Qless is a powerful Redis
-based job queueing system inspired by
resque,
but built on a collection of Lua scripts, maintained in the
qless-core repo. Be sure to check the
changelog below.
A job
is a unit of work identified by a job id or jid
. A queue
can
contain several jobs that are scheduled to be run at a certain time, several
jobs that are waiting to run, and jobs that are currently running. A worker
is a process on a host, identified uniquely, that asks for jobs from the
queue, performs some process associated with that job, and then marks it as
complete. When it's completed, it can be put into another queue.
Jobs can only be in one queue at a time. That queue is whatever queue they were last put in. So if a worker is working on a job, and you move it, the worker's request to complete the job will be ignored.
A job can be canceled
, which means it disappears into the ether, and we'll
never pay it any mind every again. A job can be dropped
, which is when a
worker fails to heartbeat or complete the job in a timely fashion, or a job
can be failed
, which is when a host recognizes some systematically
problematic state about the job. A worker should only fail a job if the error
is likely not a transient one; otherwise, that worker should just drop it and
let the system reclaim it.
qless
automatically keeps statistics about how long jobs wait
to be processed and how long they take to be processed. Currently, we keep
track of the count, mean, standard deviation, and a histogram of these
times.Interest piqued? Then read on!
Install from pip:
pip install qless-py
Alternatively, install qless-py from source by checking it out from github, and checking out the qless-core submodule:
git clone git://github.com/seomoz/qless-py.git
cd qless-py
# qless-core is a submodule
git submodule init
git submodule update
sudo python setup.py install
You've read this far -- you probably want to write some code now and turn them
into jobs. Jobs are described essentially by two pieces of information -- a
classand
data`. The class should have static methods that know how to
process this type of job depending on the queue it's in. For those thrown for
a loop by this example, it's in reference to a
South Park
episode where a group of enterprising gnomes set on world domination through
three steps: 1) collect underpants, 2) ? 3) profit!
# In gnomes.py
class GnomesJob(object):
# This would be invoked when a GnomesJob is popped off the 'underpants' queue
@staticmethod
def underpants(job):
# 1) Collect Underpants
...
# Complete and advance to the next step, 'unknown'
job.complete('unknown')
@staticmethod
def unknown(job):
# 2) ?
...
# Complete and advance to the next step, 'profit'
job.complete('profit')
@staticmethod
def profit(job):
# 3) Profit
...
# Complete the job
job.complete()
This makes it easy to describe how a GnomesJob
might move through a pipeline,
first in the 'underpants' step, then 'unknown', and lastly 'profit.'
Alternatively, you can define a single method process
that knows how to
complete the job, no matter what queue it was popped from. The above is just
meant as a convenience for pipelines:
# Alternative gnomes.py
class GnomesJob(object):
# This method would be invoked at every stage
@staticmethod
def process(job):
if job['queue'] == 'underpants':
...
job.complete('underpants')
elif job['queue'] == 'unknown':
...
job.complete('profit')
elif job['queue'] == 'profit':
...
job.complete()
else:
job.fail('unknown-stage', 'What what?')
Jobs have user data associated with them that can be modified as it goes
through a pipeline. In general, you should make this data a dictionary, in
which case it's accessible through __getitem__
and __setitem__
. Otherwise,
it's accessible through job.data
. For example, you might update the data...
@staticmethod
def underpants(job):
# Record how many underpants we collected
job['collected'] = ...
@staticmethod
def unknown(job):
# Make some decision based on how many we've collected.
if job['collected'] ...:
...
Great! With all this in place, let's put them in the queue so that they can get run
import qless
# Connecting to localhost on 6379
client = qless.Client()
# Connecting to a remote machine
client = qless.Client('redis://foo.bar.com:1234')
Now, reference a queue, and start putting your gnomes to work:
queue = client.queues['underpants']
import gnomes
for i in range(1000):
queue.put(gnomes.GnomesJob, {})
Alternatively, if the job class is not importable from where you're adding jobs, you can use the full path of the job class as a string:
...
for i in range(1000):
queue.put('gnomes.GnomesJob', {})
By way of a quick note, it's important that your job class can be imported -- you can't create a job class in an interactive prompt, for example. You can add jobs in an interactive prompt, but just can't define new job types.
