Changed docs to use Github compatible code block markup

docs/docs/connections.md

@@ -23,20 +23,20 @@ pass in Redis connection references to queues directly.
 
 In development mode, to connect to a default, local Redis server:
 
-{% highlight python %}
+```python
 from rq import use_connection
 use_connection()
-{% endhighlight %}
+```
 
 In production, to connect to a specific Redis server:
 
-{% highlight python %}
+```python
 from redis import Redis
 from rq import use_connection
 
 redis = Redis('my.host.org', 6789, password='secret')
 use_connection(redis)
-{% endhighlight %}
+```
 
 Be aware of the fact that `use_connection` pollutes the global namespace.  It
 also implies that you can only ever use a single connection.
@@ -59,7 +59,7 @@ Each RQ object instance (queues, workers, jobs) has a `connection` keyword
 argument that can be passed to the constructor.  Using this, you don't need to
 use `use_connection()`.  Instead, you can create your queues like this:
 
-{% highlight python %}
+```python
 from rq import Queue
 from redis import Redis
 
@@ -68,7 +68,7 @@ conn2 = Redis('remote.host.org', 9836)
 
 q1 = Queue('foo', connection=conn1)
 q2 = Queue('bar', connection=conn2)
-{% endhighlight %}
+```
 
 Every job that is enqueued on a queue will know what connection it belongs to.
 The same goes for the workers.
@@ -85,7 +85,7 @@ default connection to be used.
 
 An example will help to understand it:
 
-{% highlight python %}
+```python
 from rq import Queue, Connection
 from redis import Redis
 
@@ -98,7 +98,7 @@ with Connection(Redis('localhost', 6379)):
 assert q1.connection != q2.connection
 assert q2.connection != q3.connection
 assert q1.connection == q3.connection
-{% endhighlight %}
+```
 
 You can think of this as if, within the `Connection` context, every newly
 created RQ object instance will have the `connection` argument set implicitly.
@@ -112,7 +112,7 @@ If your code does not allow you to use a `with` statement, for example, if you
 want to use this to set up a unit test, you can use the `push_connection()` and
 `pop_connection()` methods instead of using the context manager.
 
-{% highlight python %}
+```python
 import unittest
 from rq import Queue
 from rq import push_connection, pop_connection
@@ -127,8 +127,7 @@ class MyTest(unittest.TestCase):
     def test_foo(self):
         """Any queues created here use local Redis."""
         q = Queue()
-        ...
+```
-{% endhighlight %}
 
 ### Sentinel support
 
@@ -136,10 +135,10 @@ To use redis sentinel, you must specify a dictionary in the configuration file.
 Using this setting in conjunction with the systemd or docker containers with the
 automatic restart option allows workers and RQ to have a fault-tolerant connection to the redis.
 
-{% highlight python %}
+```python
 SENTINEL: {'INSTANCES':[('remote.host1.org', 26379), ('remote.host2.org', 26379), ('remote.host3.org', 26379)],
            'SOCKET_TIMEOUT': None,
            'PASSWORD': 'secret',
            'DB': 2,
            'MASTER_NAME': 'master'}
-{% endhighlight %}
+```

docs/docs/exceptions.md

@@ -23,29 +23,27 @@ exception occurs.
 
 This is how you register custom exception handler(s) to an RQ worker:
 
-{% highlight python %}
+```python
 from rq.handlers import move_to_failed_queue  # RQ's default exception handler
 
 w = Worker([q], exception_handlers=[my_handler, move_to_failed_queue])
-...
+```
-{% endhighlight %}
 
 The handler itself is a function that takes the following parameters: `job`,
 `exc_type`, `exc_value` and `traceback`:
 
-{% highlight python %}
+```python
 def my_handler(job, exc_type, exc_value, traceback):
     # do custom things here
     # for example, write the exception info to a DB
-    ...
+
-{% endhighlight %}
+```
 
 You might also see the three exception arguments encoded as:
 
 ```python
 def my_handler(job, *exc_info):
     # do custom things here
-    ...
 ```
 
 ## Chaining exception handlers
@@ -63,7 +61,7 @@ as `True` (i.e.  continue with the next handler).
 To replace the default behaviour (i.e. moving the job to the `failed` queue),
 use a custom exception handler that doesn't fall through, for example:
 
-{% highlight python %}
+```python
 def black_hole(job, *exc_info):
     return False
-{% endhighlight %}
+```

docs/docs/index.md

@@ -13,13 +13,13 @@ arguments onto a queue.  This is called _enqueueing_.
 
