` query provides several types of score functions. \* <> \* <> \* <> \* <> \* <>: `gauss`, `linear`, `exp` [[function-script-score]] ==== Script score The `script\_score` function allows you to wrap another query and customize the scoring of it optionally with a computation derived from other numeric field values in the doc using a script expression. Here is a simple sample: [source,js] -------------------------------------------------- GET /\_search { "query": { "function\_score": { "query": { "match": { "message": "elasticsearch" } }, "script\_score" : { "script" : { "source": "Math.log(2 + doc['likes'].value)" } } } } } -------------------------------------------------- // CONSOLE // TEST[setup:twitter] On top of the different scripting field values and expression, the `\_score` script parameter can be used to retrieve the score based on the wrapped query. Scripts compilation is cached for faster execution. If the script has parameters that it needs to take into account, it is preferable to reuse the same script, and provide parameters to it: [source,js] -------------------------------------------------- GET /\_search { "query": { "function\_score": { "query": { "match": { "message": "elasticsearch" } }, "script\_score" : { "script" : { "params": { "a": 5, "b": 1.2 }, "source": "params.a / Math.pow(params.b, doc['likes'].value)" } } } } } -------------------------------------------------- // CONSOLE // TEST[setup:twitter] Note that unlike the `custom\_score` query, the score of the query is multiplied with the result of the script scoring. If you wish to inhibit this, set `"boost\_mode": "replace"` [[function-weight]] ==== Weight The `weight` score allows you to multiply the score by the provided `weight`. This can sometimes be desired since boost value set on specific queries gets normalized, while for this score function it does not. The number value is of type float. [source,js] -------------------------------------------------- "weight" : number -------------------------------------------------- // NOTCONSOLE // I couldn't come up with a good example for this one. [[function-random]] ==== Random The `random\_score` generates scores that are uniformly distributed in [0, 1[. By default, it uses the internal Lucene doc ids as a source of randomness, which is very efficient but unfortunately not reproducible since documents might be renumbered by merges. In case you want scores to be reproducible, it is possible to provide a `seed` and `field`. The final score will then be computed based on this seed, the minimum value of `field` for the considered document and a salt that is computed based on the index name and shard id so that documents that have the same value but are stored in different indexes get different scores. Note that documents that are within the same shard and have the same value for `field` will however get the same score, so it is usually desirable to use a field that has unique values for all documents. A good default choice might be to use the `\_seq\_no` field, whose only drawback is that scores will change if the document is updated since update operations also update the value of the `\_seq\_no` field. NOTE: It was possible to set a seed without setting a field, but this has been deprecated as this requires loading fielddata on the `\_id` field which consumes a lot of memory. [source,js] -------------------------------------------------- GET /\_search { "query": { "function\_score": { "random\_score": { "seed": 10, "field": "\_seq\_no" } } } } -------------------------------------------------- // CONSOLE // TEST[setup:twitter] [[function-field-value-factor]] ==== Field Value factor The `field\_value\_factor` function allows you to use a field from a document to influence the score. It's similar to
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/function-score-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
[
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|
of memory. [source,js] -------------------------------------------------- GET /\_search { "query": { "function\_score": { "random\_score": { "seed": 10, "field": "\_seq\_no" } } } } -------------------------------------------------- // CONSOLE // TEST[setup:twitter] [[function-field-value-factor]] ==== Field Value factor The `field\_value\_factor` function allows you to use a field from a document to influence the score. It's similar to using the `script\_score` function, however, it avoids the overhead of scripting. If used on a multi-valued field, only the first value of the field is used in calculations. As an example, imagine you have a document indexed with a numeric `likes` field and wish to influence the score of a document with this field, an example doing so would look like: [source,js] -------------------------------------------------- GET /\_search { "query": { "function\_score": { "field\_value\_factor": { "field": "likes", "factor": 1.2, "modifier": "sqrt", "missing": 1 } } } } -------------------------------------------------- // CONSOLE // TEST[setup:twitter] Which will translate into the following formula for scoring: `sqrt(1.2 \* doc['likes'].value)` There are a number of options for the `field\_value\_factor` function: [horizontal] `field`:: Field to be extracted from the document. `factor`:: Optional factor to multiply the field value with, defaults to `1`. `modifier`:: Modifier to apply to the field value, can be one of: `none`, `log`, `log1p`, `log2p`, `ln`, `ln1p`, `ln2p`, `square`, `sqrt`, or `reciprocal`. Defaults to `none`. [cols="<,<",options="header",] |======================================================================= | Modifier | Meaning | `none` | Do not apply any multiplier to the field value | `log` | Take the https://en.wikipedia.org/wiki/Common\_logarithm[common logarithm] of the field value | `log1p` | Add 1 to the field value and take the common logarithm | `log2p` | Add 2 to the field value and take the common logarithm | `ln` | Take the https://en.wikipedia.org/wiki/Natural\_logarithm[natural logarithm] of the field value | `ln1p` | Add 1 to the field value and take the natural logarithm | `ln2p` | Add 2 to the field value and take the natural logarithm | `square` | Square the field value (multiply it by itself) | `sqrt` | Take the https://en.wikipedia.org/wiki/Square\_root[square root] of the field value | `reciprocal` | https://en.wikipedia.org/wiki/Multiplicative\_inverse[Reciprocate] the field value, same as `1/x` where `x` is the field's value |======================================================================= `missing`:: Value used if the document doesn't have that field. The modifier and factor are still applied to it as though it were read from the document. Keep in mind that taking the log() of 0, or the square root of a negative number is an illegal operation, and an exception will be thrown. Be sure to limit the values of the field with a range filter to avoid this, or use `log1p` and `ln1p`. [[function-decay]] ==== Decay functions Decay functions score a document with a function that decays depending on the distance of a numeric field value of the document from a user given origin. This is similar to a range query, but with smooth edges instead of boxes. To use distance scoring on a query that has numerical fields, the user has to define an `origin` and a `scale` for each field. The `origin` is needed to define the ``central point'' from which the distance is calculated, and the `scale` to define the rate of decay. The decay function is specified as [source,js] -------------------------------------------------- "DECAY\_FUNCTION": { <1> "FIELD\_NAME": { <2> "origin": "11, 12", "scale": "2km", "offset": "0km", "decay": 0.33 } } -------------------------------------------------- // NOTCONSOLE <1> The `DECAY\_FUNCTION` should be one of `linear`, `exp`, or `gauss`. <2> The specified field must be a numeric, date, or geo-point field. In the above example, the field is a <> and origin can be provided in geo format. `scale` and `offset` must be given with a unit in this case. If your field is a date field, you can
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/function-score-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.049581
|
<2> The specified field must be a numeric, date, or geo-point field. In the above example, the field is a <> and origin can be provided in geo format. `scale` and `offset` must be given with a unit in this case. If your field is a date field, you can set `scale` and `offset` as days, weeks, and so on. Example: [source,js] -------------------------------------------------- GET /\_search { "query": { "function\_score": { "gauss": { "date": { "origin": "2013-09-17", <1> "scale": "10d", "offset": "5d", <2> "decay" : 0.5 <2> } } } } } -------------------------------------------------- // CONSOLE // TEST[setup:twitter] <1> The date format of the origin depends on the <> defined in your mapping. If you do not define the origin, the current time is used. <2> The `offset` and `decay` parameters are optional. [horizontal] `origin`:: The point of origin used for calculating distance. Must be given as a number for numeric field, date for date fields and geo point for geo fields. Required for geo and numeric field. For date fields the default is `now`. Date math (for example `now-1h`) is supported for origin. `scale`:: Required for all types. Defines the distance from origin + offset at which the computed score will equal `decay` parameter. For geo fields: Can be defined as number+unit (1km, 12m,...). Default unit is meters. For date fields: Can to be defined as a number+unit ("1h", "10d",...). Default unit is milliseconds. For numeric field: Any number. `offset`:: If an `offset` is defined, the decay function will only compute the decay function for documents with a distance greater that the defined `offset`. The default is 0. `decay`:: The `decay` parameter defines how documents are scored at the distance given at `scale`. If no `decay` is defined, documents at the distance `scale` will be scored 0.5. In the first example, your documents might represents hotels and contain a geo location field. You want to compute a decay function depending on how far the hotel is from a given location. You might not immediately see what scale to choose for the gauss function, but you can say something like: "At a distance of 2km from the desired location, the score should be reduced to one third." The parameter "scale" will then be adjusted automatically to assure that the score function computes a score of 0.33 for hotels that are 2km away from the desired location. In the second example, documents with a field value between 2013-09-12 and 2013-09-22 would get a weight of 1.0 and documents which are 15 days from that date a weight of 0.5. ===== Supported decay functions The `DECAY\_FUNCTION` determines the shape of the decay: `gauss`:: + -- Normal decay, computed as: image:images/Gaussian.png[] where image:images/sigma.png[] is computed to assure that the score takes the value `decay` at distance `scale` from `origin`+-`offset` // \sigma^2 = -scale^2/(2 \cdot ln(decay)) image:images/sigma\_calc.png[] See <> for graphs demonstrating the curve generated by the `gauss` function. -- `exp`:: + -- Exponential decay, computed as: image:images/Exponential.png[] where again the parameter image:images/lambda.png[] is computed to assure that the score takes the value `decay` at distance `scale` from `origin`+-`offset` // \lambda = ln(decay)/scale image:images/lambda\_calc.png[] See <> for graphs demonstrating the curve generated by the `exp` function. -- `linear`:: + -- Linear decay, computed as: image:images/Linear.png[]. where again the parameter `s` is computed to assure that the score takes the value `decay` at distance `scale` from `origin`+-`offset` image:images/s\_calc.png[] In contrast to the normal and exponential decay, this function actually sets the score to 0 if the field value exceeds twice the user given scale value. -- For single functions the three decay functions together with their parameters
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/function-score-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.036402
|
score takes the value `decay` at distance `scale` from `origin`+-`offset` image:images/s\_calc.png[] In contrast to the normal and exponential decay, this function actually sets the score to 0 if the field value exceeds twice the user given scale value. -- For single functions the three decay functions together with their parameters can be visualized like this (the field in this example called "age"): image:images/decay\_2d.png[width=600] ===== Multi-values fields If a field used for computing the decay contains multiple values, per default the value closest to the origin is chosen for determining the distance. This can be changed by setting `multi\_value\_mode`. [horizontal] `min`:: Distance is the minimum distance `max`:: Distance is the maximum distance `avg`:: Distance is the average distance `sum`:: Distance is the sum of all distances Example: [source,js] -------------------------------------------------- "DECAY\_FUNCTION": { "FIELD\_NAME": { "origin": ..., "scale": ... }, "multi\_value\_mode": "avg" } -------------------------------------------------- // NOTCONSOLE ==== Detailed example Suppose you are searching for a hotel in a certain town. Your budget is limited. Also, you would like the hotel to be close to the town center, so the farther the hotel is from the desired location the less likely you are to check in. You would like the query results that match your criterion (for example, "hotel, Nancy, non-smoker") to be scored with respect to distance to the town center and also the price. Intuitively, you would like to define the town center as the origin and maybe you are willing to walk 2km to the town center from the hotel. + In this case your \*origin\* for the location field is the town center and the \*scale\* is ~2km. If your budget is low, you would probably prefer something cheap above something expensive. For the price field, the \*origin\* would be 0 Euros and the \*scale\* depends on how much you are willing to pay, for example 20 Euros. In this example, the fields might be called "price" for the price of the hotel and "location" for the coordinates of this hotel. The function for `price` in this case would be [source,js] -------------------------------------------------- "gauss": { <1> "price": { "origin": "0", "scale": "20" } } -------------------------------------------------- // NOTCONSOLE <1> This decay function could also be `linear` or `exp`. and for `location`: [source,js] -------------------------------------------------- "gauss": { <1> "location": { "origin": "11, 12", "scale": "2km" } } -------------------------------------------------- // NOTCONSOLE <1> This decay function could also be `linear` or `exp`. Suppose you want to multiply these two functions on the original score, the request would look like this: [source,js] -------------------------------------------------- GET /\_search { "query": { "function\_score": { "functions": [ { "gauss": { "price": { "origin": "0", "scale": "20" } } }, { "gauss": { "location": { "origin": "11, 12", "scale": "2km" } } } ], "query": { "match": { "properties": "balcony" } }, "score\_mode": "multiply" } } } -------------------------------------------------- // CONSOLE Next, we show how the computed score looks like for each of the three possible decay functions. [[gauss-decay]] ===== Normal decay, keyword `gauss` When choosing `gauss` as the decay function in the above example, the contour and surface plot of the multiplier looks like this: image::https://f.cloud.github.com/assets/4320215/768157/cd0e18a6-e898-11e2-9b3c-f0145078bd6f.png[width="700px"] image::https://f.cloud.github.com/assets/4320215/768160/ec43c928-e898-11e2-8e0d-f3c4519dbd89.png[width="700px"] Suppose your original search results matches three hotels : \* "Backback Nap" \* "Drink n Drive" \* "BnB Bellevue". "Drink n Drive" is pretty far from your defined location (nearly 2 km) and is not too cheap (about 13 Euros) so it gets a low factor a factor of 0.56. "BnB Bellevue" and "Backback Nap" are both pretty close to the defined location but "BnB Bellevue" is cheaper, so it gets a multiplier of 0.86 whereas "Backpack Nap" gets a value of 0.66. [[exp-decay]] ===== Exponential
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/function-score-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.080064
|
cheap (about 13 Euros) so it gets a low factor a factor of 0.56. "BnB Bellevue" and "Backback Nap" are both pretty close to the defined location but "BnB Bellevue" is cheaper, so it gets a multiplier of 0.86 whereas "Backpack Nap" gets a value of 0.66. [[exp-decay]] ===== Exponential decay, keyword `exp` When choosing `exp` as the decay function in the above example, the contour and surface plot of the multiplier looks like this: image::https://f.cloud.github.com/assets/4320215/768161/082975c0-e899-11e2-86f7-174c3a729d64.png[width="700px"] image::https://f.cloud.github.com/assets/4320215/768162/0b606884-e899-11e2-907b-aefc77eefef6.png[width="700px"] [[linear-decay]] ===== Linear decay, keyword `linear` When choosing `linear` as the decay function in the above example, the contour and surface plot of the multiplier looks like this: image::https://f.cloud.github.com/assets/4320215/768164/1775b0ca-e899-11e2-9f4a-776b406305c6.png[width="700px"] image::https://f.cloud.github.com/assets/4320215/768165/19d8b1aa-e899-11e2-91bc-6b0553e8d722.png[width="700px"] ==== Supported fields for decay functions Only numeric, date, and geo-point fields are supported. ==== What if a field is missing? If the numeric field is missing in the document, the function will return 1.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/function-score-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.027238
|
[[query-dsl-bool-query]] === Bool Query A query that matches documents matching boolean combinations of other queries. The bool query maps to Lucene `BooleanQuery`. It is built using one or more boolean clauses, each clause with a typed occurrence. The occurrence types are: [cols="<,<",options="header",] |======================================================================= |Occur |Description |`must` |The clause (query) must appear in matching documents and will contribute to the score. |`filter` |The clause (query) must appear in matching documents. However unlike `must` the score of the query will be ignored. Filter clauses are executed in <>, meaning that scoring is ignored and clauses are considered for caching. |`should` |The clause (query) should appear in the matching document. If the `bool` query is in a <> and has a `must` or `filter` clause then a document will match the `bool` query even if none of the `should` queries match. In this case these clauses are only used to influence the score. If the `bool` query is a <> or has neither `must` or `filter` then at least one of the `should` queries must match a document for it to match the `bool` query. This behavior may be explicitly controlled by settings the <> parameter. |`must\_not` |The clause (query) must not appear in the matching documents. Clauses are executed in <> meaning that scoring is ignored and clauses are considered for caching. Because scoring is ignored, a score of `0` for all documents is returned. |======================================================================= [IMPORTANT] .Bool query in filter context ======================================================================== If this query is used in a filter context and it has `should` clauses then at least one `should` clause is required to match. ======================================================================== The `bool` query takes a \_more-matches-is-better\_ approach, so the score from each matching `must` or `should` clause will be added together to provide the final `\_score` for each document. [source,js] -------------------------------------------------- POST \_search { "query": { "bool" : { "must" : { "term" : { "user" : "kimchy" } }, "filter": { "term" : { "tag" : "tech" } }, "must\_not" : { "range" : { "age" : { "gte" : 10, "lte" : 20 } } }, "should" : [ { "term" : { "tag" : "wow" } }, { "term" : { "tag" : "elasticsearch" } } ], "minimum\_should\_match" : 1, "boost" : 1.0 } } } -------------------------------------------------- // CONSOLE [[score-bool-filter]] ==== Scoring with `bool.filter` Queries specified under the `filter` element have no effect on scoring -- scores are returned as `0`. Scores are only affected by the query that has been specified. For instance, all three of the following queries return all documents where the `status` field contains the term `active`. This first query assigns a score of `0` to all documents, as no scoring query has been specified: [source,js] --------------------------------- GET \_search { "query": { "bool": { "filter": { "term": { "status": "active" } } } } } --------------------------------- // CONSOLE This `bool` query has a `match\_all` query, which assigns a score of `1.0` to all documents. [source,js] --------------------------------- GET \_search { "query": { "bool": { "must": { "match\_all": {} }, "filter": { "term": { "status": "active" } } } } } --------------------------------- // CONSOLE This `constant\_score` query behaves in exactly the same way as the second example above. The `constant\_score` query assigns a score of `1.0` to all documents matched by the filter. [source,js] --------------------------------- GET \_search { "query": { "constant\_score": { "filter": { "term": { "status": "active" } } } } } --------------------------------- // CONSOLE ==== Using named queries to see which clauses matched If you need to know which of the clauses in the bool query matched the documents returned from the query, you can use <>
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/bool-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.07773
|
{ "constant\_score": { "filter": { "term": { "status": "active" } } } } } --------------------------------- // CONSOLE ==== Using named queries to see which clauses matched If you need to know which of the clauses in the bool query matched the documents returned from the query, you can use <> to assign a name to each clause.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/bool-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.056759
|
[[full-text-queries]] == Full text queries The high-level full text queries are usually used for running full text queries on full text fields like the body of an email. They understand how the field being queried is <> and will apply each field's `analyzer` (or `search\_analyzer`) to the query string before executing. The queries in this group are: <>:: The standard query for performing full text queries, including fuzzy matching and phrase or proximity queries. <>:: Like the `match` query but used for matching exact phrases or word proximity matches. <>:: The poor man's \_search-as-you-type\_. Like the `match\_phrase` query, but does a wildcard search on the final word. <>:: The multi-field version of the `match` query. <>:: A more specialized query which gives more preference to uncommon words. <>:: Supports the compact Lucene <>, allowing you to specify AND|OR|NOT conditions and multi-field search within a single query string. For expert users only. <>:: A simpler, more robust version of the `query\_string` syntax suitable for exposing directly to users. include::match-query.asciidoc[] include::match-phrase-query.asciidoc[] include::match-phrase-prefix-query.asciidoc[] include::multi-match-query.asciidoc[] include::common-terms-query.asciidoc[] include::query-string-query.asciidoc[] include::simple-query-string-query.asciidoc[]
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/full-text-queries.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.129851
|
