Project: Druid (App Definition and Development / Database)
File: data-formats.md

id: data-formats title: Source input formats sidebar_label: Source input formats <!-- ~ Licensed to the Apache Software Foundation (ASF) under one ~ or more contributor license agreements. See the NOTICE file ~ distributed with this work for additional information ~ regarding copyright ownership. The ASF licenses this file ~ to you under the Apache License, Version 2.0 (the ~ "License"); you may not use this file except in compliance ~ with the License. You may obtain a copy of the License at ~ ~ http://www.apache.org/licenses/LICENSE-2.0 ~ ~ Unless required by applicable law or agreed to in writing, ~ software distributed under the License is distributed on an ~ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY ~ KIND, either express or implied. See the License for the ~ specific language governing permissions and limitations ~ under the License. --> Apache Druid can ingest denormalized data in JSON, CSV, or a delimited form such as TSV, or any custom format. While most examples in the documentation use data in JSON format, it is not difficult to configure Druid to ingest any other delimited data. We welcome any contributions to new formats. This page lists all default and core extension data formats supported by Druid. For additional data formats supported with community extensions, please see our . The following samples show data formats that are natively supported in Druid: JSON ```json {"timestamp": "2013-08-31T01:02:33Z", "page": "Gypsy Danger", "language" : "en", "user" : "nuclear", "unpatrolled" : "true", "newPage" : "true", "robot": "false", "anonymous": "false", "namespace":"article", "continent":"North America", "country":"United States", "region":"Bay Area", "city":"San Francisco", "added": 57, "deleted": 200, "delta": -143} {"timestamp": "2013-08-31T03:32:45Z", "page": "Striker Eureka", "language" : "en", "user" : "speed", "unpatrolled" : "false", "newPage" : "true", "robot": "true", "anonymous": "false", "namespace":"wikipedia", "continent":"Australia", "country":"Australia", "region":"Cantebury", "city":"Syndey", "added": 459, "deleted": 129, "delta": 330} {"timestamp": "2013-08-31T07:11:21Z", "page": "Cherno Alpha", "language" : "ru", "user" : "masterYi", "unpatrolled" : "false", "newPage" : "true", "robot": "true", "anonymous": "false", "namespace":"article", "continent":"Asia", "country":"Russia", "region":"Oblast", "city":"Moscow", "added": 123, "deleted": 12, "delta": 111} {"timestamp": "2013-08-31T11:58:39Z", "page": "Crimson Typhoon", "language" : "zh", "user" : "triplets", "unpatrolled" : "true", "newPage" : "false", "robot": "true", "anonymous": "false", "namespace":"wikipedia", "continent":"Asia", "country":"China", "region":"Shanxi", "city":"Taiyuan", "added": 905, "deleted": 5, "delta": 900} {"timestamp": "2013-08-31T12:41:27Z", "page": "Coyote Tango", "language" : "ja", "user" : "cancer", "unpatrolled" : "true", "newPage" : "false", "robot": "true", "anonymous": "false", "namespace":"wikipedia", "continent":"Asia", "country":"Japan", "region":"Kanto", "city":"Tokyo", "added": 1, "deleted": 10, "delta": -9} ``` CSV ``` 2013-08-31T01:02:33Z,"Gypsy Danger","en","nuclear","true","true","false","false","article","North America","United States","Bay Area","San Francisco",57,200,-143 2013-08-31T03:32:45Z,"Striker Eureka","en","speed","false","true","true","false","wikipedia","Australia","Australia","Cantebury","Syndey",459,129,330 2013-08-31T07:11:21Z,"Cherno Alpha","ru","masterYi","false","true","true","false","article","Asia","Russia","Oblast","Moscow",123,12,111 2013-08-31T11:58:39Z,"Crimson Typhoon","zh","triplets","true","false","true","false","wikipedia","Asia","China","Shanxi","Taiyuan",905,5,900 2013-08-31T12:41:27Z,"Coyote Tango","ja","cancer","true","false","true","false","wikipedia","Asia","Japan","Kanto","Tokyo",1,10,-9 ``` TSV (Delimited) ``` 2013-08-31T01:02:33Z "Gypsy Danger" "en" "nuclear" "true" "true" "false" "false" "article" "North America" "United States" "Bay Area" "San Francisco" 57 200 -143 2013-08-31T03:32:45Z "Striker Eureka" "en" "speed" "false" "true" "true" "false" "wikipedia" "Australia" "Australia" "Cantebury" "Syndey" 459 129 330 2013-08-31T07:11:21Z "Cherno Alpha" "ru" "masterYi" "false" "true" "true" "false" "article" "Asia" "Russia" "Oblast" "Moscow" 123 12 111 2013-08-31T11:58:39Z "Crimson Typhoon" "zh" "triplets" "true" "false" "true" "false" "wikipedia" "Asia" "China" "Shanxi" "Taiyuan" 905 5 900 2013-08-31T12:41:27Z "Coyote Tango" "ja" "cancer" "true" "false" "true" "false" "wikipedia" "Asia" "Japan" "Kanto" "Tokyo" 1 10 -9 ``` Note that the CSV and TSV data do not contain column heads. This becomes important when you specify the data for ingesting. Besides text formats, Druid also supports binary formats such as and formats. Druid supports custom data formats and can use the Regex parser or the JavaScript parsers to parse these formats. Using any of these parsers for parsing data is less efficient than writing a native Java parser or using an external stream processor. We welcome contributions of new

You can use the `inputFormat` field to specify the data format for your input data. :::info `inputFormat` doesn't support all data formats or ingestion methods supported by Druid. ::: Especially if you want to use the Hadoop ingestion, you still need to use the . If your data is formatted in some format not listed in this section, please consider using the Parser instead. All forms of Druid ingestion require some form of schema object. The format of the data to be ingested is specified using the `inputFormat` entry in your . Configure the JSON `inputFormat` to load JSON data as follows: | Field | Type | Description | Required | |-||-|-| | type | String | Set value to `json`. | yes | | flattenSpec | JSON Object | Specifies flattening configuration for nested JSON data. See for more info. | no | | featureSpec | JSON Object | supported by Jackson, a JSON processor for Java. The features control parsing of the input JSON data. To enable a feature, map the feature name to a Boolean value of "true". For example: `"featureSpec": {"ALLOWSINGLEQUOTES": true, "ALLOWUNQUOTEDFIELD_NAMES": true}` | no | The following properties are specialized properties that only apply when the JSON `inputFormat` is used in streaming ingestion, and they are related to how parsing exceptions are handled. In streaming ingestion, multi-line JSON events can be ingested (i.e. where a single JSON event spans multiple lines). However, if a parsing exception occurs, all JSON events that are present in the same streaming record will be discarded. | Field | Type | Description | Required | |-||-|-| | assumeNewlineDelimited | Boolean | If the input is known to be newline delimited JSON (each individual JSON event is contained in a single line, separated by newlines), setting this option to true allows for more flexible parsing exception handling. Only the lines with invalid JSON syntax will be discarded, while lines containing valid JSON events will still be ingested. | no (Default false) | | useJsonNodeReader | Boolean | When ingesting multi-line JSON events, enabling this option will enable the use of a JSON parser which will retain any valid JSON events encountered within a streaming record prior to when a parsing exception occurred. | no (Default false) | For example: ```json "ioConfig": { "inputFormat": { "type": "json" }, ... } ``` Configure the CSV `inputFormat` to load CSV data as follows: | Field | Type | Description | Required | |-||-|-| | type | String | Set value to `csv`. | yes | | listDelimiter | String | A custom delimiter for multi-value dimensions. | no (default = ctrl+A) | | columns | JSON array | Specifies the columns of the data. The columns should be in the same order with the columns of your data. | yes if `findColumnsFromHeader` is false or missing | | findColumnsFromHeader | Boolean | If this is set, the task will find the column names from the header row. Note that `skipHeaderRows` will be applied before finding column names from the header. For example, if you set `skipHeaderRows` to 2 and `findColumnsFromHeader` to true, the task will skip the first two lines and then extract column information from the third line. `columns` will be ignored if this is set to true. | no (default = false if `columns` is set; otherwise null) | | skipHeaderRows | Integer | If this is set, the task will skip the first `skipHeaderRows` rows. | no (default = 0) | For example: ```json "ioConfig": { "inputFormat": { "type": "csv", "columns" : ["timestamp","page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city","added","deleted","delta"] },

