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# How to share data across jobs

## Python Code

The full code is available here.

## Background:

> Each Ruffus job runs in a separate process. To synchronise across multiple jobs requires passing data across processes.

> This is generally to be avoided because

> 1. Synchronising across parallel tasks is inherently tricky
> 2. Passing large amounts of data across processes is inefficient
> 3. Some objects are not suitable for passing across the process boundary.

> For more information, see the documentation for the python multiprocessing module.

## Aim:

> We create a shared python logging object. All the jobs will write to this log without trampling over each other.

> This programme demonstrates

> - calling functions in shared objects from different jobs
> - synchronising calls to prevent them from being mixed up

### Create the shared object

> The object which will be shared between the jobs will be created in a separate Manager Server Process (See Server Process)

> We need to write a function which will be called in this process:

>     #
>     #   Create logger object
>     #
>     def setup_shared_logger(LOGGING_LEVEL, LOG_FILENAME):
>         """
>         Function to setup logger shared between all processes
>         The logger object will be created within a separate (special) process
>             run by multiprocessing.BaseManager.start()
>         """

>         #
>         #   Log file name with logger level
>         #
>         my_ruffus_logger = logging.getLogger('simpler_example_logger')
>         my_ruffus_logger.setLevel(LOGGING_LEVEL)

>         #
>         #   Add handler to print to file, with the specified format
>         #
>         handler = logging.handlers.RotatingFileHandler(
>                       LOG_FILENAME, maxBytes=100000, backupCount=5)

>         return my_ruffus_logger

### Create proxies to forward calls to the shared object

> We then need to specify proxy objects which will forward function calls to the real (shared) object which is owned by the Manager Server Process:

>     #
>     #   Proxy object for logging
>     #       Logging messages will be marshalled (forwarded) to the process where the
>     #       shared log lives
>     #
>     class LoggerProxy(multiprocessing.managers.BaseProxy):
>         def debug(self, message):
>             return self._callmethod('debug', [message])
>         def info(self, message):
>             return self._callmethod('info', [message])
>         def __str__ (self):
>             return "Logging proxy"

> In this case, we are only interested in two function calls to forward: debug and info. We also override the __str__ function so that ruffus output looks nicer.

### Register proxy and shared object with Manager Server

> Now we need to tell the multiprocessing library how to create our shared object, and forward calls from the proxies:

>     #
>     #   Register the setup_logger function as a proxy for setup_logger
>     #
>     #   We use SyncManager as a base class so we can get a lock proxy for synchronising
>     #       logging later on
>     #
>     class LoggingManager(multiprocessing.managers.SyncManager):
>         """
>         Logging manager sets up its own process and will create the real Log object there
>         We refer to this (real) log via proxies
>         """
>         pass
>     LoggingManager.register('setup_logger', setup_shared_logger, proxytype=LoggerProxy, exposed = ('info', 'debug', '__str__'))

> We link the proxy by creating an instance of SyncManager.

> We could also have used multiprocessing::BaseManager but SyncManager includes the Lock() function which we shall use to synchronise the writing to the log.

### Create Manager Server Process

> We need to create the Manager Server Process, the logs and the proxy:

>     if __name__ == '__main__':

>         #
>         #   make shared log and proxy
>         #
>         manager = LoggingManager()
>         manager.register('setup_logger', setup_shared_logger,
>                          proxytype=LoggerProxy, exposed = ('info', 'debug'))

>         manager.start()
>         LOG_FILENAME  = options.log_file_name
>         LOGGING_LEVEL = logging.DEBUG
>         logger_proxy = manager.setup_logger(LOGGING_LEVEL, LOG_FILENAME)

>         #
>         #   make sure we are not logging at the same time in different processes
>         #
>         logging_mutex = manager.Lock()

### Run ruffus

> The shared proxy can be passed to the task function:

>     @files(None, 'a.1', logger_proxy, logging_mutex)
>     def task1(no_infile, outfile, *extra_params):

>         # Synchronised logging
>         logger_proxy, logging_mutex = extra_params
>         with logging_mutex:
>             logger_proxy.debug("task1, process name = %s" %
>                                 (multiprocessing.current_process().name))

>         # do actual work
>         do_some_hard_task(outfile)

> Because `logging_mutex` is used, log entries will not be jumbled up or interleaved.

### Navigation

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Ruffus v. 1.0.6
- Home |
- Contents |
- Cheat sheet |
- Full tutorial |
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- Future plans |
- Search »