 To put jobs on queues, first declare a function:
 
-{% highlight python %}
+```python
 import requests
 
 def count_words_at_url(url):
     resp = requests.get(url)
     return len(resp.text.split())
-{% endhighlight %}
+```
 
 Noticed anything?  There's nothing special about this function!  Any Python
 function call can be put on an RQ queue.
@@ -27,7 +27,7 @@ function call can be put on an RQ queue.
 To put this potentially expensive word count for a given URL in the background,
 simply do this:
 
-{% highlight python %}
+```python
 from rq import Queue
 from redis import Redis
 from somewhere import count_words_at_url
@@ -43,14 +43,14 @@ print(job.result)   # => None
 # Now, wait a while, until the worker is finished
 time.sleep(2)
 print(job.result)   # => 889
-{% endhighlight %}
+```
 
 If you want to put the work on a specific queue, simply specify its name:
 
-{% highlight python %}
+```python
 q = Queue('low', connection=redis_conn)
 q.enqueue(count_words_at_url, 'http://nvie.com')
-{% endhighlight %}
+```
 
 Notice the `Queue('low')` in the example above?  You can use any queue name, so
 you can quite flexibly distribute work to your own desire.  A common naming
@@ -78,28 +78,28 @@ job function.
 In the last case, it may be advantageous to instead use the explicit version of
 `.enqueue()`, `.enqueue_call()`:
 
-{% highlight python %}
+```python
 q = Queue('low', connection=redis_conn)
 q.enqueue_call(func=count_words_at_url,
                args=('http://nvie.com',),
                timeout=30)
-{% endhighlight %}
+```
 
 For cases where the web process doesn't have access to the source code running
 in the worker (i.e. code base X invokes a delayed function from code base Y),
 you can pass the function as a string reference, too.
 
-{% highlight python %}
+```python
 q = Queue('low', connection=redis_conn)
 q.enqueue('my_package.my_module.my_func', 3, 4)
-{% endhighlight %}
+```
 
 
 ## Working with Queues
 
 Besides enqueuing jobs, Queues have a few useful methods:
 
-{% highlight python %}
+```python
 from rq import Queue
 from redis import Redis
 
@@ -117,7 +117,7 @@ job = q.fetch_job('my_id') # Returns job having ID "my_id"
 # Deleting the queue
 q.delete(delete_jobs=True) # Passing in `True` will remove all jobs in the queue
 # queue is unusable now unless re-instantiated
-{% endhighlight %}
+```
 
 
 ### On the Design
@@ -151,7 +151,7 @@ of course).
 If you're familiar with Celery, you might be used to its `@task` decorator.
 Starting from RQ >= 0.3, there exists a similar decorator:
 
-{% highlight python %}
+```python
 from rq.decorators import job
 
 @job('low', connection=my_redis_conn, timeout=5)
@@ -161,7 +161,7 @@ def add(x, y):
 job = add.delay(3, 4)
 time.sleep(1)
 print(job.result)
-{% endhighlight %}
+```
 
 
 ## Bypassing workers
@@ -170,12 +170,12 @@ For testing purposes, you can enqueue jobs without delegating the actual
 execution to a worker (available since version 0.3.1). To do this, pass the
 `is_async=False` argument into the Queue constructor:
 
-{% highlight pycon %}
+```python
 >>> q = Queue('low', is_async=False, connection=my_redis_conn)
 >>> job = q.enqueue(fib, 8)
 >>> job.result
 21
-{% endhighlight %}
+```
 
 The above code runs without an active worker and executes `fib(8)`
 synchronously within the same process. You may know this behaviour from Celery
@@ -188,11 +188,11 @@ a redis instance for storing states related to job execution and completion.
 New in RQ 0.4.0 is the ability to chain the execution of multiple jobs.
 To execute a job that depends on another job, use the `depends_on` argument:
 
-{% highlight python %}
+```python
 q = Queue('low', connection=my_redis_conn)
 report_job = q.enqueue(generate_report)
 q.enqueue(send_report, depends_on=report_job)
-{% endhighlight %}
+```
 
 The ability to handle job dependencies allows you to split a big job into
 several smaller ones. A job that is dependent on another is enqueued only when

docs/docs/jobs.md

@@ -13,14 +13,14 @@ jobs.
 All job information is stored in Redis. You can inspect a job and its attributes
 by using `Job.fetch()`.
 
