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
Here is an example of a search engine with a JameSQL back-end:
Screen.Recording.2024-10-16.at.17.44.38.mov
To install this project, run:
pip install jamesql
Here is a quickstart with a string-based query:
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' ...}]To create a database, use the following code:
from jamesql import JameSQL
index = JameSQL()To load the database you initialized in your last session, use the following code:
from jamesql import JameSQL
index = JameSQL.load()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.
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 theCONTAINStype.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 astarts_withquery, you may choose a flat index to allow for efficientequalsandcontainsqueries.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:
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.
A query has the following format:
{
"query": {},
"limit": 2,
"sort_by": "song",
"skip": 1
}queryis a dictionary that contains the fields to search for.limitis the maximum number of documents to return. (default 10)sort_byis the field to sort by. (default None)skipis 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.
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.
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.
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:
{
"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)
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.
There are three operators you can use for condition matching:
equalscontainsstarts_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:
andornot
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"
}
}
]
}
}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.
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",
}To search for documents that match a query, use the following code:
result = index.search(query)This returns a JSON payload with the following structure:
{
"documents": [
{"uuid": "1", ...}
{"uuid": "2", ...}
...
],
"query_time": 0.0001,
"total_results": 200
}You can search through multiple pages with the scroll() method:
result = index.scroll(query)scroll() returns a generator that yields documents in the same format as search().
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.
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
}
}
}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.
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
}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_thanless_thangreater_than_or_equalless_than_or_equal
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.
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".
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.
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}}
}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.
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"]) == 0JameSQL 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 containterm.term: Search for documents that containterm.term1 term2: Search for documents that containterm1andterm2.'term1 term2': Search for the literal phraseterm1 term2in documents.field:'term': Search for documents where thefieldfield containsterm(i.e.title:"tolerate it").field^2 term: Boost the score of documents where thefieldfield matches the querytermby2.
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")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.
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:
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:
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:
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:
startedstarted 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.
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.
To recommend a spelling correction for a query, use the following code:
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 aylorswiftta 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.
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.
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:
{
"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.
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.
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.
By default, JameSQL assigns scores to the top 1,000 documents in each clause in a query. Consider the following query;
query = {
"query": {
"and": [
{
"artist": {
"equals": "Taylor Swift"
}
},
{
"title": {
"equals": "tolerate it"
}
}
]
},
"limit": 10
}
The { "artist": { "equals": "Taylor Swift" } } clause will return the top 1,000 documents that match the query. The { "title": { "equals": "tolerate it" } } clause will return the top 1,000 documents that match the query.
These will then be combine and sorted to return the 10 documents of the 2,000 processed that have the highest score.
This means that if you have a large number of documents that match a query, you may not get precisely the most relevant documents in the top 10 results, rather an approximation of the most relevant documents.
You can override the number of documents to consider with:
index.match_limit_for_large_result_pages = 10_000
The higher this number, the longer it will take to process results with a large number of matching documents.
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.
You can run the project unit tests with the following command:
pytest tests/*.py
The tests have three modes:
- Run all unit tests.
- Run all unit tests with an index of 30,000 small documents and ensure the query engine is fast.
- 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.
Progress is being made on making JameSQL thread safe, but there are still some issues to work out. It is recommended that you run JameSQL in a single-threaded environment.
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.
The following are notes that describe limitations of which I am aware, and may fix in the future:
boostdoes 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 ownuuid.
This project is licensed under an MIT license.