TUTORIAL

Welcome, dear stranger, to a tour de force through beanstalkd's capabilities.
Say hello to your fellow travel companion, the beanstalkc client library for
Python. You'll get to know each other fairly well during this trip, so better
start off on a friendly note. And now, let's go!

Getting Started
---------------

You'll need beanstalkd listening at port 14711 to follow along. So simply start
it using: `beanstalkd -l 127.0.0.1 -p 14711`

Besides having beanstalkc installed, you'll typically also need PyYAML. If you
insist, you can also use beanstalkc without PyYAML. For more details see
Appendix A of this tutorial.

To use beanstalkc we have to import the library and set up a connection to an
(already running) beanstalkd server:

    >>> import beanstalkc
    >>> beanstalk = beanstalkc.Connection(host='localhost', port=14711)

If we leave out the `host` and/or `port` parameters, `'localhost'` and `11300`
would be used as defaults, respectively.


Basic Operation
---------------

Now that we have a connection set up, we can enqueue jobs:

    >>> beanstalk.put('hey!')
    1

Or we can request jobs:

    >>> job = beanstalk.reserve()
    >>> job.body
    'hey!'

Once we are done with processing a job, we have to mark it as done, otherwise
jobs are re-queued by beanstalkd after a "time to run" (120 seconds, per
default) is surpassed. A job is marked as done, by calling `delete`:

    >>> job.delete()

`reserve` blocks until a job is ready, possibly forever. If that is not desired,
we can invoke `reserve` with a timeout (in seconds) how long we want to wait to
receive a job. If such a `reserve` times out, it will return `None`:

    >>> beanstalk.reserve(timeout=0) is None
    True

If you use a timeout of 0, `reserve` will immediately return either a job or
`None`.

Note that beanstalkc requires job bodies to be strings, conversion to/from
strings is left up to you:

    >>> beanstalk.put(42)
    Traceback (most recent call last):
    ...
    AssertionError: Job body must be a str instance


Tube Management
---------------

A single beanstalkd server can provide many different queues, called "tubes" in
beanstalkd. To see all available tubes:

    >>> beanstalk.tubes()
    ['default']

A beanstalkd client can choose one tube into which its job are put. This is the
tube "used" by the client. To see what tube you are currently using:

    >>> beanstalk.using()
    'default'

Unless told otherwise, a client uses the 'default' tube. If you want to use a
different tube:

    >>> beanstalk.use('foo')
    'foo'
    >>> beanstalk.using()
    'foo'

If you decide to use a tube, that does not yet exist, the tube is automatically
created by beanstalkd:

    >>> beanstalk.tubes()
    ['default', 'foo']

Of course, you can always switch back to the default tube. Tubes that don't have
any client using or watching, vanish automatically:

    >>> beanstalk.use('default')
    'default'
    >>> beanstalk.using()
    'default'
    >>> beanstalk.tubes()
    ['default']

Further, a beanstalkd client can choose many tubes to reserve jobs from. These
tubes are "watched" by the client. To see what tubes you are currently watching:

    >>> beanstalk.watching()
    ['default']

To watch an additional tube:

    >>> beanstalk.watch('bar')
    2
    >>> beanstalk.watching()
    ['default', 'bar']

As before, tubes that do not yet exist are created automatically once you start
watching them:

    >>> beanstalk.tubes()
    ['default', 'bar']

To stop watching a tube:

    >>> beanstalk.ignore('bar')
    1
    >>> beanstalk.watching()
    ['default']

You can't watch zero tubes. So if you try to ignore the last tube you are
watching, this is silently ignored:

    >>> beanstalk.ignore('default')
    1
    >>> beanstalk.watching()
    ['default']

To recap: each beanstalkd client manages two separate concerns: which tube
newly created jobs are put into, and which tube(s) jobs are reserved from.
Accordingly, there are two separate sets of functions for these concerns:

  - `use` and `using` affect where jobs are `put`;
  - `watch` and `watching` control where jobs are `reserve`d from.

Note that these concerns are fully orthogonal: for example, when you `use` a
tube, it is not automatically `watch`ed. Neither does `watch`ing a tube affect
the tube you are `using`.


