Speculative Execution #

This page describes the background of speculative execution, how to use it, and how to check the effectiveness of it.

Background #

Speculative execution is a mechanism to mitigate job slowness which is caused by problematic nodes. A problematic node may have hardware problems, accident I/O busy, or high CPU load. These problems may make the hosted tasks run much slower than tasks on other nodes, and affect the overall execution time of a batch job.

In such cases, speculative execution will start new attempts of the slow task on nodes that are not detected as problematic. The new attempts process the same input data and produces the same data as the old one. The old attempt will not be affected and will keep running. The first finished attempt will be admitted, its output will be seen and consumed by the downstream tasks, and the remaining attempts will be canceled.

To achieve this, Flink uses the slow task detector to detect slow tasks. The nodes that the slow tasks locate in will be identified as problematic nodes and get blocked via the blocklist mechanism. The scheduler will create new attempts for the slow tasks and deploy them on nodes that are not blocked.

Usage #

This section describes how to use speculative execution, including how to enable it, how to tune it, and how to develop/improve custom sources to work with speculative execution.

Note: Flink does not support speculative execution of sinks yet and will support it in follow-up releases.

Note: Flink does not support speculative execution of DataSet jobs because DataSet will be deprecated in near future. DataStream API is now the recommended low level API to develop Flink batch jobs.

Enable Speculative Execution #

To enable speculative execution, you need to set the following configuration options:

• jobmanager.scheduler: AdaptiveBatch
  • Because only Adaptive Batch Scheduler supports speculative execution.
• jobmanager.adaptive-batch-scheduler.speculative.enabled: true

Tuning Configuration #

To make speculative execution work better for different jobs, you can tune below configuration options of the scheduler:

• jobmanager.adaptive-batch-scheduler.speculative.max-concurrent-executions
• jobmanager.adaptive-batch-scheduler.speculative.block-slow-node-duration

You can also tune below configuration options of the slow task detector:

• slow-task-detector.check-interval
• slow-task-detector.execution-time.baseline-lower-bound
• slow-task-detector.execution-time.baseline-multiplier
• slow-task-detector.execution-time.baseline-ratio

Enable Sources for Speculative Execution #

If your job uses a custom Source , and the source uses custom SourceEvent , you need to change the SplitEnumerator of that source to implement SupportsHandleExecutionAttemptSourceEvent interface.

public interface SupportsHandleExecutionAttemptSourceEvent {
    void handleSourceEvent(int subtaskId, int attemptNumber, SourceEvent sourceEvent);
}

This means the SplitEnumerator should be aware of the attempt which sends the event. Otherwise, exceptions will happen when the job manager receives a source event from the tasks and lead to job failures.

No extra change is required for other sources to work with speculative execution, including SourceFunction sources , InputFormat sources , and new sources . All the source connectors offered by Apache Flink can work with speculative execution.

Checking the Effectiveness of Speculative Execution #

After enabling speculative execution, when there are slow tasks that trigger speculative execution, the web UI will show the speculative attempts on the SubTasks tab of vertices on the job page. The web UI also shows the blocked taskmanagers on the Flink cluster Overview and Task Managers pages.

You can also check these metrics to see the effectiveness of speculative execution.

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