Parallel evaluation of Vitrage templates

Parallel evaluation of Vitrage templates

https://blueprints.launchpad.net/vitrage/+spec/parallel-evaluation

Currently Vitrage sequentially processes and evaluates incoming events. While it is a necessity to run sequential processing, template evaluation for a single event is independent and can be paralleled. The challenge is NetworkX in-memory graph held by a single process, thus preventing parallelism, this can be addressed by creating evaluator worker processes that maintain clones of the graph.

Problem description

Each and every graph change triggers evaluation of all relevant template conditions, thus run time of event processing is dependent upon the number of loaded templates.

  • Processor receives an event, from either the datasource or evaluator queue
  • Processor updates the graph
  • Evaluator is triggered to run relevant templates
  • Evaluation may result in an event writen to the evaluator queue

The above flow runs on in memory NetworkX graph in a single thread

Problem description

Proposed change

One or more EvaluatorWorker processes will be added to vitrage-graph service. These processes keep their own graph instance. A new component EvaluatorManager manages the communication with the EvaluatorWorkers. Per each graph change resulted in the processor, it will request the EvaluatorManager to inform all the EvaluatorWorkers, so in effect these hold an identical graph clone. Each EvaluatorWorker runs a portion of the templates, writing its results to evaluator queue.

The flow will be as follows:

  • Processor receives an event, from either the datasource or evaluator queue
  • Processor updates the graph
  • EvaluatorManager is triggered, sending the event to the N EvaluatorWorkers, via N multiprocessing queues, then waits for their ack signal
  • Each EvaluatorWorker updates it’s own graph
  • In each EvaluatorWorker the evaluator is triggered to run a portion of the templates
  • Evaluation may result in an event writen to the evaluator queue
Proposed change

Alternatives

None

Data model impact

None

REST API impact

None

Versioning impact

None

Other end user impact

None

Deployer impact

Each EvaluatorWorker holds a clone of the in memory Entity Graph, hence memory consumption will increase as the configured number of workers increases.

Developer impact

None

Horizon impact

None

Implementation

Assignee(s)

Primary assignee:
idan-hefetz
Other contributors:
None

Work Items

  • processor should not hold a ScenarioEvaluator
  • create EvaluatorManager
  • create EvaluatorWorker
  • change main in graph.py
  • change GraphService to handle these changes
  • Choose the best way to assign tasks to workers

Dependencies

None

Testing

The implementation will be covered by additional unit test

Documentation Impact

None

References

None

Creative Commons Attribution 3.0 License

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vitrage-specs