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A thread-safe disk based persistent queue in Python.

Project Description

This project is based on the achievements of python-pqueue and queuelib

persist-queue implements a file-based queue and a serial of sqlite3-based queues. The goals is to achieve following requirements:

  • Disk-based: each queued item should be stored in disk in case of any crash.
  • Thread-safe: can be used by multi-threaded producers and multi-threaded consumers.
  • Recoverable: Items can be read after process restart.
  • Green-compatible: can be used in greenlet or eventlet environment.

While queuelib and python-pqueue cannot fulfil all of above. After some try, I found it’s hard to achieve based on their current implementation without huge code change. this is the motivation to start this project.

persist-queue use pickle object serialization module to support object instances. Most built-in type, like int, dict, list are able to be persisted by persist-queue directly, to support customized objects, please refer to Pickling and unpickling extension types(Python2) and Pickling Class Instances(Python3)

Requirements

  • Python 2.7 or Python 3.x.
  • Full support for Linux.
  • Windows support (with Caution if persistqueue.Queue is used).

Installation

from pypi

pip install persist-queue

from source code

git clone https://github.com/peter-wangxu/persist-queue
cd persist-queue
python setup.py install

Benchmark

Here is the result for writing/reading 10000 items to the disk comparing the sqlite3 and file queue.

Environment:
  • OS: Windows 10
  • Disk: SATA3 SSD
  • RAM: 16 GiB
  Transaction write (s) Bulk write (s) Transaction write/read (s) Bulk write/read (s)
SQLite3 64.98 0.19 142.82 63.82
File 89.68 85.78 101.37 85.76
  • Transaction refers to commit the change to disk on every write.
  • Bulk refers to only commit the change to disk on last write.

To see the real performance on your host, run the script under benchmark/run_benchmark.py:

python benchmark/run_benchmark.py

Examples

Example usage with a SQLite3 based queue

>>> import persistqueue
>>> q = persistqueue.SQLiteQueue('mypath', auto_commit=True)
>>> q.put('str1')
>>> q.put('str2')
>>> q.put('str3')
>>> q.get()
'str1'
>>> del q

Close the console, and then recreate the queue:

>>> import persistqueue
>>> q = persistqueue.SQLiteQueue('mypath', auto_commit=True)
>>> q.get()
'str2'
>>>

Example usage with a file based queue

>>> from persistqueue import Queue
>>> q = Queue("mypath")
>>> q.put('a')
>>> q.put('b')
>>> q.put('c')
>>> q.get()
'a'
>>> q.task_done()

Close the python console, and then we restart the queue from the same path,

>>> from persistqueue import Queue
>>> q = Queue('mypath')
>>> q.get()
'b'
>>> q.task_done()

Example usage with a SQLite3 based dict

>>> from persisitqueue import PDict
>>> q = PDict("testpath", "testname")
>>> q['key1'] = 123
>>> q['key2'] = 321
>>> q['key1']
123
>>> len(q)
2
>>> del q['key1']
>>> q['key1']
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "persistqueue\pdict.py", line 58, in __getitem__
    raise KeyError('Key: {} not exists.'.format(item))
KeyError: 'Key: key1 not exists.'

Close the console and restart the PDict

>>> from persisitqueue import PDict
>>> q = PDict("testpath", "testname")
>>> q['key2']
321

Multi-thread usage for SQLite3 based queue

from persistqueue import FIFOSQLiteQueue

q = FIFOSQLiteQueue(path="./test", multithreading=True)

def worker():
    while True:
        item = q.get()
        do_work(item)

for i in range(num_worker_threads):
     t = Thread(target=worker)
     t.daemon = True
     t.start()

for item in source():
    q.put(item)

multi-thread usage for Queue

from persistqueue import Queue

q = Queue()

def worker():
    while True:
        item = q.get()
        do_work(item)
        q.task_done()

for i in range(num_worker_threads):
     t = Thread(target=worker)
     t.daemon = True
     t.start()

for item in source():
    q.put(item)

q.join()       # block until all tasks are done

Performance impact

Since persistqueue v0.3.0, a new parameter auto_commit is introduced to tweak the performance for sqlite3 based queues as needed. When specify auto_commit=False, user needs to perform queue.task_done() to persist the changes made to the disk since last task_done invocation.

Tests

persist-queue use tox to trigger tests.

to trigger tests based on python2.7/python3.x, use:

tox -e py27
tox -e py34
tox -e py35
tox -e py36

to trigger pep8 check, use:

tox -e pep8

pyenv is usually a helpful tool to manage multiple versions of Python.

Caution

Currently, the atomic operation is not supported on Windows due to the limitation of Python’s os.rename, That’s saying, the data in persistqueue.Queue could be in unreadable state when an incidental failure occurs during Queue.task_done.

DO NOT PUT ANY CRITICAL DATA ON persistqueue.QUEUE WHEN RUNNING ON WINDOWS.

Contribution

Simply fork this repo and send PR for your code change(also tests to cover your change), remember to give a title and description of your PR. I am willing to enhance this project with you :).

License

BSD

FAQ

  • sqlite3.OperationalError: database is locked is raised.

persistquest open 2 connections for the db if multithreading=True, the SQLite database is locked until that transaction is committed. The timeout parameter specifies how long the connection should wait for the lock to go away until raising an exception. Default time is 10, increase timeout when creating the queue if above error occurs.

Release History

Release History

This version
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0.3.1

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0.3.0

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0.2.3

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0.2.2

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0.2.1

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0.1.6

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0.1.5

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0.1.4

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0.1.3

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0.1.2

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0.1.1

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0.1.0

Download Files

Download Files

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
persist_queue-0.3.1-py2.py3-none-any.whl (18.7 kB) Copy SHA256 Checksum SHA256 py2.py3 Wheel Oct 10, 2017
persist-queue-0.3.1.tar.gz (15.9 kB) Copy SHA256 Checksum SHA256 Source Oct 10, 2017

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