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.

By default, 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)

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

Join persist-queue channel

• Python 3.5 or newer versions (refer to Deprecation for older Python versions)
• Full support for Linux and MacOS.
• Windows support (with Caution if persistqueue.Queue is used).

• Multiple platforms support: Linux, macOS, Windows
• Pure python
• Both filed based queues and sqlite3 based queues are supported
• Filed based queue: multiple serialization protocol support: pickle(default), msgpack, cbor, json

• persist-queue drops Python 2 support since version 1.0.0, no new feature will be developed under Python 2 as Python 2 was sunset on January 1, 2020.
• Python 3.4 release has reached end of life and DBUtils ceased support for Python 3.4, persist queue drops MySQL based queue for python 3.4 since version 0.8.0. other queue implementations such as file based queue and sqlite3 based queue are still workable.

pip install persist-queue
# for msgpack, cbor and mysql support, use following command
pip install "persist-queue[extra]"

git clone https://github.com/peter-wangxu/persist-queue
cd persist-queue
# for msgpack and cbor support, run 'pip install -r extra-requirements.txt' first
python setup.py install

Here are the time spent(in seconds) for writing/reading 1000 items to the disk comparing the sqlite3 and file queue.

• Windows

  • OS: Windows 10
  • Disk: SATA3 SSD
  • RAM: 16 GiB

windows note Performance of Windows File Queue has dramatic improvement since v0.4.1 due to the atomic renaming support(3-4X faster)

• Linux

  • OS: Ubuntu 16.04 (VM)
  • Disk: SATA3 SSD
  • RAM: 4 GiB

• Mac OS

  • OS: 10.14 (macOS Mojave)
  • Disk: PCIe SSD
  • RAM: 16 GiB

note

• The value above is in seconds for reading/writing 1000 items, the less the better
• Above result was got from:

python benchmark/run_benchmark.py 1000

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

python benchmark/run_benchmark.py <COUNT, default to 100>

>>> 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'
>>>

New functions: Available since v0.8.0

• shrink_disk_usage perform a VACUUM against the sqlite, and rebuild the database file, this usually takes long time and frees a lot of disk space after get()

This queue does not allow duplicate items.

>>> import persistqueue
>>> q = persistqueue.UniqueQ('mypath')
>>> q.put('str1')
>>> q.put('str1')
>>> q.size
1
>>> q.put('str2')
>>> q.size
2
>>>

The core functions:

• put: add item to the queue. Returns id
• get: get item from queue and mark as unack. Returns item, Optional paramaters (block, timeout, id, next_in_order, raw)
• update: update an item. Returns id, Paramaters (item), Optional parameter if item not in raw format (id)
• ack: mark item as acked. Returns id, Parameters (item or id)
• nack: there might be something wrong with current consumer, so mark item as ready and new consumer will get it. Returns id, Parameters (item or id)
• ack_failed: there might be something wrong during process, so just mark item as failed. Returns id, Parameters (item or id)
• clear_acked_data: perform a sql delete agaist sqlite. It removes 1000 items, while keeping 1000 of the most recent, whose status is AckStatus.acked (note: this does not shrink the file size on disk) Optional paramters (max_delete, keep_latest, clear_ack_failed)
• shrink_disk_usage perform a VACUUM against the sqlite, and rebuild the database file, this usually takes long time and frees a lot of disk space after clear_acked_data
• queue: returns the database contents as a Python List[Dict]
• active_size: The active size changes when an item is added (put) and completed (ack/ack_failed) unlike qsize which changes when an item is pulled (get) or returned (nack).

>>> import persistqueue
>>> ackq = persistqueue.SQLiteAckQueue('path')
>>> ackq.put('str1')
>>> item = ackq.get()
>>> # Do something with the item
>>> ackq.ack(item) # If done with the item
>>> ackq.nack(item) # Else mark item as `nack` so that it can be proceeded again by any worker
>>> ackq.ack_failed(item) # Or else mark item as `ack_failed` to discard this item

Parameters:

• clear_acked_data

  • max_delete (defaults to 1000): This is the LIMIT. How many items to delete.
  • keep_latest (defaults to 1000): This is the OFFSET. How many recent items to keep.
  • clear_ack_failed (defaults to False): Clears the AckStatus.ack_failed in addition to the AckStatus.ack.