[[query-dsl-exists-query]] === Exists Query Returns documents that have at least one non-`null` value in the original field: [source,js] -------------------------------------------------- GET /\_search { "query": { "exists" : { "field" : "user" } } } -------------------------------------------------- // CONSOLE For instance, these documents would all match the above query: [source,js] -------------------------------------------------- { "user": "jane" } { "user": "" } <1> { "user": "-" } <2> { "user": ["jane"] } { "user": ["jane", null ] } <3> -------------------------------------------------- // NOTCONSOLE <1> An empty string is a non-`null` value. <2> Even though the `standard` analyzer would emit zero tokens, the original field is non-`null`. <3> At least one non-`null` value is required. These documents would \*not\* match the above query: [source,js] -------------------------------------------------- { "user": null } { "user": [] } <1> { "user": [null] } <2> { "foo": "bar" } <3> -------------------------------------------------- // NOTCONSOLE <1> This field has no values. <2> At least one non-`null` value is required. <3> The `user` field is missing completely. [float] [[null-value-mapping]] ==== `null\_value` mapping If the field mapping includes the <> setting then explicit `null` values are replaced with the specified `null\_value`. For instance, if the `user` field were mapped as follows: [source,js] -------------------------------------------------- PUT /example { "mappings": { "doc": { "properties": { "user": { "type": "keyword", "null\_value": "\_null\_" } } } } } -------------------------------------------------- // CONSOLE then explicit `null` values would be indexed as the string `\_null\_`, and the following docs would match the `exists` filter: [source,js] -------------------------------------------------- { "user": null } { "user": [null] } -------------------------------------------------- // NOTCONSOLE However, these docs--without explicit `null` values--would still have no values in the `user` field and thus would not match the `exists` filter: [source,js] -------------------------------------------------- { "user": [] } { "foo": "bar" } -------------------------------------------------- // NOTCONSOLE [[missing-query]] ==== `missing` query There isn't a `missing` query. Instead use the `exists` query inside a `must\_not` clause as follows: [source,js] -------------------------------------------------- GET /\_search { "query": { "bool": { "must\_not": { "exists": { "field": "user" } } } } } -------------------------------------------------- // CONSOLE This query returns documents that have no value in the user field.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/exists-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.019151
|
[[query-dsl-span-not-query]] === Span Not Query Removes matches which overlap with another span query or which are within x tokens before (controlled by the parameter `pre`) or y tokens after (controled by the parameter `post`) another SpanQuery. The span not query maps to Lucene `SpanNotQuery`. Here is an example: [source,js] -------------------------------------------------- GET /\_search { "query": { "span\_not" : { "include" : { "span\_term" : { "field1" : "hoya" } }, "exclude" : { "span\_near" : { "clauses" : [ { "span\_term" : { "field1" : "la" } }, { "span\_term" : { "field1" : "hoya" } } ], "slop" : 0, "in\_order" : true } } } } } -------------------------------------------------- // CONSOLE The `include` and `exclude` clauses can be any span type query. The `include` clause is the span query whose matches are filtered, and the `exclude` clause is the span query whose matches must not overlap those returned. In the above example all documents with the term hoya are filtered except the ones that have 'la' preceding them. Other top level options: [horizontal] `pre`:: If set the amount of tokens before the include span can't have overlap with the exclude span. Defaults to 0. `post`:: If set the amount of tokens after the include span can't have overlap with the exclude span. Defaults to 0. `dist`:: If set the amount of tokens from within the include span can't have overlap with the exclude span. Equivalent of setting both `pre` and `post`.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/span-not-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.029157
|
[[query-dsl-match-query-phrase-prefix]] === Match Phrase Prefix Query The `match\_phrase\_prefix` is the same as `match\_phrase`, except that it allows for prefix matches on the last term in the text. For example: [source,js] -------------------------------------------------- GET /\_search { "query": { "match\_phrase\_prefix" : { "message" : "quick brown f" } } } -------------------------------------------------- // CONSOLE It accepts the same parameters as the phrase type. In addition, it also accepts a `max\_expansions` parameter (default `50`) that can control to how many suffixes the last term will be expanded. It is highly recommended to set it to an acceptable value to control the execution time of the query. For example: [source,js] -------------------------------------------------- GET /\_search { "query": { "match\_phrase\_prefix" : { "message" : { "query" : "quick brown f", "max\_expansions" : 10 } } } } -------------------------------------------------- // CONSOLE [IMPORTANT] =================================================== The `match\_phrase\_prefix` query is a poor-man's autocomplete. It is very easy to use, which lets you get started quickly with \_search-as-you-type\_ but its results, which usually are good enough, can sometimes be confusing. Consider the query string `quick brown f`. This query works by creating a phrase query out of `quick` and `brown` (i.e. the term `quick` must exist and must be followed by the term `brown`). Then it looks at the sorted term dictionary to find the first 50 terms that begin with `f`, and adds these terms to the phrase query. The problem is that the first 50 terms may not include the term `fox` so the phrase `quick brown fox` will not be found. This usually isn't a problem as the user will continue to type more letters until the word they are looking for appears. For better solutions for \_search-as-you-type\_ see the <> and {defguide}/\_index\_time\_search\_as\_you\_type.html[Index-Time Search-as-You-Type]. ===================================================
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/match-phrase-prefix-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.066965
|
[[query-dsl-match-query]] === Match Query `match` queries accept text/numerics/dates, analyzes them, and constructs a query. For example: [source,js] -------------------------------------------------- GET /\_search { "query": { "match" : { "message" : "this is a test" } } } -------------------------------------------------- // CONSOLE Note, `message` is the name of a field, you can substitute the name of any field instead. [[query-dsl-match-query-boolean]] ==== match The `match` query is of type `boolean`. It means that the text provided is analyzed and the analysis process constructs a boolean query from the provided text. The `operator` flag can be set to `or` or `and` to control the boolean clauses (defaults to `or`). The minimum number of optional `should` clauses to match can be set using the <> parameter. The `analyzer` can be set to control which analyzer will perform the analysis process on the text. It defaults to the field explicit mapping definition, or the default search analyzer. The `lenient` parameter can be set to `true` to ignore exceptions caused by data-type mismatches, such as trying to query a numeric field with a text query string. Defaults to `false`. [[query-dsl-match-query-fuzziness]] ===== Fuzziness `fuzziness` allows \_fuzzy matching\_ based on the type of field being queried. See <> for allowed settings. The `prefix\_length` and `max\_expansions` can be set in this case to control the fuzzy process. If the fuzzy option is set the query will use `top\_terms\_blended\_freqs\_${max\_expansions}` as its <> the `fuzzy\_rewrite` parameter allows to control how the query will get rewritten. Fuzzy transpositions (`ab` -> `ba`) are allowed by default but can be disabled by setting `fuzzy\_transpositions` to `false`. Note that fuzzy matching is not applied to terms with synonyms, as under the hood these terms are expanded to a special synonym query that blends term frequencies, which does not support fuzzy expansion. [source,js] -------------------------------------------------- GET /\_search { "query": { "match" : { "message" : { "query" : "this is a test", "operator" : "and" } } } } -------------------------------------------------- // CONSOLE [[query-dsl-match-query-zero]] ===== Zero terms query If the analyzer used removes all tokens in a query like a `stop` filter does, the default behavior is to match no documents at all. In order to change that the `zero\_terms\_query` option can be used, which accepts `none` (default) and `all` which corresponds to a `match\_all` query. [source,js] -------------------------------------------------- GET /\_search { "query": { "match" : { "message" : { "query" : "to be or not to be", "operator" : "and", "zero\_terms\_query": "all" } } } } -------------------------------------------------- // CONSOLE [[query-dsl-match-query-cutoff]] ===== Cutoff frequency The match query supports a `cutoff\_frequency` that allows specifying an absolute or relative document frequency where high frequency terms are moved into an optional subquery and are only scored if one of the low frequency (below the cutoff) terms in the case of an `or` operator or all of the low frequency terms in the case of an `and` operator match. This query allows handling `stopwords` dynamically at runtime, is domain independent and doesn't require a stopword file. It prevents scoring / iterating high frequency terms and only takes the terms into account if a more significant / lower frequency term matches a document. Yet, if all of the query terms are above the given `cutoff\_frequency` the query is automatically transformed into a pure conjunction (`and`) query to ensure fast execution. The `cutoff\_frequency` can either be relative to the total number of documents if in the range `[0..1)` or absolute if greater or equal to `1.0`. Here is an example showing a query composed of stopwords exclusively: [source,js] -------------------------------------------------- GET /\_search { "query": { "match" : { "message" : { "query" : "to be or not to be",
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/match-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.086091
|
the total number of documents if in the range `[0..1)` or absolute if greater or equal to `1.0`. Here is an example showing a query composed of stopwords exclusively: [source,js] -------------------------------------------------- GET /\_search { "query": { "match" : { "message" : { "query" : "to be or not to be", "cutoff\_frequency" : 0.001 } } } } -------------------------------------------------- // CONSOLE IMPORTANT: The `cutoff\_frequency` option operates on a per-shard-level. This means that when trying it out on test indexes with low document numbers you should follow the advice in {defguide}/relevance-is-broken.html[Relevance is broken]. [[query-dsl-match-query-synonyms]] ===== Synonyms The `match` query supports multi-terms synonym expansion with the <> token filter. When this filter is used, the parser creates a phrase query for each multi-terms synonyms. For example, the following synonym: `"ny, new york" would produce:` `(ny OR ("new york"))` It is also possible to match multi terms synonyms with conjunctions instead: [source,js] -------------------------------------------------- GET /\_search { "query": { "match" : { "message": { "query" : "ny city", "auto\_generate\_synonyms\_phrase\_query" : false } } } } -------------------------------------------------- // CONSOLE The example above creates a boolean query: `(ny OR (new AND york)) city)` that matches documents with the term `ny` or the conjunction `new AND york`. By default the parameter `auto\_generate\_synonyms\_phrase\_query` is set to `true`. .Comparison to query\_string / field \*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\* The match family of queries does not go through a "query parsing" process. It does not support field name prefixes, wildcard characters, or other "advanced" features. For this reason, chances of it failing are very small / non existent, and it provides an excellent behavior when it comes to just analyze and run that text as a query behavior (which is usually what a text search box does). Also, the `phrase\_prefix` type can provide a great "as you type" behavior to automatically load search results. \*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*\*
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/match-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.046602
|
[[query-dsl-geo-bounding-box-query]] === Geo Bounding Box Query A query allowing to filter hits based on a point location using a bounding box. Assuming the following indexed document: [source,js] -------------------------------------------------- PUT /my\_locations { "mappings": { "\_doc": { "properties": { "pin": { "properties": { "location": { "type": "geo\_point" } } } } } } } PUT /my\_locations/\_doc/1 { "pin" : { "location" : { "lat" : 40.12, "lon" : -71.34 } } } -------------------------------------------------- // CONSOLE // TESTSETUP Then the following simple query can be executed with a `geo\_bounding\_box` filter: [source,js] -------------------------------------------------- GET /\_search { "query": { "bool" : { "must" : { "match\_all" : {} }, "filter" : { "geo\_bounding\_box" : { "pin.location" : { "top\_left" : { "lat" : 40.73, "lon" : -74.1 }, "bottom\_right" : { "lat" : 40.01, "lon" : -71.12 } } } } } } } -------------------------------------------------- // CONSOLE [float] ==== Query Options [cols="<,<",options="header",] |======================================================================= |Option |Description |`\_name` |Optional name field to identify the filter |`validation\_method` |Set to `IGNORE\_MALFORMED` to accept geo points with invalid latitude or longitude, set to `COERCE` to also try to infer correct latitude or longitude. (default is `STRICT`). |`type` |Set to one of `indexed` or `memory` to defines whether this filter will be executed in memory or indexed. See <> below for further details Default is `memory`. |======================================================================= [float] ==== Accepted Formats In much the same way the geo\_point type can accept different representations of the geo point, the filter can accept it as well: [float] ===== Lat Lon As Properties [source,js] -------------------------------------------------- GET /\_search { "query": { "bool" : { "must" : { "match\_all" : {} }, "filter" : { "geo\_bounding\_box" : { "pin.location" : { "top\_left" : { "lat" : 40.73, "lon" : -74.1 }, "bottom\_right" : { "lat" : 40.01, "lon" : -71.12 } } } } } } } -------------------------------------------------- // CONSOLE [float] ===== Lat Lon As Array Format in `[lon, lat]`, note, the order of lon/lat here in order to conform with http://geojson.org/[GeoJSON]. [source,js] -------------------------------------------------- GET /\_search { "query": { "bool" : { "must" : { "match\_all" : {} }, "filter" : { "geo\_bounding\_box" : { "pin.location" : { "top\_left" : [-74.1, 40.73], "bottom\_right" : [-71.12, 40.01] } } } } } } -------------------------------------------------- // CONSOLE [float] ===== Lat Lon As String Format in `lat,lon`. [source,js] -------------------------------------------------- GET /\_search { "query": { "bool" : { "must" : { "match\_all" : {} }, "filter" : { "geo\_bounding\_box" : { "pin.location" : { "top\_left" : "40.73, -74.1", "bottom\_right" : "40.01, -71.12" } } } } } } -------------------------------------------------- // CONSOLE [float] ===== Bounding Box as Well-Known Text (WKT) [source,js] -------------------------------------------------- GET /\_search { "query": { "bool" : { "must" : { "match\_all" : {} }, "filter" : { "geo\_bounding\_box" : { "pin.location" : { "wkt" : "BBOX (-74.1, -71.12, 40.73, 40.01)" } } } } } } -------------------------------------------------- // CONSOLE [float] ===== Geohash [source,js] -------------------------------------------------- GET /\_search { "query": { "bool" : { "must" : { "match\_all" : {} }, "filter" : { "geo\_bounding\_box" : { "pin.location" : { "top\_left" : "dr5r9ydj2y73", "bottom\_right" : "drj7teegpus6" } } } } } } -------------------------------------------------- // CONSOLE [float] ==== Vertices The vertices of the bounding box can either be set by `top\_left` and `bottom\_right` or by `top\_right` and `bottom\_left` parameters. More over the names `topLeft`, `bottomRight`, `topRight` and `bottomLeft` are supported. Instead of setting the values pairwise, one can use the simple names `top`, `left`, `bottom` and `right` to set the values separately. [source,js] -------------------------------------------------- GET /\_search { "query": { "bool" : { "must" : { "match\_all" : {} }, "filter" : { "geo\_bounding\_box" : { "pin.location" : { "top" : 40.73,
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/geo-bounding-box-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.011826
|
of setting the values pairwise, one can use the simple names `top`, `left`, `bottom` and `right` to set the values separately. [source,js] -------------------------------------------------- GET /\_search { "query": { "bool" : { "must" : { "match\_all" : {} }, "filter" : { "geo\_bounding\_box" : { "pin.location" : { "top" : 40.73, "left" : -74.1, "bottom" : 40.01, "right" : -71.12 } } } } } } -------------------------------------------------- // CONSOLE [float] ==== geo\_point Type The filter \*requires\* the `geo\_point` type to be set on the relevant field. [float] ==== Multi Location Per Document The filter can work with multiple locations / points per document. Once a single location / point matches the filter, the document will be included in the filter [float] [[geo-bbox-type]] ==== Type The type of the bounding box execution by default is set to `memory`, which means in memory checks if the doc falls within the bounding box range. In some cases, an `indexed` option will perform faster (but note that the `geo\_point` type must have lat and lon indexed in this case). Note, when using the indexed option, multi locations per document field are not supported. Here is an example: [source,js] -------------------------------------------------- GET /\_search { "query": { "bool" : { "must" : { "match\_all" : {} }, "filter" : { "geo\_bounding\_box" : { "pin.location" : { "top\_left" : { "lat" : 40.73, "lon" : -74.1 }, "bottom\_right" : { "lat" : 40.10, "lon" : -71.12 } }, "type" : "indexed" } } } } } -------------------------------------------------- // CONSOLE [float] ==== Ignore Unmapped When set to `true` the `ignore\_unmapped` option will ignore an unmapped field and will not match any documents for this query. This can be useful when querying multiple indexes which might have different mappings. When set to `false` (the default value) the query will throw an exception if the field is not mapped.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/geo-bounding-box-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | -0.028994
|
[[query-dsl-geo-shape-query]] === GeoShape Query Filter documents indexed using the `geo\_shape` type. Requires the <>. The `geo\_shape` query uses the same grid square representation as the `geo\_shape` mapping to find documents that have a shape that intersects with the query shape. It will also use the same PrefixTree configuration as defined for the field mapping. The query supports two ways of defining the query shape, either by providing a whole shape definition, or by referencing the name of a shape pre-indexed in another index. Both formats are defined below with examples. ==== Inline Shape Definition Similar to the `geo\_shape` type, the `geo\_shape` query uses http://www.geojson.org[GeoJSON] to represent shapes. Given the following index: [source,js] -------------------------------------------------- PUT /example { "mappings": { "\_doc": { "properties": { "location": { "type": "geo\_shape" } } } } } POST /example/\_doc?refresh { "name": "Wind & Wetter, Berlin, Germany", "location": { "type": "point", "coordinates": [13.400544, 52.530286] } } -------------------------------------------------- // CONSOLE // TESTSETUP The following query will find the point using the Elasticsearch's `envelope` GeoJSON extension: [source,js] -------------------------------------------------- GET /example/\_search { "query":{ "bool": { "must": { "match\_all": {} }, "filter": { "geo\_shape": { "location": { "shape": { "type": "envelope", "coordinates" : [[13.0, 53.0], [14.0, 52.0]] }, "relation": "within" } } } } } } -------------------------------------------------- // CONSOLE ==== Pre-Indexed Shape The Query also supports using a shape which has already been indexed in another index and/or index type. This is particularly useful for when you have a pre-defined list of shapes which are useful to your application and you want to reference this using a logical name (for example 'New Zealand') rather than having to provide their coordinates each time. In this situation it is only necessary to provide: \* `id` - The ID of the document that containing the pre-indexed shape. \* `index` - Name of the index where the pre-indexed shape is. Defaults to 'shapes'. \* `type` - Index type where the pre-indexed shape is. \* `path` - The field specified as path containing the pre-indexed shape. Defaults to 'shape'. The following is an example of using the Filter with a pre-indexed shape: [source,js] -------------------------------------------------- PUT /shapes { "mappings": { "\_doc": { "properties": { "location": { "type": "geo\_shape" } } } } } PUT /shapes/\_doc/deu { "location": { "type": "envelope", "coordinates" : [[13.0, 53.0], [14.0, 52.0]] } } GET /example/\_search { "query": { "bool": { "filter": { "geo\_shape": { "location": { "indexed\_shape": { "index": "shapes", "type": "\_doc", "id": "deu", "path": "location" } } } } } } } -------------------------------------------------- // CONSOLE ==== Spatial Relations The <> mapping parameter determines which spatial relation operators may be used at search time. The following is a complete list of spatial relation operators available: \* `INTERSECTS` - (default) Return all documents whose `geo\_shape` field intersects the query geometry. \* `DISJOINT` - Return all documents whose `geo\_shape` field has nothing in common with the query geometry. \* `WITHIN` - Return all documents whose `geo\_shape` field is within the query geometry. \* `CONTAINS` - Return all documents whose `geo\_shape` field contains the query geometry. [float] ==== Ignore Unmapped When set to `true` the `ignore\_unmapped` option will ignore an unmapped field and will not match any documents for this query. This can be useful when querying multiple indexes which might have different mappings. When set to `false` (the default value) the query will throw an exception if the field is not mapped.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/geo-shape-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.026241
|
an exception if the field is not mapped.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/geo-shape-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.014405
|
[[specialized-queries]] == Specialized queries This group contains queries which do not fit into the other groups: <>:: This query finds documents which are similar to the specified text, document, or collection of documents. <>:: This query allows a script to act as a filter. Also see the <>. <>:: This query finds queries that are stored as documents that match with the specified document. <>:: A query that accepts other queries as json or yaml string. include::mlt-query.asciidoc[] include::script-query.asciidoc[] include::percolate-query.asciidoc[] include::wrapper-query.asciidoc[]
