ezpq 0.2.2
an easy parallel queueing system
License: MIT
Keywords: Parallel,Queue

``ezpq``: an easy parallel queueing system.
===========================================

   Read this on `GitHub <https://github.com/dm3ll3n/ezpq>`__ or `my
   site <https://www.donaldmellenbruch.com/project/ezpq/>`__.

-  `How to get it <#how-to-get-it>`__
-  `Overview <#overview>`__
-  `Features <#features>`__
-  `Quickstart <#quickstart>`__
-  `ezpq.Queue <#ezpq.queue>`__
-  `ezpq.Job <#ezpq.job>`__

   -  `put <#put>`__
   -  `size <#size>`__
   -  `wait <#wait>`__
   -  `get <#get>`__
   -  `collect <#collect>`__
   -  `map <#map>`__
   -  `starmap <#starmap>`__
   -  `startmapkw <#startmapkw>`__
   -  `dispose <#dispose>`__

-  `Synchronous Lanes <#synchronous-lanes>`__

   -  `Lane Error Handling <#lane-error-handling>`__

-  `ezpq.Plot <#ezpq.plot>`__
-  `More Examples <#more-examples>`__

How to get it
-------------

Install from `PyPI <https://pypi.org/project/ezpq/>`__ with:

.. code:: python

   pip install ezpq

Optional packages:

.. code:: python

   pip install pandas    # required for plots
   pip install plotnine  # required for plots
   pip install tqdm      # required for progress bars

Overview
--------

``ezpq`` implements a parallel queueing system consisting of:

1. a priority “waiting” queue in.
2. a lookup table of “working” jobs.
3. a priority “completed” queue out.

The queueing system uses ``multiprocessing.Process`` by default and can
also run jobs with ``threading.Thread``.

|image0|

Features
--------

-  Simple interface; pure Python.
-  No required dependencies outside of standard library.
-  Optional integration with ```tqdm`` <https://github.com/tqdm/tqdm>`__
   progress bars.
-  Compatible with Python 2 & 3.
-  Cross platform with MacOS, Linux, and Windows.
-  Data remains in-memory.
-  Priority Queueing, both in and out and within lanes.
-  Synchronous lanes allow dependent jobs to execute in the desired
   order.
-  Easily switch from processes to threads.
-  Automatic handling of output.
-  Rich job details, easily viewed as pandas dataframe.
-  Built-in logging to CSV.
-  Customizable visualizations of queue operations.

Quickstart
----------

Suppose you wanted to speed up the following code, which runs 60
operations that take anywhere from 0s to 2s. With an average job time of
~1s, this operation should take ~60s.

.. code:: python

   import time
   import random
   def random_sleep(x):
     random.seed(x)
     n = random.uniform(0.5, 1.5)
     time.sleep(n)
     return n

.. code:: python

   start = time.time()
   output = [random_sleep(x) for x in range(60)]
   end = time.time()
   print('> Runtime: ' + str(end - start))

::

   ## '> Runtime: 58.932034969329834'

Here is the function ran in parallel with an ``ezpq`` Queue of 6
workers. Thus, the runtime of the above operation will be reduced from
~60s to ~10s.

.. code:: python

   import time
   import random
   import ezpq
   start = time.time()
   with ezpq.Queue(6) as Q:
     output = Q.map(random_sleep, range(60))
   end = time.time()
   print('> Runtime: ' + str(end - start))

Here is the same scenario, using the ``@ezpq.Queue`` decorator.

.. code:: python

   @ezpq.Queue(6)
   def random_sleep(x):
       random.seed(x)
       n = random.uniform(0.5, 1.5)
       time.sleep(n)
       return n
   output = random_sleep(iterable=range(60))

While ``map()`` and the decorator are useful for quick-n-simple
parallization, the essential functions of an ``ezpq`` Queue include
``put()``, ``wait()``, and ``get()`` (or ``collect()``).

.. code:: python

   with ezpq.Queue(6) as Q:
       for x in range(60):
           Q.put(random_sleep, args=x)
       Q.wait()
       output = Q.collect()

The output is a list of dicts containing verbose information about each
job, along with its output, and exit code.

