Advanced Python Scheduler ========================= .. contents:: Introduction ------------ Advanced Python Scheduler (APScheduler) is a light but powerful in-process task scheduler that lets you schedule functions (or any other python callables) to be executed at times of your choosing. This can be a far better alternative to externally run cron scripts for long-running applications (e.g. web applications), as it is platform neutral and can directly access your application's variables and functions. The development of APScheduler was heavily influenced by the `Quartz `_ task scheduler written in Java. APScheduler provides most of the major features that Quartz does, but it also provides features not present in Quartz (such as multiple job stores). Features -------- * Thread-safe API * Excellent test coverage (tested on CPython 2.6 - 2.7, 3.2 - 3.4, PyPy 2.2) * Configurable scheduling mechanisms (triggers): * Cron-like scheduling * Delayed scheduling of single run jobs (like the UNIX "at" command) * Interval-based (run a job at specified time intervals) * Integrates with several frameworks: * `asyncio `_ (`PEP 3156 `_) * `gevent `_ * `Tornado `_ * `Twisted `_ * `Qt `_ (using either `PyQt `_ or `PySide `_) * Multiple, simultaneously active job stores: * Memory * `SQLAlchemy `_ (any supported RDBMS works) * `MongoDB `_ Usage ===== Installing APScheduler ---------------------- The preferred installation method is by using `pip `_:: $ pip install apscheduler or `easy_install `_:: $ easy_install apscheduler If that doesn't work, you can manually `download the APScheduler distribution `_ from PyPI, extract and then install it:: $ python setup.py install Starting the scheduler ---------------------- To start the scheduler with default settings:: from apscheduler.scheduler import Scheduler sched = Scheduler() sched.start() The constructor takes as its first, optional parameter a dictionary of "global" options to facilitate configuration from .ini files. All APScheduler options given in the global configuration must begin with "apscheduler." to avoid name clashes with other software. The constructor also takes options as keyword arguments (without the prefix). You can also configure the scheduler after its instantiation, if necessary. This is handy if you use the decorators for scheduling and must have a Scheduler instance available from the very beginning:: from apscheduler.scheduler import Scheduler sched = Scheduler() @sched.scheduled_job('interval', {'hours': 3}) def some_job(): print "Decorated job" sched.configure(options_from_ini_file) sched.start() Scheduling jobs --------------- The simplest way to schedule jobs using the built-in triggers is to use one of the shortcut methods provided by the scheduler: .. toctree:: :maxdepth: 1 dateschedule intervalschedule cronschedule These shortcuts cover the vast majority of use cases. However, if you need to use a custom trigger, you need to use the :meth:`~apscheduler.scheduler.Scheduler.add_job` method. When a scheduled job is triggered, it is handed over to the thread pool for execution. You can request a job to be added to a specific job store by giving the target job store's alias in the ``jobstore`` option to :meth:`~apscheduler.scheduler.Scheduler.add_job` or any of the above shortcut methods. You can schedule jobs on the scheduler **at any time**. If the scheduler is not running when the job is added, the job will be scheduled `tentatively` and its first run time will only be computed when the scheduler starts. Jobs will not run retroactively in such cases. .. warning:: Scheduling new jobs from existing jobs is not currently reliable. This will likely be fixed in the next major release. .. _job_options: Job options ----------- The following options can be given as keyword arguments to :meth:`~apscheduler.scheduler.Scheduler.add_job` or one of the shortcut methods, including the decorators. ===================== ========================================================= Option Definition ===================== ========================================================= name Name of the job (informative, does not have to be unique) misfire_grace_time Time in seconds that the job is allowed to miss the the designated run time before being considered to have misfired (see :ref:`coalescing`) (overrides the global scheduler setting) coalesce Run once instead of many times if the scheduler determines that the job should be run more than once in succession (see :ref:`coalescing`) (overrides the global scheduler setting) max_runs Maximum number of times this job is allowed to be triggered before being removed max_instances Maximum number of concurrently running instances allowed for this job (see :ref:`_max_instances`) ===================== ========================================================= Shutting down the scheduler --------------------------- To shut down the scheduler:: sched.shutdown() By default, the scheduler shuts down its thread pool and waits until all currently executing jobs are finished. For a faster exit you can do:: sched.shutdown(wait=False) This will still shut down the thread pool but does not wait for any running tasks to complete. Also, if you gave the scheduler a thread pool that you want to manage elsewhere, you probably want to