Statistics
----------

Beanstalkd accumulates various statistics at the server, tube and job level.
Statistical details for a job can only be retrieved during the job's lifecycle.
So let's create another job:

    >>> beanstalk.put('ho?')
    2

    >>> job = beanstalk.reserve()

Now we retrieve job-level statistics:

    >>> from pprint import pprint
    >>> pprint(job.stats())
    {'age': 0,
     ...
     'id': 2,
     ...
     'state': 'reserved',
     ...
     'tube': 'default'}

If you try to access job stats after the job was deleted, you'll get a
`CommandFailed` exception:

    >>> job.delete()
    >>> job.stats()
    Traceback (most recent call last):
    ...
    CommandFailed: ('stats-job', 'NOT_FOUND', [])

Let's have a look at some numbers for the `'default'` tube:

    >>> pprint(beanstalk.stats_tube('default'))
    {...
     'current-jobs-ready': 0,
     'current-jobs-reserved': 0,
     'current-jobs-urgent': 0,
     ...
     'name': 'default',
     ...}

Finally, there's an abundant amount of server-level statistics accessible via
the `Connection`'s `stats` method. We won't go into details here, but:

    >>> pprint(beanstalk.stats())
    {...
     'current-connections': 1,
     'current-jobs-buried': 0,
     'current-jobs-delayed': 0,
     'current-jobs-ready': 0,
     'current-jobs-reserved': 0,
     'current-jobs-urgent': 0,
     ...}


Advanced Operation
------------------

In "Basic Operation" above, we discussed the typical lifecycle of a job:

     put            reserve               delete
    -----> [READY] ---------> [RESERVED] --------> *poof*


    (This picture was taken from beanstalkd's protocol documentation. It is
    originally contained in `protocol.txt`, part of the beanstalkd
    distribution.) #doctest:+SKIP

But besides `ready` and `reserved`, a job can also be `delayed` or `buried`.
Along with those states come a few transitions, so the full picture looks like
the following:

       put with delay               release with delay
      ----------------> [DELAYED] <------------.
                            |                   |
                            | (time passes)     |
                            |                   |
       put                  v     reserve       |       delete
      -----------------> [READY] ---------> [RESERVED] --------> *poof*
                           ^  ^                |  |
                           |   \  release      |  |
                           |    `-------------'   |
                           |                      |
                           | kick                 |
                           |                      |
                           |       bury           |
                        [BURIED] <---------------'
                           |
                           |  delete
                            `--------> *poof*


      (This picture was taken from beanstalkd's protocol documentation. It is
      originally contained in `protocol.txt`, part of the beanstalkd
      distribution.) #doctest:+SKIP

Now let's have a practical look at those new possibilities. For a start, we can
create a job with a delay. Such a job will only be available for reservation
once this delay passes:

    >>> beanstalk.put('yes!', delay=1)
    3

    >>> beanstalk.reserve(timeout=0) is None
    True

    >>> job = beanstalk.reserve(timeout=1)
    >>> job.body
    'yes!'

If we are not interested in a job anymore (e.g. after we failed to
process it), we can simply release the job back to the tube it came from:

    >>> job.release()
    >>> job.stats()['state']
    'ready'

Want to get rid of a job? Well, just "bury" it! A buried job is put aside and is
therefore not available for reservation anymore:

    >>> job = beanstalk.reserve()
    >>> job.bury()
    >>> job.stats()['state']
    'buried'

    >>> beanstalk.reserve(timeout=0) is None
    True

Buried jobs are maintained in a special FIFO-queue outside of the normal job
processing lifecycle until they are kicked alive again:

    >>> beanstalk.kick()
    1

You can request many jobs to be kicked alive at once, `kick`'s return value will
tell you how many jobs were actually kicked alive again:

    >>> beanstalk.kick(42)
    0


Inspecting Jobs
---------------

Besides reserving jobs, a client can also "peek" at jobs. This allows to inspect
jobs without modifying their state. If you know the `id` of a job you're
interested, you can directly peek at the job. We still have job #3 hanging
around from our previous examples, so:

    >>> job = beanstalk.peek(3)
    >>> job.body
    'yes!'

Note that this peek did *not* reserve the job:

    >>> job.stats()['state']
    'ready'

If you try to peek at a non-existing job, you'll simply see nothing:

    >>> beanstalk.peek(42) is None
    True

If you are not interested in a particular job, but want to see jobs according to
their state, beanstalkd also provides a few commands. To peek at the same job
that would be returned by `reserve` -- the next ready job -- use `peek-ready`:

    >>> job = beanstalk.peek_ready()
    >>> job.body
    'yes!'

