jamesql 0.3.0
A JameSQL database implemented in Python.
Requires Python: >=3.7

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# JameSQL

An in-memory, NoSQL database implemented in Python, with support for building custom ranking algorithms.

You can run full text search queries on thousands of documents with multiple fields in < 1ms.

[Try a site search engine built with JameSQL](https://jamesg.blog/search-pages/)

Here is an example of a search engine with a JameSQL back-end:

https://github.com/user-attachments/assets/f1bf931d-6601-4fc8-b43c-d284853bce8f

## Installation

To install this project, run:

```
pip install git+https://github.com/capjamesg/jamesql
```

## Quickstart

Here is a quickstart with a string-based query:

```python
from jamesql import JameSQL

index = JameSQL.load()

index.add({"title": "tolerate it", "lyric": "Use my best colors for your portrait"})

# results should return in < 1ms, whether you have one or 1k documents
results = index.string_query_search("title:'tolerate it' colors")

print(results)
# {'documents': [{'title': 'tolerate it', 'lyric': 'Use my best colors for your portrait' ...}]
```

## Usage

### Create a database

To create a database, use the following code:

```python
from jamesql import JameSQL

index = JameSQL()
```

### Load a database

To load the database you initialized in your last session, use the following code:

```python
from jamesql import JameSQL

index = JameSQL.load()
```

### Add documents to a database

To add documents to a database, use the following code:

```python
index.add({"title": "tolerate it", "artist": "Taylor Swift"})
index.insert({"title": "betty", "artist": "Taylor Swift"})
```

Values within documents can have the following data types:

- String
- Integer
- Float
- List

You cannot currently index a document whose value is a dictionary.

When documents are added, a `uuid` key is added for use in uniquely identifying the document.

### Indexing strategies

When you run a query on a field for the first time, JameSQL will automatically set up an index for the field. The index type will be chosen based on what is most likely to be effective at querying the type of data in the field.

There are four indexing strategies currently implemented:

- `GSI_INDEX_STRATEGIES.CONTAINS`: Creates a reverse index for the field. This is useful for fields that contain longer strings (i.e. body text in a blog post). TF-IDF is used to search fields structured with the `CONTAINS` type.
- `GSI_INDEX_STRATEGIES.NUMERIC`: Creates several buckets to allow for efficient search of numeric values, especially values with high cardinality.
- `GSI_INDEX_STRATEGIES.FLAT`: Stores the field as the data type it is. A flat index is created of values that are not strings or numbers. This is the default. For example, if you are indexing document titles and don't need to do a `starts_with` query, you may choose a flat index to allow for efficient `equals` and `contains` queries.
- `GSI_INDEX_STRATEGIES.PREFIX`: Creates a trie index for the field. This is useful for fields that contain short strings (i.e. titles).
- `GSI_INDEX_STRATEGIES.CATEGORICAL`: Creates a categorical index for the field. This is useful for fields that contain specific categories (i.e. genres).
- `GSI_INDEX_STRATEGIES.TRIGRAM_CODE`: Creates a character-level trigram index for the field. This is useful for efficient code search. See the "Code Search" documentation later in this README for more information about using code search with JameSQL.

You can manually set an index type by creating a index (called a GSI), like so:

```python
index.create_gsi("title", strategy=GSI_INDEX_STRATEGIES.PREFIX)
```

If you manually set an indexing startegy, any document currently in or added to the database will be indexed according to the strategy provided.

### Search for documents

A query has the following format:

```python
{
    "query": {},
    "limit": 2,
    "sort_by": "song",
    "skip": 1
}
```

- `query` is a dictionary that contains the fields to search for.
- `limit` is the maximum number of documents to return. (default 10)
- `sort_by` is the field to sort by. (default None)
- `skip` is the number of documents to skip. This is useful for implementing pagination. (default 0)

`limit`, `sort_by`, and `skip` are optional.

Within the `query` key you can query for documents that match one or more conditions.

An empty query returns no documents.

You can retrieve all documents by using a catch-all query, which uses the following syntax:

```python
{
    "query": "*",
    "limit": 2,
    "sort_by": "song",
    "skip": 1
}
```

This is useful if you want to page through documents. You should supply a `sort_by` field to ensure the order of documents is consistent.