-{% highlight python %}
+```python
 from redis import Redis
 from rq.job import Job
 
 connection = Redis()
 job = Job.fetch('my_job_id', connection=redis)
 print('Status: %s' $ job.get_status())
-{% endhighlight %}
+```
 
 Some interesting job attributes include:
 * `job.status`
@@ -38,14 +38,14 @@ Some interesting job attributes include:
 Since job functions are regular Python functions, you have to ask RQ for the
 current job ID, if any.  To do this, you can use:
 
-{% highlight python %}
+```python
 from rq import get_current_job
 
 def add(x, y):
     job = get_current_job()
     print('Current job: %s' % (job.id,))
     return x + y
-{% endhighlight %}
+```
 
 
 ## Storing arbitrary data on jobs
@@ -56,7 +56,7 @@ To add/update custom status information on this job, you have access to the
 `meta` property, which allows you to store arbitrary pickleable data on the job
 itself:
 
-{% highlight python %}
+```python
 import socket
 
 def add(x, y):
@@ -67,7 +67,7 @@ def add(x, y):
     # do more work
     time.sleep(1)
     return x + y
-{% endhighlight %}
+```
 
 
 ## Time to live for job in queue
@@ -77,13 +77,13 @@ _New in version 0.4.7._
 A job has two TTLs, one for the job result and one for the job itself. This means that if you have
 job that shouldn't be executed after a certain amount of time, you can define a TTL as such:
 
-{% highlight python %}
+```python
 # When creating the job:
 job = Job.create(func=say_hello, ttl=43)
 
 # or when queueing a new job:
 job = q.enqueue(count_words_at_url, 'http://nvie.com', ttl=43)
-{% endhighlight %}
+```
 
 
 ## Failed Jobs
@@ -92,7 +92,7 @@ If a job fails and raises an exception, the worker will put the job in a failed
 On the Job instance, the `is_failed` property will be true. To fetch all failed jobs, scan
 through the `get_failed_queue()` queue.
 
-{% highlight python %}
+```python
 from redis import StrictRedis
 from rq import push_connection, get_failed_queue, Queue
 from rq.job import Job
@@ -115,4 +115,4 @@ fq.requeue(job.id)
 
 assert fq.count == 0
 assert Queue('fake').count == 1
-{% endhighlight %}
+```

docs/docs/monitoring.md

@@ -13,10 +13,10 @@ which looks like this:
 
 To install, just do:
 
-{% highlight console %}
+```console
 $ pip install rq-dashboard
 $ rq-dashboard
-{% endhighlight %}
+```
 
 It can also be integrated easily in your Flask app.
 
@@ -25,7 +25,7 @@ It can also be integrated easily in your Flask app.
 
 To see what queues exist and what workers are active, just type `rq info`:
 
-{% highlight console %}
+```console
 $ rq info
 high       |██████████████████████████ 20
 low        |██████████████ 12
@@ -36,14 +36,14 @@ Bricktop.19233 idle: low
 Bricktop.19232 idle: high, default, low
 Bricktop.18349 idle: default
 3 workers, 3 queues
-{% endhighlight %}
+```
 
 
 ## Querying by queue names
 
 You can also query for a subset of queues, if you're looking for specific ones:
 
-{% highlight console %}
+```console
 $ rq info high default
 high       |██████████████████████████ 20
 default    |█████████ 8
@@ -52,7 +52,7 @@ default    |█████████ 8
 Bricktop.19232 idle: high, default
 Bricktop.18349 idle: default
 2 workers, 2 queues
-{% endhighlight %}
+```
 
 
 ## Organising workers by queue
@@ -60,7 +60,7 @@ Bricktop.18349 idle: default
 By default, `rq info` prints the workers that are currently active, and the
 queues that they are listening on, like this:
 
-{% highlight console %}
+```console
 $ rq info
 ...
 
@@ -68,12 +68,12 @@ Mickey.26421 idle: high, default
 Bricktop.25458 busy: high, default, low
 Turkish.25812 busy: high, default
 3 workers, 3 queues
-{% endhighlight %}
+```
 
 To see the same data, but organised by queue, use the `-R` (or `--by-queue`)
 flag:
 
-{% highlight console %}
+```console
 $ rq info -R
 ...
 