} ``` Configure the TSV `inputFormat` to load TSV data as follows: | Field | Type | Description | Required | |-||-|-| | type | String | Set value to `tsv`. | yes | | delimiter | String | A custom delimiter for data values. | no (default = `\t`) | | listDelimiter | String | A custom delimiter for multi-value dimensions. | no (default = ctrl+A) | | columns | JSON array | Specifies the columns of the data. The columns should be in the same order with the columns of your data. | yes if `findColumnsFromHeader` is false or missing | | findColumnsFromHeader | Boolean | If this is set, the task will find the column names from the header row. Note that `skipHeaderRows` will be applied before finding column names from the header. For example, if you set `skipHeaderRows` to 2 and `findColumnsFromHeader` to true, the task will skip the first two lines and then extract column information from the third line. `columns` will be ignored if this is set to true. | no (default = false if `columns` is set; otherwise null) | | skipHeaderRows | Integer | If this is set, the task will skip the first `skipHeaderRows` rows. | no (default = 0) | Be sure to change the `delimiter` to the appropriate delimiter for your data. Like CSV, you must specify the columns and which subset of the columns you want indexed. For example: ```json "ioConfig": { "inputFormat": { "type": "tsv", "columns" : ["timestamp","page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city","added","deleted","delta"], "delimiter":"|" }, ... } ``` To use the ORC input format, load the Druid Orc extension ( ). :::info To upgrade from versions earlier than 0.15.0 to 0.15.0 or new, read . ::: Configure the ORC `inputFormat` to load ORC data as follows: | Field | Type | Description | Required | |-||-|-| | type | String | Set value to `orc`. | yes | | flattenSpec | JSON Object | Specifies flattening configuration for nested ORC data. Only 'path' expressions are supported ('jq' and 'tree' are unavailable). See for more info. | no | | binaryAsString | Boolean | Specifies if the binary orc column which is not logically marked as a string should be treated as a UTF-8 encoded string. | no (default = false) | For example: ```json "ioConfig": { "inputFormat": { "type": "orc", "flattenSpec": { "useFieldDiscovery": true, "fields": [ { "type": "path", "name": "nested", "expr": "$.path.to.nested" } ] }, "binaryAsString": false }, ... } ``` To use the Parquet input format load the Druid Parquet extension (). Configure the Parquet `inputFormat` to load Parquet data as follows: | Field | Type | Description | Required | ||||| | `type` | String | Set value to `parquet`. | yes | | `flattenSpec` | JSON Object | Define a to extract nested values from a Parquet file. Only 'path' expressions are supported ('jq' and 'tree' are unavailable). | no (default will auto-discover 'root' level properties) | | `binaryAsString` | Boolean | Specifies if the bytes parquet column which is not logically marked as a string or enum type should be treated as a UTF-8 encoded string. | no (default = false) | For example: ```json "ioConfig": { "inputFormat": { "type": "parquet", "flattenSpec": { "useFieldDiscovery": true, "fields": [ { "type": "path", "name": "nested", "expr": "$.path.to.nested" } ] }, "binaryAsString": false }, ... } ``` To use the Avro Stream input format load the Druid Avro extension

For more information on how Druid handles Avro types, see section for Configure the Avro `inputFormat` to load Avro data as follows: | Field | Type | Description | Required | |-||-|-| |type| String| Set value to `avro_stream`. | yes | |flattenSpec| JSON Object |Define a to extract nested values from a Avro record. Only 'path' expressions are supported ('jq' is unavailable).| no (default will auto-discover 'root' level properties) | |`avroBytesDecoder`| JSON Object |Specifies how to decode bytes to Avro record. | yes | | binaryAsString | Boolean | Specifies if the bytes Avro column which is not logically marked as a string or enum type should be treated as a UTF-8 encoded string. | no (default = false) | For example: ```json "ioConfig": { "inputFormat": { "type": "avro_stream", "avroBytesDecoder": { "type": "schema_inline", "schema": { //your schema goes here, for example "namespace": "org.apache.druid.data", "name": "User", "type": "record", "fields": [ { "name": "FullName", "type": "string" }, { "name": "Country", "type": "string" } ] } }, "flattenSpec": { "useFieldDiscovery": true, "fields": [ { "type": "path", "name": "someRecord_subInt", "expr": "$.someRecord.subInt" } ] }, "binaryAsString": false }, ... } ``` If `type` is not included, the avroBytesDecoder defaults to `schema_repo`. :::info The "schema_inline" decoder reads Avro records using a fixed schema and does not support schema migration. If you may need to migrate schemas in the future, consider one of the other decoders, all of which use a message header that allows the parser to identify the proper Avro schema for reading records. ::: This decoder can be used if all the input events can be read using the same schema. In this case, specify the schema in the input task JSON itself, as described below. ``` ... "avroBytesDecoder": { "type": "schema_inline", "schema": { //your schema goes here, for example "namespace": "org.apache.druid.data", "name": "User", "type": "record", "fields": [ { "name": "FullName", "type": "string" }, { "name": "Country", "type": "string" } ] } } ... ``` Use this decoder if different input events can have different read schemas. In this case, specify the schema in the input task JSON itself, as described below. ``` ... "avroBytesDecoder": { "type": "multipleschemasinline", "schemas": { //your id -> schema map goes here, for example "1": { "namespace": "org.apache.druid.data", "name": "User", "type": "record", "fields": [ { "name": "FullName", "type": "string" }, { "name": "Country", "type": "string" } ] }, "2": { "namespace": "org.apache.druid.otherdata", "name": "UserIdentity", "type": "record", "fields": [ { "name": "Name", "type": "string" }, { "name": "Location", "type": "string" } ] }, ... ... } } ... ``` Note that it is essentially a map of integer schema ID to avro schema object. This parser assumes that record has following format. first 1 byte is version and must always be 1. next 4 bytes are integer schema ID serialized using big-endian byte order. remaining bytes contain serialized avro message. This Avro bytes decoder first extracts `subject` and `id` from the input message bytes, and then uses them to look up the Avro schema used to decode the Avro record from bytes. For details, see the . You need an HTTP service like schema repo to hold the Avro schema. For information on registering a schema on the message producer side, see `org.apache.druid.data.input.AvroStreamInputRowParserTest#testParse()`. | Field | Type | Description | Required | |-||-|-| | type | String | Set value to `schema_repo`. | no | | subjectAndIdConverter | JSON Object | Specifies how to extract the subject and id from message bytes. | yes | | schemaRepository | JSON Object | Specifies how to look up the Avro schema from subject and id. | yes | This section describes the format of the `subjectAndIdConverter` object for the `schema_repo` Avro bytes