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/special-queries.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.132905
|
[[query-dsl-dis-max-query]] === Dis Max Query A query that generates the union of documents produced by its subqueries, and that scores each document with the maximum score for that document as produced by any subquery, plus a tie breaking increment for any additional matching subqueries. This is useful when searching for a word in multiple fields with different boost factors (so that the fields cannot be combined equivalently into a single search field). We want the primary score to be the one associated with the highest boost, not the sum of the field scores (as Boolean Query would give). If the query is "albino elephant" this ensures that "albino" matching one field and "elephant" matching another gets a higher score than "albino" matching both fields. To get this result, use both Boolean Query and DisjunctionMax Query: for each term a DisjunctionMaxQuery searches for it in each field, while the set of these DisjunctionMaxQuery's is combined into a BooleanQuery. The tie breaker capability allows results that include the same term in multiple fields to be judged better than results that include this term in only the best of those multiple fields, without confusing this with the better case of two different terms in the multiple fields.The default `tie\_breaker` is `0.0`. This query maps to Lucene `DisjunctionMaxQuery`. [source,js] -------------------------------------------------- GET /\_search { "query": { "dis\_max" : { "tie\_breaker" : 0.7, "boost" : 1.2, "queries" : [ { "term" : { "age" : 34 } }, { "term" : { "age" : 35 } } ] } } } -------------------------------------------------- // CONSOLE
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/dis-max-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.016891
|
[[query-dsl-regexp-query]] === Regexp Query The `regexp` query allows you to use regular expression term queries. See <> for details of the supported regular expression language. The "term queries" in that first sentence means that Elasticsearch will apply the regexp to the terms produced by the tokenizer for that field, and not to the original text of the field. \*Note\*: The performance of a `regexp` query heavily depends on the regular expression chosen. Matching everything like `.\*` is very slow as well as using lookaround regular expressions. If possible, you should try to use a long prefix before your regular expression starts. Wildcard matchers like `.\*?+` will mostly lower performance. [source,js] -------------------------------------------------- GET /\_search { "query": { "regexp":{ "name.first": "s.\*y" } } } -------------------------------------------------- // CONSOLE Boosting is also supported [source,js] -------------------------------------------------- GET /\_search { "query": { "regexp":{ "name.first":{ "value":"s.\*y", "boost":1.2 } } } } -------------------------------------------------- // CONSOLE You can also use special flags [source,js] -------------------------------------------------- GET /\_search { "query": { "regexp":{ "name.first": { "value": "s.\*y", "flags" : "INTERSECTION|COMPLEMENT|EMPTY" } } } } -------------------------------------------------- // CONSOLE Possible flags are `ALL` (default), `ANYSTRING`, `COMPLEMENT`, `EMPTY`, `INTERSECTION`, `INTERVAL`, or `NONE`. Please check the http://lucene.apache.org/core/4\_9\_0/core/org/apache/lucene/util/automaton/RegExp.html[Lucene documentation] for their meaning Regular expressions are dangerous because it's easy to accidentally create an innocuous looking one that requires an exponential number of internal determinized automaton states (and corresponding RAM and CPU) for Lucene to execute. Lucene prevents these using the `max\_determinized\_states` setting (defaults to 10000). You can raise this limit to allow more complex regular expressions to execute. [source,js] -------------------------------------------------- GET /\_search { "query": { "regexp":{ "name.first": { "value": "s.\*y", "flags" : "INTERSECTION|COMPLEMENT|EMPTY", "max\_determinized\_states": 20000 } } } } -------------------------------------------------- // CONSOLE include::regexp-syntax.asciidoc[]
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/regexp-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.035908
|
[[query-dsl-span-term-query]] === Span Term Query Matches spans containing a term. The span term query maps to Lucene `SpanTermQuery`. Here is an example: [source,js] -------------------------------------------------- GET /\_search { "query": { "span\_term" : { "user" : "kimchy" } } } -------------------------------------------------- // CONSOLE A boost can also be associated with the query: [source,js] -------------------------------------------------- GET /\_search { "query": { "span\_term" : { "user" : { "value" : "kimchy", "boost" : 2.0 } } } } -------------------------------------------------- // CONSOLE Or : [source,js] -------------------------------------------------- GET /\_search { "query": { "span\_term" : { "user" : { "term" : "kimchy", "boost" : 2.0 } } } } -------------------------------------------------- // CONSOLE
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/span-term-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.035932
|
[[query-dsl-prefix-query]] === Prefix Query Matches documents that have fields containing terms with a specified prefix (\*not analyzed\*). The prefix query maps to Lucene `PrefixQuery`. The following matches documents where the user field contains a term that starts with `ki`: [source,js] -------------------------------------------------- GET /\_search { "query": { "prefix" : { "user" : "ki" } } } -------------------------------------------------- // CONSOLE A boost can also be associated with the query: [source,js] -------------------------------------------------- GET /\_search { "query": { "prefix" : { "user" : { "value" : "ki", "boost" : 2.0 } } } } -------------------------------------------------- // CONSOLE This multi term query allows you to control how it gets rewritten using the <> parameter.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/prefix-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.085977
|
[[compound-queries]] == Compound queries Compound queries wrap other compound or leaf queries, either to combine their results and scores, to change their behaviour, or to switch from query to filter context. The queries in this group are: <>:: A query which wraps another query, but executes it in filter context. All matching documents are given the same ``constant'' `\_score`. <>:: The default query for combining multiple leaf or compound query clauses, as `must`, `should`, `must\_not`, or `filter` clauses. The `must` and `should` clauses have their scores combined -- the more matching clauses, the better -- while the `must\_not` and `filter` clauses are executed in filter context. <>:: A query which accepts multiple queries, and returns any documents which match any of the query clauses. While the `bool` query combines the scores from all matching queries, the `dis\_max` query uses the score of the single best- matching query clause. <>:: Modify the scores returned by the main query with functions to take into account factors like popularity, recency, distance, or custom algorithms implemented with scripting. <>:: Return documents which match a `positive` query, but reduce the score of documents which also match a `negative` query. include::constant-score-query.asciidoc[] include::bool-query.asciidoc[] include::dis-max-query.asciidoc[] include::function-score-query.asciidoc[] include::boosting-query.asciidoc[]
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/compound-queries.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.111096
|
[[query-dsl-geo-polygon-query]] === Geo Polygon Query A query allowing to include hits that only fall within a polygon of points. Here is an example: [source,js] -------------------------------------------------- GET /\_search { "query": { "bool" : { "must" : { "match\_all" : {} }, "filter" : { "geo\_polygon" : { "person.location" : { "points" : [ {"lat" : 40, "lon" : -70}, {"lat" : 30, "lon" : -80}, {"lat" : 20, "lon" : -90} ] } } } } } } -------------------------------------------------- // CONSOLE [float] ==== Query Options [cols="<,<",options="header",] |======================================================================= |Option |Description |`\_name` |Optional name field to identify the filter |`validation\_method` |Set to `IGNORE\_MALFORMED` to accept geo points with invalid latitude or longitude, `COERCE` to try and infer correct latitude or longitude, or `STRICT` (default is `STRICT`). |======================================================================= [float] ==== Allowed Formats [float] ===== Lat Long as Array Format in `[lon, lat]`, note, the order of lon/lat here in order to conform with http://geojson.org/[GeoJSON]. [source,js] -------------------------------------------------- GET /\_search { "query": { "bool" : { "must" : { "match\_all" : {} }, "filter" : { "geo\_polygon" : { "person.location" : { "points" : [ [-70, 40], [-80, 30], [-90, 20] ] } } } } } } -------------------------------------------------- // CONSOLE [float] ===== Lat Lon as String Format in `lat,lon`. [source,js] -------------------------------------------------- GET /\_search { "query": { "bool" : { "must" : { "match\_all" : {} }, "filter" : { "geo\_polygon" : { "person.location" : { "points" : [ "40, -70", "30, -80", "20, -90" ] } } } } } } -------------------------------------------------- // CONSOLE [float] ===== Geohash [source,js] -------------------------------------------------- GET /\_search { "query": { "bool" : { "must" : { "match\_all" : {} }, "filter" : { "geo\_polygon" : { "person.location" : { "points" : [ "drn5x1g8cu2y", "30, -80", "20, -90" ] } } } } } } -------------------------------------------------- // CONSOLE [float] ==== geo\_point Type The query \*requires\* the <> type to be set on the relevant field. [float] ==== Ignore Unmapped When set to `true` the `ignore\_unmapped` option will ignore an unmapped field and will not match any documents for this query. This can be useful when querying multiple indexes which might have different mappings. When set to `false` (the default value) the query will throw an exception if the field is not mapped.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/geo-polygon-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.037496
|
[[query-dsl-wildcard-query]] === Wildcard Query Matches documents that have fields matching a wildcard expression (\*not analyzed\*). Supported wildcards are `\*`, which matches any character sequence (including the empty one), and `?`, which matches any single character. Note that this query can be slow, as it needs to iterate over many terms. In order to prevent extremely slow wildcard queries, a wildcard term should not start with one of the wildcards `\*` or `?`. The wildcard query maps to Lucene `WildcardQuery`. [source,js] -------------------------------------------------- GET /\_search { "query": { "wildcard" : { "user" : "ki\*y" } } } -------------------------------------------------- // CONSOLE A boost can also be associated with the query: [source,js] -------------------------------------------------- GET /\_search { "query": { "wildcard" : { "user" : { "value" : "ki\*y", "boost" : 2.0 } } } } -------------------------------------------------- // CONSOLE Or : [source,js] -------------------------------------------------- GET /\_search { "query": { "wildcard" : { "user" : { "wildcard" : "ki\*y", "boost" : 2.0 } } } } -------------------------------------------------- // CONSOLE This multi term query allows to control how it gets rewritten using the <> parameter.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/wildcard-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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|
[[span-queries]] == Span queries Span queries are low-level positional queries which provide expert control over the order and proximity of the specified terms. These are typically used to implement very specific queries on legal documents or patents. Span queries cannot be mixed with non-span queries (with the exception of the `span\_multi` query). The queries in this group are: <>:: The equivalent of the <> but for use with other span queries. <>:: Wraps a <>, <>, <>, <>, <>, or <> query. <>:: Accepts another span query whose matches must appear within the first N positions of the field. <>:: Accepts multiple span queries whose matches must be within the specified distance of each other, and possibly in the same order. <>:: Combines multiple span queries -- returns documents which match any of the specified queries. <>:: Wraps another span query, and excludes any documents which match that query. <>:: Accepts a list of span queries, but only returns those spans which also match a second span query. <>:: The result from a single span query is returned as long is its span falls within the spans returned by a list of other span queries. <>:: Allows queries like `span-near` or `span-or` across different fields. include::span-term-query.asciidoc[] include::span-multi-term-query.asciidoc[] include::span-first-query.asciidoc[] include::span-near-query.asciidoc[] include::span-or-query.asciidoc[] include::span-not-query.asciidoc[] include::span-containing-query.asciidoc[] include::span-within-query.asciidoc[] include::span-field-masking-query.asciidoc[]
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/span-queries.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.115706
|
[[query-dsl-common-terms-query]] === Common Terms Query The `common` terms query is a modern alternative to stopwords which improves the precision and recall of search results (by taking stopwords into account), without sacrificing performance. [float] ==== The problem Every term in a query has a cost. A search for `"The brown fox"` requires three term queries, one for each of `"the"`, `"brown"` and `"fox"`, all of which are executed against all documents in the index. The query for `"the"` is likely to match many documents and thus has a much smaller impact on relevance than the other two terms. Previously, the solution to this problem was to ignore terms with high frequency. By treating `"the"` as a \_stopword\_, we reduce the index size and reduce the number of term queries that need to be executed. The problem with this approach is that, while stopwords have a small impact on relevance, they are still important. If we remove stopwords, we lose precision, (eg we are unable to distinguish between `"happy"` and `"not happy"`) and we lose recall (eg text like `"The The"` or `"To be or not to be"` would simply not exist in the index). [float] ==== The solution The `common` terms query divides the query terms into two groups: more important (ie \_low frequency\_ terms) and less important (ie \_high frequency\_ terms which would previously have been stopwords). First it searches for documents which match the more important terms. These are the terms which appear in fewer documents and have a greater impact on relevance. Then, it executes a second query for the less important terms -- terms which appear frequently and have a low impact on relevance. But instead of calculating the relevance score for \*all\* matching documents, it only calculates the `\_score` for documents already matched by the first query. In this way the high frequency terms can improve the relevance calculation without paying the cost of poor performance. If a query consists only of high frequency terms, then a single query is executed as an `AND` (conjunction) query, in other words all terms are required. Even though each individual term will match many documents, the combination of terms narrows down the resultset to only the most relevant. The single query can also be executed as an `OR` with a specific <>, in this case a high enough value should probably be used. Terms are allocated to the high or low frequency groups based on the `cutoff\_frequency`, which can be specified as an absolute frequency (`>=1`) or as a relative frequency (`0.0 .. 1.0`). (Remember that document frequencies are computed on a per shard level as explained in the blog post {defguide}/relevance-is-broken.html[Relevance is broken].) Perhaps the most interesting property of this query is that it adapts to domain specific stopwords automatically. For example, on a video hosting site, common terms like `"clip"` or `"video"` will automatically behave as stopwords without the need to maintain a manual list. [float] ==== Examples In this example, words that have a document frequency greater than 0.1% (eg `"this"` and `"is"`) will be treated as \_common terms\_. [source,js] -------------------------------------------------- GET /\_search { "query": { "common": { "body": { "query": "this is bonsai cool", "cutoff\_frequency": 0.001 } } } } -------------------------------------------------- // CONSOLE The number of terms which should match can be controlled with the <> (`high\_freq`, `low\_freq`), `low\_freq\_operator` (default `"or"`) and `high\_freq\_operator` (default `"or"`) parameters. For low frequency terms, set the `low\_freq\_operator` to `"and"` to make all terms required: [source,js] -------------------------------------------------- GET /\_search { "query": { "common": { "body": { "query": "nelly the elephant as a cartoon", "cutoff\_frequency": 0.001, "low\_freq\_operator": "and" } } }
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/common-terms-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.044055
|
<> (`high\_freq`, `low\_freq`), `low\_freq\_operator` (default `"or"`) and `high\_freq\_operator` (default `"or"`) parameters. For low frequency terms, set the `low\_freq\_operator` to `"and"` to make all terms required: [source,js] -------------------------------------------------- GET /\_search { "query": { "common": { "body": { "query": "nelly the elephant as a cartoon", "cutoff\_frequency": 0.001, "low\_freq\_operator": "and" } } } } -------------------------------------------------- // CONSOLE which is roughly equivalent to: [source,js] -------------------------------------------------- GET /\_search { "query": { "bool": { "must": [ { "term": { "body": "nelly"}}, { "term": { "body": "elephant"}}, { "term": { "body": "cartoon"}} ], "should": [ { "term": { "body": "the"}}, { "term": { "body": "as"}}, { "term": { "body": "a"}} ] } } } -------------------------------------------------- // CONSOLE Alternatively use <> to specify a minimum number or percentage of low frequency terms which must be present, for instance: [source,js] -------------------------------------------------- GET /\_search { "query": { "common": { "body": { "query": "nelly the elephant as a cartoon", "cutoff\_frequency": 0.001, "minimum\_should\_match": 2 } } } } -------------------------------------------------- // CONSOLE which is roughly equivalent to: [source,js] -------------------------------------------------- GET /\_search { "query": { "bool": { "must": { "bool": { "should": [ { "term": { "body": "nelly"}}, { "term": { "body": "elephant"}}, { "term": { "body": "cartoon"}} ], "minimum\_should\_match": 2 } }, "should": [ { "term": { "body": "the"}}, { "term": { "body": "as"}}, { "term": { "body": "a"}} ] } } } -------------------------------------------------- // CONSOLE minimum\_should\_match A different <> can be applied for low and high frequency terms with the additional `low\_freq` and `high\_freq` parameters. Here is an example when providing additional parameters (note the change in structure): [source,js] -------------------------------------------------- GET /\_search { "query": { "common": { "body": { "query": "nelly the elephant not as a cartoon", "cutoff\_frequency": 0.001, "minimum\_should\_match": { "low\_freq" : 2, "high\_freq" : 3 } } } } } -------------------------------------------------- // CONSOLE which is roughly equivalent to: [source,js] -------------------------------------------------- GET /\_search { "query": { "bool": { "must": { "bool": { "should": [ { "term": { "body": "nelly"}}, { "term": { "body": "elephant"}}, { "term": { "body": "cartoon"}} ], "minimum\_should\_match": 2 } }, "should": { "bool": { "should": [ { "term": { "body": "the"}}, { "term": { "body": "not"}}, { "term": { "body": "as"}}, { "term": { "body": "a"}} ], "minimum\_should\_match": 3 } } } } } -------------------------------------------------- // CONSOLE In this case it means the high frequency terms have only an impact on relevance when there are at least three of them. But the most interesting use of the <> for high frequency terms is when there are only high frequency terms: [source,js] -------------------------------------------------- GET /\_search { "query": { "common": { "body": { "query": "how not to be", "cutoff\_frequency": 0.001, "minimum\_should\_match": { "low\_freq" : 2, "high\_freq" : 3 } } } } } -------------------------------------------------- // CONSOLE which is roughly equivalent to: [source,js] -------------------------------------------------- GET /\_search { "query": { "bool": { "should": [ { "term": { "body": "how"}}, { "term": { "body": "not"}}, { "term": { "body": "to"}}, { "term": { "body": "be"}} ], "minimum\_should\_match": "3<50%" } } } -------------------------------------------------- // CONSOLE The high frequency generated query is then slightly less restrictive than with an `AND`. The `common` terms query also supports `boost` and `analyzer` as parameters.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/query-dsl/common-terms-query.asciidoc
|
6.2
|
elasticsearch-6-2
|
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|