#### Response

All valid queries return responses in the following form:

```json
{
    "documents": [
        {"uuid": "1", "title": "test", "artist": "..."},
        {"uuid": "2", "title": "test", "artist": "..."},
        ...
    ],
    "query_time": 0.0001,
    "total_results": 200
}
```

`documents` is a list of documents that match the query. `query_time` is the amount of time it took to execute the query. `total_results` is the total number of documents that match the query before applying any `limit`.

`total_results` is useful for implementing pagination.

If an error was encountered, the response will be in the following form:

```json
{
    "documents": [],
    "query_time": 0.0001,
    "error": "Invalid query"
}
```

The `error` key contains a message describing the exact error encountered.

### Document ranking

By default, documents are ranked in no order. If you provide a `sort_by` field, documents are sorted by that field.

For more advanced ranking, you can use the `boost` feature. This feature lets you boost the value of a field in a document to calculate a final score.

The default score for each field is `1`.

To use this feature, you must use `boost` on fields that have an index.

Here is an example of a query that uses the `boost` feature:

```python
{
    "query": {
        "or": {
            "post": {
                "contains": "taylor swift",
                "strict": False,
                "boost": 1
            },
            "title": {
                "contains": "desk",
                "strict": True,
                "boost": 25
            }
        }
    },
    "limit": 4,
    "sort_by": "_score",
}
```

This query would search for documents whose `post` field contains `taylor swift` or whose `title` field contains `desk`. The `title` field is boosted by 25, so documents that match the `title` field are ranked higher.

The score for each document before boosting is equal to the number of times the query condition is satisfied. For example, if a post contains `taylor swift` twice, the score for that document is `2`; if a title contains `desk` once, the score for that document is `1`.

Documents are then ranked in decreasing order of score.

#### Document ranking with script scores

The script score feature lets you write custom scripts to calculate the score for each document. This is useful if you want to calculate a score based on multiple fields, including numeric fields.

Script scores are applied after all documents are retrieved.

The script score feature supports the following mathematical operations:

- `+` (addition)
- `-` (subtraction)
- `*` (multiplication)
- `/` (division)
- `log` (logarithm)
- `decay` (timeseries decay)

You can apply a script score at the top level of your query:

```python
{
    "query": {
        "or": {
            "post": {
                "contains": "taylor swift",
                "strict": False,
                "boost": 1
            },
            "title": {
                "contains": "desk",
                "strict": True,
                "boost": 25
            }
        }
    },
    "limit": 4,
    "sort_by": "_score",
    "script_score": "((post + title) * 2)"
}
```

The above example will calculate the score of documents by adding the score of the `post` field and the `title` field, then multiplying the result by `2`.

A script score is made up of terms. A term is a field name or number (float or int), followed by an operator, followed by another term or number. Terms can be nested.

All terms must be enclosed within parentheses.

To compute a score that adds the `post` score to `title` and multiplies the result by `2`, use the following code:

```text
((post + title) * 2)
```

Invalid forms of this query include:

- `post + title * 2` (missing parentheses)
- `(post + title * 2)` (terms can only include one operator)

The `decay` function lets you decay a value by `0.9 ** days_since_post / 30`. This is useful for gradually decreasing the rank for older documents as time passes. This may be particularly useful if you are working with data where you want more recent documents to be ranked higher. `decay` only works with timeseries.

Here is an example of `decay` in use:

```
(_score * decay published)
```

This will apply the `decay` function to the `published` field.

Data must be stored as a Python `datetime` object for the `decay` function to work.

### Condition matching

There are three operators you can use for condition matching:

- `equals`
- `contains`
- `starts_with`

Here is an example of a query that searches for documents that have the `artist` field set to `Taylor Swift`:

```python
query = {
    "query": {
        "artist": {
            "equals": "Taylor Swift"
        }
    }
}
```

These operators can be used with three query types:

- `and`
- `or`
- `not`

### and

You can also search for documents that have the `artist` field set to `Taylor Swift` and the `title` field set to `tolerate it`:

```python
query = {
    "query": {
        "and": [
            {
                "artist": {
                    "equals": "Taylor Swift"
                }
            },
            {
                "title": {
                    "equals": "tolerate it"
                }
            }
        ]
    }
}
```

### or

You can nest conditions to create complex queries, like:

```python
query = {
    "query": {
        "or": {
            "and": [
                {"title": {"starts_with": "tolerate"}},
                {"title": {"contains": "it"}},
            ],
            "lyric": {"contains": "kiss"},
        }
    },
    "limit": 2,
    "sort_by": "title",
}
```

This will return a list of documents that match the query.