| Field | Type | Description | Required | |-||-|-| | type | String | Set value to `avro_1124`. | no | | topic | String | Specifies the topic of your Kafka stream. | yes | This section describes the format of the `schemaRepository` object for the `schema_repo` Avro bytes decoder. | Field | Type | Description | Required | |-||-|-| | type | String | Set value to `avro1124rest_client`. | no | | url | String | Specifies the endpoint URL of your Avro-1124 schema repository. | yes | This Avro bytes decoder first extracts a unique `id` from input message bytes, and then uses it to look up the schema in the Schema Registry used to decode the Avro record from bytes. For details, see the Schema Registry and . | Field | Type | Description | Required | |-||-|-| | type | String | Set value to `schema_registry`. | no | | url | String | Specifies the URL endpoint of the Schema Registry. | yes | | capacity | Integer | Specifies the max size of the cache (default = Integer.MAX_VALUE). | no | | urls | Array<String\> | Specifies the URL endpoints of the multiple Schema Registry instances. | yes (if `url` is not provided) | | config | Json | To send additional configurations, configured for Schema Registry. This can be supplied via a | no | | headers | Json | To send headers to the Schema Registry. This can be supplied via a | no | For a single schema registry instance, use Field `url` or `urls` for multi instances. Single Instance: ```json ... "avroBytesDecoder" : { "type" : "schema_registry", "url" : <schema-registry-url> } ... ``` Multiple Instances: ```json ... "avroBytesDecoder" : { "type" : "schema_registry", "urls" : [<schema-registry-url-1>, <schema-registry-url-2>, ...], "config" : { "basic.auth.credentials.source": "USER_INFO", "basic.auth.user.info": "fred:letmein", "schema.registry.ssl.truststore.location": "/some/secrets/kafka.client.truststore.jks", "schema.registry.ssl.truststore.password": "<password>", "schema.registry.ssl.keystore.location": "/some/secrets/kafka.client.keystore.jks", "schema.registry.ssl.keystore.password": "<password>", "schema.registry.ssl.key.password": "<password>", "schema.registry.ssl.key.password", ... }, "headers": { "traceID" : "b29c5de2-0db4-490b-b421", "timeStamp" : "1577191871865", "druid.dynamic.config.provider":{ "type":"mapString", "config":{ "registry.header.prop.1":"value.1", "registry.header.prop.2":"value.2" } } ... } } ... ``` The following errors when reading records will be considered parse exceptions, which can be limited and logged with ingestion task configurations such as `maxParseExceptions` and `maxSavedParseExceptions`: Failure to retrieve a schema due to misconfiguration or corrupt records (invalid schema IDs) Failure to decode an Avro message To load the Avro OCF input format, load the Druid Avro extension (). See the section for how Avro types are handled in Druid Configure the Avro OCF `inputFormat` to load Avro OCF data as follows: | Field | Type | Description | Required | |-||-|-| |type| String| Set value to `avro_ocf`. | yes | |flattenSpec| JSON Object | Define a to extract nested values from Avro records. Only 'path' expressions are supported ('jq' and 'tree' are unavailable). | no (default will auto-discover 'root' level properties) | |schema| JSON Object | Define a reader schema to be used when parsing Avro records. This is useful when parsing multiple versions of Avro OCF file data. | no (default will decode using the writer schema contained in the OCF file) | | binaryAsString | Boolean | Specifies if the bytes parquet column which is not logically marked as a string or enum type should be treated as a UTF-8 encoded string. | no (default = false) | For example: ```json "ioConfig": { "inputFormat": { "type": "avro_ocf", "flattenSpec": { "useFieldDiscovery": true, "fields": [ { "type": "path", "name": "someRecord_subInt", "expr": "$.someRecord.subInt" } ] }, "schema": { "namespace":

"name": "SomeDatum", "type": "record", "fields" : [ { "name": "timestamp", "type": "long" }, { "name": "eventType", "type": "string" }, { "name": "id", "type": "long" }, { "name": "someRecord", "type": { "type": "record", "name": "MySubRecord", "fields": [ { "name": "subInt", "type": "int"}, { "name": "subLong", "type": "long"} ] }}] }, "binaryAsString": false }, ... } ``` :::info You need to include the as an extension to use the Protobuf input format. ::: Configure the Protobuf `inputFormat` to load Protobuf data as follows: | Field | Type | Description | Required | ||||| | `type` | String | Set value to `protobuf`. | yes | | `flattenSpec` | JSON Object | Define a to extract nested values from a Protobuf record. Note that only 'path' expression are supported ('jq' and 'tree' is unavailable). | no (default will auto-discover 'root' level properties) | | `protoBytesDecoder` | JSON Object | Specifies how to decode bytes to Protobuf record. | yes | For example: ```json "ioConfig": { "inputFormat": { "type": "protobuf", "protoBytesDecoder": { "type": "file", "descriptor": "file:///tmp/metrics.desc", "protoMessageType": "Metrics" } "flattenSpec": { "useFieldDiscovery": true, "fields": [ { "type": "path", "name": "someRecord_subInt", "expr": "$.someRecord.subInt" } ] } }, ... } ``` The `kafka` input format lets you parse the Kafka metadata fields in addition to the Kafka payload value contents. It should only be used when ingesting from Apache Kafka. The `kafka` input format wraps around the payload parsing input format and augments the data it outputs with the Kafka event timestamp, topic name, event headers, and the key field that itself can be parsed using any available input format. If there are conflicts between column names in the payload and those created from the metadata, the payload takes precedence. This ensures that upgrading a Kafka ingestion to use the Kafka input format (by taking its existing input format and setting it as the `valueFormat`) can be done without losing any of the payload data. Configure the Kafka `inputFormat` as follows: | Field | Type | Description | Required | Default | |-||-|-|| | `type` | String | Set value to `kafka`. | yes || | `valueFormat` | | The to parse the Kafka value payload. | yes || | `timestampColumnName` | String | The name of the column for the Kafka timestamp.| no |`kafka.timestamp`| | `topicColumnName` | String |The name of the column for the Kafka topic. This field is useful when ingesting data from multiple topics into same datasource.| no |`kafka.topic`| | `headerColumnPrefix` | String | The custom prefix for all the header columns. | no | `kafka.header`| | `headerFormat` | Object | Specifies how to parse the Kafka headers. Supports String types. Because Kafka header values are bytes, the parser decodes them as UTF-8 encoded strings. To change this behavior, implement your own parser based on the encoding style. Change the `encoding` type in `KafkaStringHeaderFormat` to match your custom implementation. See for supported encoding formats.| no || | `keyFormat` | | The to parse the Kafka key. It only processes the first entry of the `inputFormat` field. If your key values are simple strings, you can use the `tsv` format to parse them. Note that for `tsv`,`csv`, and `regex` formats, you need to provide a `columns` array to make a valid input format. Only the first one is used, and its name will be ignored in favor of `keyColumnName`. | no || | `keyColumnName` | String | The name of the column for the Kafka key.| no |`kafka.key`| `headerFormat` supports the following encoding formats: `ISO-8859-1`: ISO Latin Alphabet No. 1, that is, ISO-LATIN-1. `US-ASCII`: Seven-bit ASCII. Also known as ISO646-US. The Basic Latin block of the Unicode character set. `UTF-8`: Eight-bit UCS Transformation

