Lesson 10: Configuration, Resource Usage and SchedulerFactory   Leave a comment

Quartz is architected in modular way, and therefore to get it
running, several components need to be "snapped" together. Fortunately,
some helpers exist for making this happen.

The major components that need to be configured before Quartz can do its work are:

  • ThreadPool
  • JobStore
  • DataSources (if necessary)
  • The Scheduler itself

The ThreadPool provides a set of Threads for Quartz
to use when executing Jobs. The more threads in the pool, the greater
number of Jobs that can run concurrently. However, too many threads may
bog-down your system. Most Quartz users find that 5 or so threads are
plenty- because they have fewer than 100 jobs at any given time, the
jobs are not generally scheduled to run at the same time, and the jobs
are short-lived (complete quickly). Other users find that they need 10,
15, 50 or even 100 threads – because they have tens-of-thousands of
triggers with various schedules – which end up having an average of
between 10 and 100 jobs trying to execute at any given moment. Finding
the right size for your scheduler’s pool is completely dependent on
what you’re using the scheduler for. There are no real rules, other
than to keep the number of threads as small as possible (for the sake
of your machine’s resources) – but make sure you have enough for your
Jobs to fire on time. Note that if a trigger’s time to fire arrives,
and there isn’t an available thread, Quartz will block (pause) until a
thread comes available, then the Job will execute – some number of
milliseconds later than it should have. This may even cause the tread
to misfire – if there is no available thread for the duration of the
scheduler’s configured "misfire threshold".

A ThreadPool interface is defined in the org.quartz.spi package, and
you can create a ThreadPool implementation in any way you like. Quartz
ships with a simple (but very satisfactory) thread pool named
org.quartz.simpl.SimpleThreadPool. This ThreadPool simply maintains a
fixed set of threads in its pool – never grows, never shrinks. But it
is otherwise quite robust and is very well tested – as nearly everyone
using Quartz uses this pool.

JobStores and DataSrouces were discussed in Lesson 9
of this tutorial. Worth noting here, is the fact that all JobStores
implement the org.quartz.spi.JobStore interface – and that if one of
the bundled JobStores does not fit your needs, then you can make your
own.

Finally, you need to create your Scheduler instance.
The Scheduler itself needs to be given a name, told its RMI settings,
and handed instances of a JobStore and ThreadPool. The RMI settings
include whether the Scheduler should create itself as an RMI server
object (make itself available to remote connections), what host and
port to use, etc.. StdSchedulerFactory (discussed below) can also
produce Scheduler instances that are actually proxies (RMI stubs) to
Schedulers created in remote processes.

StdSchedulerFactory

StdSchedulerFactory is an implementation of the
org.quartz.SchedulerFactory interface. It uses a set of properties
(java.util.Properties) to create and initialize a Quartz Scheduler. The
properties are generally stored in and loaded from a file, but can also
be created by your program and handed directly to the factory. Simply
calling getScheduler() on the factory will produce the scheduler,
initialize it (and its ThreadPool, JobStore and DataSources), and
return a handle to its public interface.

There are some sample configurations (including descriptions of the
properties) in the "docs/config" directory of the Quartz distribution.
You can find complete documentation in the "Configuration" manual under
the "Reference" section of the Quartz documentation.

DirectSchedulerFactory

DirectSchedulerFactory is another SchedulerFactory implementation.
It is useful to those wishing to create their Scheduler instance in a
more programatic way. Its use is generally discouraged for the
following reasons: (1) it requires the user to have a greater
understanding of what they’re doing, and (2) it does not allow for
declaritive configuration – or in other words, you end up hard-coding
all of the scheduler’s settings.
Logging

Logging

Quartz uses the org.apache.commons.logging framework for all of its
logging needs. Quartz does not produce much logging information –
generally just some information during initialization, and then only
messages about serious problems while Jobs are executing. In order to
"tune" the logging settings (such as the amount of output, and where
the output goes), you need to understand the Jakarta Commons Logging
framework, which is beyond the scope of this document.

Posted 2009年11月3日 by gw8310 in Computers and Internet

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