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!
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. There is also a connect_timeout
parameter which determines how long, in seconds, the socket will wait for the
server to respond to its initial connection attempt. If it is None, then
there will be no timeout; it defaults to the result of your system's
socket.getdefaulttimeout().
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 or bytes objects:
>>> beanstalk.put(42)
Traceback (most recent call last):
...
ValueError: Job body must be a str or bytes instance
There is no restriction on what characters you can put in a job body, so they can be used to hold arbitrary binary data:
>>> beanstalk_binary = beanstalkc.Connection(host='localhost', port=14711, encoding=None)
>>> _ = beanstalk.put(b'\x00\x01\xfe\xff')
>>> job = beanstalk_binary.reserve() ; print(repr(job.body).replace('b','')) ; job.delete()
'\x00\x01\xfe\xff'
When using python 2, bytes and string objects can be used for input
interchangably, and reserve will return string objects. Because python 3 is
stricter when it comes to encodings, the rules are slightly different there and
you you can specify an encoding to be used for job bodies. This encoding
defaults to your system's default encoding and can be set to None if you do
not want beanstalkc to try to encode/decode job bodies. In that case, put
will only accept bytes objects and reserve and the peek functions will
return bytes objects.
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:
useandusingaffect where jobs areput;watchandwatchingcontrol where jobs arereserved from.
Note that these concerns are fully orthogonal: for example, when you use a
tube, it is not automatically watched. Neither does watching a tube affect
the tube you are using.
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?')
3
>>> job = beanstalk.reserve()
Now we retrieve job-level statistics:
>>> from pprint import pprint
>>> pprint(job.stats()) # doctest: +ELLIPSIS
{'age': 0,
...
'id': 3,
...
'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() # doctest: +IGNORE_EXCEPTION_DETAIL
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')) # doctest: +ELLIPSIS
{...
'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()) # doctest: +ELLIPSIS
{...
'current-connections': 2,
'current-jobs-buried': 0,
'current-jobs-delayed': 0,
'current-jobs-ready': 0,
'current-jobs-reserved': 0,
'current-jobs-urgent': 0,
...}
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)
4
>>> 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
If you still have a handle to a job (or know its job ID), you can also kick alive this particular job, overriding the FIFO operation of the burial queue:
>>> job = beanstalk.reserve()
>>> job.bury()
>>> job.stats()['state']
'buried'
>>> job.kick()
>>> job.stats()['state']
'ready'
(NOTE: The kick-job command was introduced in beanstalkd v1.8.)
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 #4 hanging
around from our previous examples, so:
>>> job = beanstalk.peek(4)
>>> 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() # doctest: +IGNORE_EXCEPTION_DETAIL
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)
>>> job = beanstalk.peek_delayed()
>>> job.stats()['state']
'delayed'
... or buried:
>>> _ = beanstalk.put('o mores!')
>>> job = beanstalk.reserve()
>>> job.bury()
>>> job = beanstalk.peek_buried()
>>> job.stats()['state']
'buried'
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:
-
Jobs with lower priority numbers are reserved before jobs with higher priority numbers.
-
beanstalkd priorities are 32-bit unsigned integers (they range from 0 to 2**32 - 1).
-
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.
>>> 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.
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.
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') # doctest: +ELLIPSIS
'---\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') # doctest: +ELLIPSIS
['---', 'name: default', 'current-jobs-urgent: 0', ...]
>>> beanstalk.close()
This should come in handy if PyYAML simply does not fit your needs.