skip the thread pool shutdown altogether:: sched.shutdown(shutdown_threadpool=False) This implies ``wait=False``, since there is no way to wait for the scheduler's tasks to finish without shutting down the thread pool. A neat trick to automatically shut down the scheduler is to use an :py:mod:`atexit` hook for that:: import atexit sched = Scheduler(daemon=True) atexit.register(lambda: sched.shutdown(wait=False)) # Proceed with starting the actual application Scheduler configuration options ------------------------------- ======================= ========== ============================================== Directive Default Definition ======================= ========== ============================================== misfire_grace_time 1 Maximum time in seconds for the job execution to be allowed to delay before it is considered a misfire (see :ref:`coalescing`) coalesce False Roll several pending executions of jobs into one (see :ref:`coalescing`) standalone False If set to ``True``, :meth:`~apscheduler.scheduler.Scheduler.start` will run the main loop in the calling thread until no more jobs are scheduled. See :ref:`modes` for more information. daemonic True Controls whether the scheduler thread is daemonic or not. This option has no effect when ``standalone`` is ``True``. If set to ``False``, then the scheduler must be shut down explicitly when the program is about to finish, or it will prevent the program from terminating. If set to ``True``, the scheduler will automatically terminate with the application, but may cause an exception to be raised on exit. Jobs are always executed in non-daemonic threads. threadpool (built-in) Instance of a :pep:`3148` compliant thread pool or a dot-notation (``x.y.z:varname``) reference to one threadpool.core_threads 0 Maximum number of persistent threads in the pool threadpool.max_threads 20 Maximum number of total threads in the pool threadpool.keepalive 1 Seconds to keep non-core worker threads waiting for new tasks jobstores.X.class Class of the jobstore named X (specified as module.name:classname) jobstores.X.Y Constructor option Y of jobstore X ======================= ========== ============================================== .. _modes: Operating modes: embedded vs standalone --------------------------------------- The scheduler has two operating modes: standalone and embedded. In embedded mode, it will spawn its own thread when :meth:`~apscheduler.scheduler.Scheduler.start` is called. In standalone mode, it will run directly in the calling thread and will block until there are no more pending jobs. The embedded mode is suitable for running alongside some application that requires scheduling capabilities. The standalone mode, on the other hand, can be used as a handy cross-platform cron replacement for executing Python code. A typical usage of the standalone mode is to have a script that only adds the jobs to the scheduler and then calls :meth:`~apscheduler.scheduler.Scheduler.start` on the scheduler. All of the examples in the examples directory demonstrate usage of the standalone mode, with the exception of ``threaded.py`` which demonstrates the embedded mode (where the "application" just prints a line every 2 seconds). Job stores ---------- APScheduler keeps all the scheduled jobs in *job stores*. Job stores are configurable adapters to some back-end that may or may not support persisting job configurations on disk, database or something else. Job stores are added to the scheduler and identified by their aliases. The alias ``default`` is special in that if the user does not explicitly specify a job store alias when scheduling a job, it goes to the ``default`` job store. If there is no job store in the scheduler by that name when the scheduler is started, a new job store of type :class:`~apscheduler.jobstores.ram_store.RAMJobStore` is created to serve as the default. The other built-in job stores are: * :class:`~apscheduler.jobstores.shelve_store.ShelveJobStore` * :class:`~apscheduler.jobstores.sqlalchemy_store.SQLAlchemyJobStore` * :class:`~apscheduler.jobstores.mongodb_store.MongoDBJobStore` * :class:`~apscheduler.jobstores.redis_store.RedisJobStore` Job stores can be added either through configuration options or the :meth:`~apscheduler.scheduler.Scheduler.add_jobstore` method. The following are therefore equal:: config = {'apscheduler.jobstore.file.class': 'apscheduler.jobstores.shelve_store:ShelveJobStore', 'apscheduler.jobstore.file.path': '/tmp/dbfile'} sched = Scheduler(config) and:: from apscheduler.jobstores.shelve_store import ShelveJobStore sched = Scheduler() sched.add_jobstore(ShelveJobStore('/tmp/dbfile'), 'file') The example configuration above results in the scheduler having two job stores -- one :class:`~apscheduler.jobstores.ram_store.RAMJobStore` and one :class:`~apscheduler.jobstores.shelve_store.ShelveJobStore`. Job persistency --------------- The built-in job stores (other than :class:`~apscheduler.jobstores.ram_store.RAMJobStore`) store jobs in a durable manner. This means that when you schedule jobs in them, shut down the scheduler, restart it and readd the job store in question, it will load the previously scheduled jobs automatically. Persistent job stores store a reference to the target callable in text form and serialize the