All that remains is to have workers actually run these jobs. This distribution comes with a script to help with this:
qless-py-worker -q underpants -q unknown -q profit
This script actually forks off several subprocesses that perform the work, and
the original process keeps tabs on them to ensure that they are all up and
running. In the future, the parent process might also perform other sanity
checks, but for the time being, it's just that the process is still alive. You
can specify the host
and port
you want to use for the qless server as well:
qless-py-worker --host foo.bar --port 1234 ...
In the absence of the --workers
argument, qless will spawn as many workers
as there are cores on the machine. The interval specifies how often to poll
in seconds) for work items. Future versions may have a mechanism to support
blocking pop.
qless-py-worker --workers 4 --interval 10
Because this works on a forked process model, it can be convenient to import
large modules before subprocesses are forked. Specify these with --import
:
qless-py-worker --import my.really.bigModule
Previous versions of qless-py
included a feature to have each worker process
run in its own sandbox directory. We've removed this feature because since
greenlets can't run in their own directory, the 'regular' and greenlet workers
behave differently.
In lieu of this behavior, each child process runs in its own sandboxed directory
and each job is given a sandbox
attribute which is the name of a directory for
the sole use of that job. It's guaranteed to be clean by the time the job is
performed, and it cleaned up afterwards.
For example, if you invoke:
qless-py-worker --workers 4 --greenlets 5 --workdir foo
Then four child processes will be spawned using the directories:
foo/qless-py-workers/sandbox-{0,1,2,3}
The jobs run by the greenlets in the first process are given their own sandboxes of the form:
foo/qless-py-workers/sandbox-0/greenlet-{0,1,2,3,4}
Some jobs are I/O-bound, and might want to, say, make use of a greenlet pool.
If you have a class where you've, say, monkey-patched socket
, you can ask
qless to create a pool of greenlets to run you job inside each process. To run
5 processes with 50 greenlets each:
qless-py-worker --workers 5 --greenlets 50
With a worker running, you can send signals to child processes to:
USR1
- Get the current stack trace in that workerUSR2
- Enter a debugger in that workerSo, for example, if one of the worker child processes is PID 1234
, then you
can invoke kill -USR1 1234
to get the backtrace in the logs (and console
output).
This is an experimental feature, but you can start workers --resume
flag
to have the worker begin its processing with the jobs it left off with. For
instance, during deployments, it's common to restart the worker processes, and
the --resume
flag has the worker first perform a check with qless
to see
which jobs it had last been running (and still has locks for).
This flag should be used with some caution. In particular, if two workers are
running with the same worker name, then this should not be used. The reason is
that through the qless
interface, it's impossible to differentiate the two,
and currently-running jobs may be confused with jobs that were simply dropped
when the worker was stopped.
Whenever a job is processed, it checks to see if the file in which your job is defined has been updated since its last import. If it has, it automatically reimports it. We think of this as a feature.
With this in mind, when I start a new project and want to make use of qless, I
first start up the web app locally (see
qless
for more), take a first pass, and
enqueue a single job while the worker is running:
# Supposing that I have /my/awesome/project/awesomeproject.py
# In one terminal...
qless-py-worker --path /my/awesome/project --queue foo --workers 1 --interval 10 --verbose
# In another terminal...
>>> import qless
>>> import awesomeproject
>>> qless.Client().queues['foo'].put(awesomeproject.Job, {'key': 'value'))
From there, I watch the output on the worker, adjust my job class, save it, watch again, etc., but without restarting the worker -- in general it shouldn't be necessary to restart the worker.
While in many cases the above is sufficient, there are also many cases where you may need something more. Hopefully after this section many of your questions will be answered.
