unqlite

Fast Python bindings for UnQLite, a lightweight, embedded NoSQL database and JSON document store.

Please note

Read the issue tracker for this database before considering using it. UnQLite has not seen any meaningful development since 2014. It is strongly recommended that you use Sqlite. Sqlite has robust support for json and is actively developed and maintained.

Features

UnQLite features:

Embedded, zero-conf database
Transactional (ACID)
Single file or in-memory database
Key/value store
Cursor support and linear record traversal
JSON document store
Thread-safe
Terabyte-sized databases

UnQLite-Python features:

Compiled library, extremely fast with minimal overhead.
Supports key/value operations, cursors, and transactions using Pythonic APIs.
Support for Jx9 scripting.
APIs for working with Jx9 JSON document collections.

Links:

Installation

You can install unqlite using pip.

pip install unqlite

Basic usage

Below is a sample interactive console session designed to show some of the basic features and functionality of the unqlite-python library. Also check out the full API documentation.

To begin, instantiate an UnQLite object. You can specify either the path to a database file, or use UnQLite as an in-memory database.

>>> from unqlite import UnQLite
>>> db = UnQLite()  # Create an in-memory database.

Key/value features

UnQLite can be used as a key/value store.

>>> db['foo'] = 'bar'  # Use as a key/value store.
>>> db['foo']  # The key/value store deals in byte-strings.
b'bar'

>>> for i in range(4):
...     db['k%s' % i] = str(i)
...

>>> 'k3' in db
True
>>> 'k4' in db
False
>>> del db['k3']

>>> db.append('k2', 'XXXX')
>>> db['k2']
b'2XXXX'

>>> db.get('k2')  # Like dict.get() -- returns None if not found.
b'2XXXX'
>>> db.get('missing', 'n/a')
'n/a'

>>> list(db.match_prefix('k'))  # Iterate keys matching a prefix (O(n) scan).
[('k0', b'0'), ('k1', b'1'), ('k2', b'2XXXX')]

The database can also be iterated through directly. Note that keys are decoded while values are left as bytestrings.

>>> [item for item in db]
[('foo', b'bar'), ('k0', b'0'), ('k1', b'1'), ('k2', b'2XXXX')]

Cursors

For finer-grained record traversal, you can use cursors.

>>> with db.cursor() as cursor:
...     cursor.seek('k0')
...     for key, value in cursor:
...         print(key, '=>', value.decode('utf8'))
...
k0 => 0
k1 => 1
k2 => 2XXXX

>>> with db.cursor() as cursor:
...     cursor.seek('k2')
...     print(cursor.value())
...
b'2XXXX'

>>> with db.cursor() as cursor:
...     cursor.seek('k0')
...     print(list(cursor.fetch_until('k2', include_stop_key=False)))
...
[('k0', b'0'), ('k1', b'1')]

There are many different ways of interacting with cursors, which are described in the Cursor API documentation.

Document store features

In my opinion the most interesting feature of UnQLite is its JSON document store. The Jx9 scripting language is used to interact with the document store, and it is a wacky mix of PHP and maybe JavaScript (?).

Note: as of v0.8.0 the document store and collections APIs treat all strings as unicode.

Interacting with the document store basically consists of creating a Jx9 script (you might think of it as an imperative SQL query), compiling it, and then executing it.

>>> script = """
...     db_create('users');
...     db_store('users', $list_of_users);
...     $users_from_db = db_fetch_all('users');
... """

>>> list_of_users = [
...     {'username': 'Huey', 'age': 3},
...     {'username': 'Mickey', 'age': 5}
... ]

>>> with db.vm(script) as vm:
...     vm['list_of_users'] = list_of_users
...     vm.execute()
...     users_from_db = vm['users_from_db']
...
True

>>> users_from_db  # UnQLite assigns items in a collection an ID.
[{'username': 'Huey', 'age': 3, '__id': 0},
 {'username': 'Mickey', 'age': 5, '__id': 1}]

This is just a taste of what is possible with Jx9. In the near future I may add some wrappers around common Jx9 collection operations, but for now hopefully it is not too difficult to work with.

More information can be found in the VM API documentation.

Collections

To simplify working with JSON document collections, unqlite-python provides a light API for executing Jx9 queries on collections. A collection is an ordered list of JSON objects (records). Records can be appended or deleted, and in the next major release of UnQLite there will be support for updates as well.

To begin working with collections, you can use the factory method on UnQLite:

>>> users = db.collection('users')
>>> users.create()  # Create the collection if it does not exist.
>>> users.exists()
True

You can use the store() method to add one or many records. To add a single record just pass in a python dict. To add multiple records, pass in a list of dicts. Records can be fetched and deleted by ID.

By default, the ID of the last-stored record is returned. At the time of writing, IDs start at 0, so when inserting 3 records the last-id is 2:

>>> users.store([
...     {'name': 'Charlie', 'color': 'green'},
...     {'name': 'Huey', 'color': 'white'},
...     {'name': 'Mickey', 'color': 'black'}])
2
>>> users.store({'name': 'Leslie', 'color': 'also green'}, return_id=False)
True

>>> users.fetch(0)  # Fetch the first record.
{'__id': 0, 'color': 'green', 'name': 'Charlie'}

>>> users.delete(0)  # Delete the first record.
True
>>> users.delete(users.last_record_id())  # Delete the last record.
True

You can retrieve all records in the collection, or specify a filtering function. The filtering function will be registered as a foreign function with the Jx9 VM and called from the VM.

