piskvorky/sqlitedict

=================================================== sqlitedict -- persistent dict, backed by SQLite

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A lightweight wrapper around Python's sqlite3 database with a simple, Pythonic dict-like interface and support for multi-thread access:

Usage

Write

.. code-block:: python

>>> from sqlitedict import SqliteDict
>>> db = SqliteDict("example.sqlite")
>>>
>>> db["1"] = {"name": "first item"}
>>> db["2"] = {"name": "second item"}
>>> db["3"] = {"name": "yet another item"}
>>>
>>> # Commit to save the objects.
>>> db.commit()
>>>
>>> db["4"] = {"name": "yet another item"}
>>> # Oops, forgot to commit here, that object will never be saved.
>>> # Always remember to commit, or enable autocommit with SqliteDict("example.sqlite", autocommit=True)
>>> # Autocommit is off by default for performance.
>>>
>>> db.close()

Read

.. code-block:: python

>>> from sqlitedict import SqliteDict
>>> db = SqliteDict("example.sqlite")
>>>
>>> print("There are %d items in the database" % len(db))
There are 3 items in the database
>>>
>>> # Standard dict interface. items() values() keys() etc...
>>> for key, item in db.items():
...     print("%s=%s" % (key, item))
1={'name': 'first item'}
2={'name': 'second item'}
3={'name': 'yet another item'}
>>>
>>> db.close()

Efficiency

By default, sqlitedict's exception handling favors verbosity over efficiency. It extracts and outputs the outer exception stack to the error logs. If you favor efficiency, then initialize the DB with outer_stack=False.

.. code-block:: python

>>> from sqlitedict import SqliteDict
>>> db = SqliteDict("example.sqlite", outer_stack=False)  # True is the default
>>> db[1]
{'name': 'first item'}

Context Manager

.. code-block:: python

>>> from sqlitedict import SqliteDict
>>>
>>> # The database is automatically closed when leaving the with section.
>>> # Uncommitted objects are not saved on close. REMEMBER TO COMMIT!
>>>
>>> with SqliteDict("example.sqlite") as db:
...     print("There are %d items in the database" % len(db))
There are 3 items in the database

Tables

A database file can store multiple tables. A default table is used when no table name is specified.

Note: Writes are serialized, having multiple tables does not improve performance.

.. code-block:: python

>>> from sqlitedict import SqliteDict
>>>
>>> products = SqliteDict("example.sqlite", tablename="product", autocommit=True)
>>> manufacturers = SqliteDict("example.sqlite", tablename="manufacturer", autocommit=True)
>>>
>>> products["1"] = {"name": "first item",  "manufacturer_id": "1"}
>>> products["2"] = {"name": "second item", "manufacturer_id": "1"}
>>>
>>> manufacturers["1"] = {"manufacturer_name": "afactory", "location": "US"}
>>> manufacturers["2"] = {"manufacturer_name": "anotherfactory", "location": "UK"}
>>>
>>> tables = products.get_tablenames('example.sqlite')
>>> print(tables)
['unnamed', 'product', 'manufacturer']
>>>
>>> products.close()
>>> manufacturers.close()

In case you're wondering, the unnamed table comes from the previous examples, where we did not specify a table name.

Serialization

Keys are strings. Values are any serializeable object.

By default Pickle is used internally to (de)serialize the values.

It's possible to use a custom (de)serializer, notably for JSON and for compression.

.. code-block:: python

>>> # Use JSON instead of pickle
>>> import json
>>> with SqliteDict("example.sqlite", encode=json.dumps, decode=json.loads) as mydict:
...     pass
>>>
>>> # Apply zlib compression after pickling
>>> import zlib, pickle, sqlite3
>>>
>>> def my_encode(obj):
...     return sqlite3.Binary(zlib.compress(pickle.dumps(obj, pickle.HIGHEST_PROTOCOL)))
>>>
>>> def my_decode(obj):
...     return pickle.loads(zlib.decompress(bytes(obj)))
>>>
>>> with SqliteDict("example.sqlite", encode=my_encode, decode=my_decode) as mydict:
...     pass

It's also possible to use a custom (de)serializer for keys to allow non-string keys.