[[index-modules-slowlog]] == Slow Log [float] [[search-slow-log]] === Search Slow Log Shard level slow search log allows to log slow search (query and fetch phases) into a dedicated log file. Thresholds can be set for both the query phase of the execution, and fetch phase, here is a sample: [source,yaml] -------------------------------------------------- index.search.slowlog.threshold.query.warn: 10s index.search.slowlog.threshold.query.info: 5s index.search.slowlog.threshold.query.debug: 2s index.search.slowlog.threshold.query.trace: 500ms index.search.slowlog.threshold.fetch.warn: 1s index.search.slowlog.threshold.fetch.info: 800ms index.search.slowlog.threshold.fetch.debug: 500ms index.search.slowlog.threshold.fetch.trace: 200ms index.search.slowlog.level: info -------------------------------------------------- All of the above settings are \_dynamic\_ and are set per-index. By default, none are enabled (set to `-1`). Levels (`warn`, `info`, `debug`, `trace`) allow to control under which logging level the log will be logged. Not all are required to be configured (for example, only `warn` threshold can be set). The benefit of several levels is the ability to quickly "grep" for specific thresholds breached. The logging is done on the shard level scope, meaning the execution of a search request within a specific shard. It does not encompass the whole search request, which can be broadcast to several shards in order to execute. Some of the benefits of shard level logging is the association of the actual execution on the specific machine, compared with request level. The logging file is configured by default using the following configuration (found in `log4j2.properties`): [source,properties] -------------------------------------------------- appender.index\_search\_slowlog\_rolling.type = RollingFile appender.index\_search\_slowlog\_rolling.name = index\_search\_slowlog\_rolling appender.index\_search\_slowlog\_rolling.fileName = ${sys:es.logs}\_index\_search\_slowlog.log appender.index\_search\_slowlog\_rolling.layout.type = PatternLayout appender.index\_search\_slowlog\_rolling.layout.pattern = [%d{ISO8601}][%-5p][%-25c] %.10000m%n appender.index\_search\_slowlog\_rolling.filePattern = ${sys:es.logs}\_index\_search\_slowlog-%d{yyyy-MM-dd}.log appender.index\_search\_slowlog\_rolling.policies.type = Policies appender.index\_search\_slowlog\_rolling.policies.time.type = TimeBasedTriggeringPolicy appender.index\_search\_slowlog\_rolling.policies.time.interval = 1 appender.index\_search\_slowlog\_rolling.policies.time.modulate = true logger.index\_search\_slowlog\_rolling.name = index.search.slowlog logger.index\_search\_slowlog\_rolling.level = trace logger.index\_search\_slowlog\_rolling.appenderRef.index\_search\_slowlog\_rolling.ref = index\_search\_slowlog\_rolling logger.index\_search\_slowlog\_rolling.additivity = false -------------------------------------------------- [float] [[index-slow-log]] === Index Slow log The indexing slow log, similar in functionality to the search slow log. The log file name ends with `\_index\_indexing\_slowlog.log`. Log and the thresholds are configured in the same way as the search slowlog. Index slowlog sample: [source,yaml] -------------------------------------------------- index.indexing.slowlog.threshold.index.warn: 10s index.indexing.slowlog.threshold.index.info: 5s index.indexing.slowlog.threshold.index.debug: 2s index.indexing.slowlog.threshold.index.trace: 500ms index.indexing.slowlog.level: info index.indexing.slowlog.source: 1000 -------------------------------------------------- All of the above settings are \_dynamic\_ and are set per-index. By default Elasticsearch will log the first 1000 characters of the \_source in the slowlog. You can change that with `index.indexing.slowlog.source`. Setting it to `false` or `0` will skip logging the source entirely an setting it to `true` will log the entire source regardless of size. The original `\_source` is reformatted by default to make sure that it fits on a single log line. If preserving the original document format is important, you can turn off reformatting by setting `index.indexing.slowlog.reformat` to `false`, which will cause the source to be logged "as is" and can potentially span multiple log lines. The index slow log file is configured by default in the `log4j2.properties` file: [source,properties] -------------------------------------------------- appender.index\_indexing\_slowlog\_rolling.type = RollingFile appender.index\_indexing\_slowlog\_rolling.name = index\_indexing\_slowlog\_rolling appender.index\_indexing\_slowlog\_rolling.fileName = ${sys:es.logs}\_index\_indexing\_slowlog.log appender.index\_indexing\_slowlog\_rolling.layout.type = PatternLayout appender.index\_indexing\_slowlog\_rolling.layout.pattern = [%d{ISO8601}][%-5p][%-25c] %marker%.10000m%n appender.index\_indexing\_slowlog\_rolling.filePattern = ${sys:es.logs}\_index\_indexing\_slowlog-%d{yyyy-MM-dd}.log appender.index\_indexing\_slowlog\_rolling.policies.type = Policies appender.index\_indexing\_slowlog\_rolling.policies.time.type = TimeBasedTriggeringPolicy appender.index\_indexing\_slowlog\_rolling.policies.time.interval = 1 appender.index\_indexing\_slowlog\_rolling.policies.time.modulate = true logger.index\_indexing\_slowlog.name = index.indexing.slowlog.index logger.index\_indexing\_slowlog.level = trace logger.index\_indexing\_slowlog.appenderRef.index\_indexing\_slowlog\_rolling.ref = index\_indexing\_slowlog\_rolling logger.index\_indexing\_slowlog.additivity = false --------------------------------------------------
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/index-modules/slowlog.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.085319
|
[[index-modules-similarity]] == Similarity module A similarity (scoring / ranking model) defines how matching documents are scored. Similarity is per field, meaning that via the mapping one can define a different similarity per field. Configuring a custom similarity is considered an expert feature and the builtin similarities are most likely sufficient as is described in <>. [float] [[configuration]] === Configuring a similarity Most existing or custom Similarities have configuration options which can be configured via the index settings as shown below. The index options can be provided when creating an index or updating index settings. [source,js] -------------------------------------------------- PUT /index { "settings" : { "index" : { "similarity" : { "my\_similarity" : { "type" : "DFR", "basic\_model" : "g", "after\_effect" : "l", "normalization" : "h2", "normalization.h2.c" : "3.0" } } } } } -------------------------------------------------- // CONSOLE Here we configure the DFRSimilarity so it can be referenced as `my\_similarity` in mappings as is illustrate in the below example: [source,js] -------------------------------------------------- PUT /index/\_mapping/\_doc { "properties" : { "title" : { "type" : "text", "similarity" : "my\_similarity" } } } -------------------------------------------------- // CONSOLE // TEST[continued] [float] === Available similarities [float] [[bm25]] ==== BM25 similarity (\*default\*) TF/IDF based similarity that has built-in tf normalization and is supposed to work better for short fields (like names). See http://en.wikipedia.org/wiki/Okapi\_BM25[Okapi\_BM25] for more details. This similarity has the following options: [horizontal] `k1`:: Controls non-linear term frequency normalization (saturation). The default value is `1.2`. `b`:: Controls to what degree document length normalizes tf values. The default value is `0.75`. `discount\_overlaps`:: Determines whether overlap tokens (Tokens with 0 position increment) are ignored when computing norm. By default this is true, meaning overlap tokens do not count when computing norms. Type name: `BM25` [float] [[classic-similarity]] ==== Classic similarity The classic similarity that is based on the TF/IDF model. This similarity has the following option: `discount\_overlaps`:: Determines whether overlap tokens (Tokens with 0 position increment) are ignored when computing norm. By default this is true, meaning overlap tokens do not count when computing norms. Type name: `classic` [float] [[drf]] ==== DFR similarity Similarity that implements the http://lucene.apache.org/core/5\_2\_1/core/org/apache/lucene/search/similarities/DFRSimilarity.html[divergence from randomness] framework. This similarity has the following options: [horizontal] `basic\_model`:: Possible values: `be`, `d`, `g`, `if`, `in`, `ine` and `p`. `after\_effect`:: Possible values: `no`, `b` and `l`. `normalization`:: Possible values: `no`, `h1`, `h2`, `h3` and `z`. All options but the first option need a normalization value. Type name: `DFR` [float] [[dfi]] ==== DFI similarity Similarity that implements the http://trec.nist.gov/pubs/trec21/papers/irra.web.nb.pdf[divergence from independence] model. This similarity has the following options: [horizontal] `independence\_measure`:: Possible values `standardized`, `saturated`, `chisquared`. Type name: `DFI` [float] [[ib]] ==== IB similarity. http://lucene.apache.org/core/5\_2\_1/core/org/apache/lucene/search/similarities/IBSimilarity.html[Information based model] . The algorithm is based on the concept that the information content in any symbolic 'distribution' sequence is primarily determined by the repetitive usage of its basic elements. For written texts this challenge would correspond to comparing the writing styles of different authors. This similarity has the following options: [horizontal] `distribution`:: Possible values: `ll` and `spl`. `lambda`:: Possible values: `df` and `ttf`. `normalization`:: Same as in `DFR` similarity. Type name: `IB` [float] [[lm\_dirichlet]] ==== LM Dirichlet similarity. http://lucene.apache.org/core/5\_2\_1/core/org/apache/lucene/search/similarities/LMDirichletSimilarity.html[LM Dirichlet similarity] . This similarity has the following options: [horizontal] `mu`:: Default to `2000`. Type name: `LMDirichlet` [float] [[lm\_jelinek\_mercer]] ==== LM Jelinek Mercer similarity. http://lucene.apache.org/core/5\_2\_1/core/org/apache/lucene/search/similarities/LMJelinekMercerSimilarity.html[LM Jelinek Mercer similarity] . The algorithm attempts to capture important patterns in the text, while leaving out noise. This similarity has the following options: [horizontal] `lambda`:: The optimal value depends on both the collection and the query. The optimal value is around `0.1` for title queries and `0.7` for long queries. Default to `0.1`. When value approaches `0`, documents that match more query terms will be ranked
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/index-modules/similarity.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.08272
|
noise. This similarity has the following options: [horizontal] `lambda`:: The optimal value depends on both the collection and the query. The optimal value is around `0.1` for title queries and `0.7` for long queries. Default to `0.1`. When value approaches `0`, documents that match more query terms will be ranked higher than those that match fewer terms. Type name: `LMJelinekMercer` [float] [[scripted\_similarity]] ==== Scripted similarity A similarity that allows you to use a script in order to specify how scores should be computed. For instance, the below example shows how to reimplement TF-IDF: [source,js] -------------------------------------------------- PUT /index { "settings": { "number\_of\_shards": 1, "similarity": { "scripted\_tfidf": { "type": "scripted", "script": { "source": "double tf = Math.sqrt(doc.freq); double idf = Math.log((field.docCount+1.0)/(term.docFreq+1.0)) + 1.0; double norm = 1/Math.sqrt(doc.length); return query.boost \* tf \* idf \* norm;" } } } }, "mappings": { "\_doc": { "properties": { "field": { "type": "text", "similarity": "scripted\_tfidf" } } } } } PUT /index/\_doc/1 { "field": "foo bar foo" } PUT /index/\_doc/2 { "field": "bar baz" } POST /index/\_refresh GET /index/\_search?explain=true { "query": { "query\_string": { "query": "foo^1.7", "default\_field": "field" } } } -------------------------------------------------- // CONSOLE Which yields: [source,js] -------------------------------------------------- { "took": 12, "timed\_out": false, "\_shards": { "total": 1, "successful": 1, "skipped": 0, "failed": 0 }, "hits": { "total": 1, "max\_score": 1.9508477, "hits": [ { "\_shard": "[index][0]", "\_node": "OzrdjxNtQGaqs4DmioFw9A", "\_index": "index", "\_type": "\_doc", "\_id": "1", "\_score": 1.9508477, "\_source": { "field": "foo bar foo" }, "\_explanation": { "value": 1.9508477, "description": "weight(field:foo in 0) [PerFieldSimilarity], result of:", "details": [ { "value": 1.9508477, "description": "score from ScriptedSimilarity(weightScript=[null], script=[Script{type=inline, lang='painless', idOrCode='double tf = Math.sqrt(doc.freq); double idf = Math.log((field.docCount+1.0)/(term.docFreq+1.0)) + 1.0; double norm = 1/Math.sqrt(doc.length); return query.boost \* tf \* idf \* norm;', options={}, params={}}]) computed from:", "details": [ { "value": 1.0, "description": "weight", "details": [] }, { "value": 1.7, "description": "query.boost", "details": [] }, { "value": 2.0, "description": "field.docCount", "details": [] }, { "value": 4.0, "description": "field.sumDocFreq", "details": [] }, { "value": 5.0, "description": "field.sumTotalTermFreq", "details": [] }, { "value": 1.0, "description": "term.docFreq", "details": [] }, { "value": 2.0, "description": "term.totalTermFreq", "details": [] }, { "value": 2.0, "description": "doc.freq", "details": [] }, { "value": 3.0, "description": "doc.length", "details": [] } ] } ] } } ] } } -------------------------------------------------- // TESTRESPONSE[s/"took": 12/"took" : $body.took/] // TESTRESPONSE[s/OzrdjxNtQGaqs4DmioFw9A/$body.hits.hits.0.\_node/] You might have noticed that a significant part of the script depends on statistics that are the same for every document. It is possible to make the above slightly more efficient by providing an `weight\_script` which will compute the document-independent part of the score and will be available under the `weight` variable. When no `weight\_script` is provided, `weight` is equal to `1`. The `weight\_script` has access to the same variables as the `script` except `doc` since it is supposed to compute a document-independent contribution to the score. The below configuration will give the same tf-idf scores but is slightly more efficient: [source,js] -------------------------------------------------- PUT /index { "settings": { "number\_of\_shards": 1, "similarity": { "scripted\_tfidf": { "type": "scripted", "weight\_script": { "source": "double idf = Math.log((field.docCount+1.0)/(term.docFreq+1.0)) + 1.0; return query.boost \* idf;" }, "script": { "source": "double tf = Math.sqrt(doc.freq); double norm = 1/Math.sqrt(doc.length); return weight \* tf \* norm;" } } } }, "mappings": { "\_doc": { "properties": { "field": { "type": "text", "similarity": "scripted\_tfidf" } } } } } -------------------------------------------------- // CONSOLE //////////////////// [source,js] -------------------------------------------------- PUT /index/\_doc/1 { "field": "foo bar foo" } PUT /index/\_doc/2 { "field": "bar baz" } POST /index/\_refresh GET /index/\_search?explain=true { "query": { "query\_string": { "query": "foo^1.7", "default\_field": "field" } } } -------------------------------------------------- // CONSOLE // TEST[continued] [source,js] -------------------------------------------------- { "took": 1, "timed\_out": false, "\_shards": { "total": 1, "successful": 1, "skipped": 0, "failed":
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/index-modules/similarity.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | -0.015445
|
{ "field": "foo bar foo" } PUT /index/\_doc/2 { "field": "bar baz" } POST /index/\_refresh GET /index/\_search?explain=true { "query": { "query\_string": { "query": "foo^1.7", "default\_field": "field" } } } -------------------------------------------------- // CONSOLE // TEST[continued] [source,js] -------------------------------------------------- { "took": 1, "timed\_out": false, "\_shards": { "total": 1, "successful": 1, "skipped": 0, "failed": 0 }, "hits": { "total": 1, "max\_score": 1.9508477, "hits": [ { "\_shard": "[index][0]", "\_node": "OzrdjxNtQGaqs4DmioFw9A", "\_index": "index", "\_type": "\_doc", "\_id": "1", "\_score": 1.9508477, "\_source": { "field": "foo bar foo" }, "\_explanation": { "value": 1.9508477, "description": "weight(field:foo in 0) [PerFieldSimilarity], result of:", "details": [ { "value": 1.9508477, "description": "score from ScriptedSimilarity(weightScript=[Script{type=inline, lang='painless', idOrCode='double idf = Math.log((field.docCount+1.0)/(term.docFreq+1.0)) + 1.0; return query.boost \* idf;', options={}, params={}}], script=[Script{type=inline, lang='painless', idOrCode='double tf = Math.sqrt(doc.freq); double norm = 1/Math.sqrt(doc.length); return weight \* tf \* norm;', options={}, params={}}]) computed from:", "details": [ { "value": 2.3892908, "description": "weight", "details": [] }, { "value": 1.7, "description": "query.boost", "details": [] }, { "value": 2.0, "description": "field.docCount", "details": [] }, { "value": 4.0, "description": "field.sumDocFreq", "details": [] }, { "value": 5.0, "description": "field.sumTotalTermFreq", "details": [] }, { "value": 1.0, "description": "term.docFreq", "details": [] }, { "value": 2.0, "description": "term.totalTermFreq", "details": [] }, { "value": 2.0, "description": "doc.freq", "details": [] }, { "value": 3.0, "description": "doc.length", "details": [] } ] } ] } } ] } } -------------------------------------------------- // TESTRESPONSE[s/"took": 1/"took" : $body.took/] // TESTRESPONSE[s/OzrdjxNtQGaqs4DmioFw9A/$body.hits.hits.0.\_node/] //////////////////// Type name: `scripted` [float] [[default-base]] ==== Default Similarity By default, Elasticsearch will use whatever similarity is configured as `default`. You can change the default similarity for all fields in an index when it is <>: [source,js] -------------------------------------------------- PUT /index { "settings": { "index": { "similarity": { "default": { "type": "classic" } } } } } -------------------------------------------------- // CONSOLE If you want to change the default similarity after creating the index you must <> your index, send the following request and <> it again afterwards: [source,js] -------------------------------------------------- POST /index/\_close PUT /index/\_settings { "index": { "similarity": { "default": { "type": "classic" } } } } POST /index/\_open -------------------------------------------------- // CONSOLE // TEST[continued]
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/index-modules/similarity.asciidoc
|
6.2
|
elasticsearch-6-2
|
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|
[[index-modules-store]] == Store The store module allows you to control how index data is stored and accessed on disk. [float] [[file-system]] === File system storage types There are different file system implementations or \_storage types\_. By default, Elasticsearch will pick the best implementation based on the operating environment. This can be overridden for all indices by adding this to the `config/elasticsearch.yml` file: [source,yaml] --------------------------------- index.store.type: niofs --------------------------------- It is a \_static\_ setting that can be set on a per-index basis at index creation time: [source,js] --------------------------------- PUT /my\_index { "settings": { "index.store.type": "niofs" } } --------------------------------- // CONSOLE WARNING: This is an expert-only setting and may be removed in the future. The following sections lists all the different storage types supported. `fs`:: Default file system implementation. This will pick the best implementation depending on the operating environment, which is currently `mmapfs` on all supported systems but is subject to change. [[simplefs]]`simplefs`:: The Simple FS type is a straightforward implementation of file system storage (maps to Lucene `SimpleFsDirectory`) using a random access file. This implementation has poor concurrent performance (multiple threads will bottleneck). It is usually better to use the `niofs` when you need index persistence. [[niofs]]`niofs`:: The NIO FS type stores the shard index on the file system (maps to Lucene `NIOFSDirectory`) using NIO. It allows multiple threads to read from the same file concurrently. It is not recommended on Windows because of a bug in the SUN Java implementation. [[mmapfs]]`mmapfs`:: The MMap FS type stores the shard index on the file system (maps to Lucene `MMapDirectory`) by mapping a file into memory (mmap). Memory mapping uses up a portion of the virtual memory address space in your process equal to the size of the file being mapped. Before using this class, be sure you have allowed plenty of <>. [[default\_fs]]`default\_fs` deprecated[5.0.0, The `default\_fs` store type is deprecated - use `fs` instead]:: The `default` type is deprecated and is aliased to `fs` for backward compatibility. === Pre-loading data into the file system cache NOTE: This is an expert setting, the details of which may change in the future. By default, Elasticsearch completely relies on the operating system file system cache for caching I/O operations. It is possible to set `index.store.preload` in order to tell the operating system to load the content of hot index files into memory upon opening. This setting accept a comma-separated list of files extensions: all files whose extension is in the list will be pre-loaded upon opening. This can be useful to improve search performance of an index, especially when the host operating system is restarted, since this causes the file system cache to be trashed. However note that this may slow down the opening of indices, as they will only become available after data have been loaded into physical memory. This setting is best-effort only and may not work at all depending on the store type and host operating system. The `index.store.preload` is a static setting that can either be set in the `config/elasticsearch.yml`: [source,yaml] --------------------------------- index.store.preload: ["nvd", "dvd"] --------------------------------- or in the index settings at index creation time: [source,js] --------------------------------- PUT /my\_index { "settings": { "index.store.preload": ["nvd", "dvd"] } } --------------------------------- // CONSOLE The default value is the empty array, which means that nothing will be loaded into the file-system cache eagerly. For indices that are actively searched, you might want to set it to `["nvd", "dvd"]`, which will cause norms and doc values to be loaded eagerly into physical memory. These are the two first extensions to look at since Elasticsearch performs random access on them. A wildcard can be used in order
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/index-modules/store.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.12499
|
that are actively searched, you might want to set it to `["nvd", "dvd"]`, which will cause norms and doc values to be loaded eagerly into physical memory. These are the two first extensions to look at since Elasticsearch performs random access on them. A wildcard can be used in order to indicate that all files should be preloaded: `index.store.preload: ["\*"]`. Note however that it is generally not useful to load all files into memory, in particular those for stored fields and term vectors, so a better option might be to set it to `["nvd", "dvd", "tim", "doc", "dim"]`, which will preload norms, doc values, terms dictionaries, postings lists and points, which are the most important parts of the index for search and aggregations. Note that this setting can be dangerous on indices that are larger than the size of the main memory of the host, as it would cause the filesystem cache to be trashed upon reopens after large merges, which would make indexing and searching \_slower\_.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/index-modules/store.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.0817
|
[[index-modules-merge]] == Merge A shard in Elasticsearch is a Lucene index, and a Lucene index is broken down into segments. Segments are internal storage elements in the index where the index data is stored, and are immutable. Smaller segments are periodically merged into larger segments to keep the index size at bay and to expunge deletes. The merge process uses auto-throttling to balance the use of hardware resources between merging and other activities like search. [float] [[merge-scheduling]] === Merge scheduling The merge scheduler (ConcurrentMergeScheduler) controls the execution of merge operations when they are needed. Merges run in separate threads, and when the maximum number of threads is reached, further merges will wait until a merge thread becomes available. The merge scheduler supports the following \_dynamic\_ setting: `index.merge.scheduler.max\_thread\_count`:: The maximum number of threads on a single shard that may be merging at once. Defaults to `Math.max(1, Math.min(4, Runtime.getRuntime().availableProcessors() / 2))` which works well for a good solid-state-disk (SSD). If your index is on spinning platter drives instead, decrease this to 1.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/index-modules/merge.asciidoc
|
6.2
|
elasticsearch-6-2
|
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|