Jobs can optionally have priority associated with them. Jobs of equal priority are popped in the order in which they were put in a queue. The higher the priority, the sooner it will be processed. If, for example, you get a new job to collect some really valuable underpants:
queue.put(qless.gnomes.GnomesJob, {'address': '123 Brief St.'}, priority = 10)
You can also adjust a job's priority while it's waiting:
job = client.jobs['83da4d32a0a811e1933012313b062cf1']
job.priority = 25
Jobs can also be scheduled for the future with a delay (in seconds). If for example, you just learned of an underpants heist opportunity, but you have to wait until later:
queue.put(qless.gnomes.GnomesJob, {}, delay=3600)
It's worth noting that it's not guaranteed that this job will run at that time. It merely means that this job will only be considered valid after the delay has passed, at which point it will be subject to the normal constraints. If you want it to be processed very soon after the delay expires, you could also boost its priority:
queue.put(qless.gnomes.GnomesJob, {}, delay=3600, priority=100)
Whether it's nightly maintainence, or weekly customer updates, you can have a job of a certain configuration set to recur. Recurring jobs still support priority, and tagging, and are attached to a queue. Let's say, for example, I need some global maintenance to run, and I don't care what machine runs it, so long as someone does:
client.queues['maintenance'].recur(myJob, {'tasks': ['sweep', 'mop', 'scrub']}, interval=60 * 60 * 24)
That will spawn a job right now, but it's possible you'd like to have it recur, but maybe the first job should wait a little bit:
client.queues['maintenance'].recur(..., interval=86400, offset=3600)
You can always update the tags, priority and even the interval of a recurring job:
job = client.jobs['83da4d32a0a811e1933012313b062cf1']
job.priority = 20
job.tag('foo', 'bar')
job.untag('hello')
job.interval = 7200
These attributes aren't attached to the recurring jobs, per se, but it's used as the template for the job that it creates. In the case where more than one interval passes before a worker tries to pop the job, more than one job is created. The thinking is that while it's completely client-managed, the state should not be dependent on how often workers are trying to pop jobs.
# Recur every minute
queue.recur(..., {'lots': 'of jobs'}, 60)
# Wait 5 minutes
len(queue.pop(10))
# => 5 jobs got popped
You can get and set global (read: in the context of the same Redis instance)
configuration to change the behavior for heartbeating, and so forth. There
aren't a tremendous number of configuration options, but an important one is
how long job data is kept around. Job data is expired after it has been
completed for jobs-history
seconds, but is limited to the last
jobs-history-count
completed jobs. These default to 50k jobs, and 30 days,
but depending on volume, your needs may change. To only keep the last 500 jobs
for up to 7 days:
client.config['jobs-history'] = 7 * 86400
client.config['jobs-history-count'] = 500
In qless, 'tracking' means flagging a job as important. Tracked jobs have a tab reserved for them in the web interface, and they also emit subscribable events as they make progress (more on that below). You can flag a job from the web interface, or the corresponding code:
client.jobs['b1882e009a3d11e192d0b174d751779d'].track()
Jobs can be tagged with strings which are indexed for quick searches. For example, jobs might be associated with customer accounts, or some other key that makes sense for your project.
queue.put(qless.gnomes.GnomesJob, {'tags': 'aplenty'}, tags=['12345', 'foo', 'bar'])
This makes them searchable in the web interface, or from code:
jids = client.jobs.tagged('foo')
You can add or remove tags at will, too:
job = client.jobs['b1882e009a3d11e192d0b174d751779d']
job.tag('howdy', 'hello')
job.untag('foo', 'bar')
Jobs can be made dependent on the completion of another job. For example, if you need to buy eggs, and buy a pan before making an omelete, you could say:
eggs_jid = client.queues['buy_eggs'].put(myJob, {'count': 12})
pan_jid = client.queues['buy_pan' ].put(myJob, {'coating': 'non-stick'})
client.queues['omelete'].put(myJob, {'toppings': ['onions', 'ham']}, depends=[eggs_jid, pan_jid])
That way, the job to make the omelete can't be performed until the pan and eggs purchases have been completed.
Tracked jobs emit events on specific pubsub channels as things happen to them. Whether it's getting popped off of a queue, completed by a worker, etc. The jist of it goes like this, though:
def callback(evt, jid):
print '%s => %s' % (jid, evt)
from functools import partial
for evt in ['canceled', 'completed', 'failed', 'popped', 'put', 'stalled', 'track', 'untrack']:
client.events.on(evt, partial(callback, evt))
client.events.listen()
If you're interested in, say, getting growl or campfire notifications, you
should check out the qless-growl
and qless-campfire
ruby gems.