.. code:: python

   print( output[0] )

::

   ## {'args': [0],
   ##  'callback': None,
   ##  'cancelled': False,
   ##  'ended': datetime.datetime(2019, 3, 13, 0, 48, 52, 811248),
   ##  'exception': None,
   ##  'exitcode': 0,
   ##  'function': 'random_sleep',
   ##  'id': 1,
   ##  'kwargs': None,
   ##  'lane': None,
   ##  'name': 1,
   ##  'output': 1.3444218515250481,
   ##  'priority': 100,
   ##  'processed': datetime.datetime(2019, 3, 13, 0, 48, 52, 867387),
   ##  'qid': '13318d36',
   ##  'runtime': 1.3500409126281738,
   ##  'started': datetime.datetime(2019, 3, 13, 0, 48, 51, 461207),
   ##  'submitted': datetime.datetime(2019, 3, 13, 0, 48, 51, 357405),
   ##  'timeout': 0}

Easily convert output to a ``pandas`` dataframe:

.. code:: python

   import pandas as pd
   df = pd.DataFrame(output)
   print( df.head()[['id', 'output', 'runtime', 'exitcode']] )

::

   ##    id    output   runtime  exitcode
   ## 0   1  1.344422  1.350041         0
   ## 1   2  0.634364  0.638938         0
   ## 2   3  1.456034  1.459830         0
   ## 3   4  0.737965  0.741742         0
   ## 4   5  0.736048  0.739848         0

Use ``ezpq.Plot`` to generate a Gannt chart of the job timings.

.. code:: python

   plt = ezpq.Plot(output).build(show_legend=False)
   plt.save('docs/imgs/quickstart.png')

|image1|

ezpq.Queue
----------

The ``Queue`` class implements the queueing system, which is itself a
3-part system composed of the:

1. waiting queue
2. working table
3. completed queue


   <!-- -->

::

   ## Help on function __init__ in module ezpq.Queue:
   ## 
   ## __init__(self, n_workers=8, max_size=0, job_runner=<class 'multiprocessing.context.Process'>, auto_remove=False, auto_start=True, auto_stop=False, callback=None, log_file=None, poll=0.1, show_progress=False, qid=None)
   ##     Implements a parallel queueing system.
   ##     
   ##     Args:
   ##         n_workers: the max number of concurrent jobs.
   ##             - Accepts: int
   ##             - Default: cpu_count()
   ##         max_size: when > 0, will throw an exception the number of enqueued jobs exceeds this value. Otherwise, no limit.
   ##             - Accepts: int
   ##             - Default: 0 (unlimited)
   ##         job_runner: the class to use to invoke new jobs.
   ##             - Accepts: multiprocessing.Process, threading.Thread
   ##             - Default: multiprocessing.Process
   ##         auto_remove: controls whether jobs are discarded of after completion.
   ##             - Accepts: bool
   ##             - Default: False
   ##         auto_start: controls whether the queue system "pulse" is started upon instantiation (default), or manually.
   ##             - Accepts: bool
   ##             - Default: True
   ##         auto_stop: controls whether the queue system "pulse" stops itself after all jobs are complete.
   ##             - Accepts: bool
   ##             - Default: False
   ##         callback: optional function to execute synchronously immediately after a job completes.
   ##             - Accepts: function object
   ##             - Default: None
   ##         log_file: if file path is specified, job data is written to this path in CSV format.
   ##             - Accepts: str
   ##             - Default: None
   ##         poll: controls the pulse frequency; the amount of time slept between operations.
   ##             - Accepts: float
   ##             - Default: 0.1
   ##     
   ##     Returns:
   ##         ezpq.Queue object.
   ## 
   ## None

ezpq.Job
--------

A ``ezpq`` job defines the function to run. It is passed to an ``ezpq``
queue with a call to ``submit()``.