Note that you can't release, or bury a job that was not reserved by you. Those
requests on unreserved jobs are silently ignored:

    >>> job.release()
    >>> job.bury()

    >>> job.stats()['state']
    'ready'

You can, though, delete a job that was not reserved by you:

    >>> job.delete()
    >>> job.stats()
    Traceback (most recent call last):
    ...
    CommandFailed: ('stats-job', 'NOT_FOUND', [])

Finally, you can also peek into the special queues for jobs that are delayed:

    >>> beanstalk.put('o tempores', delay=120)
    4
    >>> job = beanstalk.peek_delayed()
    >>> job.stats()['state']
    'delayed'

... or buried:

    >>> beanstalk.put('o mores!')
    5
    >>> job = beanstalk.reserve()
    >>> job.bury()

    >>> job = beanstalk.peek_buried()
    >>> job.stats()['state']
    'buried'


Job Priorities
--------------

Without job priorities, beanstalkd operates as a FIFO queue:

    >>> _ = beanstalk.put('1')
    >>> _ = beanstalk.put('2')

    >>> job = beanstalk.reserve() ; print job.body ; job.delete()
    1
    >>> job = beanstalk.reserve() ; print job.body ; job.delete()
    2

If need arises, you can override this behaviour by giving different jobs
different priorities. There are three hard facts to know about job priorities:

  1. Jobs with lower priority numbers are reserved before jobs with higher
  priority numbers.

  2. beanstalkd priorities are 32-bit unsigned integers (they range from 0 to
  2**32 - 1).

  3. beanstalkc uses 2**31 as default job priority
  (`beanstalkc.DEFAULT_PRIORITY`).

To create a job with a custom priority, use the keyword-argument `priority`:

    >>> _ = beanstalk.put('foo', priority=42)
    >>> _ = beanstalk.put('bar', priority=21)
    >>> _ = beanstalk.put('qux', priority=21)

    >>> job = beanstalk.reserve() ; print job.body ; job.delete()
    bar
    >>> job = beanstalk.reserve() ; print job.body ; job.delete()
    qux
    >>> job = beanstalk.reserve() ; print job.body ; job.delete()
    foo

Note how `'bar'` and `'qux'` left the queue before `'foo'`, even though they
were enqueued well after `'foo'`. Note also that within the same priority jobs
are still handled in a FIFO manner.


Fin!
----

    >>> beanstalk.close()

That's it, for now. We've left a few capabilities untouched (touch and
time-to-run). But if you've really read through all of the above, send me a
message and tell me what you think of it. And then go get yourself a treat. You
certainly deserve it.


Appendix A: beanstalkc and YAML
-------------------------------

As beanstalkd uses YAML for diagnostic information (like the results of
`stats()` or `tubes()`), you'll typically need [PyYAML](). Depending on your
performance needs, you may want to supplement that with the [libyaml]() C
extension.

[PyYAML]: http://pyyaml.org/
[libyaml]: http://pyyaml.org/wiki/LibYAML

If, for whatever reason, you cannot use PyYAML, you can still use beanstalkc
and just leave the YAML responses unparsed. To do that, pass `parse_yaml=False`
when creating the `Connection`:

    >>> beanstalk = beanstalkc.Connection(host='localhost',
    ...                                   port=14711,
    ...                                   parse_yaml=False)

    >>> beanstalk.tubes()
    '---\n- default\n'

    >>> beanstalk.stats_tube('default')
    '---\nname: default\ncurrent-jobs-urgent: 0\ncurrent-jobs-ready: 0\n...'

    >>> beanstalk.close()

This possibility is mostly useful if you don't use the introspective
capabilities of beanstalkd (`Connection#tubes`, `Connection#watching`,
`Connection#stats`, `Connection#stats_tube`, and `Job#stats`).

Alternatively, you can also pass a function to be used as YAML parser:

    >>> beanstalk = beanstalkc.Connection(host='localhost',
    ...                                   port=14711,
    ...                                   parse_yaml=lambda x: x.split('\n'))

    >>> beanstalk.tubes()
    ['---', '- default', '']

    >>> beanstalk.stats_tube('default')
    ['---', 'name: default', 'current-jobs-urgent: 0', ...]

    >>> beanstalk.close()

This should come in handy if PyYAML simply does not fit your needs.