`UTF-16`: Sixteen-bit UCS Transformation Format, byte order identified by an optional byte-order mark. `UTF-16BE`: Sixteen-bit UCS Transformation Format, big-endian byte order. `UTF-16LE`: Sixteen-bit UCS Transformation Format, little-endian byte order. `headerColumnPrefix`: Supply a prefix to the Kafka headers to avoid any conflicts with columns from the payload. The default is `kafka.header.`. Using `{ "type": "json" }` as the input format would only parse the payload value. To parse the Kafka metadata in addition to the payload, use the `kafka` input format. For example, consider the following structure for a Kafka message that represents an edit in a development environment: Kafka timestamp: `1680795276351` Kafka topic: `wiki-edits` Kafka headers: `env=development` `zone=z1` Kafka key: `wiki-edit` Kafka payload value: `{"channel":"#sv.wikipedia","timestamp":"2016-06-27T00:00:11.080Z","page":"Salo Toraut","delta":31,"namespace":"Main"}` You would configure it as follows: ```json "ioConfig": { "inputFormat": { "type": "kafka", "valueFormat": { "type": "json" }, "timestampColumnName": "kafka.timestamp", "topicColumnName": "kafka.topic", "headerFormat": { "type": "string", "encoding": "UTF-8" }, "headerColumnPrefix": "kafka.header.", "keyFormat": { "type": "tsv", "findColumnsFromHeader": false, "columns": ["x"] }, "keyColumnName": "kafka.key", } } ``` You would parse the example message as follows: ```json { "channel": "#sv.wikipedia", "timestamp": "2016-06-27T00:00:11.080Z", "page": "Salo Toraut", "delta": 31, "namespace": "Main", "kafka.timestamp": 1680795276351, "kafka.topic": "wiki-edits", "kafka.header.env": "development", "kafka.header.zone": "z1", "kafka.key": "wiki-edit" } ``` If you want to use `kafka.timestamp` as Druid's primary timestamp (`time`), specify it as the value for `column` in the `timestampSpec`: ```json "timestampSpec": { "column": "kafka.timestamp", "format": "millis" } ``` Similarly, if you want to use a timestamp extracted from the Kafka header: ```json "timestampSpec": { "column": "kafka.header.myTimestampHeader", "format": "millis" } ``` Finally, add these Kafka metadata columns to the `dimensionsSpec` or set your `dimensionsSpec` to auto-detect columns. The following supervisor spec demonstrates how to ingest the Kafka header, key, timestamp, and topic into Druid dimensions: <details> <summary>Click to view the example</summary> ```json { "type": "kafka", "spec": { "ioConfig": { "type": "kafka", "consumerProperties": { "bootstrap.servers": "localhost:9092" }, "topic": "wiki-edits", "inputFormat": { "type": "kafka", "valueFormat": { "type": "json" }, "headerFormat": { "type": "string" }, "keyFormat": { "type": "tsv", "findColumnsFromHeader": false, "columns": ["x"] } }, "useEarliestOffset": true }, "dataSchema": { "dataSource": "wikiticker", "timestampSpec": { "column": "timestamp", "format": "posix" }, "dimensionsSpec": "dimensionsSpec": { "useSchemaDiscovery": true, "includeAllDimensions": true }, "granularitySpec": { "queryGranularity": "none", "rollup": false, "segmentGranularity": "day" } }, "tuningConfig": { "type": "kafka" } } } ``` </details> After Druid ingests the data, you can query the Kafka metadata columns as follows: ```sql SELECT "kafka.header.env", "kafka.key", "kafka.timestamp", "kafka.topic" FROM "wikiticker" ``` This query returns: | `kafka.header.env` | `kafka.key` | `kafka.timestamp` | `kafka.topic` | |--|--||| | `development` | `wiki-edit` | `1680795276351` | `wiki-edits` | You can use the `flattenSpec` object to flatten nested data, as an alternative to the Druid feature, and for nested input formats unsupported by the feature. It is an object within the `inputFormat` object. See for information on ingesting and storing nested data in an Apache Druid column as a `COMPLEX<json>` data type. Configure your `flattenSpec` as follows: | Field | Description | Default | |-|-|| | useFieldDiscovery | If true, interpret all root-level fields as available fields for usage by , , , and .<br /><br />If false, only explicitly specified fields (see `fields`) will be available for use. | `true` | | fields | Specifies the fields of interest and how they are accessed. See for more detail. | `[]` | For example: ```json "flattenSpec": { "useFieldDiscovery": true, "fields": [ { "name": "baz", "type": "root" }, { "name": "foo_bar", "type": "path", "expr": "$.foo.bar" }, { "name": "foootherbar", "type": "tree", "nodes": ["foo", "other", "bar"] }, { "name": "first_food", "type": "jq", "expr":

} ] } ``` After Druid reads the input data records, it applies the flattenSpec before applying any other specs such as , , , or . This makes it possible to extract timestamps from flattened data, for example, and to refer to flattened data in transformations, in your dimension list, and when generating metrics. Flattening is only supported for that support nesting, including `avro`, `json`, `orc`, and `parquet`. Each entry in the `fields` list can have the following components: | Field | Description | Default | |-|-|| | type | Options are as follows:<br /><br /><ul><li>`root`, referring to a field at the root level of the record. Only really useful if `useFieldDiscovery` is false.</li><li>`path`, referring to a field using notation. Supported by most data formats that offer nesting, including `avro`, `json`, `orc`, and `parquet`.</li><li>`jq`, referring to a field using notation. Only supported for the `json` format.</li><li>`tree`, referring to a nested field from the root level of the record. Useful and more efficient than `path` or `jq` if a simple hierarchical fetch is required. Only supported for the `json` format.</li></ul> | none (required) | | name | Name of the field after flattening. This name can be referred to by the , , , and .| none (required) | | expr | Expression for accessing the field while flattening. For type `path`, this should be . For type `jq`, this should be notation. For other types, this parameter is ignored. | none (required for types `path` and `jq`) | | nodes | For `tree` only. Multiple-expression field for accessing the field while flattening, representing the hierarchy of field names to read. For other types, this parameter must not be provided. | none (required for type `tree`) | For convenience, when defining a root-level field, it is possible to define only the field name, as a string, instead of a JSON object. For example, `{"name": "baz", "type": "root"}` is equivalent to `"baz"`. Enabling `useFieldDiscovery` will only automatically detect "simple" fields at the root level that correspond to data types that Druid supports. This includes strings, numbers, and lists of strings or numbers. Other types will not be automatically detected, and must be specified explicitly in the `fields` list. Duplicate field `name`s are not allowed. An exception will be thrown. If `useFieldDiscovery` is enabled, any discovered field with the same name as one already defined in the `fields` list will be skipped, rather than added twice. is useful for testing `path`-type expressions. jackson-jq supports a subset of the full syntax. Please refer to the for details. supports a bunch of functions, but not all of these functions are supported by Druid now. Following matrix shows the current supported JsonPath functions and corresponding data formats. Please also note the output data type of these functions. | Function | Description | Output type | json | orc | avro | parquet | | :-| : |:-- |:--|:-|:--|:--| | min() | Provides the min value of an array of numbers | Double | &#10003; | &#10003; | &#10003; | &#10003; | | max() | Provides the max value of an array of numbers | Double | &#10003; | &#10003; | &#10003; | &#10003; | | avg() | Provides the average value of an array of numbers | Double | &#10003; | &#10003; | &#10003; | &#10003; | | stddev() | Provides the standard deviation value of an array of numbers | Double | &#10003; | &#10003; | &#10003; | &#10003; | | length() | Provides the length of an array | Integer | &#10003; | &#10003; | &#10003; | &#10003; | | sum() | Provides the sum value of an array of numbers | Double | &#10003; | &#10003;