>>> users.all()
[{'__id': 1, 'color': 'white', 'name': 'Huey'},
 {'__id': 2, 'color': 'black', 'name': 'Mickey'}]

>>> users.filter(lambda obj: obj['name'].startswith('H'))
[{'__id': 1, 'color': 'white', 'name': 'Huey'}]

Transactions

UnQLite supports transactions for file-backed databases (since transactions occur at the filesystem level, they have no effect on in-memory databases).

The easiest way to create a transaction is with the context manager:

>>> db = UnQLite('/tmp/test.db')
>>> with db.transaction():
...     db['k1'] = 'v1'
...     db['k2'] = 'v2'
...
>>> db['k1']
b'v1'

You can also use the transaction decorator which will wrap a function call in a transaction and commit upon successful execution (rolling back if an exception occurs).

>>> @db.commit_on_success
... def save_value(key, value, exc=False):
...     db[key] = value
...     if exc:
...         raise Exception('uh-oh')
...
>>> save_value('k3', 'v3')
>>> save_value('k3', 'vx', True)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "unqlite/core.py", line 312, in wrapper
    return fn(*args, **kwargs)
  File "<stdin>", line 5, in save_value
Exception: uh-oh
>>> db['k3']
b'v3'

For finer-grained control you can call db.begin(), db.rollback() and db.commit() manually:

>>> db.begin()
>>> db['k3'] = 'v3-xx'
>>> db.commit()
True
>>> db['k3']
b'v3-xx'

This code is based in part on buaabyl's pyUnQLite.

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unqlite 1.1.0

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Algorithm	Hash digest
SHA256	`a31c499eb4c471b950cec1d573c50539b065f1e74befe790e2b36a7815bf3d68`
MD5	`a1ebb4e5ac3b6095302f59fda5e908b3`
BLAKE2b-256	`c948c4ce451bed3716aef4b5fbcdd7149c9a15d0d9c469d582414a95eafb3baa`

Algorithm	Hash digest
SHA256	`7598c45b500d3d085e701598c8fae5390be8955156f7aafb4c4645adb4a143fe`
MD5	`bff48af86f9b302b6f6db4e7a5b65003`
BLAKE2b-256	`2961d1430ee42daa71e9b639f635d1b050569ff5d838266fe07202fe6824aad0`

Algorithm	Hash digest
SHA256	`2a56173ed3b3a0d9e012c8af1f281d7a874cf55e859ef76356efbdaed1beb0c9`
MD5	`cd498153ea44bb5e85992ba86c2dfdbf`
BLAKE2b-256	`13f37a377832ef9e138aa628d002f7302ea8925c182c90fed8f68d35269de35c`

Algorithm	Hash digest
SHA256	`56860ed6dc520854ac8049732209cc02ee97c0615d39e79e12af42609be80351`
MD5	`3dc686c929eb37030c2c4829a41578b2`
BLAKE2b-256	`ff1e545e8a4b532c3e4d911b4d76c7fec7ea3568283beaf6dff019f4f1f2521b`

Algorithm	Hash digest
SHA256	`6e24349351e47770fffb999b85e9698f532b11af659be10a14fa195685f6e9a4`
MD5	`12cf5037c3ab07788279d7896eac015b`
BLAKE2b-256	`54dde8bac331f1937bc7fd51812ac0f5973c26ab6cc48130b92daf87f48256e3`

Algorithm	Hash digest
SHA256	`9cf0e5b8af33b50d6acdcf9c48b3f2a9a295c03f391261804a9c62c956c0a941`
MD5	`99f174c85f18fa0f8bc3f25371ac4423`
BLAKE2b-256	`cffbb9c4347a6e68902e6a0292d5084f10a1862079dbee56b4598eb88f924704`

Algorithm	Hash digest
SHA256	`8bb5b23734142206dfdad5b546ae14919b30d02cc5757683825875e4d2249c66`
MD5	`92655fefee927195ef74a939f1cb87cb`
BLAKE2b-256	`7d4872f1a92018ab8d754cee1a17dfb2a6fbe9cbfafe77e626251a5def799c7c`

Algorithm	Hash digest
SHA256	`de0f566ab5fff3d1d3cb618f418151df3890aab80436a798a49dbae21e0cff6e`
MD5	`fc746ce7f2085bf9225e25caacb8f38b`
BLAKE2b-256	`984200be79bde20269280ee0ff45af49f90cc1284dc1358c5e6581d28ff62a4d`

Algorithm	Hash digest
SHA256	`9fe5acb60a095f3e947255dd5f72c9e37e16a2fdb50bfabd090036d9cac97b1d`
MD5	`36cde1733db38a30009765d27a8b486a`
BLAKE2b-256	`21a0071a28dec44357555e13d9d8c0f93bc880a0c0c44596f40a9660ba7b2dbf`