::

   ## Help on function __init__ in module ezpq.Job:
   ## 
   ## __init__(self, function, args=None, kwargs=None, name=None, priority=100, lane=None, timeout=0, suppress_errors=False, stop_on_lane_error=False)
   ##     Defines what to run within a `ezpq.Queue`, and how to run it.
   ##     
   ##     Args:
   ##         function: the function to run.
   ##             - Accepts: function object
   ##         args: optional positional arguments to pass to the function.
   ##             - Accepts: list, tuple
   ##             - Default: None
   ##         kwargs: optional keyword arguments to pass to the function.
   ##             - Accepts: dict
   ##             - Default: None
   ##         name: optional name to give to the job. Does not have to be unique.
   ##             - Accepts: str
   ##             - Default: None; assumes same name as job id.
   ##         priority: priority value to assign. Lower values get processed sooner.
   ##             - Accepts: int
   ##             - Default: 100
   ##         lane: a sequential lane to place the job in. if it does not already exist, it will be created.
   ##             - Accepts: int, str; any hashable object
   ##             - Default: None; no lanes.
   ##         timeout: When > 0, if this value (in seconds) is exceeded, the job is terminated. Otherwise, no limit enforced.
   ##             - Accepts: float
   ##             - Default: 0 (unlimited)
   ##     
   ##     Returns:
   ##         ezpq.Job object
   ## 
   ## None

.. code:: python

   with ezpq.Queue(6) as Q:
     for x in range(60):
       priority = x % 2 # give even numbers higher priority.
       job = ezpq.Job(random_sleep, args=x, priority=priority)
       Q.submit(job)
     Q.wait()
     output = Q.collect()

|image2|

put
~~~

The ``put`` method creates a job and submits it to an ``ezpq`` queue.
All of its arguments are passed to ``ezpq.Job()``.

.. code:: python

   with ezpq.Queue(6) as Q:
       for x in range(60):
           Q.put(random_sleep, args=x)
       Q.wait()
       output = Q.collect()

size
~~~~

``size()`` returns a count of all items across all three queue
components. It accepts three boolean parameters, ``waiting``,
``working``, and ``completed``. If all of these are ``False`` (default),
all jobs are counted. If any combination of these is ``True``, only the
corresponding queue(s) will be counted. For example:

.. code:: python

   def print_sizes(Q):
       msg = 'Total: {0}; Waiting: {1}; Working: {2}; Completed: {3}'.format(
           Q.size(),
           Q.size(waiting=True),
           Q.size(working=True),
           Q.size(completed=True)
       )
       print(msg)

.. code:: python

   with ezpq.Queue(6) as Q:
       # enqueue jobs
       for x in range(60):
           Q.put(random_sleep, x)
       # repeatedly print sizes until complete.
       while Q.size(waiting=True, working=True):
           print_sizes(Q)
           time.sleep(1)
       print_sizes(Q)

::

   ## 'Total: 60; Waiting: 60; Working: 0; Completed: 0'
   ## 'Total: 60; Waiting: 51; Working: 6; Completed: 3'
   ## 'Total: 60; Waiting: 46; Working: 6; Completed: 8'
   ## 'Total: 60; Waiting: 39; Working: 6; Completed: 15'
   ## 'Total: 60; Waiting: 34; Working: 6; Completed: 20'
   ## 'Total: 60; Waiting: 31; Working: 6; Completed: 23'
   ## 'Total: 60; Waiting: 24; Working: 6; Completed: 30'
   ## 'Total: 60; Waiting: 17; Working: 6; Completed: 37'
   ## 'Total: 60; Waiting: 11; Working: 6; Completed: 43'
   ## 'Total: 60; Waiting: 6; Working: 6; Completed: 48'
   ## 'Total: 60; Waiting: 0; Working: 5; Completed: 55'
   ## 'Total: 60; Waiting: 0; Working: 1; Completed: 59'
   ## 'Total: 60; Waiting: 0; Working: 0; Completed: 60'

wait
~~~~

The ``wait()`` method will block execution until all jobs complete. It
also accepts a ``timeout`` parameter, given in seconds. The return value
is the count of jobs that did not complete. Thus, a return value greater
than 0 indicates the timeout was exceeded. The parameter ``poll`` can be
used to adjust how frequently (in seconds) the operation checks for
completed jobs.

New in v0.2.0, include ``show_progress=True`` to show a progress bar
while waiting. This is equivalent to a call to ``waitpb()``.

|image3|

get
~~~