&#10003; | &#10003; | | concat(X) | Provides a concatenated version of the path output with a new item | like input | &#10003; | &#10007; | &#10007; | &#10007; | | append(X) | add an item to the json path output array | like input | &#10003; | &#10007; | &#10007; | &#10007; | | keys() | Provides the property keys (An alternative for terminal tilde ~) | Set<E\> | &#10007; | &#10007; | &#10007; | &#10007; | :::info The Parser is deprecated for , , and . Consider using the instead for these types of ingestion. ::: This section lists all default and core extension parsers. For community extension parsers, please see our . `string` typed parsers operate on text based inputs that can be split into individual records by newlines. Each line can be further parsed using . | Field | Type | Description | Required | |-||-|-| | type | String | Set value to `string` for most cases. Otherwise use `hadoopyString` for Hadoop indexing. | yes | | parseSpec | JSON Object | Specifies the format, timestamp, and dimensions of the data. | yes | :::info You need to include the as an extension to use the Avro Hadoop Parser. ::: :::info See the section for how Avro types are handled in Druid ::: This parser is for . The `inputFormat` of `inputSpec` in `ioConfig` must be set to `"org.apache.druid.data.input.avro.AvroValueInputFormat"`. You may want to set Avro reader's schema in `jobProperties` in `tuningConfig`, e.g.: `"avro.schema.input.value.path": "/path/to/your/schema.avsc"` or `"avro.schema.input.value": "yourschemaJSON_object"`. If the Avro reader's schema is not set, the schema in Avro object container file will be used. See for more information. | Field | Type | Description | Required | |-||-|-| | type | String | Set value to `avro_hadoop`. | yes | | parseSpec | JSON Object | Specifies the timestamp and dimensions of the data. Should be an "avro" parseSpec. | yes | | fromPigAvroStorage | Boolean | Specifies whether the data file is stored using AvroStorage. | no(default == false) | An Avro parseSpec can contain a using either the "root" or "path" field types, which can be used to read nested Avro records. The "jq" and "tree" field type is not currently supported for Avro. For example, using Avro Hadoop parser with custom reader's schema file: ```json { "type" : "index_hadoop", "spec" : { "dataSchema" : { "dataSource" : "", "parser" : { "type" : "avro_hadoop", "parseSpec" : { "format": "avro", "timestampSpec": <standard timestampSpec>, "dimensionsSpec": <standard dimensionsSpec>, "flattenSpec": <optional> } } }, "ioConfig" : { "type" : "hadoop", "inputSpec" : { "type" : "static", "inputFormat": "org.apache.druid.data.input.avro.AvroValueInputFormat", "paths" : "" } }, "tuningConfig" : { "jobProperties" : { "avro.schema.input.value.path" : "/path/to/my/schema.avsc" } } } } ``` :::info You need to include the as an extension to use the ORC Hadoop Parser. ::: :::info If you are considering upgrading from earlier than 0.15.0 to 0.15.0 or a higher version, please read carefully. ::: This parser is for . The `inputFormat` of `inputSpec` in `ioConfig` must be set to `"org.apache.orc.mapreduce.OrcInputFormat"`. |Field | Type | Description | Required| |-|-|-|| | type | String | Set value to `orc`. | yes | |parseSpec | JSON Object | Specifies the timestamp and dimensions of the data (`timeAndDims` and `orc` format) and a `flattenSpec` (`orc` format). | yes| The parser supports two `parseSpec` formats: `orc` and `timeAndDims`. `orc` supports auto field discovery and flattening, if specified with a . If no `flattenSpec` is specified, `useFieldDiscovery` will be enabled by

