An notification event processing library based on persistent streams.
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An OpenStack notification event processing library based on persistant streams.

Winchester is designed to process event streams, such as those produced from OpenStack notifications. Events are represented as simple python dictionaries. They should be flat dictionaries (not nested), with a minimum of three keys:

"message_id":   A unique identifier for this event, such as a uuid.
"event_type":   A string identifying the event's type. Usually a hierarchical dotted name like ""
"timestamp":    Time the event occurred (a python datetime, in UTC)

The individual keys of the event dictionary are called traits and can be strings, integers, floats or datetimes. For processing of the (often large) notifications that come out of OpenStack, winchester uses the StackDistiller library to extract flattened events from the notifications, that only contain the data you actually need for processing.

Winchester’s processing is done through triggers and pipelines.

A trigger is composed of a match_criteria which is like a persistant query, collecting events you want to process into a persistant stream (stored in a sql database), a set of distinguishing traits, which can separate your list of events into distinct streams, similar to a GROUP BY clause in an SQL query, and a fire_criteria, which specifies the conditions a given stream has to match for the trigger to fire. When it does, the events in the stream are sent to a pipeline listed as the fire_pipeline for processing as a batch. Also listed is an expire_timestamp. If a given stream does not meet the fire_criteria by that time, it is expired, and can be sent to an expire_pipeline for alternate processing. Both fire_pipeline and expire_pipeline are optional, but at least one of them must be specified.

A pipeline is simply a list of simple handlers. Each handler in the pipeline receives the list of events in a given stream, sorted by timestamp, in turn. Handlers can filter events from the list, or add new events to it. These changes will be seen by handlers further down the pipeline. Handlers should avoid operations with side-effects, other than modifying the list of events, as pipeline processing can be re-tried later if there is an error. Instead, if all handlers process the list of events without raising an exception, a commit call is made on each handler, giving it the chance to perform actions, like sending data to external systems. Handlers are simple to write, as pretty much any object that implements the appropriate handle_events, commit and rollback methods can be a handler.

Installing and running.

Winchster is installable as a simple python package. Once installed, and the appropriate database url is specified in the winchester.yaml config file (example included in the etc directory), you can create the appropriate database schema with:

winchester_db -c <path_to_your_config_files>/winchester.yaml upgrade head

If you need to run the SQL by hand, or just want to look at the schema, the following will print out the appropriate table creation SQL:

winchester_db -c <path_to_your_config_files>/winchester.yaml upgrade --sql head

Once you have done that, and configured the appropriate triggers.yaml, pipelines.yaml, and, if using StackDistiller, event_definitions.yaml configs (again, examples are in etc in the winchester codebase), you can add events into the system by calling the add_event method of Winchester’s TriggerManager. If you are processing OpenStack notifications, you can call add_notification, which will pare down the notification into an event with StackDistiller, and then call add_event with that. If you are reading OpenStack notifications off of a RabbitMQ queue, there is a plugin for the Yagi notification processor included with Winchester. Simply add “winchester.yagi_handler.WinchesterHandler” to the “apps” line in your yagi.conf section for the queues you want to listen to, and add a:

config_file = <path_to_your_config_files>/winchester.yaml

section to the yagi.conf.

To run the actual pipeline processing, which is run as a separate daemon, run:

pipeline_worker -c <path_to_your_config_files>/winchester.yaml

You can pass the -d flag to the pipeline_worker to tell it to run as a background daemon.

Winchester uses an optimistic locking scheme in the database to coordinate firing, expiring, and processing of streams, so you can run as many processes (like Yagi’s yagi-event daemon) feeding TriggerManagers as you need to handle the incoming events, and as many *pipeline_worker*s as you need to handle the resulting processing load, scaling the system horizontally.