[[index-modules-index-sorting]] == Index Sorting beta[] When creating a new index in Elasticsearch it is possible to configure how the Segments inside each Shard will be sorted. By default Lucene does not apply any sort. The `index.sort.\*` settings define which fields should be used to sort the documents inside each Segment. [WARNING] nested fields are not compatible with index sorting because they rely on the assumption that nested documents are stored in contiguous doc ids, which can be broken by index sorting. An error will be thrown if index sorting is activated on an index that contains nested fields. For instance the following example shows how to define a sort on a single field: [source,js] -------------------------------------------------- PUT twitter { "settings" : { "index" : { "sort.field" : "date", <1> "sort.order" : "desc" <2> } }, "mappings": { "\_doc": { "properties": { "date": { "type": "date" } } } } } -------------------------------------------------- // CONSOLE <1> This index is sorted by the `date` field <2> ... in descending order. It is also possible to sort the index by more than one field: [source,js] -------------------------------------------------- PUT twitter { "settings" : { "index" : { "sort.field" : ["username", "date"], <1> "sort.order" : ["asc", "desc"] <2> } }, "mappings": { "\_doc": { "properties": { "username": { "type": "keyword", "doc\_values": true }, "date": { "type": "date" } } } } } -------------------------------------------------- // CONSOLE <1> This index is sorted by `username` first then by `date` <2> ... in ascending order for the `username` field and in descending order for the `date` field. Index sorting supports the following settings: `index.sort.field`:: The list of fields used to sort the index. Only `boolean`, `numeric`, `date` and `keyword` fields with `doc\_values` are allowed here. `index.sort.order`:: The sort order to use for each field. The order option can have the following values: \* `asc`: For ascending order \* `desc`: For descending order. `index.sort.mode`:: Elasticsearch supports sorting by multi-valued fields. The mode option controls what value is picked to sort the document. The mode option can have the following values: \* `min`: Pick the lowest value. \* `max`: Pick the highest value. `index.sort.missing`:: The missing parameter specifies how docs which are missing the field should be treated. The missing value can have the following values: \* `\_last`: Documents without value for the field are sorted last. \* `\_first`: Documents without value for the field are sorted first. [WARNING] Index sorting can be defined only once at index creation. It is not allowed to add or update a sort on an existing index. Index sorting also has a cost in terms of indexing throughput since documents must be sorted at flush and merge time. You should test the impact on your application before activating this feature. [float] [[early-terminate]] === Early termination of search request By default in Elasticsearch a search request must visit every document that match a query to retrieve the top documents sorted by a specified sort. Though when the index sort and the search sort are the same it is possible to limit the number of documents that should be visited per segment to retrieve the N top ranked documents globally. For example, let's say we have an index that contains events sorted by a timestamp field: [source,js] -------------------------------------------------- PUT events { "settings" : { "index" : { "sort.field" : "timestamp", "sort.order" : "desc" <1> } }, "mappings": { "doc": { "properties": { "timestamp": { "type": "date" } } } } } -------------------------------------------------- // CONSOLE <1> This index is sorted by timestamp in descending order (most recent first) You can search for the last 10 events with: [source,js] -------------------------------------------------- GET /events/\_search
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/index-modules/index-sorting.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.017087
|
"sort.order" : "desc" <1> } }, "mappings": { "doc": { "properties": { "timestamp": { "type": "date" } } } } } -------------------------------------------------- // CONSOLE <1> This index is sorted by timestamp in descending order (most recent first) You can search for the last 10 events with: [source,js] -------------------------------------------------- GET /events/\_search { "size": 10, "sort": [ { "timestamp": "desc" } ] } -------------------------------------------------- // CONSOLE // TEST[continued] Elasticsearch will detect that the top docs of each segment are already sorted in the index and will only compare the first N documents per segment. The rest of the documents matching the query are collected to count the total number of results and to build aggregations. If you're only looking for the last 10 events and have no interest in the total number of documents that match the query you can set `track\_total\_hits` to false: [source,js] -------------------------------------------------- GET /events/\_search { "size": 10, "sort": [ <1> { "timestamp": "desc" } ], "track\_total\_hits": false } -------------------------------------------------- // CONSOLE // TEST[continued] <1> The index sort will be used to rank the top documents and each segment will early terminate the collection after the first 10 matches. This time, Elasticsearch will not try to count the number of documents and will be able to terminate the query as soon as N documents have been collected per segment. [source,js] -------------------------------------------------- { "\_shards": ... "hits" : { "total" : -1, <1> "max\_score" : null, "hits" : [] }, "took": 20, "timed\_out": false } -------------------------------------------------- // TESTRESPONSE[s/"\_shards": \.\.\./"\_shards": "$body.\_shards",/] // TESTRESPONSE[s/"took": 20,/"took": "$body.took",/] <1> The total number of hits matching the query is unknown because of early termination. NOTE: Aggregations will collect all documents that match the query regardless of the value of `track\_total\_hits` [[index-modules-index-sorting-conjunctions]] === Use index sorting to speed up conjunctions Index sorting can be useful in order to organize Lucene doc ids (not to be conflated with `\_id`) in a way that makes conjunctions (a AND b AND ...) more efficient. In order to be efficient, conjunctions rely on the fact that if any clause does not match, then the entire conjunction does not match. By using index sorting, we can put documents that do not match together, which will help skip efficiently over large ranges of doc IDs that do not match the conjunction. This trick only works with low-cardinality fields. A rule of thumb is that you should sort first on fields that both have a low cardinality and are frequently used for filtering. The sort order (`asc` or `desc`) does not matter as we only care about putting values that would match the same clauses close to each other. For instance if you were indexing cars for sale, it might be interesting to sort by fuel type, body type, make, year of registration and finally mileage.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/index-modules/index-sorting.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | -0.007799
|
[[index-modules-translog]] == Translog Changes to Lucene are only persisted to disk during a Lucene commit, which is a relatively expensive operation and so cannot be performed after every index or delete operation. Changes that happen after one commit and before another will be removed from the index by Lucene in the event of process exit or hardware failure. Because Lucene commits are too expensive to perform on every individual change, each shard copy also has a \_transaction log\_ known as its \_translog\_ associated with it. All index and delete operations are written to the translog after being processed by the internal Lucene index but before they are acknowledged. In the event of a crash, recent transactions that have been acknowledged but not yet included in the last Lucene commit can instead be recovered from the translog when the shard recovers. An Elasticsearch flush is the process of performing a Lucene commit and starting a new translog. Flushes are performed automatically in the background in order to make sure the translog doesn't grow too large, which would make replaying its operations take a considerable amount of time during recovery. The ability to perform a flush manually is also exposed through an API, although this is rarely needed. [float] === Translog settings The data in the translog is only persisted to disk when the translog is ++fsync++ed and committed. In the event of hardware failure, any data written since the previous translog commit will be lost. By default, Elasticsearch ++fsync++s and commits the translog every 5 seconds if `index.translog.durability` is set to `async` or if set to `request` (default) at the end of every <>, <>, <>, or <> request. More precisely, if set to `request`, Elasticsearch will only report success of an index, delete, update, or bulk request to the client after the translog has been successfully ++fsync++ed and committed on the primary and on every allocated replica. The following <> per-index settings control the behaviour of the translog: `index.translog.sync\_interval`:: How often the translog is ++fsync++ed to disk and committed, regardless of write operations. Defaults to `5s`. Values less than `100ms` are not allowed. `index.translog.durability`:: + -- Whether or not to `fsync` and commit the translog after every index, delete, update, or bulk request. This setting accepts the following parameters: `request`:: (default) `fsync` and commit after every request. In the event of hardware failure, all acknowledged writes will already have been committed to disk. `async`:: `fsync` and commit in the background every `sync\_interval`. In the event of hardware failure, all acknowledged writes since the last automatic commit will be discarded. -- `index.translog.flush\_threshold\_size`:: The translog stores all operations that are not yet safely persisted in Lucene (i.e., are not part of a Lucene commit point). Although these operations are available for reads, they will need to be reindexed if the shard was to shutdown and has to be recovered. This settings controls the maximum total size of these operations, to prevent recoveries from taking too long. Once the maximum size has been reached a flush will happen, generating a new Lucene commit point. Defaults to `512mb`. `index.translog.retention.size`:: The total size of translog files to keep. Keeping more translog files increases the chance of performing an operation based sync when recovering replicas. If the translog files are not sufficient, replica recovery will fall back to a file based sync. Defaults to `512mb` `index.translog.retention.age`:: The maximum duration for which translog files will be kept. Defaults to `12h`. [float] [[corrupt-translog-truncation]] === What to do if the translog becomes corrupted? In some cases (a bad drive, user error) the translog on a shard copy can become
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/index-modules/translog.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.152097
|
back to a file based sync. Defaults to `512mb` `index.translog.retention.age`:: The maximum duration for which translog files will be kept. Defaults to `12h`. [float] [[corrupt-translog-truncation]] === What to do if the translog becomes corrupted? In some cases (a bad drive, user error) the translog on a shard copy can become corrupted. When this corruption is detected by Elasticsearch due to mismatching checksums, Elasticsearch will fail that shard copy and refuse to use that copy of the data. If there are other copies of the shard available then Elasticsearch will automatically recover from one of them using the normal shard allocation and recovery mechanism. In particular, if the corrupt shard copy was the primary when the corruption was detected then one of its replicas will be promoted in its place. If there is no copy of the data from which Elasticsearch can recover successfully, a user may want to recover the data that is part of the shard at the cost of losing the data that is currently contained in the translog. We provide a command-line tool for this, `elasticsearch-translog`. [WARNING] The `elasticsearch-translog` tool should \*not\* be run while Elasticsearch is running. If you attempt to run this tool while Elasticsearch is running, you will permanently lose the documents that were contained only in the translog! In order to run the `elasticsearch-translog` tool, specify the `truncate` subcommand as well as the directory for the corrupted translog with the `-d` option: [source,txt] -------------------------------------------------- $ bin/elasticsearch-translog truncate -d /var/lib/elasticsearchdata/nodes/0/indices/P45vf\_YQRhqjfwLMUvSqDw/0/translog/ Checking existing translog files !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! ! WARNING: Elasticsearch MUST be stopped before running this tool ! ! ! ! WARNING: Documents inside of translog files will be lost ! ! ! ! WARNING: The following files will be DELETED! ! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! --> data/nodes/0/indices/P45vf\_YQRhqjfwLMUvSqDw/0/translog/translog-41.ckp --> data/nodes/0/indices/P45vf\_YQRhqjfwLMUvSqDw/0/translog/translog-6.ckp --> data/nodes/0/indices/P45vf\_YQRhqjfwLMUvSqDw/0/translog/translog-37.ckp --> data/nodes/0/indices/P45vf\_YQRhqjfwLMUvSqDw/0/translog/translog-24.ckp --> data/nodes/0/indices/P45vf\_YQRhqjfwLMUvSqDw/0/translog/translog-11.ckp Continue and DELETE files? [y/N] y Reading translog UUID information from Lucene commit from shard at [data/nodes/0/indices/P45vf\_YQRhqjfwLMUvSqDw/0/index] Translog Generation: 3 Translog UUID : AxqC4rocTC6e0fwsljAh-Q Removing existing translog files Creating new empty checkpoint at [data/nodes/0/indices/P45vf\_YQRhqjfwLMUvSqDw/0/translog/translog.ckp] Creating new empty translog at [data/nodes/0/indices/P45vf\_YQRhqjfwLMUvSqDw/0/translog/translog-3.tlog] Done. -------------------------------------------------- You can also use the `-h` option to get a list of all options and parameters that the `elasticsearch-translog` tool supports.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/index-modules/translog.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.071325
|
[[delayed-allocation]] === Delaying allocation when a node leaves When a node leaves the cluster for whatever reason, intentional or otherwise, the master reacts by: \* Promoting a replica shard to primary to replace any primaries that were on the node. \* Allocating replica shards to replace the missing replicas (assuming there are enough nodes). \* Rebalancing shards evenly across the remaining nodes. These actions are intended to protect the cluster against data loss by ensuring that every shard is fully replicated as soon as possible. Even though we throttle concurrent recoveries both at the <> and at the <>, this ``shard-shuffle'' can still put a lot of extra load on the cluster which may not be necessary if the missing node is likely to return soon. Imagine this scenario: \* Node 5 loses network connectivity. \* The master promotes a replica shard to primary for each primary that was on Node 5. \* The master allocates new replicas to other nodes in the cluster. \* Each new replica makes an entire copy of the primary shard across the network. \* More shards are moved to different nodes to rebalance the cluster. \* Node 5 returns after a few minutes. \* The master rebalances the cluster by allocating shards to Node 5. If the master had just waited for a few minutes, then the missing shards could have been re-allocated to Node 5 with the minimum of network traffic. This process would be even quicker for idle shards (shards not receiving indexing requests) which have been automatically <>. The allocation of replica shards which become unassigned because a node has left can be delayed with the `index.unassigned.node\_left.delayed\_timeout` dynamic setting, which defaults to `1m`. This setting can be updated on a live index (or on all indices): [source,js] ------------------------------ PUT \_all/\_settings { "settings": { "index.unassigned.node\_left.delayed\_timeout": "5m" } } ------------------------------ // CONSOLE // TEST[s/^/PUT test\n/] With delayed allocation enabled, the above scenario changes to look like this: \* Node 5 loses network connectivity. \* The master promotes a replica shard to primary for each primary that was on Node 5. \* The master logs a message that allocation of unassigned shards has been delayed, and for how long. \* The cluster remains yellow because there are unassigned replica shards. \* Node 5 returns after a few minutes, before the `timeout` expires. \* The missing replicas are re-allocated to Node 5 (and sync-flushed shards recover almost immediately). NOTE: This setting will not affect the promotion of replicas to primaries, nor will it affect the assignment of replicas that have not been assigned previously. In particular, delayed allocation does not come into effect after a full cluster restart. Also, in case of a master failover situation, elapsed delay time is forgotten (i.e. reset to the full initial delay). ==== Cancellation of shard relocation If delayed allocation times out, the master assigns the missing shards to another node which will start recovery. If the missing node rejoins the cluster, and its shards still have the same sync-id as the primary, shard relocation will be cancelled and the synced shard will be used for recovery instead. For this reason, the default `timeout` is set to just one minute: even if shard relocation begins, cancelling recovery in favour of the synced shard is cheap. ==== Monitoring delayed unassigned shards The number of shards whose allocation has been delayed by this timeout setting can be viewed with the <>: [source,js] ------------------------------ GET \_cluster/health <1> ------------------------------ // CONSOLE <1> This request will return a `delayed\_unassigned\_shards` value. ==== Removing a node permanently If a node is not going to return and
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/index-modules/allocation/delayed.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.092826
|
shards The number of shards whose allocation has been delayed by this timeout setting can be viewed with the <>: [source,js] ------------------------------ GET \_cluster/health <1> ------------------------------ // CONSOLE <1> This request will return a `delayed\_unassigned\_shards` value. ==== Removing a node permanently If a node is not going to return and you would like Elasticsearch to allocate the missing shards immediately, just update the timeout to zero: [source,js] ------------------------------ PUT \_all/\_settings { "settings": { "index.unassigned.node\_left.delayed\_timeout": "0" } } ------------------------------ // CONSOLE // TEST[s/^/PUT test\n/] You can reset the timeout as soon as the missing shards have started to recover.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/index-modules/allocation/delayed.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.03325
|
[[recovery-prioritization]] === Index recovery prioritization Unallocated shards are recovered in order of priority, whenever possible. Indices are sorted into priority order as follows: \* the optional `index.priority` setting (higher before lower) \* the index creation date (higher before lower) \* the index name (higher before lower) This means that, by default, newer indices will be recovered before older indices. Use the per-index dynamically updateable `index.priority` setting to customise the index prioritization order. For instance: [source,js] ------------------------------ PUT index\_1 PUT index\_2 PUT index\_3 { "settings": { "index.priority": 10 } } PUT index\_4 { "settings": { "index.priority": 5 } } ------------------------------ // CONSOLE In the above example: \* `index\_3` will be recovered first because it has the highest `index.priority`. \* `index\_4` will be recovered next because it has the next highest priority. \* `index\_2` will be recovered next because it was created more recently. \* `index\_1` will be recovered last. This setting accepts an integer, and can be updated on a live index with the <>: [source,js] ------------------------------ PUT index\_4/\_settings { "index.priority": 1 } ------------------------------ // CONSOLE // TEST[continued]
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/index-modules/allocation/prioritization.asciidoc
|
6.2
|
elasticsearch-6-2
|
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|
[[shard-allocation-filtering]] === Shard Allocation Filtering Shard allocation filtering allows you to specify which nodes are allowed to host the shards of a particular index. NOTE: The per-index shard allocation filters explained below work in conjunction with the cluster-wide allocation filters explained in <>. It is possible to assign arbitrary metadata attributes to each node at startup. For instance, nodes could be assigned a `rack` and a `size` attribute as follows: [source,sh] ------------------------ bin/elasticsearch -Enode.attr.rack=rack1 -Enode.attr.size=big <1> ------------------------ <1> These attribute settings can also be specified in the `elasticsearch.yml` config file. These metadata attributes can be used with the `index.routing.allocation.\*` settings to allocate an index to a particular group of nodes. For instance, we can move the index `test` to either `big` or `medium` nodes as follows: [source,js] ------------------------ PUT test/\_settings { "index.routing.allocation.include.size": "big,medium" } ------------------------ // CONSOLE // TEST[s/^/PUT test\n/] Alternatively, we can move the index `test` away from the `small` nodes with an `exclude` rule: [source,js] ------------------------ PUT test/\_settings { "index.routing.allocation.exclude.size": "small" } ------------------------ // CONSOLE // TEST[s/^/PUT test\n/] Multiple rules can be specified, in which case all conditions must be satisfied. For instance, we could move the index `test` to `big` nodes in `rack1` with the following: [source,js] ------------------------ PUT test/\_settings { "index.routing.allocation.include.size": "big", "index.routing.allocation.include.rack": "rack1" } ------------------------ // CONSOLE // TEST[s/^/PUT test\n/] NOTE: If some conditions cannot be satisfied then shards will not be moved. The following settings are \_dynamic\_, allowing live indices to be moved from one set of nodes to another: `index.routing.allocation.include.{attribute}`:: Assign the index to a node whose `{attribute}` has at least one of the comma-separated values. `index.routing.allocation.require.{attribute}`:: Assign the index to a node whose `{attribute}` has \_all\_ of the comma-separated values. `index.routing.allocation.exclude.{attribute}`:: Assign the index to a node whose `{attribute}` has \_none\_ of the comma-separated values. These special attributes are also supported: [horizontal] `\_name`:: Match nodes by node name `\_host\_ip`:: Match nodes by host IP address (IP associated with hostname) `\_publish\_ip`:: Match nodes by publish IP address `\_ip`:: Match either `\_host\_ip` or `\_publish\_ip` `\_host`:: Match nodes by hostname All attribute values can be specified with wildcards, eg: [source,js] ------------------------ PUT test/\_settings { "index.routing.allocation.include.\_ip": "192.168.2.\*" } ------------------------ // CONSOLE // TEST[skip:indexes don't assign]
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/index-modules/allocation/filtering.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.044452
|