Specifying a `dimensionSpec` is optional if `useFieldDiscovery` is enabled: if a `dimensionSpec` is supplied, the list of `dimensions` it defines will be the set of ingested dimensions, if missing the discovered fields will make up the list. `timeAndDims` parse spec must specify which fields will be extracted as dimensions through the `dimensionSpec`. are supported, with the exception of `union` types. Columns of `list` type, if filled with primitives, may be used as a multi-value dimension, or specific elements can be extracted with `flattenSpec` expressions. Likewise, primitive fields may be extracted from `map` and `struct` types in the same manner. Auto field discovery will automatically create a string dimension for every (non-timestamp) primitive or `list` of primitives, as well as any flatten expressions defined in the `flattenSpec`. Like most Hadoop jobs, the best outcomes will add `"mapreduce.job.user.classpath.first": "true"` or `"mapreduce.job.classloader": "true"` to the `jobProperties` section of `tuningConfig`. Note that it is likely if using `"mapreduce.job.classloader": "true"` that you will need to set `mapreduce.job.classloader.system.classes` to include `-org.apache.hadoop.hive.` to instruct Hadoop to load `org.apache.hadoop.hive` classes from the application jars instead of system jars, e.g. ```json ... "mapreduce.job.classloader": "true", "mapreduce.job.classloader.system.classes" : "java., javax.accessibility., javax.activation., javax.activity., javax.annotation., javax.annotation.processing., javax.crypto., javax.imageio., javax.jws., javax.lang.model., -javax.management.j2ee., javax.management., javax.naming., javax.net., javax.print., javax.rmi., javax.script., -javax.security.auth.message., javax.security.auth., javax.security.cert., javax.security.sasl., javax.sound., javax.sql., javax.swing., javax.tools., javax.transaction., -javax.xml.registry., -javax.xml.rpc., javax.xml., org.w3c.dom., org.xml.sax., org.apache.commons.logging., org.apache.log4j., -org.apache.hadoop.hbase., -org.apache.hadoop.hive., org.apache.hadoop., core-default.xml, hdfs-default.xml, mapred-default.xml, yarn-default.xml", ... ``` This is due to the `hive-storage-api` dependency of the `orc-mapreduce` library, which provides some classes under the `org.apache.hadoop.hive` package. If instead using the setting `"mapreduce.job.user.classpath.first": "true"`, then this will not be an issue. ```json { "type": "index_hadoop", "spec": { "ioConfig": { "type": "hadoop", "inputSpec": { "type": "static", "inputFormat": "org.apache.orc.mapreduce.OrcInputFormat", "paths": "path/to/file.orc" }, ... }, "dataSchema": { "dataSource": "example", "parser": { "type": "orc", "parseSpec": { "format": "orc", "flattenSpec": { "useFieldDiscovery": true, "fields": [ { "type": "path", "name": "nestedDim", "expr": "$.nestedData.dim1" }, { "type": "path", "name": "listDimFirstItem", "expr": "$.listDim[1]" } ] }, "timestampSpec": { "column": "timestamp", "format": "millis" } } }, ... }, "tuningConfig": <hadoop-tuning-config> } } } ``` ```json { "type": "index_hadoop", "spec": { "ioConfig": { "type": "hadoop", "inputSpec": { "type": "static", "inputFormat": "org.apache.orc.mapreduce.OrcInputFormat", "paths": "path/to/file.orc" }, ... }, "dataSchema": { "dataSource": "example", "parser": { "type": "orc", "parseSpec": { "format": "orc", "timestampSpec": { "column": "timestamp", "format": "millis" } } }, ... }, "tuningConfig": <hadoop-tuning-config> } } } ``` ```json { "type": "index_hadoop", "spec": { "ioConfig": { "type": "hadoop", "inputSpec": { "type": "static", "inputFormat": "org.apache.orc.mapreduce.OrcInputFormat", "paths": "path/to/file.orc" }, ... }, "dataSchema": { "dataSource": "example", "parser": { "type": "orc", "parseSpec": { "format": "orc", "flattenSpec": { "useFieldDiscovery": false, "fields": [ { "type": "path", "name": "nestedDim", "expr": "$.nestedData.dim1" }, { "type": "path", "name": "listDimFirstItem", "expr": "$.listDim[1]" } ] }, "timestampSpec": { "column": "timestamp", "format": "millis" }, "dimensionsSpec": { "dimensions": [ "dim1", "dim3", "nestedDim", "listDimFirstItem" ], "dimensionExclusions": [], "spatialDimensions": [] } } }, ... }, "tuningConfig": <hadoop-tuning-config> } } } ``` ```json { "type": "index_hadoop", "spec": { "ioConfig": { "type": "hadoop", "inputSpec": { "type": "static", "inputFormat": "org.apache.orc.mapreduce.OrcInputFormat", "paths": "path/to/file.orc" }, ... }, "dataSchema": { "dataSource": "example", "parser": { "type": "orc", "parseSpec": { "format": "timeAndDims", "timestampSpec": { "column": "timestamp", "format": "auto" }, "dimensionsSpec": { "dimensions": [ "dim1", "dim2", "dim3", "listDim" ], "dimensionExclusions": [], "spatialDimensions": [] } } }, ... }, "tuningConfig": <hadoop-tuning-config> } } ``` :::info You need to include the as an extension to use the Parquet Hadoop Parser. ::: The Parquet Hadoop parser is for and parses Parquet files directly. The `inputFormat` of `inputSpec` in `ioConfig` must be set to `org.apache.druid.data.input.parquet.DruidParquetInputFormat`. The Parquet Hadoop Parser supports auto field discovery and flattening if provided with a with the `parquet`

Parquet nested list and map should operate correctly with JSON path expressions for all supported types. |Field | Type | Description | Required| |-|-|-|| | type | String | Set value to `parquet`. | yes | | parseSpec | JSON Object | Specifies the timestamp and dimensions of the data, and optionally, a flatten spec. Valid parseSpec formats are `timeAndDims` and `parquet`. | yes | | binaryAsString | Boolean | Specifies if the bytes parquet column which is not logically marked as a string or enum type should be treated as a UTF-8 encoded string. | no(default = false) | When the time dimension is a , a format should not be supplied. When the format is UTF8 (String), either `auto` or a explicitly defined is required. Both parsers read from Parquet files, but slightly differently. The main differences are: The Parquet Hadoop Parser uses a simple conversion while the Parquet Avro Hadoop Parser converts Parquet data into avro records first with the `parquet-avro` library and then parses avro data using the `druid-avro-extensions` module to ingest into Druid. The Parquet Hadoop Parser sets a hadoop job property `parquet.avro.add-list-element-records` to `false` (which normally defaults to `true`), in order to 'unwrap' primitive list elements into multi-value dimensions. The Parquet Hadoop Parser supports `int96` Parquet values, while the Parquet Avro Hadoop Parser does not. There may also be some subtle differences in the behavior of JSON path expression evaluation of `flattenSpec`. Based on those differences, we suggest using the Parquet Hadoop Parser over the Parquet Avro Hadoop Parser to allow ingesting data beyond the schema constraints of Avro conversion. However, the Parquet Avro Hadoop Parser was the original basis for supporting the Parquet format, and as such it is a bit more mature. ```json { "type": "index_hadoop", "spec": { "ioConfig": { "type": "hadoop", "inputSpec": { "type": "static", "inputFormat": "org.apache.druid.data.input.parquet.DruidParquetInputFormat", "paths": "path/to/file.parquet" }, ... }, "dataSchema": { "dataSource": "example", "parser": { "type": "parquet", "parseSpec": { "format": "parquet", "flattenSpec": { "useFieldDiscovery": true, "fields": [ { "type": "path", "name": "nestedDim", "expr": "$.nestedData.dim1" }, { "type": "path", "name": "listDimFirstItem", "expr": "$.listDim[1]" } ] }, "timestampSpec": { "column": "timestamp", "format": "auto" }, "dimensionsSpec": { "dimensions": [], "dimensionExclusions": [], "spatialDimensions": [] } } }, ... }, "tuningConfig": <hadoop-tuning-config> } } } ``` ```json { "type": "index_hadoop", "spec": { "ioConfig": { "type": "hadoop", "inputSpec": { "type": "static", "inputFormat": "org.apache.druid.data.input.parquet.DruidParquetInputFormat", "paths": "path/to/file.parquet" }, ... }, "dataSchema": { "dataSource": "example", "parser": { "type": "parquet", "parseSpec": { "format": "timeAndDims", "timestampSpec": { "column": "timestamp", "format": "auto" }, "dimensionsSpec": { "dimensions": [ "dim1", "dim2", "dim3", "listDim" ], "dimensionExclusions": [], "spatialDimensions": [] } } }, ... }, "tuningConfig": <hadoop-tuning-config> } } ``` :::info Consider using the over this parser to ingest Parquet files. See for the differences between those parsers. ::: :::info You need to include both the [`druid-avro-extensions`] as extensions to use the Parquet Avro Hadoop Parser. ::: The Parquet Avro Hadoop Parser is for . This parser first converts the Parquet data into Avro records, and then parses them to ingest into Druid. The `inputFormat` of `inputSpec` in `ioConfig` must be set to `org.apache.druid.data.input.parquet.DruidParquetAvroInputFormat`. The Parquet Avro Hadoop Parser supports auto field discovery and flattening if provided with a with the `avro` `parseSpec`. Parquet nested list and map should operate correctly with JSON path expressions for all supported types. This parser sets a hadoop job property `parquet.avro.add-list-element-records` to `false` (which normally defaults to `true`), in order to 'unwrap' primitive list elements into multi-value dimensions. Note that the `int96` Parquet value type is not supported with this parser. |Field | Type | Description | Required| |-|-|-|| | type | String | Set value to

