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Easily save, load, query your Python objects with SQLite3.

Project description

Here, a persistent object is one that persists or lives beyond the life of a program by being stored in a database in a serialized form. The object can be loaded which deserializes its stored form back into its original class type with all attributes restored


  • Your regular Python objects are serialized with jsonpickle

  • Serialized objects are stored in a SQLite3 database

  • Each object must have a globally (across all class types) unique identifier in its id property.

  • References between persistent objects are supported. See below for details.


pip install python-object-persistence

System Requirements

SQLite 3.9.0 or later is required for the JSON functionality which was added about 2 months prior to this writing.

Python’s standard library version is older than the required minimum SQLite3 version.

However, Python will use whatever SQLite3 version you have installed. Thus, if you update SQLite3 to the latest stable version you are good to go.

Eventually, Python will bundle SQLite 3.9.0+ and none of this will be required.


Install Homebrew if you haven’t already.

$ brew update
$ brew install sqlite3 --with-json1

$ python
>>> import sqlite3
>>> sqlite3.sqlite_version


Install latest SQLite3 from source to home directory:

$ LOCAL=$HOME/local
$ sqlite_version=3110000  # or whatever
$ wget${sqlite_version}.tar.gz -O- | tar xzv
$ cd sqlite-autoconf-${sqlite_version}
$ ./configure --enable-json1 --prefix=$LOCAL
$ make
$ make install

I use pyenv to manage install Python versions.

$ curl -L | bash
$ echo 'export PATH="$HOME/.pyenv/bin:$PATH"' >> ~/.bash_profile
$ echo 'eval "$(pyenv init -)"' >> ~/.bash_profile
$ echo 'eval "$(pyenv virtualenv-init -)"' >> ~/.bash_profile
$ echo 'export PYENV_VERSION=3.5.1' >> ~/.bash_profile
$ source ~/.bash_profile
$ LD_RUN_PATH=$HOME/local/lib pyenv install $PYENV_VERSION
$ pyenv version

$ python
>>> import sqlite3
>>> sqlite3.sqlite_version


Connect to the database to use via:

import persistent

persistent.connect(db_path=':memory:', debug=True)

Subclass Persistent. Create objects of your class type. Call save on them.

class Foo(persistent.Persistent):

f = Foo() = 'hello'

Load an object by id:

x = persistent.get(
assert ==
assert ==

Make updates and save the object. = 'monkey'

Inter-Object References

A persistent object may refer to another persistent object by setting an attribute to the referenced object as in any other Python program. By default, when the source object is saved, a copy of referenced objects is saved with it.

If you would prefer to save an explicit reference, add the source object attributes that contain references to the source class’s references. On save, the attributes on the source object are stored as the referenced object’s id. On load, the source object’s references are scanned and the referenced objects loaded, replacing the corresponding attribute on the source object.

class Bar(persistent.Persistent):
    references = [ 'a_ref' ]

b = Bar()
c = Bar()
c.baz = 'yes'
b.a_ref = c
x = persistent.get(
assert ==
assert type(x.a_ref) is Bar
assert ==
assert x.a_ref.baz == c.baz


The system automatically adds created_at and updated_at which are datetime objects in UTC.

class Baz(persistent.Persistent): pass
x = Baz()
from datetime import datetime
assert type(x.created_at) is datetime
assert not hasattr(x, 'updated_at')
x.quux = 'doo'
assert type(x.updated_at) is datetime


A least-recently-used (LRU) cache is used to hold the latest copy of each object by object id. On a cache miss, the desired object is loaded from the database and placed into the cache. If more than N objects (by default, N=1000) objects are stored in the cache, the least-recently-used object is evicted from the cache.

To change the default size of the cache, use the cache_size parameter when calling persistent.connect. To disable caching entirely, set the cache_size to 0.


To enforce that only a single object may contain some value for a set of “key paths”, create a “unique index”:

persistent.add_index(['a', 'b.c'], unique=True)

x = Bar()
x.a = 1
x.b = dict(c=1)  # OK

y = Bar()
y.a = 1
y.b = dict(c=1)
except persistent.UniquenessError as err: assert True
    # Fails as y is non-unique for ['a', 'b.c']

Note that such an index is scoped to the same object class. If you wish to make the index span all persistent objects stored, pass global_scope=True to add_index.

By default, an index has a generated name which is returned by add_index.

A non-unique index can be created to speed up queries.


To query or find objects, create a persist.Query object, passing the class of object. Only objects of the given class will be returned.

q = persist.Query(Bar)
q.equal_to(key_path, value)
objects = q.find()

Key Paths

A key path is a string with elements separated by a period (.). Following a key path in an object leads to a particular value. The value at a key path is what is used as the test value.

Consider key path “a.b.c”:

o = Persistent()
o.a = dict(b=dict(c=1))

The value at the key path “a.b.c” is 1

See keypath for more details.


q.equal_to(key_path, value)
q.not_equal_to(key_path, value)


q.contained_in(key_path, values)
q.not_contained_in(key_path, values)

q.starts_with(key_path, substr, case_insensitive=False)
q.contains(key_path, substr, case_insensitive=False)
q.ends_with(key_path, substr, case_insensitive=False)

q.greater_than(key_path, n, is_list=False)
q.greater_than_or_equal_to(key_path, n, is_list=False)

q.less_than(key_path, n, is_list=False)
q.less_than_or_equal_to(key_path, n, is_list=False)

q.matches(key_path, regex_pattern, case_insensitive=False)

Note: when is_list is True the test/comparison is between the length of the list at key_path and the operand n.



These can be called multiple times to sort on multiple key paths.



Running the Query

objs = q.find()
obj = q.first()
n = q.count()

find and first return None if no object(s) were found.

AND or OR Queries

A Query is in effect an AND query in that all conditions specified must be met by an object for that object to be included in the result set.

To create an OR query, use OrQuery:

q = OrQuery(
  Query(A).equal_to('foo', 'bar'),
  Query(B).equal_to('baz', 'buz'),
  Query(C).equal_to('buz', 'quux'))

objs = q.find()

You may pass an arbitrary number of queries to an OrQuery.


Pass debug=True to persistent.connect and submitted SQL statements will be logged using Python’s built-in logging module at the debug level.


Run make init to install Python package dependencies with pip.


Run make test to run the test suite with pytest including coverage reporting.

I aim for 100% code coverage in tests. See

When Python’s standard library version of SQLite3 is updated, I will include Tox reports here.

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