### not

You can search for documents that do not match a query by using the `not` operator. Here is an example of a query that searches for lyrics that contain `sky` but not `kiss`:

```python
query = {
    "query": {
        "and": {
            "or": [
                {"lyric": {"contains": "sky", "boost": 3}},
            ],
            "not": {"lyric": {"contains": "kiss"}},
        }
    },
    "limit": 10,
    "sort_by": "title",
}
```

### Running a search

To search for documents that match a query, use the following code:

```python
result = index.search(query)
```

This returns a JSON payload with the following structure:

```json
{
    "documents": [
        {"uuid": "1", ...}
        {"uuid": "2", ...}
        ...
    ],
    "query_time": 0.0001,
    "total_results": 200
}
```

You can search through multiple pages with the `scroll()` method:

```python
result = index.scroll(query)
```

`scroll()` returns a generator that yields documents in the same format as `search()`.

### Strict matching

By default, a search query on a text field will find any document where the field contains any word in the query string. For example, a query for `tolerate it` on a `title` field will match any document whose `title` that contains `tolerate` or `it`. This is called a non-strict match.

Non-strict matches are the default because they are faster to compute than strict matches.

If you want to find documents where terms appear next to each other in a field, you can do so with a strict match. Here is an example of a strict match:

```python
query = {
    "query": {
        "title": {
            "contains": "tolerate it",
            "strict": True
        }
    }
}
```

This will return documents whose title contains `tolerate it` as a single phrase.

### Fuzzy matching

By default, search queries look for the exact string provided. This means that if a query contains a typo (i.e. searching for `tolerate ip` instead of `tolerate it`), no documents will be returned.

JameSQL implements a limited form of fuzzy matching. This means that if a query contains a typo, JameSQL will still return documents that match the query.

The fuzzy matching feature matches documents that contain one typo. If a document contains more than one typo, it will not be returned. A typo is an incorrectly typed character. JameSQL does not support fuzzy matching that accounts for missing or additional characters (i.e. `tolerate itt` will not match `tolerate it`).

You can enable fuzzy matching by setting the `fuzzy` key to `True` in the query. Here is an example of a query that uses fuzzy matching:

```python
query = {
    "query": {
        "title": {
            "contains": "tolerate ip",
            "fuzzy": True
        }
    }
}
```

### Wildcard matching

You can match documents using a single wildcard character. This character is represented by an asterisk `*`.

```python
query = {
    "query": {
        "title": {
            "contains": "tolerat* it",
            "fuzzy": True
        }
    }
}
```

This query will look for all words that match the pattern `tolerat* it`, where the `*` character can be any single character.

### Look for terms close to each other

You can find terms that appear close to each other with a `close_to` query. Here is an example of a query that looks for documents where `made` and `temple` appear within `7` words of each other and `my` appears within `7` words of `temple`:

```python
query = {
    "query": {
        "close_to": [
            {"lyric": "made"},
            {"lyric": "temple,"},
            {"lyric": "my"},
        ],
        "distance": 7
    },
    "limit": 10
}
```

### Less than, greater than, less than or equal to, greater than or equal to

You can find documents where a field is less than, greater than, less than or equal to, or greater than or equal to a value with a range query. Here is an example of a query that looks for documents where the `year` field is greater than `2010`:

```python
query = {
    "query": {
        "year": {
            "greater_than": 2010
        }
    }
}
```

The following operators are supported:

- `greater_than`
- `less_than`
- `greater_than_or_equal`
- `less_than_or_equal`

### Range queries

You can find values in a numeric range with a range query. Here is an example of a query that looks for documents where the `year` field is between `2010` and `2020`:

```python
query = {
    "query": {
        "year": {
            "range": [2010, 2020]
        }
    }
}
```

The first value in the range is the lower bound to use in the search, and the second value is the upper bound.

### Highlight results

You can extract context around results. This data can be used to show a snippet of the document that contains the query term.

Here is an example of a query that highlights context around all instances of the term "sky" in the `lyric` field:

```python
query = {
    "query": {
        "lyric": {
            "contains": "sky",
            "highlight": True,
            "highlight_stride": 3
        }
    }
}
```

`highlight_stride` states how many words to retrieve before and after the match.

All documents returned by this query will have a `_context` key that contains the context around all instances of the term "sky".

### Group by

You can group results by a single key. This is useful for presenting aggregate views of data.

To group results by a key, use the following code:

```python
query = {
    "query": {
        "lyric": {
            "contains": "sky"
        }
    },
    "group_by": "title"
}
```

This query will search for all `lyric` fields that contain the term "sky" and group the results by the `title` field.