| yes | | parseSpec | JSON Object | Specifies the timestamp and dimensions of the data, and optionally, a flatten spec. Should be `avro`. | yes | | binaryAsString | Boolean | Specifies if the bytes parquet column which is not logically marked as a string or enum type should be treated as a UTF-8 encoded string. | no(default = false) | When the time dimension is a , a format should not be supplied. When the format is UTF8 (String), either `auto` or an explicitly defined is required. ```json { "type": "index_hadoop", "spec": { "ioConfig": { "type": "hadoop", "inputSpec": { "type": "static", "inputFormat": "org.apache.druid.data.input.parquet.DruidParquetAvroInputFormat", "paths": "path/to/file.parquet" }, ... }, "dataSchema": { "dataSource": "example", "parser": { "type": "parquet-avro", "parseSpec": { "format": "avro", "flattenSpec": { "useFieldDiscovery": true, "fields": [ { "type": "path", "name": "nestedDim", "expr": "$.nestedData.dim1" }, { "type": "path", "name": "listDimFirstItem", "expr": "$.listDim[1]" } ] }, "timestampSpec": { "column": "timestamp", "format": "auto" }, "dimensionsSpec": { "dimensions": [], "dimensionExclusions": [], "spatialDimensions": [] } } }, ... }, "tuningConfig": <hadoop-tuning-config> } } } ``` :::info You need to include the as an extension to use the Avro Stream Parser. ::: :::info See the section for how Avro types are handled in Druid ::: This parser is for and reads Avro data from a stream directly. | Field | Type | Description | Required | |-||-|-| | type | String | Set value to `avro_stream`. | no | | avroBytesDecoder | JSON Object | Specifies to decode bytes to Avro record. | yes | | parseSpec | JSON Object | Specifies the timestamp and dimensions of the data. Should be an "avro" parseSpec. | yes | An Avro parseSpec can contain a using either the "root" or "path" field types, which can be used to read nested Avro records. The "jq" and "tree" field type is not currently supported for Avro. For example, using Avro stream parser with schema repo Avro bytes decoder: ```json "parser" : { "type" : "avro_stream", "avroBytesDecoder" : { "type" : "schema_repo", "subjectAndIdConverter" : { "type" : "avro_1124", "topic" : "${YOUR_TOPIC}" }, "schemaRepository" : { "type" : "avro1124rest_client", "url" : "${YOURSCHEMAREPOENDPOINT}", } }, "parseSpec" : { "format": "avro", "timestampSpec": <standard timestampSpec>, "dimensionsSpec": <standard dimensionsSpec>, "flattenSpec": <optional> } } ``` :::info You need to include the as an extension to use the Protobuf Parser. ::: This parser is for and reads Protocol buffer data from a stream directly. | Field | Type | Description | Required | |-||-|-| | type | String | Set value to `protobuf`. | yes | | `protoBytesDecoder` | JSON Object | Specifies how to decode bytes to Protobuf record. | yes | | parseSpec | JSON Object | Specifies the timestamp and dimensions of the data. The format must be JSON. See for more configuration options. Note that `timeAndDims` `parseSpec` is no longer supported. | yes | Sample spec: ```json "parser": { "type": "protobuf", "protoBytesDecoder": { "type": "file", "descriptor": "file:///tmp/metrics.desc", "protoMessageType": "Metrics" }, "parseSpec": { "format": "json", "timestampSpec": { "column": "timestamp", "format": "auto" }, "dimensionsSpec": { "dimensions": [ "unit", "http_method", "http_code", "page", "metricType", "server" ], "dimensionExclusions": [ "timestamp", "value" ] } } } ``` See the for more details and examples. If `type` is not included, the `protoBytesDecoder` defaults to `schema_registry`. This Protobuf bytes decoder first read a descriptor file, and then parse it to get schema used to decode the Protobuf record from bytes. | Field | Type | Description | Required | |-||-|-| | type | String | Set value to `file`. | yes | | descriptor | String | Protobuf descriptor file name in the classpath or

| yes | | protoMessageType | String | Protobuf message type in the descriptor. Both short name and fully qualified name are accepted. The parser uses the first message type found in the descriptor if not specified. | no | Sample spec: ```json "protoBytesDecoder": { "type": "file", "descriptor": "file:///tmp/metrics.desc", "protoMessageType": "Metrics" } ``` This Protobuf bytes decoder allows the user to provide the contents of a Protobuf descriptor file inline, encoded as a Base64 string, and then parse it to get schema used to decode the Protobuf record from bytes. | Field | Type | Description | Required | |-||-|-| | type | String | Set value to `inline`. | yes | | descriptorString | String | A compiled Protobuf descriptor, encoded as a Base64 string. | yes | | protoMessageType | String | Protobuf message type in the descriptor. Both short name and fully qualified name are accepted. The parser uses the first message type found in the descriptor if not specified. | no | Sample spec: ```json "protoBytesDecoder": { "type": "inline", "descriptorString": <Contents of a Protobuf descriptor file encoded as Base64 string>, "protoMessageType": "Metrics" } ``` This Protobuf bytes decoder first extracts a unique `id` from input message bytes, and then uses it to look up the schema in the Schema Registry used to decode the Avro record from bytes. For details, see the Schema Registry and . | Field | Type | Description | Required | |-||-|-| | type | String | Set value to `schema_registry`. | yes | | url | String | Specifies the URL endpoint of the Schema Registry. | yes | | capacity | Integer | Specifies the max size of the cache (default = Integer.MAX_VALUE). | no | | urls | Array<String\> | Specifies the URL endpoints of the multiple Schema Registry instances. | yes (if `url` is not provided) | | config | Json | To send additional configurations, configured for Schema Registry. This can be supplied via a . | no | | headers | Json | To send headers to the Schema Registry. This can be supplied via a | no | For a single schema registry instance, use Field `url` or `urls` for multi instances. Single Instance: ```json ... "protoBytesDecoder": { "url": <schema-registry-url>, "type": "schema_registry" } ... ``` Multiple Instances: ```json ... "protoBytesDecoder": { "urls": [<schema-registry-url-1>, <schema-registry-url-2>, ...], "type": "schema_registry", "capacity": 100, "config" : { "basic.auth.credentials.source": "USER_INFO", "basic.auth.user.info": "fred:letmein", "schema.registry.ssl.truststore.location": "/some/secrets/kafka.client.truststore.jks", "schema.registry.ssl.truststore.password": "<password>", "schema.registry.ssl.keystore.location": "/some/secrets/kafka.client.keystore.jks", "schema.registry.ssl.keystore.password": "<password>", "schema.registry.ssl.key.password": "<password>", ... }, "headers": { "traceID" : "b29c5de2-0db4-490b-b421", "timeStamp" : "1577191871865", "druid.dynamic.config.provider":{ "type":"mapString", "config":{ "registry.header.prop.1":"value.1", "registry.header.prop.2":"value.2" } } ... } } ... ``` :::info The Parser is deprecated for , , and . Consider using the instead for these types of ingestion. ::: ParseSpecs serve two purposes: The String Parser use them to determine the format (i.e., JSON, CSV, TSV) of incoming rows. All Parsers use them to determine the timestamp and dimensions of incoming rows. If `format` is not included, the parseSpec defaults to `tsv`. Use this with the String Parser to load JSON. | Field | Type | Description | Required | |-||-|-| | format | String |`json`| no | | timestampSpec | JSON Object | Specifies the column and format of the timestamp. | yes | | dimensionsSpec | JSON Object | Specifies the dimensions of the data. | yes | | flattenSpec | JSON Object | Specifies flattening configuration for nested JSON data. See for more