[[allocation-total-shards]] === Total Shards Per Node The cluster-level shard allocator tries to spread the shards of a single index across as many nodes as possible. However, depending on how many shards and indices you have, and how big they are, it may not always be possible to spread shards evenly. The following \_dynamic\_ setting allows you to specify a hard limit on the total number of shards from a single index allowed per node: `index.routing.allocation.total\_shards\_per\_node`:: The maximum number of shards (replicas and primaries) that will be allocated to a single node. Defaults to unbounded. You can also limit the amount of shards a node can have regardless of the index: `cluster.routing.allocation.total\_shards\_per\_node`:: The maximum number of shards (replicas and primaries) that will be allocated to a single node globally. Defaults to unbounded (-1). [WARNING] ======================================= These settings impose a hard limit which can result in some shards not being allocated. Use with caution. =======================================
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/index-modules/allocation/total_shards.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.025513
|
[[modules-scripting]] == Scripting The scripting module enables you to use scripts to evaluate custom expressions. For example, you could use a script to return "script fields" as part of a search request or evaluate a custom score for a query. The default scripting language is <>. Additional `lang` plugins enable you to run scripts written in other languages. Everywhere a script can be used, you can include a `lang` parameter to specify the language of the script. [float] === General-purpose languages: These languages can be used for any purpose in the scripting APIs, and give the most flexibility. [cols="<,<,<",options="header",] |======================================================================= |Language |Sandboxed |Required plugin |<> |yes |built-in |======================================================================= [float] === Special-purpose languages: These languages are less flexible, but typically have higher performance for certain tasks. [cols="<,<,<,<",options="header",] |======================================================================= |Language |Sandboxed |Required plugin |Purpose |<> |yes |built-in |fast custom ranking and sorting |<> |yes |built-in |templates |<> |n/a |you write it! |expert API |======================================================================= [WARNING] .Scripts and security ================================================= Languages that are sandboxed are designed with security in mind. However, non- sandboxed languages can be a security issue, please read <> for more details. ================================================= include::scripting/using.asciidoc[] include::scripting/fields.asciidoc[] include::scripting/security.asciidoc[] include::scripting/painless.asciidoc[] include::scripting/expression.asciidoc[] include::scripting/engine.asciidoc[]
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/modules/scripting.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.11895
|
[[modules-thrift]] == Thrift The https://thrift.apache.org/[thrift] transport module allows to expose the REST interface of Elasticsearch using thrift. Thrift should provide better performance over http. Since thrift provides both the wire protocol and the transport, it should make using Elasticsearch more efficient (though it has limited documentation). Using thrift requires installing the `transport-thrift` plugin, located https://github.com/elastic/elasticsearch-transport-thrift[here]. The thrift https://github.com/elastic/elasticsearch-transport-thrift/blob/master/elasticsearch.thrift[schema] can be used to generate thrift clients. [cols="<,<",options="header",] |======================================================================= |Setting |Description |`thrift.port` |The port to bind to. Defaults to 9500-9600 |`thrift.frame` |Defaults to `-1`, which means no framing. Set to a higher value to specify the frame size (like `15mb`). |=======================================================================
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/modules/thrift.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.093407
|
[[modules-gateway]] == Local Gateway The local gateway module stores the cluster state and shard data across full cluster restarts. The following \_static\_ settings, which must be set on every master node, control how long a freshly elected master should wait before it tries to recover the cluster state and the cluster's data: `gateway.expected\_nodes`:: The number of (data or master) nodes that are expected to be in the cluster. Recovery of local shards will start as soon as the expected number of nodes have joined the cluster. Defaults to `0` `gateway.expected\_master\_nodes`:: The number of master nodes that are expected to be in the cluster. Recovery of local shards will start as soon as the expected number of master nodes have joined the cluster. Defaults to `0` `gateway.expected\_data\_nodes`:: The number of data nodes that are expected to be in the cluster. Recovery of local shards will start as soon as the expected number of data nodes have joined the cluster. Defaults to `0` `gateway.recover\_after\_time`:: If the expected number of nodes is not achieved, the recovery process waits for the configured amount of time before trying to recover regardless. Defaults to `5m` if one of the `expected\_nodes` settings is configured. Once the `recover\_after\_time` duration has timed out, recovery will start as long as the following conditions are met: `gateway.recover\_after\_nodes`:: Recover as long as this many data or master nodes have joined the cluster. `gateway.recover\_after\_master\_nodes`:: Recover as long as this many master nodes have joined the cluster. `gateway.recover\_after\_data\_nodes`:: Recover as long as this many data nodes have joined the cluster. NOTE: These settings only take effect on a full cluster restart.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/modules/gateway.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.08943
|
[[modules-network]] == Network Settings Elasticsearch binds to localhost only by default. This is sufficient for you to run a local development server (or even a development cluster, if you start multiple nodes on the same machine), but you will need to configure some <> in order to run a real production cluster across multiple servers. [WARNING] .Be careful with the network configuration! ============================= Never expose an unprotected node to the public internet. ============================= [float] [[common-network-settings]] === Commonly Used Network Settings `network.host`:: The node will bind to this hostname or IP address and \_publish\_ (advertise) this host to other nodes in the cluster. Accepts an IP address, hostname, a <>, or an array of any combination of these. Note that any values containing a `:` (e.g., an IPv6 address or containing one of the <>) must be quoted because `:` is a special character in YAML. `0.0.0.0` is an acceptable IP address and will bind to all network interfaces. The value `0` has the same effect as the value `0.0.0.0`. + Defaults to `\_local\_`. `discovery.zen.ping.unicast.hosts`:: In order to join a cluster, a node needs to know the hostname or IP address of at least some of the other nodes in the cluster. This setting provides the initial list of other nodes that this node will try to contact. Accepts IP addresses or hostnames. If a hostname lookup resolves to multiple IP addresses then each IP address will be used for discovery. https://en.wikipedia.org/wiki/Round-robin\_DNS[Round robin DNS] -- returning a different IP from a list on each lookup -- can be used for discovery; non- existent IP addresses will throw exceptions and cause another DNS lookup on the next round of pinging (subject to JVM DNS caching). + Defaults to `["127.0.0.1", "[::1]"]`. `http.port`:: Port to bind to for incoming HTTP requests. Accepts a single value or a range. If a range is specified, the node will bind to the first available port in the range. + Defaults to `9200-9300`. `transport.tcp.port`:: Port to bind for communication between nodes. Accepts a single value or a range. If a range is specified, the node will bind to the first available port in the range. + Defaults to `9300-9400`. [float] [[network-interface-values]] === Special values for `network.host` The following special values may be passed to `network.host`: [horizontal] `\_[networkInterface]\_`:: Addresses of a network interface, for example `\_en0\_`. `\_local\_`:: Any loopback addresses on the system, for example `127.0.0.1`. `\_site\_`:: Any site-local addresses on the system, for example `192.168.0.1`. `\_global\_`:: Any globally-scoped addresses on the system, for example `8.8.8.8`. [float] ==== IPv4 vs IPv6 These special values will work over both IPv4 and IPv6 by default, but you can also limit this with the use of `:ipv4` of `:ipv6` specifiers. For example, `\_en0:ipv4\_` would only bind to the IPv4 addresses of interface `en0`. [TIP] .Discovery in the cloud ================================ More special settings are available when running in the cloud with either the {plugins}/discovery-ec2.html[EC2 discovery plugin] or the {plugins}/discovery-gce-network-host.html#discovery-gce-network-host[Google Compute Engine discovery plugin] installed. ================================ [float] [[advanced-network-settings]] === Advanced network settings The `network.host` setting explained in <> is a shortcut which sets the \_bind host\_ and the \_publish host\_ at the same time. In advanced used cases, such as when running behind a proxy server, you may need to set these settings to different values: `network.bind\_host`:: This specifies which network interface(s) a node should bind to in order to listen for incoming requests. A node can bind to multiple interfaces, e.g. two network cards, or a site-local address and a local address. Defaults to `network.host`. `network.publish\_host`:: The publish host is the single interface that the node advertises to other nodes in the cluster,
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/modules/network.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.077909
|
should bind to in order to listen for incoming requests. A node can bind to multiple interfaces, e.g. two network cards, or a site-local address and a local address. Defaults to `network.host`. `network.publish\_host`:: The publish host is the single interface that the node advertises to other nodes in the cluster, so that those nodes can connect to it. Currently an Elasticsearch node may be bound to multiple addresses, but only publishes one. If not specified, this defaults to the ``best'' address from `network.host`, sorted by IPv4/IPv6 stack preference, then by reachability. If you set a `network.host` that results in multiple bind addresses yet rely on a specific address for node-to-node communication, you should explicitly set `network.publish\_host`. Both of the above settings can be configured just like `network.host` -- they accept IP addresses, host names, and <>. [float] [[tcp-settings]] === Advanced TCP Settings Any component that uses TCP (like the <> and <> modules) share the following settings: [horizontal] `network.tcp.no\_delay`:: Enable or disable the https://en.wikipedia.org/wiki/Nagle%27s\_algorithm[TCP no delay] setting. Defaults to `true`. `network.tcp.keep\_alive`:: Enable or disable https://en.wikipedia.org/wiki/Keepalive[TCP keep alive]. Defaults to `true`. `network.tcp.reuse\_address`:: Should an address be reused or not. Defaults to `true` on non-windows machines. `network.tcp.send\_buffer\_size`:: The size of the TCP send buffer (specified with <>). By default not explicitly set. `network.tcp.receive\_buffer\_size`:: The size of the TCP receive buffer (specified with <>). By default not explicitly set. [float] === Transport and HTTP protocols An Elasticsearch node exposes two network protocols which inherit the above settings, but may be further configured independently: TCP Transport:: Used for communication between nodes in the cluster, by the Java {javaclient}/transport-client.html[Transport client] and by the <>. See the <> for more information. HTTP:: Exposes the JSON-over-HTTP interface used by all clients other than the Java clients. See the <> for more information.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/modules/network.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.066439
|
[[modules-http]] == HTTP The http module allows to expose \*Elasticsearch\* APIs over HTTP. The http mechanism is completely asynchronous in nature, meaning that there is no blocking thread waiting for a response. The benefit of using asynchronous communication for HTTP is solving the http://en.wikipedia.org/wiki/C10k\_problem[C10k problem]. When possible, consider using http://en.wikipedia.org/wiki/Keepalive#HTTP\_Keepalive[HTTP keep alive] when connecting for better performance and try to get your favorite client not to do http://en.wikipedia.org/wiki/Chunked\_transfer\_encoding[HTTP chunking]. [float] === Settings The settings in the table below can be configured for HTTP. Note that none of them are dynamically updatable so for them to take effect they should be set in `elasticsearch.yml`. [cols="<,<",options="header",] |======================================================================= |Setting |Description |`http.port` |A bind port range. Defaults to `9200-9300`. |`http.publish\_port` |The port that HTTP clients should use when communicating with this node. Useful when a cluster node is behind a proxy or firewall and the `http.port` is not directly addressable from the outside. Defaults to the actual port assigned via `http.port`. |`http.bind\_host` |The host address to bind the HTTP service to. Defaults to `http.host` (if set) or `network.bind\_host`. |`http.publish\_host` |The host address to publish for HTTP clients to connect to. Defaults to `http.host` (if set) or `network.publish\_host`. |`http.host` |Used to set the `http.bind\_host` and the `http.publish\_host` Defaults to `http.host` or `network.host`. |`http.max\_content\_length` |The max content of an HTTP request. Defaults to `100mb`. If set to greater than `Integer.MAX\_VALUE`, it will be reset to 100mb. |`http.max\_initial\_line\_length` |The max length of an HTTP URL. Defaults to `4kb` |`http.max\_header\_size` | The max size of allowed headers. Defaults to `8kB` |`http.compression` |Support for compression when possible (with Accept-Encoding). Defaults to `true`. |`http.compression\_level` |Defines the compression level to use for HTTP responses. Valid values are in the range of 1 (minimum compression) and 9 (maximum compression). Defaults to `3`. |`http.cors.enabled` |Enable or disable cross-origin resource sharing, i.e. whether a browser on another origin can execute requests against Elasticsearch. Set to `true` to enable Elasticsearch to process pre-flight https://en.wikipedia.org/wiki/Cross-origin\_resource\_sharing[CORS] requests. Elasticsearch will respond to those requests with the `Access-Control-Allow-Origin` header if the `Origin` sent in the request is permitted by the `http.cors.allow-origin` list. Set to `false` (the default) to make Elasticsearch ignore the `Origin` request header, effectively disabling CORS requests because Elasticsearch will never respond with the `Access-Control-Allow-Origin` response header. Note that if the client does not send a pre-flight request with an `Origin` header or it does not check the response headers from the server to validate the `Access-Control-Allow-Origin` response header, then cross-origin security is compromised. If CORS is not enabled on Elasticsearch, the only way for the client to know is to send a pre-flight request and realize the required response headers are missing. |`http.cors.allow-origin` |Which origins to allow. Defaults to no origins allowed. If you prepend and append a `/` to the value, this will be treated as a regular expression, allowing you to support HTTP and HTTPs. for example using `/https?:\/\/localhost(:[0-9]+)?/` would return the request header appropriately in both cases. `\*` is a valid value but is considered a \*security risk\* as your Elasticsearch instance is open to cross origin requests from \*anywhere\*. |`http.cors.max-age` |Browsers send a "preflight" OPTIONS-request to determine CORS settings. `max-age` defines how long the result should be cached for. Defaults to `1728000` (20 days) |`http.cors.allow-methods` |Which methods to allow. Defaults to `OPTIONS, HEAD, GET, POST, PUT, DELETE`. |`http.cors.allow-headers` |Which headers to allow. Defaults to `X-Requested-With, Content-Type, Content-Length`. |`http.cors.allow-credentials` | Whether the `Access-Control-Allow-Credentials` header should be returned. Note: This header is only returned, when the setting is set to `true`. Defaults to `false` |`http.detailed\_errors.enabled` |Enables or disables the output of detailed error messages and stack traces in response output. Note: When set to
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/modules/http.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.102678
|
Defaults to `X-Requested-With, Content-Type, Content-Length`. |`http.cors.allow-credentials` | Whether the `Access-Control-Allow-Credentials` header should be returned. Note: This header is only returned, when the setting is set to `true`. Defaults to `false` |`http.detailed\_errors.enabled` |Enables or disables the output of detailed error messages and stack traces in response output. Note: When set to `false` and the `error\_trace` request parameter is specified, an error will be returned; when `error\_trace` is not specified, a simple message will be returned. Defaults to `true` |`http.pipelining` |Enable or disable HTTP pipelining, defaults to `true`. |`http.pipelining.max\_events` |The maximum number of events to be queued up in memory before a HTTP connection is closed, defaults to `10000`. |======================================================================= It also uses the common <>. [float] === Disable HTTP The http module can be completely disabled and not started by setting `http.enabled` to `false`. Elasticsearch nodes (and Java clients) communicate internally using the <>, not HTTP. It might make sense to disable the `http` layer entirely on nodes which are not meant to serve REST requests directly. For instance, you could disable HTTP on <> if you also have <> which are intended to serve all REST requests. Be aware, however, that you will not be able to send any REST requests (eg to retrieve node stats) directly to nodes which have HTTP disabled.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/modules/http.asciidoc
|
6.2
|
elasticsearch-6-2
|
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|
[[modules-snapshots]] == Snapshot And Restore A snapshot is a backup taken from a running Elasticsearch cluster. You can take a snapshot of individual indices or of the entire cluster and store it in a repository on a shared filesystem, and there are plugins that support remote repositories on S3, HDFS, Azure, Google Cloud Storage and more. Snapshots are taken incrementally. This means that when creating a snapshot of an index Elasticsearch will avoid copying any data that is already stored in the repository as part of an earlier snapshot of the same index. Therefore it can be efficient to take snapshots of your cluster quite frequently. Snapshots can be restored into a running cluster via the restore API. When restoring an index it is possible to alter the name of the restored index as well as some of its settings, allowing a great deal of flexibility in how the snapshot and restore functionality can be used. WARNING: It is not possible to back up an Elasticsearch cluster simply by taking a copy of the data directories of all of its nodes. Elasticsearch may be making changes to the contents of its data directories while it is running, and this means that copying its data directories cannot be expected to capture a consistent picture of their contents. Attempts to restore a cluster from such a backup may fail, reporting corruption and/or missing files, or may appear to have succeeded having silently lost some of its data. The only reliable way to back up a cluster is by using the snapshot and restore functionality. [float] === Version compatibility A snapshot contains a copy of the on-disk data structures that make up an index. This means that snapshots can only be restored to versions of Elasticsearch that can read the indices: \* A snapshot of an index created in 5.x can be restored to 6.x. \* A snapshot of an index created in 2.x can be restored to 5.x. \* A snapshot of an index created in 1.x can be restored to 2.x. Conversely, snapshots of indices created in 1.x \*\*cannot\*\* be restored to 5.x or 6.x, and snapshots of indices created in 2.x \*\*cannot\*\* be restored to 6.x. Each snapshot can contain indices created in various versions of Elasticsearch, and when restoring a snapshot it must be possible to restore all of the indices into the target cluster. If any indices in a snapshot were created in an incompatible version, you will not be able restore the snapshot. IMPORTANT: When backing up your data prior to an upgrade, keep in mind that you won't be able to restore snapshots after you upgrade if they contain indices created in a version that's incompatible with the upgrade version. If you end up in a situation where you need to restore a snapshot of an index that is incompatible with the version of the cluster you are currently running, you can restore it on the latest compatible version and use <> to rebuild the index on the current version. Reindexing from remote is only possible if the original index has source enabled. Retrieving and reindexing the data can take significantly longer than simply restoring a snapshot. If you have a large amount of data, we recommend testing the reindex from remote process with a subset of your data to understand the time requirements before proceeding. [float] === Repositories You must register a snapshot repository before you can perform snapshot and restore operations. We recommend creating a new snapshot repository for each major version. The valid repository settings depend on the repository type. If you register
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/modules/snapshots.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.108836
|