.. code-block:: python

>>> # Use key encoding instead of default string keys only
>>> from sqlitedict import encode_key, decode_key
>>> with SqliteDict("example.sqlite", encode_key=encode_key, decode_key=decode_key) as mydict:
...     pass

More

Functions are well documented, see docstrings directly in sqlitedict.py or call help(sqlitedict).

Beware: because of Python semantics, sqlitedict cannot know when a mutable SqliteDict-backed entry was modified in RAM. You'll need to explicitly assign the mutated object back to SqliteDict:

.. code-block:: python

>>> from sqlitedict import SqliteDict
>>> db = SqliteDict("example.sqlite")
>>> db["colors"] = {"red": (255, 0, 0)}
>>> db.commit()
>>>
>>> colors = db["colors"]
>>> colors["blue"] = (0, 0, 255) # sqlite DB not updated here!
>>> db["colors"] = colors  # now updated
>>>
>>> db.commit() # remember to commit (or set autocommit)
>>> db.close()

Features

• Values can be any picklable objects (uses pickle with the highest protocol).

• Support for multiple tables (=dicts) living in the same database file.

• Support for access from multiple threads to the same connection (needed by e.g. Pyro). Vanilla sqlite3 gives you ProgrammingError: SQLite objects created in a thread can only be used in that same thread.

  Concurrent requests are still serialized internally, so this "multithreaded support" doesn't give you any performance benefits. It is a work-around for sqlite limitations in Python.

• Support for custom serialization or compression:

.. code-block:: python

use JSON instead of pickle

import json mydict = SqliteDict('./my_db.sqlite', encode=json.dumps, decode=json.loads)

apply zlib compression after pickling

import zlib, pickle, sqlite3 def my_encode(obj): ... return sqlite3.Binary(zlib.compress(pickle.dumps(obj, pickle.HIGHEST_PROTOCOL))) def my_decode(obj): ... return pickle.loads(zlib.decompress(bytes(obj))) mydict = SqliteDict('./my_db.sqlite', encode=my_encode, decode=my_decode)

• sqlite is efficient and can work effectively with large databases (multi gigabytes), not limited by memory.
• sqlitedict is mostly a thin wrapper around sqlite.
• items() keys() values() are iterating one by one, the rows are loaded in a worker thread and queued in memory.
• len() is calling sqlite to count rows, that is scanning the whole table.
• For better performance, write objects in batch and commit() once.

Installation

The module has no dependencies beyond Python itself. The minimum supported Python version is 3.7, continuously tested on Python 3.7, 3.8, 3.9, and 3.10 on Travis <https://travis-ci.org/RaRe-Technologies/sqlitedict>_.

Install or upgrade with::

pip install -U sqlitedict

or from the source tar.gz <http://pypi.python.org/pypi/sqlitedict>_::

python setup.py install

Contributions

Testing

Install::

$ pip install pytest coverage pytest-coverage

To perform all tests::

$ mkdir -p tests/db
$ pytest tests
$ python -m doctest README.rst

To perform all tests with coverage::

$ pytest tests --cov=sqlitedict

Comments, bug reports

sqlitedict resides on github <https://github.com/RaRe-Technologies/sqlitedict>_. You can file issues or pull requests there.

License

sqlitedict is open source software released under the Apache 2.0 license <http://opensource.org/licenses/apache2.0.php>. Copyright (c) 2011-now Radim Řehůřek <http://radimrehurek.com> and contributors.

Housekeeping

Clean up the test database to keep each doctest run idempotent:

.. code-block:: python

import os if name == 'main': ... os.unlink('example.sqlite')