arguments using pickle. This unfortunately adds some restrictions: * You cannot schedule static methods, inner functions or lambdas. * You cannot update the objects given as arguments to the callable. Technically you *can* update the state of the argument objects, but those changes are never persisted back to the job store. .. note:: None of these restrictions apply to ``RAMJobStore``. .. _max_instances: Limiting the number of concurrently executing instances of a job ---------------------------------------------------------------- By default, no two instances of the same job will be run concurrently. This means that if the job is about to be run but the previous run hasn't finished yet, then the latest run is considered a misfire. It is possible to set the maximum number of instances for a particular job that the scheduler will let run concurrently, by using the ``max_instances`` keyword argument when adding the job. .. _coalescing: Missed job executions and coalescing ------------------------------------ Sometimes the scheduler may be unable to execute a scheduled job at the time it was scheduled to run. The most common case is when a job is scheduled in a persistent job store and the scheduler is shut down and restarted after the job was supposed to execute. When this happens, the job is considered to have "misfired". The scheduler will then check each missed execution time against the job's ``misfire_grace_time`` option (which can be set on per-job basis or globally in the scheduler) to see if the execution should still be triggered. This can lead into the job being executed several times in succession. If this behavior is undesirable for your particular use case, it is possible to use `coalescing` to roll all these missed executions into one. In other words, if coalescing is enabled for the job and the scheduler sees one or more queued executions for the job, it will only trigger it once. The "bypassed" runs of the job are not considered misfires nor do they count towards any maximum run count of the job. Scheduler events ---------------- It is possible to attach event listeners to the scheduler. Scheduler events are fired on certain occasions, and may carry additional information in them concerning the details of that particular event. It is possible to listen to only particular types of events by giving the appropriate ``mask`` argument to :meth:`~apscheduler.scheduler.Scheduler.add_listener`, OR'ing the different constants together. The listener callable is called with one argument, the event object. The type of the event object is tied to the event code as shown below: ========================== ============== ========================================== Constant Event class Triggered when... ========================== ============== ========================================== EVENT_SCHEDULER_START SchedulerEvent The scheduler is started EVENT_SCHEDULER_SHUTDOWN SchedulerEvent The scheduler is shut down EVENT_JOBSTORE_ADDED JobStoreEvent A job store is added to the scheduler EVENT_JOBSTORE_REMOVED JobStoreEvent A job store is removed from the scheduler EVENT_JOBSTORE_JOB_ADDED JobStoreEvent A job is added to a job store EVENT_JOBSTORE_JOB_REMOVED JobStoreEvent A job is removed from a job store EVENT_JOB_EXECUTED JobEvent A job is executed successfully EVENT_JOB_ERROR JobEvent A job raised an exception during execution EVENT_JOB_MISSED JobEvent A job's execution time is missed ========================== ============== ========================================== See the documentation for the :mod:`~apscheduler.events` module for specifics on the available event attributes. Example:: def my_listener(event): if event.exception: print 'The job crashed :(' else: print 'The job worked :)' scheduler.add_listener(my_listener, EVENT_JOB_EXECUTED | EVENT_JOB_ERROR) Getting a list of scheduled jobs -------------------------------- If you want to see which jobs are currently added in the scheduler, you can simply do:: sched.print_jobs() This will print a human-readable listing of scheduled jobs, their triggering mechanisms and the next time they will fire. If you supply a file-like object as an argument to this method, it will output the results in that file. To get a machine processable list of the scheduled jobs, you can use the :meth:`~apscheduler.scheduler.Scheduler.get_jobs` scheduler method. It will return a list of :class:`~apscheduler.job.Job` instances. Extending APScheduler ===================== It is possible to extend APScheduler to support alternative job stores and triggers. See the :doc:`Extending APScheduler ` document for details. FAQ === Q: Why do my processes hang instead of exiting when they are finished? A: A scheduled job may still be executing. APScheduler's thread pool is wired to wait for the job threads to exit before allowing the interpreter to exit to avoid unpredictable behavior caused by the shutdown procedures of the Python interpreter. A more thorough explanation `can be found here `_. Reporting bugs ============== A `bug tracker `_ is provided by bitbucket.org. Getting help ============ If you have problems or other questions, you can either: * Ask on the `APScheduler Google group `_, or * Ask on the ``#apscheduler`` channel on `Freenode IRC `_ .. include:: ../CHANGES.rst