of your data to understand the time requirements before proceeding. [float] === Repositories You must register a snapshot repository before you can perform snapshot and restore operations. We recommend creating a new snapshot repository for each major version. The valid repository settings depend on the repository type. If you register same snapshot repository with multiple clusters, only one cluster should have write access to the repository. All other clusters connected to that repository should set the repository to `readonly` mode. NOTE: The snapshot format can change across major versions, so if you have clusters on different major versions trying to write the same repository, new snapshots written by one version will not be visible to the other. While setting the repository to `readonly` on all but one of the clusters should work with multiple clusters differing by one major version, it is not a supported configuration. [source,js] ----------------------------------- PUT /\_snapshot/my\_backup { "type": "fs", "settings": { "location": "my\_backup\_location" } } ----------------------------------- // CONSOLE // TESTSETUP To retrieve information about a registered repository, use a GET request: [source,js] ----------------------------------- GET /\_snapshot/my\_backup ----------------------------------- // CONSOLE which returns: [source,js] ----------------------------------- { "my\_backup": { "type": "fs", "settings": { "location": "my\_backup\_location" } } } ----------------------------------- // TESTRESPONSE To retrieve information about multiple repositories, specify a a comma-delimited list of repositories. You can also use the \* wildcard when specifying repository names. For example, the following request retrieves information about all of the snapshot repositories that start with `repo` or contain `backup`: [source,js] ----------------------------------- GET /\_snapshot/repo\*,\*backup\* ----------------------------------- // CONSOLE To retrieve information about all registered snapshot repositories, omit the repository name or specify `\_all`: [source,js] ----------------------------------- GET /\_snapshot ----------------------------------- // CONSOLE or [source,js] ----------------------------------- GET /\_snapshot/\_all ----------------------------------- // CONSOLE [float] ===== Shared File System Repository The shared file system repository (`"type": "fs"`) uses the shared file system to store snapshots. In order to register the shared file system repository it is necessary to mount the same shared filesystem to the same location on all master and data nodes. This location (or one of its parent directories) must be registered in the `path.repo` setting on all master and data nodes. Assuming that the shared filesystem is mounted to `/mount/backups/my\_fs\_backup\_location`, the following setting should be added to `elasticsearch.yml` file: [source,yaml] -------------- path.repo: ["/mount/backups", "/mount/longterm\_backups"] -------------- The `path.repo` setting supports Microsoft Windows UNC paths as long as at least server name and share are specified as a prefix and back slashes are properly escaped: [source,yaml] -------------- path.repo: ["\\\\MY\_SERVER\\Snapshots"] -------------- After all nodes are restarted, the following command can be used to register the shared file system repository with the name `my\_fs\_backup`: [source,js] ----------------------------------- PUT /\_snapshot/my\_fs\_backup { "type": "fs", "settings": { "location": "/mount/backups/my\_fs\_backup\_location", "compress": true } } ----------------------------------- // CONSOLE // TEST[skip:no access to absolute path] If the repository location is specified as a relative path this path will be resolved against the first path specified in `path.repo`: [source,js] ----------------------------------- PUT /\_snapshot/my\_fs\_backup { "type": "fs", "settings": { "location": "my\_fs\_backup\_location", "compress": true } } ----------------------------------- // CONSOLE // TEST[continued] The following settings are supported: [horizontal] `location`:: Location of the snapshots. Mandatory. `compress`:: Turns on compression of the snapshot files. Compression is applied only to metadata files (index mapping and settings). Data files are not compressed. Defaults to `true`. `chunk\_size`:: Big files can be broken down into chunks during snapshotting if needed. The chunk size can be specified in bytes or by using size value notation, i.e. 1g, 10m, 5k. Defaults to `null` (unlimited chunk size). `max\_restore\_bytes\_per\_sec`:: Throttles per node restore rate. Defaults to `40mb` per second. `max\_snapshot\_bytes\_per\_sec`:: Throttles per node snapshot rate. Defaults to `40mb` per second. `readonly`:: Makes repository read-only. Defaults
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/modules/snapshots.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | -0.006712
|
chunk size can be specified in bytes or by using size value notation, i.e. 1g, 10m, 5k. Defaults to `null` (unlimited chunk size). `max\_restore\_bytes\_per\_sec`:: Throttles per node restore rate. Defaults to `40mb` per second. `max\_snapshot\_bytes\_per\_sec`:: Throttles per node snapshot rate. Defaults to `40mb` per second. `readonly`:: Makes repository read-only. Defaults to `false`. [float] ===== Read-only URL Repository The URL repository (`"type": "url"`) can be used as an alternative read-only way to access data created by the shared file system repository. The URL specified in the `url` parameter should point to the root of the shared filesystem repository. The following settings are supported: [horizontal] `url`:: Location of the snapshots. Mandatory. URL Repository supports the following protocols: "http", "https", "ftp", "file" and "jar". URL repositories with `http:`, `https:`, and `ftp:` URLs has to be whitelisted by specifying allowed URLs in the `repositories.url.allowed\_urls` setting. This setting supports wildcards in the place of host, path, query, and fragment. For example: [source,yaml] ----------------------------------- repositories.url.allowed\_urls: ["http://www.example.org/root/\*", "https://\*.mydomain.com/\*?\*#\*"] ----------------------------------- URL repositories with `file:` URLs can only point to locations registered in the `path.repo` setting similar to shared file system repository. [float] ===== Repository plugins Other repository backends are available in these official plugins: \* {plugins}/repository-s3.html[repository-s3] for S3 repository support \* {plugins}/repository-hdfs.html[repository-hdfs] for HDFS repository support in Hadoop environments \* {plugins}/repository-azure.html[repository-azure] for Azure storage repositories \* {plugins}/repository-gcs.html[repository-gcs] for Google Cloud Storage repositories [float] ===== Repository Verification When a repository is registered, it's immediately verified on all master and data nodes to make sure that it is functional on all nodes currently present in the cluster. The `verify` parameter can be used to explicitly disable the repository verification when registering or updating a repository: [source,js] ----------------------------------- PUT /\_snapshot/my\_unverified\_backup?verify=false { "type": "fs", "settings": { "location": "my\_unverified\_backup\_location" } } ----------------------------------- // CONSOLE // TEST[continued] The verification process can also be executed manually by running the following command: [source,js] ----------------------------------- POST /\_snapshot/my\_unverified\_backup/\_verify ----------------------------------- // CONSOLE // TEST[continued] It returns a list of nodes where repository was successfully verified or an error message if verification process failed. [float] === Snapshot A repository can contain multiple snapshots of the same cluster. Snapshots are identified by unique names within the cluster. A snapshot with the name `snapshot\_1` in the repository `my\_backup` can be created by executing the following command: [source,js] ----------------------------------- PUT /\_snapshot/my\_backup/snapshot\_1?wait\_for\_completion=true ----------------------------------- // CONSOLE // TEST[continued] The `wait\_for\_completion` parameter specifies whether or not the request should return immediately after snapshot initialization (default) or wait for snapshot completion. During snapshot initialization, information about all previous snapshots is loaded into the memory, which means that in large repositories it may take several seconds (or even minutes) for this command to return even if the `wait\_for\_completion` parameter is set to `false`. By default a snapshot of all open and started indices in the cluster is created. This behavior can be changed by specifying the list of indices in the body of the snapshot request. [source,js] ----------------------------------- PUT /\_snapshot/my\_backup/snapshot\_2?wait\_for\_completion=true { "indices": "index\_1,index\_2", "ignore\_unavailable": true, "include\_global\_state": false } ----------------------------------- // CONSOLE // TEST[continued] The list of indices that should be included into the snapshot can be specified using the `indices` parameter that supports <>. The snapshot request also supports the `ignore\_unavailable` option. Setting it to `true` will cause indices that do not exist to be ignored during snapshot creation. By default, when `ignore\_unavailable` option is not set and an index is missing the snapshot request will fail. By setting `include\_global\_state` to false it's possible to prevent the cluster global state to be stored as part of the snapshot. By default, the entire snapshot will fail if one or more indices participating in the snapshot don't have all primary
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/modules/snapshots.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.075902
|
an index is missing the snapshot request will fail. By setting `include\_global\_state` to false it's possible to prevent the cluster global state to be stored as part of the snapshot. By default, the entire snapshot will fail if one or more indices participating in the snapshot don't have all primary shards available. This behaviour can be changed by setting `partial` to `true`. The index snapshot process is incremental. In the process of making the index snapshot Elasticsearch analyses the list of the index files that are already stored in the repository and copies only files that were created or changed since the last snapshot. That allows multiple snapshots to be preserved in the repository in a compact form. Snapshotting process is executed in non-blocking fashion. All indexing and searching operation can continue to be executed against the index that is being snapshotted. However, a snapshot represents the point-in-time view of the index at the moment when snapshot was created, so no records that were added to the index after the snapshot process was started will be present in the snapshot. The snapshot process starts immediately for the primary shards that has been started and are not relocating at the moment. Before version 1.2.0, the snapshot operation fails if the cluster has any relocating or initializing primaries of indices participating in the snapshot. Starting with version 1.2.0, Elasticsearch waits for relocation or initialization of shards to complete before snapshotting them. Besides creating a copy of each index the snapshot process can also store global cluster metadata, which includes persistent cluster settings and templates. The transient settings and registered snapshot repositories are not stored as part of the snapshot. Only one snapshot process can be executed in the cluster at any time. While snapshot of a particular shard is being created this shard cannot be moved to another node, which can interfere with rebalancing process and allocation filtering. Elasticsearch will only be able to move a shard to another node (according to the current allocation filtering settings and rebalancing algorithm) once the snapshot is finished. Once a snapshot is created information about this snapshot can be obtained using the following command: [source,sh] ----------------------------------- GET /\_snapshot/my\_backup/snapshot\_1 ----------------------------------- // CONSOLE // TEST[continued] This command returns basic information about the snapshot including start and end time, version of Elasticsearch that created the snapshot, the list of included indices, the current state of the snapshot and the list of failures that occurred during the snapshot. The snapshot `state` can be [horizontal] `IN\_PROGRESS`:: The snapshot is currently running. `SUCCESS`:: The snapshot finished and all shards were stored successfully. `FAILED`:: The snapshot finished with an error and failed to store any data. `PARTIAL`:: The global cluster state was stored, but data of at least one shard wasn't stored successfully. The `failure` section in this case should contain more detailed information about shards that were not processed correctly. `INCOMPATIBLE`:: The snapshot was created with an old version of Elasticsearch and therefore is incompatible with the current version of the cluster. Similar as for repositories, information about multiple snapshots can be queried in one go, supporting wildcards as well: [source,sh] ----------------------------------- GET /\_snapshot/my\_backup/snapshot\_\*,some\_other\_snapshot ----------------------------------- // CONSOLE // TEST[continued] All snapshots currently stored in the repository can be listed using the following command: [source,sh] ----------------------------------- GET /\_snapshot/my\_backup/\_all ----------------------------------- // CONSOLE // TEST[continued] The command fails if some of the snapshots are unavailable. The boolean parameter `ignore\_unavailable` can be used to return all snapshots that are currently available. Getting all snapshots in the repository can be costly on cloud-based repositories, both from a cost and performance perspective. If the only information required
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/modules/snapshots.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.040228
|
TEST[continued] The command fails if some of the snapshots are unavailable. The boolean parameter `ignore\_unavailable` can be used to return all snapshots that are currently available. Getting all snapshots in the repository can be costly on cloud-based repositories, both from a cost and performance perspective. If the only information required is the snapshot names/uuids in the repository and the indices in each snapshot, then the optional boolean parameter `verbose` can be set to `false` to execute a more performant and cost-effective retrieval of the snapshots in the repository. Note that setting `verbose` to `false` will omit all other information about the snapshot such as status information, the number of snapshotted shards, etc. The default value of the `verbose` parameter is `true`. A currently running snapshot can be retrieved using the following command: [source,sh] ----------------------------------- GET /\_snapshot/my\_backup/\_current ----------------------------------- // CONSOLE // TEST[continued] A snapshot can be deleted from the repository using the following command: [source,sh] ----------------------------------- DELETE /\_snapshot/my\_backup/snapshot\_2 ----------------------------------- // CONSOLE // TEST[continued] When a snapshot is deleted from a repository, Elasticsearch deletes all files that are associated with the deleted snapshot and not used by any other snapshots. If the deleted snapshot operation is executed while the snapshot is being created the snapshotting process will be aborted and all files created as part of the snapshotting process will be cleaned. Therefore, the delete snapshot operation can be used to cancel long running snapshot operations that were started by mistake. A repository can be deleted using the following command: [source,sh] ----------------------------------- DELETE /\_snapshot/my\_backup ----------------------------------- // CONSOLE // TEST[continued] When a repository is deleted, Elasticsearch only removes the reference to the location where the repository is storing the snapshots. The snapshots themselves are left untouched and in place. [float] === Restore A snapshot can be restored using the following command: [source,sh] ----------------------------------- POST /\_snapshot/my\_backup/snapshot\_1/\_restore ----------------------------------- // CONSOLE // TEST[continued] By default, all indices in the snapshot are restored, and the cluster state is \*not\* restored. It's possible to select indices that should be restored as well as to allow the global cluster state from being restored by using `indices` and `include\_global\_state` options in the restore request body. The list of indices supports <>. The `rename\_pattern` and `rename\_replacement` options can be also used to rename indices on restore using regular expression that supports referencing the original text as explained http://docs.oracle.com/javase/6/docs/api/java/util/regex/Matcher.html#appendReplacement(java.lang.StringBuffer,%20java.lang.String)[here]. Set `include\_aliases` to `false` to prevent aliases from being restored together with associated indices [source,js] ----------------------------------- POST /\_snapshot/my\_backup/snapshot\_1/\_restore { "indices": "index\_1,index\_2", "ignore\_unavailable": true, "include\_global\_state": true, "rename\_pattern": "index\_(.+)", "rename\_replacement": "restored\_index\_$1" } ----------------------------------- // CONSOLE // TEST[continued] The restore operation can be performed on a functioning cluster. However, an existing index can be only restored if it's <> and has the same number of shards as the index in the snapshot. The restore operation automatically opens restored indices if they were closed and creates new indices if they didn't exist in the cluster. If cluster state is restored with `include\_global\_state` (defaults to `false`), the restored templates that don't currently exist in the cluster are added and existing templates with the same name are replaced by the restored templates. The restored persistent settings are added to the existing persistent settings. [float] ==== Partial restore By default, the entire restore operation will fail if one or more indices participating in the operation don't have snapshots of all shards available. It can occur if some shards failed to snapshot for example. It is still possible to restore such indices by setting `partial` to `true`. Please note, that only successfully snapshotted shards will be restored in this case and all missing shards will be recreated empty. [float] ==== Changing
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/modules/snapshots.asciidoc
|
6.2
|
elasticsearch-6-2
|
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shards available. It can occur if some shards failed to snapshot for example. It is still possible to restore such indices by setting `partial` to `true`. Please note, that only successfully snapshotted shards will be restored in this case and all missing shards will be recreated empty. [float] ==== Changing index settings during restore Most of index settings can be overridden during the restore process. For example, the following command will restore the index `index\_1` without creating any replicas while switching back to default refresh interval: [source,js] ----------------------------------- POST /\_snapshot/my\_backup/snapshot\_1/\_restore { "indices": "index\_1", "index\_settings": { "index.number\_of\_replicas": 0 }, "ignore\_index\_settings": [ "index.refresh\_interval" ] } ----------------------------------- // CONSOLE // TEST[continued] Please note, that some settings such as `index.number\_of\_shards` cannot be changed during restore operation. [float] ==== Restoring to a different cluster The information stored in a snapshot is not tied to a particular cluster or a cluster name. Therefore it's possible to restore a snapshot made from one cluster into another cluster. All that is required is registering the repository containing the snapshot in the new cluster and starting the restore process. The new cluster doesn't have to have the same size or topology. However, the version of the new cluster should be the same or newer (only 1 major version newer) than the cluster that was used to create the snapshot. For example, you can restore a 1.x snapshot to a 2.x cluster, but not a 1.x snapshot to a 5.x cluster. If the new cluster has a smaller size additional considerations should be made. First of all it's necessary to make sure that new cluster have enough capacity to store all indices in the snapshot. It's possible to change indices settings during restore to reduce the number of replicas, which can help with restoring snapshots into smaller cluster. It's also possible to select only subset of the indices using the `indices` parameter. If indices in the original cluster were assigned to particular nodes using <>, the same rules will be enforced in the new cluster. Therefore if the new cluster doesn't contain nodes with appropriate attributes that a restored index can be allocated on, such index will not be successfully restored unless these index allocation settings are changed during restore operation. The restore operation also checks that restored persistent settings are compatible with the current cluster to avoid accidentally restoring an incompatible settings such as `discovery.zen.minimum\_master\_nodes` and as a result disable a smaller cluster until the required number of master eligible nodes is added. If you need to restore a snapshot with incompatible persistent settings, try restoring it without the global cluster state. [float] === Snapshot status A list of currently running snapshots with their detailed status information can be obtained using the following command: [source,sh] ----------------------------------- GET /\_snapshot/\_status ----------------------------------- // CONSOLE // TEST[continued] In this format, the command will return information about all currently running snapshots. By specifying a repository name, it's possible to limit the results to a particular repository: [source,sh] ----------------------------------- GET /\_snapshot/my\_backup/\_status ----------------------------------- // CONSOLE // TEST[continued] If both repository name and snapshot id are specified, this command will return detailed status information for the given snapshot even if it's not currently running: [source,sh] ----------------------------------- GET /\_snapshot/my\_backup/snapshot\_1/\_status ----------------------------------- // CONSOLE // TEST[continued] Multiple ids are also supported: [source,sh] ----------------------------------- GET /\_snapshot/my\_backup/snapshot\_1,snapshot\_2/\_status ----------------------------------- // CONSOLE // TEST[continued] [float] [[monitor-snapshot-restore-progress]] === Monitoring snapshot/restore progress There are several ways to monitor the progress of the snapshot and restores processes while they are running. Both operations support `wait\_for\_completion` parameter that would block client until the operation is completed. This is the simplest method that can be used to get notified
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/modules/snapshots.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.005703
|