``get()`` retrieves and deletes (“pop”) the highest priority job from
the completed queue, if one is available. If the completed queue is
empty, ``get()`` returns ``None``. However, ``get()`` will wait for a
completed job if ``wait``, ``poll``, or ``timeout`` are specified. If
the timeout is exceeded, ``None`` is returned.

.. code:: python

   with ezpq.Queue(6) as Q:
       n_inputs = 60
       output = [None] * n_inputs
       # enqueue jobs
       for x in range(n_inputs):
           Q.put(random_sleep, args=x)

       # repeatedly `get()` until queue is empty.
       for i in range(n_inputs):
           output[i] = Q.get(wait=True)

collect
~~~~~~~

``collect()`` is similar to ``get()``, but it will return a list of
*all* completed jobs and clear the completed queue. It does not support
the ``poll`` or ``timeout`` parameters, but you can call ``wait()``
before ``collect()`` if desired.

.. code:: python

   with ezpq.Queue(6) as Q:
       # enqueue jobs
       for x in range(60):
           Q.put(random_sleep, x)
       # wait and collect all jobs
       print('Queue size before: {0}'.format(Q.size()))
       Q.wait()
       output = Q.collect()
       print('Queue size after: {0}'.format(Q.size()))
       print('Output size: {0}'.format(len(output)))

::

   ## 'Queue size before: 60'
   ## 'Queue size after: 0'
   ## 'Output size: 60'

map
~~~

``map`` encapsulates the logic of ``put``, ``wait``, and ``collect`` in
one call. Include ``show_progress=True`` to get output ``tqdm`` progress
bar.

|image4|

starmap
~~~~~~~

``starmap`` is similar to ``map``, but operates on a list of lists, with
each nested list being unpacked as arguments to the function.

.. code:: python

   def my_pow(x, k):
       return '{}^{} = {}'.format(x, k, x**k)
   # list of lists to iterate over.
   args_list = [[x, x%4] # (x, k)
               for x in range(100)]
   # starmap
   with ezpq.Queue(10) as Q:
       output = Q.starmap(my_pow, iterable=args_list)

   [x['output'] for x in output[:10]]

startmapkw
~~~~~~~~~~

Same as ``starmap``, but operations on a list of *dicts* to be expanded
as kwargs to the function.

.. code:: python

   def my_pow(x, k):
       return '{}^{} = {}'.format(x, k, x**k)
   # list of dicts to iterate over.
   kwargs_list = [{ 'x':x, 'k':x%4 } # (x, k)
                   for x in range(100)]
   # starmapkw
   with ezpq.Queue(10) as Q:
       output = Q.starmapkw(my_pow, iterable=kwargs_list)

   [x['output'] for x in output[:10]]

dispose
~~~~~~~

The queueing operations performed by ``ezpq.Queue`` are performed on a
periodic basis. By default, the ``poll`` parameter for a Queue is
``0.1`` seconds. This “pulse” thread will continue firing until the
Queue is disposed of.

In the previous examples, use of the context manager
(``with ezpq.Queue() as Q:``) results in automatic disposal. If not
using the context manager (or decorator), clean up after yourself with
``dispose()``.

Synchronous Lanes
-----------------

When you have jobs that are dependent upon another, you can use “lanes”
to execute them in sequence. All that is required is an arbitrary lane
name/id passed to the ``lane`` parameter of ``put``. Empty lanes are
automatically removed.

|image5|

In the above graphic, notice how same-colored bars never overlap. These
bars represent jobs that are in the same lane, which executed
synchronously.

Lane Error Handling
~~~~~~~~~~~~~~~~~~~

You may want to short-circuit a synchronous lane if a job in the lane
fails. You can do this by specifying ``stop_on_lane_error=True`` when
putting a job in the queue. If specified and the preceding job has a
non-zero exit code, this job will not be run.

