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Implement Distributed Runner

We need a Distributed Runner in Rally that will run tasks on many nodes simultaneously.

Problem description

Currently there are several runners in Rally, but they all can only run on the same host that Rally itself runs on. It limits test load that Rally can generate. In some cases required load can not be generated from one host.

In current implementation Runner object runs actual subtask and generates test results while TaskEngine via ResultConsumer retrieves these results, checks them against specified SLA and stores in DB.

There are several aspects that should be kept in mind when reasoning about distributed load generation:

  • Even one active runner is able to produce significant amounts of result data so that TaskEngine could barely process it in time. We assume that the single TaskEngine instance definitely will not be able to process several streams of raw test result data from several simultaneous runners.
  • We need test results to be checked against SLA as soon as possible so that we could stop load generation on SLA violation immediately (or close to) and protect the environment being tested. On the other hand we need results from all runners to be analysed, i.e. checking SLA on a single runner is not enough.
  • Since we expect long task duration we want to provide to user at least partial information about task execution as soon as possible.

Proposed change

It is proposed to introduce two new component, RunnerAgent and a new plugin of runner type, DistributedRunner, and refactor existing components, TaskEngine, Runner and SLA, so that overall interaction will look as follows.

../../source/images/Rally_Distributed_Runner.png

  1. TaskEngine

    • create subtask context
    • create instance of Runner
    • run Runner.run() with context object and info about sceanario
    • in separated thread consume iteration result chunks & SLA from Runner
    • delete context
  2. RunnerAgent
    • is executed on agent nodes
    • runs Runner for received task iterations with given context and args
    • collects iteration result chunks, stores them on local filesystem, sends them on request to DistributedRunner
    • aggregates SLA data and periodically sends it to DistributedRunner
    • stops Runner on receive of corresponding message
  3. DistributedRunner
    • is a regular plugin of Runner type
    • communicates with remote RunnerAgents wia message queue (ZeroMQ)
    • provides context, args and SLA to RunnerAgents
    • distributes task iterations to RunnerAgents
    • aggregates SLA data from RunnerAgents
    • merges chunks of task result data

It is supposed to use separate communication channels for task results and SLA data.

  • SLA data is sent periodically (e.g. once per second) for iterations that are already finished.
  • Task results are collected into chunks and stored locally by RunnerAgent and only send on request.

Alternatives

No way

Implementation

Assignee(s)

Primary assignee:
Illia Khudoshyn

Work Items

  • Refactor current SLA mechanism to support aggregated SLA data
  • Refactor current Runner base class
    • collect iteration results into chunks, ordered by timestamp
    • perform local SLA checks
    • aggregate SLA data
  • Refactor TaskEngine to reflect changes in Runner
    • operate chunks of ordered test results rather then stream of raw result items
    • apply SLA checks to aggregated SLA data
    • analyze SLA data and consume test results in separate threads
  • Develop infrastructure that will allow multi-node Rally configuration and run
  • Implement RunnerAgent
    • run Runner
    • cache prepared chunks of iteration results
    • comunicate via ZMQ with DistributedRunner(send task results and SLA on separate channels)
    • terminate Runner on 'stop' command from TaskEngine
  • Implement DistributedRunner that will
    • feed tasks to RunnerAgents
    • receive chunks of result data from RunnerAgents, merge it and provide merged data to TaskEngine
    • receive aggregated SLA data from RunnerAgents, merge it and provide data to TaskEngine
    • translate 'stop' command from TaskEngine to RunnerAgents

Dependencies

  • DB model refactoring (boris-42)
  • Report generation refactoring (amaretsky)
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