@@ -82,7 +82,7 @@ low:     Bricktop.25458 (busy)
 default: Bricktop.25458 (busy), Mickey.26421 (idle), Turkish.25812 (busy)
 failed:  –
 3 workers, 4 queues
-{% endhighlight %}
+```
 
 
 ## Interval polling
@@ -90,16 +90,16 @@ failed:  –
 By default, `rq info` will print stats and exit.
 You can specify a poll interval, by using the `--interval` flag.
 
-{% highlight console %}
+```console
 $ rq info --interval 1
-{% endhighlight %}
+```
 
 `rq info` will now update the screen every second.  You may specify a float
 value to indicate fractions of seconds.  Be aware that low interval values will
 increase the load on Redis, of course.
 
-{% highlight console %}
+```console
 $ rq info --interval 0.5
-{% endhighlight %}
+```
 
 [dashboard]: https://github.com/nvie/rq-dashboard

docs/docs/results.md

@@ -93,15 +93,15 @@ If a job requires more (or less) time to complete, the default timeout period
 can be loosened (or tightened), by specifying it as a keyword argument to the
 `enqueue()` call, like so:
 
-{% highlight python %}
+```python
 q = Queue()
 q.enqueue(mytask, args=(foo,), kwargs={'bar': qux}, timeout=600)  # 10 mins
-{% endhighlight %}
+```
 
 You can also change the default timeout for jobs that are enqueued via specific
 queue instances at once, which can be useful for patterns like this:
 
-{% highlight python %}
+```python
 # High prio jobs should end in 8 secs, while low prio
 # work may take up to 10 mins
 high = Queue('high', default_timeout=8)  # 8 secs
@@ -109,7 +109,7 @@ low = Queue('low', default_timeout=600)  # 10 mins
 
 # Individual jobs can still override these defaults
 low.enqueue(really_really_slow, timeout=3600)  # 1 hr
-{% endhighlight %}
+```
 
 Individual jobs can still specify an alternative timeout, as workers will
 respect these.

docs/docs/testing.md

@@ -9,7 +9,7 @@ You may wish to include your RQ tasks inside unit tests. However many frameworks
 
 Therefore, you must use the SimpleWorker class to avoid fork();
 
-{% highlight python %}
+```python
 from redis import Redis
 from rq import SimpleWorker, Queue
 
@@ -18,7 +18,7 @@ queue.enqueue(my_long_running_job)
 worker = SimpleWorker([queue], connection=queue.connection)
 worker.work(burst=True)  # Runs enqueued job
 # Check for result...
-{% endhighlight %}
+```
 
 
 ## Running Jobs in unit tests
@@ -31,11 +31,11 @@ Additionally, we can use fakeredis to mock a redis instance, so we don't have to
 run a redis server separately. The instance of the fake redis server can
 be directly passed as the connection argument to the queue:
 
-{% highlight python %}
+```python
 from fakeredis import FakeStrictRedis
 from rq import Queue
 
 queue = Queue(is_async=False, connection=FakeStrictRedis())
 job = queue.enqueue(my_long_running_job)
 assert job.is_finished
-{% endhighlight %}
+```

docs/docs/workers.md

@@ -13,14 +13,14 @@ to perform inside web processes.
 To start crunching work, simply start a worker from the root of your project
 directory:
 
-{% highlight console %}
+```console
 $ rq worker high normal low
 *** Listening for work on high, normal, low
 Got send_newsletter('me@nvie.com') from default
 Job ended normally without result
 *** Listening for work on high, normal, low
 ...
-{% endhighlight %}
+```
 
 Workers will read jobs from the given queues (the order is important) in an
 endless loop, waiting for new work to arrive when all jobs are done.
@@ -37,14 +37,14 @@ new work when they run out of work.  Workers can also be started in _burst
 mode_ to finish all currently available work and quit as soon as all given
 queues are emptied.
 