Workers sometimes die. That's an unfortunate reality of life. We try to mitigate the effects of this by insisting that workers heartbeat their jobs to ensure that they do not get dropped. That said, qless will automatically requeue jobs that do get 'stalled' up to the provided number of retries (default is 5). Since underpants profit can sometimes go awry, maybe you want to retry a particular heist several times:
queue.put(qless.gnomes.GnomesJob, {}, retries=10)
A client pops one or more jobs from a queue:
# Get a single job
job = queue.pop()
# Get 20 jobs
jobs = queue.pop(20)
Each job object has a notion of when you must either check in with a heartbeat or turn it in as completed. You can get the absolute time until it expires, or how long you have left:
# When I have to heartbeat / complete it by (seconds since epoch)
job.expires_at
# How long until it expires
job.ttl
If your lease on the job will expire before you have a chance to complete it, then you should heartbeat it to make sure that no other worker gets access to it. Or, if you are done, you should complete it so that the job can move on:
# I call stay-offsies!
job.heartbeat()
# I'm done!
job.complete()
# I'm done with this step, but need to go into another queue
job.complete('anotherQueue')
One of the selling points of qless is that it keeps stats for you about your underpants hijinks. It tracks the average wait time, number of jobs that have waited in a queue, failures, retries, and average running time. It also keeps histograms for the number of jobs that have waited x time, and the number that took x time to run.
Frankly, these are best viewed using the web app.
Qless is a set of client language bindings, but the majority of the work is
done in a collection of Lua scripts that comprise the
core functionality. These scripts run
on the Redis 2.6+ server atomically and allow for portability with the same
functionality guarantees. Consult the documentation for qless-core
to learn
more about its internals.
Qless
also comes with a web app for administrative tasks, like keeping tabs
on the progress of jobs, tracking specific jobs, retrying failed jobs, etc.
It's available in the qless
library as a
mountable Sinatra
app. The web app is language
agnostic and was one of the major desires out of this project, so you should
consider using it even if you're not planning on using the Ruby client.
Things that have changed over time.
The major change was the switch to unified
qless. This change is
semi-incompatibile. In particular, it changes the job history format but the new
version knows how to convert the old format forward. Upgrades to your workers
should be made from the end of pipelines towards the start. It will also be
necessary to upgrade your qless-web
install if you're using it.
localhost:6379
, but rather than specify host
and port
, you
should provide a single host
argument of a Redis URL format. For example,
redis://user:auth@host:port/db
. Many of these paremeters are optional, but
it seems to be the convention recently.Some notes, instructions and potential road blocks to the upgrade. This version has much better coverage, and a few added features, including stalled job preemption, pauseable queues, unified sandboxing and the ability to use the cleaner web interface.
Before we talk about how to install the updated client, here are a couple potential road blocks that will need to be addressed before you can make the switch.
If you were using sandboxes (if using the non-greenlet client) and relying on
using the current working directory as the sandbox, that interface has been
done away with. The replacement is that each job comes with a sandbox
attribute which is guaranteed to be a directory that exists and empty at the
start of the job, and which is cleaned up after the job. It's a great place for
temporary files. This only applies if you are running a qless-worker, and not
if you are using the qless client directly to work on jobs.
The directories are made up of subdirectories under the directory provided as
--workdir
, defaulting to the current directory.
If you are using the qless
client directly, all instances of qless.client
will have to change to qless.Client
. It was an unfortunate mistake that it was
ever named client
to begin with, but hopefully this change won't be painful.
There was a feature request to be able to provide redis auth credentials, and rather than support any new attributes to the redis client that might come along, we'll now use a redis url.
For example:
# Instead of this
client = qless.Client(host='foo', port=6380)
# Now it's this
client = qless.Client(url='redis://foo:6380')
This allows users to provide auth, select a database, etc. Remember to change this in worker invocations and config files.
With an existing copy of qless-py
checked out
# Get the most recent version
git fetch
git checkout v0.10.0
# Checkout, update and build the submodule
git submodule init
git submodule update
make -C qless/qless-core
# Install dependencies and then qless
sudo pip install -r requirements.txt
sudo python setup.py install