### Aggregate metrics

You can find the total number of unique values for the fields returned by a query using an `aggregate` query. This is useful for presenting the total number of options available in a search space to a user.

You can use the following query to find the total number of unique values for all fields whose `lyric` field contains the term "sky":

```python
query = {
    "query": {
        "lyric": {
            "contains": "sky"
        }
    },
    "metrics": ["aggregate"]
}
```

The aggregate results are presented in an `unique_record_values` key with the following structure:

```python
{
    "documents": [...],
    "query_time": 0.0001,
    {'unique_record_values': {'title': 2, 'lyric': 2, 'listens': 2, 'categories': 3}}
}
```

### Update documents

You need a document UUID to update a document. You can retrieve a UUID by searching for a document.

Here is an example showing how to update a document:

```python
response = index.search(
    {
        "query": {"title": {"equals": "tolerate it"}},
        "limit": 10,
        "sort_by": "title",
    }
)

uuid = response["documents"][0]["uuid"]

index.update(uuid, {"title": "tolerate it (folklore)", "artist": "Taylor Swift"})
```

`update` is an override operation. This means you must provide the full document that you want to save, instead of only the fields you want to update.

### Delete documents

You need a document UUID to delete a document. You can retrieve a UUID by searching for a document.

Here is an example showing how to delete a document:

```python
response = index.search(
    {
        "query": {"title": {"equals": "tolerate it"}},
        "limit": 10,
        "sort_by": "title",
    }
)

uuid = response["documents"][0]["uuid"]

index.remove(uuid)
```

You can validate the document has been deleted using this code:

```python
response = index.search(
    {
        "query": {"title": {"equals": "tolerate it"}},
        "limit": 10,
        "sort_by": "title",
    }
)

assert len(response["documents"]) == 0
```

## String queries

JameSQL supports string queries. String queries are single strings that use special syntax to assert the meaning of parts of a string.

For example, you could use the following query to find documents where the `title` field contains `tolerate it` and any field contains `mural`:

```
title:"tolerate it" mural
```

The following operators are supported:

- `-term`: Search for documents that do not contain `term`.
- `term`: Search for documents that contain `term`.
- `term1 term2`: Search for documents that contain `term1` and `term2`.
- `'term1 term2'`: Search for the literal phrase `term1 term2` in documents.
- `field:'term'`: Search for documents where the `field` field contains `term` (i.e. `title:"tolerate it"`).
- `field^2 term`: Boost the score of documents where the `field` field matches the query `term` by `2`.

This feature turns a string query into a JameSQL query, which is then executed and the results returned.

To run a string query, use the following code:

```python
results = index.string_query_search("title:'tolerate it' mural")
```

When you run a string query, JameSQL will attempt to simplify the query to make it more efficient. For example, if you search for `-sky sky mural`, the query will be `mural` because `-sky` negates the `sky` mention.

## Autosuggest

You can enable autosuggest using one or more fields in an index. This can be used to efficiently find records that start with a given prefix.

To enable autosuggest on an index, run:

```python
index = JameSQL()

...

index.enable_autosuggest("field")
```

Where `field` is the name of the field on which you want to enable autosuggest.

You can enable autosuggest on multiple fields:

```python
index.enable_autosuggest("field1")
index.enable_autosuggest("field2")
```

When you enable autosuggest on a field, JameSQL will create a trie index for that field. This index is used to efficiently find records that start with a given prefix.

To run an autosuggest query, use the following code:

```python
suggestions = index.autosuggest("started", match_full_record=True, limit = 1)
```

This will automatically return records that start with the prefix `started`.

The `match_full_record` parameter indicates whether to return full record names, or any records starting with a term.

`match_full_record=True` means that the full record name will be returned. This is ideal to enable selection between full records.

`match_full_record=False` means that any records starting with the term will be returned. This is ideal for autosuggesting single words.

For example, given the query `start`, matching against full records with `match_full_record=True` would return:

- `Started with a kiss`

This is the content of a full document.

`match_full_record=False`, on the other hand, would return:

- `started`
- `started with a kiss`

This contains both a root word starting with `start` and full documents starting with `start`.

This feature is case insensitive.

The `limit` argument limits the number of results returned.

## Spelling correction

It is recommended that you check the spelling of words before you run a query. 

This is because correcting the spelling of a word can improve the accuracy of your search results.

