jamesql 0.2.3

A JameSQL database implemented in Python.

JameSQL

A JameSQL database implemented in Python.

This project has support for:

• Inserting records
• Deleting records
• Searching for records that match a query
• Searching for records that match multiple query conditions

This database does not enforce a schema, so you can insert records with different fields.

Here is an example of a query run in the JameSQL web interface:

JamesQL is designed for use in single-threaded applications. It is not designed for use in multi-threaded applications.

Installation

To install this project, run:

pip install jamesql

Usage

Create a database

To create a database, use the following code:

from nosql import NoSQL

index = JameSQL()

Add documents to a database

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

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).
• 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).

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

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:

{
    "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:

{
    "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:

{
    "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:

{
    "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:

{
    "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)

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

{
    "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:

((post + title) * 2)

Invalid forms of this query include:

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

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:

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:

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

or

You can nest conditions to create complex queries, like:

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:

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:

result = index.search(query)

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:

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:

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 *.

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:

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:

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:

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:

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:

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":

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

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

{
    "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:

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:

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:

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").

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:

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

Note: Single quotes (') must be used to denote literal terms. Quotation marks (") do not yet work.

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.

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 uuids 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.