.. code:: python

   def reciprocal(x):
       time.sleep(0.1) # slow things down
       return 1/x      # will throw DivideByZero exception

.. code:: python

   import random
   with ezpq.Queue(6) as Q:
       for i in range(100):
           Q.put(reciprocal, random.randint(0, 10), lane=i%5, suppress_errors=True, stop_on_lane_error=True)
       Q.wait()
       output = Q.collect()
   plt = ezpq.Plot(output).build(facet_by='lane', color_by='exitcode', color_pal=['red', 'blue'])
   plt.save('docs/imgs/lane_error.png')

|image6|

ezpq.Plot
---------

The ``Plot`` class is used to visualize the wait, start, and end times
for each job that entered the queueing system. The class is initialized
with a list of dicts; exactly what is returned from a call to
``collect()`` or ``map()``.

Arguments given to ``build()`` control various aspects of the plot, from
coloring, to faceting,

::

   ## Help on function build in module ezpq.Plot:
   ## 
   ## build(self, color_by='qid', facet_by='qid', facet_scale='fixed', show_legend=True, bar_width=1, title=None, color_pal=None, theme='bw')
   ##     Produces a plot based on the data and options provided to a `ezpq.Plot()` object.
   ##     
   ##     Args:
   ##         color_by: controls the column to use for coloring the bars.
   ##             - Accepts: one of 'qid', 'priority', 'lane', 'cancelled', 'exitcode', 'name', 'output'
   ##             - Default: 'qid'
   ##         facet_by: controls the column to use for facetting the plot.
   ##             - Accepts: one of 'qid', 'priority', 'lane', 'cancelled', 'exitcode', 'name', 'output'
   ##             - Default: 'qid'
   ##         facet_scale: controls the scale of the x/y axis across facets.
   ##             - Accepts: one of 'fixed', 'free', 'free_x', 'free_y'
   ##             - Default: 'fixed'
   ##         show_legend: controls whether the legend is drawn.
   ##             - Accepts: bool
   ##             - Default: True
   ##         bar_width: controls the bar width
   ##             - Accepts: float
   ##             - Default: 1
   ##         title: optional title to be drawn above the plot.
   ##             - Accepts: str, None
   ##             - Default: None
   ##         theme:
   ##             - Accepts: 'bw', 'classic', 'gray', 'grey', 'seaborn', '538', 'dark', 'matplotlib', 'minimal', 'xkcd', 'light'
   ##             - Default: 'bw'
   ##     Returns:
   ##         The plot produced from plotnine.ggplot().
   ## 
   ## None

.. code:: python

   with ezpq.Queue(6) as Q:
     for x in range(60):
       lane = x % 5
       Q.put(random_sleep, x, timeout=1, lane=lane)
     Q.wait()
     output = Q.collect()

.. code:: python

   plt = ezpq.Plot(output).build(facet_by='lane', show_legend=False)
   plt.save('docs/imgs/lanes2.png')

|image7|

Each horizontal bar represents an independent job id. The start of the
gray bar indicates when the job entered the queuing system. The start of
the colored bar indicates when the job started running, and when it
ended. The gray bar that follows (if any) reflects how long it took for
the queue operations to recognize the finished job, join the job data
with its output, remove it from the working table, and place it in the
completed queue.

More Examples
-------------

Many more examples can be found in
`docs/examples.ipynb <//github.com/dm3ll3n/ezpq/blob/master/docs/examples.ipynb>`__.

.. |image0| image:: docs/imgs/ezpq.png
.. |image1| image:: docs/imgs/quickstart.png
.. |image2| image:: docs/imgs/submit.png
.. |image3| image:: docs/imgs/tqdm.gif
.. |image4| image:: docs/imgs/tqdm_map.gif
.. |image5| image:: docs/imgs/lanes.gif
.. |image6| image:: docs/imgs/lane_error.png
.. |image7| image:: docs/imgs/lanes2.png