| no | Sample spec: ```json "parseSpec": { "format" : "json", "timestampSpec" : { "column" : "timestamp" }, "dimensionSpec" : { "dimensions" : ["page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city"] } } ``` :::info The jsonLowercase parser is deprecated and may be removed in a future version of Druid. ::: This is a special variation of the JSON ParseSpec that lower cases all the column names in the incoming JSON data. This parseSpec is required if you are updating to Druid 0.7.x from Druid 0.6.x, are directly ingesting JSON with mixed case column names, do not have any ETL in place to lower case those column names, and would like to make queries that include the data you created using 0.6.x and 0.7.x. | Field | Type | Description | Required | |-||-|-| | format | String | `jsonLowercase` | yes | | timestampSpec | JSON Object | Specifies the column and format of the timestamp. | yes | | dimensionsSpec | JSON Object | Specifies the dimensions of the data. | yes | Use this with the String Parser to load CSV. Strings are parsed using the com.opencsv library. | Field | Type | Description | Required | |-||-|-| | format | String | `csv` | yes | | timestampSpec | JSON Object | Specifies the column and format of the timestamp. | yes | | dimensionsSpec | JSON Object | Specifies the dimensions of the data. | yes | | listDelimiter | String | A custom delimiter for multi-value dimensions. | no (default = ctrl+A) | | columns | JSON array | Specifies the columns of the data. | yes | Sample spec: ```json "parseSpec": { "format" : "csv", "timestampSpec" : { "column" : "timestamp" }, "columns" : ["timestamp","page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city","added","deleted","delta"], "dimensionsSpec" : { "dimensions" : ["page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city"] } } ``` If your input files contain a header, the `columns` field is optional and you don't need to set. Instead, you can set the `hasHeaderRow` field to true, which makes Druid automatically extract the column information from the header. Otherwise, you must set the `columns` field and ensure that field must match the columns of your input data in the same order. Also, you can skip some header rows by setting `skipHeaderRows` in your parseSpec. If both `skipHeaderRows` and `hasHeaderRow` options are set, `skipHeaderRows` is first applied. For example, if you set `skipHeaderRows` to 2 and `hasHeaderRow` to true, Druid will skip the first two lines and then extract column information from the third line. Note that `hasHeaderRow` and `skipHeaderRows` are effective only for non-Hadoop batch index tasks. Other types of index tasks will fail with an exception. The `columns` field must be included and and ensure that the order of the fields matches the columns of your input data in the same order. Use this with the String Parser to load any delimited text that does not require special escaping. By default, the delimiter is a tab, so this will load TSV. | Field | Type | Description | Required | |-||-|-| | format | String | `tsv` | yes | | timestampSpec | JSON Object | Specifies the column and format of the timestamp. | yes | | dimensionsSpec | JSON Object | Specifies the dimensions of the data. | yes | | delimiter | String | A custom delimiter for data values. | no (default = \t) | | listDelimiter | String | A custom delimiter for multi-value dimensions. | no (default = ctrl+A) | | columns | JSON String array | Specifies the columns of the

| yes | Sample spec: ```json "parseSpec": { "format" : "tsv", "timestampSpec" : { "column" : "timestamp" }, "columns" : ["timestamp","page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city","added","deleted","delta"], "delimiter":"|", "dimensionsSpec" : { "dimensions" : ["page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city"] } } ``` Be sure to change the `delimiter` to the appropriate delimiter for your data. Like CSV, you must specify the columns and which subset of the columns you want indexed. If your input files contain a header, the `columns` field is optional and doesn't need to be set. Instead, you can set the `hasHeaderRow` field to true, which makes Druid automatically extract the column information from the header. Otherwise, you must set the `columns` field and ensure that field must match the columns of your input data in the same order. Also, you can skip some header rows by setting `skipHeaderRows` in your parseSpec. If both `skipHeaderRows` and `hasHeaderRow` options are set, `skipHeaderRows` is first applied. For example, if you set `skipHeaderRows` to 2 and `hasHeaderRow` to true, Druid will skip the first two lines and then extract column information from the third line. Note that `hasHeaderRow` and `skipHeaderRows` are effective only for non-Hadoop batch index tasks. Other types of index tasks will fail with an exception. The `columns` field must be included and and ensure that the order of the fields matches the columns of your input data in the same order. ```json "parseSpec":{ "format" : "regex", "timestampSpec" : { "column" : "timestamp" }, "dimensionsSpec" : { "dimensions" : [<yourlistof_dimensions>] }, "columns" : [<yourcolumnshere>], "pattern" : <regex pattern for partitioning data> } ``` The `columns` field must match the columns of your regex matching groups in the same order. If columns are not provided, default columns names ("column1", "column2", ... "columnn") will be assigned. Ensure that your column names include all your dimensions. ```json "parseSpec":{ "format" : "javascript", "timestampSpec" : { "column" : "timestamp" }, "dimensionsSpec" : { "dimensions" : ["page","language","user","unpatrolled","newPage","robot","anonymous","namespace","continent","country","region","city"] }, "function" : "function(str) { var parts = str.split(\"-\"); return { one: parts[0], two: parts[1] } }" } ``` Note with the JavaScript parser that data must be fully parsed and returned as a `{key:value}` format in the JS logic. This means any flattening or parsing multi-dimensional values must be done here. :::info JavaScript-based functionality is disabled by default. Please refer to the Druid for guidelines about using Druid's JavaScript functionality, including instructions on how to enable it. ::: Use this with non-String Parsers to provide them with timestamp and dimensions information. Non-String Parsers handle all formatting decisions on their own, without using the ParseSpec. | Field | Type | Description | Required | |-||-|-| | format | String | `timeAndDims` | yes | | timestampSpec | JSON Object | Specifies the column and format of the timestamp. | yes | | dimensionsSpec | JSON Object | Specifies the dimensions of the data. | yes | Use this with the Hadoop ORC Parser to load ORC files. | Field | Type | Description | Required | |-||-|-| | format | String | `orc`| no | | timestampSpec | JSON Object | Specifies the column and format of the timestamp. | yes | | dimensionsSpec | JSON Object | Specifies the dimensions of the data. | yes | | flattenSpec | JSON Object | Specifies flattening configuration for nested JSON data. See for more info. | no | Use this with the Hadoop Parquet Parser to load Parquet files. | Field | Type | Description | Required | |-||-|-| | format | String | `parquet`| no | | timestampSpec | JSON Object | Specifies the column and format of the timestamp. | yes | | dimensionsSpec | JSON Object