[float] [[monitor-snapshot-restore-progress]] === Monitoring snapshot/restore progress There are several ways to monitor the progress of the snapshot and restores processes while they are running. Both operations support `wait\_for\_completion` parameter that would block client until the operation is completed. This is the simplest method that can be used to get notified about operation completion. The snapshot operation can be also monitored by periodic calls to the snapshot info: [source,sh] ----------------------------------- GET /\_snapshot/my\_backup/snapshot\_1 ----------------------------------- // CONSOLE // TEST[continued] Please note that snapshot info operation uses the same resources and thread pool as the snapshot operation. So, executing a snapshot info operation while large shards are being snapshotted can cause the snapshot info operation to wait for available resources before returning the result. On very large shards the wait time can be significant. To get more immediate and complete information about snapshots the snapshot status command can be used instead: [source,sh] ----------------------------------- GET /\_snapshot/my\_backup/snapshot\_1/\_status ----------------------------------- // CONSOLE // TEST[continued] While snapshot info method returns only basic information about the snapshot in progress, the snapshot status returns complete breakdown of the current state for each shard participating in the snapshot. The restore process piggybacks on the standard recovery mechanism of the Elasticsearch. As a result, standard recovery monitoring services can be used to monitor the state of restore. When restore operation is executed the cluster typically goes into `red` state. It happens because the restore operation starts with "recovering" primary shards of the restored indices. During this operation the primary shards become unavailable which manifests itself in the `red` cluster state. Once recovery of primary shards is completed Elasticsearch is switching to standard replication process that creates the required number of replicas at this moment cluster switches to the `yellow` state. Once all required replicas are created, the cluster switches to the `green` states. The cluster health operation provides only a high level status of the restore process. It's possible to get more detailed insight into the current state of the recovery process by using <> and <> APIs. [float] === Stopping currently running snapshot and restore operations The snapshot and restore framework allows running only one snapshot or one restore operation at a time. If a currently running snapshot was executed by mistake, or takes unusually long, it can be terminated using the snapshot delete operation. The snapshot delete operation checks if the deleted snapshot is currently running and if it does, the delete operation stops that snapshot before deleting the snapshot data from the repository. [source,sh] ----------------------------------- DELETE /\_snapshot/my\_backup/snapshot\_1 ----------------------------------- // CONSOLE // TEST[continued] The restore operation uses the standard shard recovery mechanism. Therefore, any currently running restore operation can be canceled by deleting indices that are being restored. Please note that data for all deleted indices will be removed from the cluster as a result of this operation. [float] === Effect of cluster blocks on snapshot and restore operations Many snapshot and restore operations are affected by cluster and index blocks. For example, registering and unregistering repositories require write global metadata access. The snapshot operation requires that all indices and their metadata as well as the global metadata were readable. The restore operation requires the global metadata to be writable, however the index level blocks are ignored during restore because indices are essentially recreated during restore. Please note that a repository content is not part of the cluster and therefore cluster blocks don't affect internal repository operations such as listing or deleting snapshots from an already registered repository.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/modules/snapshots.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.080619
|
content is not part of the cluster and therefore cluster blocks don't affect internal repository operations such as listing or deleting snapshots from an already registered repository.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/modules/snapshots.asciidoc
|
6.2
|
elasticsearch-6-2
|
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|
[[modules-cluster]] == Cluster One of the main roles of the master is to decide which shards to allocate to which nodes, and when to move shards between nodes in order to rebalance the cluster. There are a number of settings available to control the shard allocation process: \* <> lists the settings to control the allocation and rebalancing operations. \* <> explains how Elasticsearch takes available disk space into account, and the related settings. \* <> and <> control how shards can be distributed across different racks or availability zones. \* <> allows certain nodes or groups of nodes excluded from allocation so that they can be decommissioned. Besides these, there are a few other <>. All of the settings in this section are \_dynamic\_ settings which can be updated on a live cluster with the <> API. include::cluster/shards\_allocation.asciidoc[] include::cluster/disk\_allocator.asciidoc[] include::cluster/allocation\_awareness.asciidoc[] include::cluster/allocation\_filtering.asciidoc[] include::cluster/misc.asciidoc[]
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/modules/cluster.asciidoc
|
6.2
|
elasticsearch-6-2
|
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|
[[modules-cross-cluster-search]] == {ccs-cap} The \_{ccs}\_ feature allows any node to act as a federated client across multiple clusters. In contrast to the <> feature, a {ccs} node won't join the remote cluster, instead it connects to a remote cluster in a light fashion in order to execute federated search requests. {ccs-cap} works by configuring a remote cluster in the cluster state and connecting only to a limited number of nodes in the remote cluster. Each remote cluster is referenced by a name and a list of seed nodes. When a remote cluster is registered, its cluster state is retrieved from one of the seed nodes so that up to 3 \_gateway nodes\_ are selected to be connected to as part of upcoming {ccs} requests. {ccs-cap} requests consist of uni-directional connections from the coordinating node to the previously selected remote nodes only. It is possible to tag which nodes should be selected through node attributes (see <>). Each node in a cluster that has remote clusters configured connects to one or more \_gateway nodes\_ and uses them to federate search requests to the remote cluster. [float] === Configuring {ccs} Remote clusters can be specified globally using <> (which can be updated dynamically), or local to individual nodes using the `elasticsearch.yml` file. If a remote cluster is configured via `elasticsearch.yml` only the nodes with that configuration will be able to connect to the remote cluster. In other words, federated search requests will have to be sent specifically to those nodes. Remote clusters set via the <> will be available on every node in the cluster. [WARNING] This feature was added as Beta in Elasticsearch `v5.3` with further improvements made in 5.4 and 5.5. It requires gateway eligible nodes to be on `v5.5` onwards. The `elasticsearch.yml` config file for a \_{ccs}\_ node just needs to list the remote clusters that should be connected to, for instance: [source,yaml] -------------------------------- search: remote: cluster\_one: <1> seeds: 127.0.0.1:9300 cluster\_two: <1> seeds: 127.0.0.1:9301 -------------------------------- <1> `cluster\_one` and `cluster\_two` are arbitrary cluster aliases representing the connection to each cluster. These names are subsequently used to distinguish between local and remote indices. The equivalent example using the <> to add remote clusters to all nodes in the cluster would look like the following: [source,js] -------------------------------- PUT \_cluster/settings { "persistent": { "search": { "remote": { "cluster\_one": { "seeds": [ "127.0.0.1:9300" ] }, "cluster\_two": { "seeds": [ "127.0.0.1:9301" ] }, "cluster\_three": { "seeds": [ "127.0.0.1:9302" ] } } } } } -------------------------------- // CONSOLE // TEST[setup:host] // TEST[s/127.0.0.1:9300/\${transport\_host}/] ////////////////////////// We want to be sure that settings have been updated, because we'll use them later. [source,js] -------------------------------------------------- { "acknowledged" : true, "persistent": { "search": { "remote": { "cluster\_one": { "seeds": [ "127.0.0.1:9300" ] }, "cluster\_two": { "seeds": [ "127.0.0.1:9301" ] }, "cluster\_three": { "seeds": [ "127.0.0.1:9302" ] } } } }, "transient" : {} } -------------------------------------------------- // TESTRESPONSE[s/127.0.0.1:9300/\${transport\_host}/] ////////////////////////// A remote cluster can be deleted from the cluster settings by setting its seeds to `null`: [source,js] -------------------------------- PUT \_cluster/settings { "persistent": { "search": { "remote": { "cluster\_three": { "seeds": null <1> } } } } } -------------------------------- // CONSOLE // TEST[continued] <1> `cluster\_three` would be removed from the cluster settings, leaving `cluster\_one` and `cluster\_two` intact. ////////////////////////// We want to be sure that settings have been updated, because we'll use them later. [source,js] -------------------------------------------------- { "acknowledged" : true, "persistent" : {}, "transient" : {} } -------------------------------------------------- // TESTRESPONSE ////////////////////////// [float] === Using {ccs} To search the `twitter` index on remote cluster `cluster\_one` the index name must be prefixed with the cluster alias separated by a `:` character: [source,js] -------------------------------------------------- GET /cluster\_one:twitter/\_search { "query": {
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/modules/cross-cluster-search.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.162408
|
{ "acknowledged" : true, "persistent" : {}, "transient" : {} } -------------------------------------------------- // TESTRESPONSE ////////////////////////// [float] === Using {ccs} To search the `twitter` index on remote cluster `cluster\_one` the index name must be prefixed with the cluster alias separated by a `:` character: [source,js] -------------------------------------------------- GET /cluster\_one:twitter/\_search { "query": { "match": { "user": "kimchy" } } } -------------------------------------------------- // CONSOLE // TEST[continued] // TEST[setup:twitter] [source,js] -------------------------------------------------- { "took": 150, "timed\_out": false, "\_shards": { "total": 1, "successful": 1, "failed": 0, "skipped": 0 }, "\_clusters": { "total": 1, "successful": 1, "skipped": 0 }, "hits": { "total": 1, "max\_score": 1, "hits": [ { "\_index": "cluster\_one:twitter", "\_type": "\_doc", "\_id": "0", "\_score": 1, "\_source": { "user": "kimchy", "date": "2009-11-15T14:12:12", "message": "trying out Elasticsearch", "likes": 0 } } ] } } -------------------------------------------------- // TESTRESPONSE[s/"took": 150/"took": "$body.took"/] // TESTRESPONSE[s/"max\_score": 1/"max\_score": "$body.hits.max\_score"/] // TESTRESPONSE[s/"\_score": 1/"\_score": "$body.hits.hits.0.\_score"/] In contrast to the `tribe` feature {ccs} can also search indices with the same name on different clusters: [source,js] -------------------------------------------------- GET /cluster\_one:twitter,twitter/\_search { "query": { "match": { "user": "kimchy" } } } -------------------------------------------------- // CONSOLE // TEST[continued] Search results are disambiguated the same way as the indices are disambiguated in the request. Even if index names are identical these indices will be treated as different indices when results are merged. All results retrieved from a remote index will be prefixed with their remote cluster name: [source,js] -------------------------------------------------- { "took": 150, "timed\_out": false, "\_shards": { "total": 2, "successful": 2, "failed": 0, "skipped": 0 }, "\_clusters": { "total": 2, "successful": 2, "skipped": 0 }, "hits": { "total": 2, "max\_score": 1, "hits": [ { "\_index": "cluster\_one:twitter", "\_type": "\_doc", "\_id": "0", "\_score": 1, "\_source": { "user": "kimchy", "date": "2009-11-15T14:12:12", "message": "trying out Elasticsearch", "likes": 0 } }, { "\_index": "twitter", "\_type": "\_doc", "\_id": "0", "\_score": 2, "\_source": { "user": "kimchy", "date": "2009-11-15T14:12:12", "message": "trying out Elasticsearch", "likes": 0 } } ] } } -------------------------------------------------- // TESTRESPONSE[s/"took": 150/"took": "$body.took"/] // TESTRESPONSE[s/"max\_score": 1/"max\_score": "$body.hits.max\_score"/] // TESTRESPONSE[s/"\_score": 1/"\_score": "$body.hits.hits.0.\_score"/] // TESTRESPONSE[s/"\_score": 2/"\_score": "$body.hits.hits.1.\_score"/] [float] === Skipping disconnected clusters By default all remote clusters that are searched via {ccs} need to be available when the search request is executed, otherwise the whole request fails and no search results are returned despite some of the clusters are available. Remote clusters can be made optional through the boolean `skip\_unavailable` setting, set to `false` by default. [source,js] -------------------------------- PUT \_cluster/settings { "persistent": { "search.remote.cluster\_two.skip\_unavailable": true <1> } } -------------------------------- // CONSOLE // TEST[continued] <1> `cluster\_two` is made optional [source,js] -------------------------------------------------- GET /cluster\_one:twitter,cluster\_two:twitter,twitter/\_search <1> { "query": { "match": { "user": "kimchy" } } } -------------------------------------------------- // CONSOLE // TEST[continued] <1> Search against the `twitter` index in `cluster\_one`, `cluster\_two` and also locally [source,js] -------------------------------------------------- { "took": 150, "timed\_out": false, "\_shards": { "total": 2, "successful": 2, "failed": 0, "skipped": 0 }, "\_clusters": { <1> "total": 3, "successful": 2, "skipped": 1 }, "hits": { "total": 2, "max\_score": 1, "hits": [ { "\_index": "cluster\_one:twitter", "\_type": "\_doc", "\_id": "0", "\_score": 1, "\_source": { "user": "kimchy", "date": "2009-11-15T14:12:12", "message": "trying out Elasticsearch", "likes": 0 } }, { "\_index": "twitter", "\_type": "\_doc", "\_id": "0", "\_score": 2, "\_source": { "user": "kimchy", "date": "2009-11-15T14:12:12", "message": "trying out Elasticsearch", "likes": 0 } } ] } } -------------------------------------------------- // TESTRESPONSE[s/"took": 150/"took": "$body.took"/] // TESTRESPONSE[s/"max\_score": 1/"max\_score": "$body.hits.max\_score"/] // TESTRESPONSE[s/"\_score": 1/"\_score": "$body.hits.hits.0.\_score"/] // TESTRESPONSE[s/"\_score": 2/"\_score": "$body.hits.hits.1.\_score"/] <1> The `clusters` section indicates that one cluster was unavailable and got skipped [float] [[cross-cluster-search-settings]] === {ccs-cap} settings `search.remote.connections\_per\_cluster`:: The number of nodes to connect to per remote cluster. The default is `3`. `search.remote.initial\_connect\_timeout`:: The time to wait for remote connections to be established when the node starts. The default is `30s`. `search.remote.node.attr`:: A node attribute to filter out nodes that
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/modules/cross-cluster-search.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.098603
|
got skipped [float] [[cross-cluster-search-settings]] === {ccs-cap} settings `search.remote.connections\_per\_cluster`:: The number of nodes to connect to per remote cluster. The default is `3`. `search.remote.initial\_connect\_timeout`:: The time to wait for remote connections to be established when the node starts. The default is `30s`. `search.remote.node.attr`:: A node attribute to filter out nodes that are eligible as a gateway node in the remote cluster. For instance a node can have a node attribute `node.attr.gateway: true` such that only nodes with this attribute will be connected to if `search.remote.node.attr` is set to `gateway`. `search.remote.connect`:: By default, any node in the cluster can act as a cross-cluster client and connect to remote clusters. The `search.remote.connect` setting can be set to `false` (defaults to `true`) to prevent certain nodes from connecting to remote clusters. {ccs-cap} requests must be sent to a node that is allowed to act as a cross-cluster client. `search.remote.${cluster\_alias}.skip\_unavailable`:: Per cluster boolean setting that allows to skip specific clusters when no nodes belonging to them are available and they are searched as part of a {ccs} request. Default is `false`, meaning that all clusters are mandatory by default, but they can selectively be made optional by setting this setting to `true`. [float] [[retrieve-remote-clusters-info]] === Retrieving remote clusters info The <> allows to retrieve information about the configured remote clusters, as well as the remote nodes that the {ccs} node is connected to.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/modules/cross-cluster-search.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.037748
|
[[modules-discovery]] == Discovery The discovery module is responsible for discovering nodes within a cluster, as well as electing a master node. Note, Elasticsearch is a peer to peer based system, nodes communicate with one another directly if operations are delegated / broadcast. All the main APIs (index, delete, search) do not communicate with the master node. The responsibility of the master node is to maintain the global cluster state, and act if nodes join or leave the cluster by reassigning shards. Each time a cluster state is changed, the state is made known to the other nodes in the cluster (the manner depends on the actual discovery implementation). [float] === Settings The `cluster.name` allows to create separated clusters from one another. The default value for the cluster name is `elasticsearch`, though it is recommended to change this to reflect the logical group name of the cluster running. include::discovery/azure.asciidoc[] include::discovery/ec2.asciidoc[] include::discovery/gce.asciidoc[] include::discovery/zen.asciidoc[]
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/modules/discovery.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.14752
|
[[modules-threadpool]] == Thread Pool A node holds several thread pools in order to improve how threads memory consumption are managed within a node. Many of these pools also have queues associated with them, which allow pending requests to be held instead of discarded. There are several thread pools, but the important ones include: `generic`:: For generic operations (e.g., background node discovery). Thread pool type is `scaling`. `index`:: For index/delete operations. Thread pool type is `fixed` with a size of `# of available processors`, queue\_size of `200`. The maximum size for this pool is `1 + # of available processors`. `search`:: For count/search/suggest operations. Thread pool type is `fixed\_auto\_queue\_size` with a size of `int((# of available\_processors \* 3) / 2) + 1`, and initial queue\_size of `1000`. `get`:: For get operations. Thread pool type is `fixed` with a size of `# of available processors`, queue\_size of `1000`. `bulk`:: For bulk operations. Thread pool type is `fixed` with a size of `# of available processors`, queue\_size of `200`. The maximum size for this pool is `1 + # of available processors`. `snapshot`:: For snapshot/restore operations. Thread pool type is `scaling` with a keep-alive of `5m` and a max of `min(5, (# of available processors)/2)`. `warmer`:: For segment warm-up operations. Thread pool type is `scaling` with a keep-alive of `5m` and a max of `min(5, (# of available processors)/2)`. `refresh`:: For refresh operations. Thread pool type is `scaling` with a keep-alive of `5m` and a max of `min(10, (# of available processors)/2)`. `listener`:: Mainly for java client executing of action when listener threaded is set to true. Thread pool type is `scaling` with a default max of `min(10, (# of available processors)/2)`. Changing a specific thread pool can be done by setting its type-specific parameters; for example, changing the `index` thread pool to have more threads: [source,yaml] -------------------------------------------------- thread\_pool: index: size: 30 -------------------------------------------------- [float] [[types]] === Thread pool types The following are the types of thread pools and their respective parameters: [float] [[fixed]] ==== `fixed` The `fixed` thread pool holds a fixed size of threads to handle the requests with a queue (optionally bounded) for pending requests that have no threads to service them. The `size` parameter controls the number of threads, and defaults to the number of cores times 5. The `queue\_size` allows to control the size of the queue of pending requests that have no threads to execute them. By default, it is set to `-1` which means its unbounded. When a request comes in and the queue is full, it will abort the request. [source,yaml] -------------------------------------------------- thread\_pool: index: size: 30 queue\_size: 1000 -------------------------------------------------- [float] [[fixed-auto-queue-size]] ==== `fixed\_auto\_queue\_size` experimental[] The `fixed\_auto\_queue\_size` thread pool holds a fixed size of threads to handle the requests with a bounded queue for pending requests that have no threads to service them. It's similar to the `fixed` threadpool, however, the `queue\_size` automatically adjusts according to calculations based on https://en.wikipedia.org/wiki/Little%27s\_law[Little's Law]. These calculations will potentially adjust the `queue\_size` up or down by 50 every time `auto\_queue\_frame\_size` operations have been completed. The `size` parameter controls the number of threads, and defaults to the number of cores times 5. The `queue\_size` allows to control the initial size of the queue of pending requests that have no threads to execute them. The `min\_queue\_size` setting controls the minimum amount the `queue\_size` can be adjusted to. The `max\_queue\_size` setting controls the maximum amount the `queue\_size` can be adjusted to. The `auto\_queue\_frame\_size` setting controls the number of operations during which measurement is taken before the queue is adjusted. It should be large enough that a single operation cannot unduly bias the calculation. The
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/modules/threadpool.asciidoc
|
6.2
|
elasticsearch-6-2
|
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] | 0.189857
|
can be adjusted to. The `max\_queue\_size` setting controls the maximum amount the `queue\_size` can be adjusted to. The `auto\_queue\_frame\_size` setting controls the number of operations during which measurement is taken before the queue is adjusted. It should be large enough that a single operation cannot unduly bias the calculation. The `target\_response\_time` is a time value setting that indicates the targeted average response time for tasks in the thread pool queue. If tasks are routinely above this time, the thread pool queue will be adjusted down so that tasks are rejected. [source,yaml] -------------------------------------------------- thread\_pool: search: size: 30 queue\_size: 500 min\_queue\_size: 10 max\_queue\_size: 1000 auto\_queue\_frame\_size: 2000 target\_response\_time: 1s -------------------------------------------------- [float] [[scaling]] ==== `scaling` The `scaling` thread pool holds a dynamic number of threads. This number is proportional to the workload and varies between the value of the `core` and `max` parameters. The `keep\_alive` parameter determines how long a thread should be kept around in the thread pool without it doing any work. [source,yaml] -------------------------------------------------- thread\_pool: warmer: core: 1 max: 8 keep\_alive: 2m -------------------------------------------------- [float] [[processors]] === Processors setting The number of processors is automatically detected, and the thread pool settings are automatically set based on it. In some cases it can be useful to override the number of detected processors. This can be done by explicitly setting the `processors` setting. [source,yaml] -------------------------------------------------- processors: 2 -------------------------------------------------- There are a few use-cases for explicitly overriding the `processors` setting: . If you are running multiple instances of Elasticsearch on the same host but want Elasticsearch to size its thread pools as if it only has a fraction of the CPU, you should override the `processors` setting to the desired fraction (e.g., if you're running two instances of Elasticsearch on a 16-core machine, set `processors` to 8). Note that this is an expert-level use-case and there's a lot more involved than just setting the `processors` setting as there are other considerations like changing the number of garbage collector threads, pinning processes to cores, etc. . Sometimes the number of processors is wrongly detected and in such cases explicitly setting the `processors` setting will workaround such issues. In order to check the number of processors detected, use the nodes info API with the `os` flag.
|
https://github.com/elastic/elasticsearch/blob/6.2//docs/reference/modules/threadpool.asciidoc
|
6.2
|
elasticsearch-6-2
|
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0.018475109711289406,
-0.04675394296646118,
0.001200154540129006
] | 0.053601
|
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