• get

  • raw (defaults to False): Returns the metadata along with the record, which includes the id (pqid) and timestamp. On the SQLiteAckQueue, the raw results can be ack, nack, ack_failed similar to the normal return.
  • id (defaults to None): Accepts an id or a raw item containing pqid. Will select the item based on the row id.
  • next_in_order (defaults to False): Requires the id attribute. This option tells the SQLiteAckQueue/UniqueAckQ to get the next item based on id, not the first available. This allows the user to get, nack, get, nack and progress down the queue, instead of continuing to get the same nack'd item over again.

raw example:

>>> q.put('val1')
>>> d = q.get(raw=True)
>>> print(d)
>>> {'pqid': 1, 'data': 'val1', 'timestamp': 1616719225.012912}
>>> q.ack(d)

next_in_order example:

>>> q.put("val1")
>>> q.put("val2")
>>> q.put("val3")
>>> item = q.get()
>>> id = q.nack(item)
>>> item = q.get(id=id, next_in_order=True)
>>> print(item)
>>> val2

Note:

1. The SQLiteAckQueue always uses "auto_commit=True".
2. The Queue could be set in non-block style, e.g. "SQLiteAckQueue.get(block=False, timeout=5)".
3. UniqueAckQ only allows for unique items

Parameters:

• path: specifies the directory wher enqueued data persisted.
• maxsize: indicates the maximum size stored in the queue, if maxsize<=0 the queue is unlimited.
• chunksize: indicates how many entries should exist in each chunk file on disk. When a all entries in a chunk file was dequeued by get(), the file would be removed from filesystem.
• tempdir: indicates where temporary files should be stored. The tempdir has to be located on the same disk as the enqueued data in order to obtain atomic operations.
• serializer: controls how enqueued data is serialized.
• auto_save: True or False. By default, the change is only persisted when task_done() is called. If autosave is enabled, info data is persisted immediately when get() is called. Adding data to the queue with put() will always persist immediately regardless of this setting.

>>> 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()

Available since: v0.5.0

By default, items added to the queue are persisted during the put() call, and items removed from a queue are only persisted when task_done() is called.

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

After exiting and restarting the queue from the same path, we see the items remain in the queue, because task_done() wasn't called before.

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

This can be advantageous. For example, if your program crashes before finishing processing an item, it will remain in the queue after restarting. You can also spread out the task_done() calls for performance reasons to avoid lots of individual writes.

Using autosave=True on a file based queue will automatically save on every call to get(). Calling task_done() is not necessary, but may still be used to join() against the queue.

>>> from persistqueue import Queue
>>> q = Queue("mypath", autosave=True)
>>> q.put('a')
>>> q.put('b')
>>> q.get()
'a'

After exiting and restarting the queue from the same path, only the second item remains:

>>> from persistqueue import Queue
>>> q = Queue('mypath', autosave=True)
>>> q.get()
'b'

>>> 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

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)

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

Available since: v0.8.0

>>> import persistqueue
>>> db_conf = {
>>>     "host": "127.0.0.1",
>>>     "user": "user",
>>>     "passwd": "passw0rd",
>>>     "db_name": "testqueue",
>>>     # "name": "",
>>>     "port": 3306
>>> }
>>> q = persistqueue.MySQLQueue(name="testtable", **db_conf)
>>> 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.MySQLQueue(name="testtable", **db_conf)
>>> q.get()
'str2'
>>>

note

Due to the limitation of file queue described in issue #89, task_done in one thread may acknowledge items in other threads which should not be. Considering the SQLiteAckQueue if you have such requirement.

• v0.4.1: Currently only available for file based Queue
• v0.4.2: Also available for SQLite3 based Queues

>>> from persistqueue
>>> q = persistqueue.Queue('mypath', serializer=persistqueue.serializers.msgpack)
>>> # via cbor2
>>> # q = persistqueue.Queue('mypath', serializer=persistqueue.serializers.cbor2)
>>> # via json
>>> # q = Queue('mypath', serializer=persistqueue.serializers.json)
>>> q.get()
'b'
>>> q.task_done()

For some reasons, an application may require explicit reclamation for file handles or sql connections before end of execution. In these cases, user can simply call: .. code-block:: python

q = Queue() # or q = persistqueue.SQLiteQueue('mypath', auto_commit=True) del q

to reclaim related file handles or sql connections.

task_done is required both for file based queue and SQLite3 based queue (when auto_commit=False) to persist the cursor of next get to the disk.

• WAL

  Starting on v0.3.2, the persistqueue is leveraging the sqlite3 builtin feature WAL which can improve the performance significantly, a general testing indicates that persistqueue is 2-4 times faster than previous version.

• auto_commit=False

  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.

• pickle protocol selection

  From v0.3.6, the persistqueue will select Protocol version 2 for python2 and Protocol version 4 for python3 respectively. This selection only happens when the directory is not present when initializing the queue.

persist-queue use tox to trigger tests.

• Unit test

tox -e <PYTHON_VERSION>

Available <PYTHON_VERSION>: py27, py34, py35, py36, py37

• PEP8 check

tox -e pep8

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

Currently, the atomic operation is supported on Windows while still in experimental, 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 on Windows.

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 :).

BSD

Contributors

• sqlite3.OperationalError: database is locked is raised.

persistqueue 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.

• sqlite3 based queues are not thread-safe.

The sqlite3 queues are heavily tested under multi-threading environment, if you find it's not thread-safe, please make sure you set the multithreading=True when initializing the queue before submitting new issue:).