### Correcting the spelling of a single word

To recommend a spelling correction for a query, use the following code:

```python
index = ...

suggestion = index.spelling_correction("taylr swift")
```

This will return a single suggestion. The suggestion will be the word that is most likely to be the correct spelling of the word you provided.

Spelling correction first generates segmentations of a word, like:

- `t aylorswift`
- `ta ylorswift`

If a segmentation is valid, it is returned.

For example, if the user types in `taylorswift`, one permutation would be segmented into `taylor swift`. If `taylor swift` is common in the index, `taylor swift` will be returned as the suggestion.

Spelling correction works by transforming the input query by inserting, deleting, and transforming one character in every position in a string. The transformed strings are then looked up in the index to find if they are present and, if so, how common they are.

The most common suggestion is then returned.

For example, if you provide the word `tayloi` and `taylor` is common in the index, the suggestion will be `taylor`.

If correction was not possible after transforming one character, correction will be attempted with two transformations given the input string.

If the word you provided is already spelled correctly, the suggestion will be the word you provided. If spelling correction is not possible (i.e. the word is too distant from any word in the index), the suggestion will be `None`.

### Correcting a string query

If you are correcting a string query submitted with the `string_query_search()` function, spelling will be automatically corrected using the algorithm above. No configuration is required.

## Code Search

You can use JameSQL to efficiently search through code.

To do so, first create a `TRIGRAM_CODE` index on the field you want to search.

When you add documents, include at least the following two fields:

- `file_name`: The name of the file the code is in.
- `code`: The code you want to index.

When you search for code, all matching documents will have a `_context` key with the following structure:

```python
{
    "line": "1",
    "code": "..."
}
```

This tells you on what line your search matched, and the code that matched. This information is ideal to highlight specific lines relevant to your query.


## Data Storage

JameSQL indices are stored in memory and on disk.

When you call the `add()` method, the document is appended to an `index.jamesql` file in the directory in which your program is running. This file is serialized as JSONL.

When you load an index, all entries in the `index.jamesql` file will be read back into memory.

_Note: You will need to manually reconstruct your indices using the `create_gsi()` method after loading an index._

## Data Consistency

When you call `add()`, a `journal.jamesql` file is created. This is used to store the contents of the `add()` operation you are executing. If JameSQL terminates during an `add()` call for any reason (i.e. system crash, program termination), this journal will be used to reconcile the database.

Next time you initialize a JameSQL instance, your documents in `index.jamesql` will be read into memory. Then, the transactions in `journal.jamesql` will be replayed to ensure the index is consistent. Finally, the `journal.jamesql` file will be deleted.

You can access the JSON of the last transaction issued, sans the `uuid`, by calling `index.last_transaction`.

If you were in the middle of ingesting data, this could be used to resume the ingestion process from where you left off by allowing you to skip records that were already ingested.

## Web Interface

JameSQL comes with a limited web interface designed for use in testing queries.

_Note: You should not use the web interface if you are extending the query engine. Full error messages are only available in the console when you run the query engine._

To start the web interface, run:

```
python3 web.py
```

The web interface will run on `localhost:5000`.

## Testing

You can run the project unit tests with the following command:

```
pytest tests/*.py
```

The tests have three modes:

1. Run all unit tests.
2. Run all unit tests with an index of 30,000 small documents and ensure the query engine is fast.
3. Run all unit tests with an index of 30,000 documents with a few dozen words and ensure the query engine is fast.

To run the 30,000 small documents benchmark tests, run:

```
pytest tests/*.py --benchmark
```

To run the 30,000 documents with a few dozen words benchmark tests, run:

```
pytest tests/*.py --long-benchmark
```

In development, the goal should be making the query engine as fast as possible. The performance tests are designed to monitor for performance regressions, not set a ceiling for acceptable performance.

## Deployment considerations

JameSQL is not thread safe. This is because non-atomic mutations are made to record `_score` values during query computation. This may be fixed in a future release, although there are no immediate plans to do so.

It is recommended that you cache responses from JameSQL. While it takes < 1ms to process many JameSQL queries, reading a set of results from a cache will be faster.

## Development notes

The following are notes that describe limitations of which I am aware, and may fix in the future:

- `boost` does not work with and/or queries.
- The query engine relies on `uuid`s to uniquely identify items. But these are treated as the partition key, which is not appropriate. Two documents should be able to have the same partition key, as long as they have their own `uuid`.

## License

This project is licensed under an [MIT license](LICENSE).