-{% highlight console %}
+```console
 $ rq worker --burst high normal low
 *** Listening for work on high, normal, low
 Got send_newsletter('me@nvie.com') from default
 Job ended normally without result
 No more work, burst finished.
 Registering death.
-{% endhighlight %}
+```
 
 This can be useful for batch work that needs to be processed periodically, or
 just to scale up your workers temporarily during peak periods.
@@ -106,7 +106,7 @@ yourself before starting the work loop.
 To do this, provide your own worker script (instead of using `rq worker`).
 A simple implementation example:
 
-{% highlight python %}
+```python
 #!/usr/bin/env python
 import sys
 from rq import Connection, Worker
@@ -121,7 +121,7 @@ with Connection():
 
     w = Worker(qs)
     w.work()
-{% endhighlight %}
+```
 
 
 ### Worker names
@@ -139,7 +139,7 @@ starting the worker, using the `--name` option.
 `Worker` instances store their runtime information in Redis. Here's how to
 retrieve them:
 
-{% highlight python %}
+```python
 from redis import Redis
 from rq import Queue, Worker
 
@@ -150,14 +150,14 @@ workers = Worker.all(connection=redis)
 # Returns all workers in this queue (new in version 0.10.0)
 queue = Queue('queue_name')
 workers = Worker.all(queue=queue)
-{% endhighlight %}
+```
 
 _New in version 0.10.0._
 
 If you only want to know the number of workers for monitoring purposes, using
 `Worker.count()` is much more performant.
 
-{% highlight python %}
+```python
 from redis import Redis
 from rq import Worker
 
@@ -169,8 +169,7 @@ workers = Worker.count(connection=redis)
 # Count the number of workers for a specific queue
 queue = Queue('queue_name', connection=redis)
 workers = Worker.all(queue=queue)
-
+```
-{% endhighlight %}
 
 
 ### Worker statistics
@@ -180,14 +179,14 @@ _New in version 0.9.0._
 If you want to check the utilization of your queues, `Worker` instances
 store a few useful information:
 
-{% highlight python %}
+```python
 from rq.worker import Worker
 worker = Worker.find_by_key('rq:worker:name')
 
 worker.successful_job_count  # Number of jobs finished successfully
 worker.failed_job_count. # Number of failed jobs processed by this worker
 worker.total_working_time  # Number of time spent executing jobs
-{% endhighlight %}
+```
 
 
 ## Taking down workers
@@ -209,7 +208,7 @@ If you'd like to configure `rq worker` via a configuration file instead of
 through command line arguments, you can do this by creating a Python file like
 `settings.py`:
 
-{% highlight python %}
+```python
 REDIS_URL = 'redis://localhost:6379/1'
 
 # You can also specify the Redis DB to use
@@ -228,7 +227,7 @@ SENTRY_DSN = 'sync+http://public:secret@example.com/1'
 
 # If you want custom worker name
 # NAME = 'worker-1024'
-{% endhighlight %}
+```
 
 The example above shows all the options that are currently supported.
 
@@ -236,9 +235,9 @@ _Note: The_ `QUEUES` _and_ `REDIS_PASSWORD` _settings are new since 0.3.3._
 
 To specify which module to read settings from, use the `-c` option:
 
-{% highlight console %}
+```console
 $ rq worker -c settings
-{% endhighlight %}
+```
 
 
 ## Custom worker classes
@@ -255,9 +254,9 @@ more common requests so far are:
 
 You can use the `-w` option to specify a different worker class to use:
 
-{% highlight console %}
+```console
 $ rq worker -w 'path.to.GeventWorker'
-{% endhighlight %}
+```
 
 
 ## Custom Job and Queue classes
@@ -267,15 +266,15 @@ _Will be available in next release._
 You can tell the worker to use a custom class for jobs and queues using
 `--job-class` and/or `--queue-class`.
 
-{% highlight console %}
+```console
 $ rq worker --job-class 'custom.JobClass' --queue-class 'custom.QueueClass'
-{% endhighlight %}
+```
 
 Don't forget to use those same classes when enqueueing the jobs.
 
 For example:
 
-{% highlight python %}
+```python
 from rq import Queue
 from rq.job import Job
 
@@ -287,7 +286,7 @@ class CustomQueue(Queue):
 
 queue = CustomQueue('default', connection=redis_conn)
 queue.enqueue(some_func)
-{% endhighlight %}
+```
 
 
 ## Custom DeathPenalty classes
@@ -307,9 +306,9 @@ _New in version 0.5.5._
 If you need to handle errors differently for different types of jobs, or simply want to customize
 RQ's default error handling behavior, run `rq worker` using the `--exception-handler` option:
 
-{% highlight console %}
+```console
 $ rq worker --exception-handler 'path.to.my.ErrorHandler'
 
 # Multiple exception handlers is also supported
 $ rq worker --exception-handler 'path.to.my.ErrorHandler' --exception-handler 'another.ErrorHandler'
-{% endhighlight %}
+```