Task Completion in MapReduce: A Summary
Hadoop MapReduce, a popular distributed computing framework, is designed to handle real-world issues and complete jobs even in the face of temporary failures or unexpected events. This article will delve into the mechanisms that ensure the integrity of the output while maximizing fault tolerance during job execution.
Marking Job Success
A MapReduce job is marked as successful by the ApplicationMaster once all map and reduce tasks complete successfully. Upon completion, the ApplicationMaster updates the job status to SUCCESSFUL and releases all the containers running those tasks [1].
Committing Output
During task execution, intermediate outputs are written to temporary locations. Only after a task successfully completes does Hadoop commit its output to the final output directory on HDFS. This two-phase commit process ensures that partial or failed outputs do not corrupt the final result.
Handling Failures
In the event of a map or reduce task failure, due to user code errors or JVM crashes, the ApplicationMaster marks that task attempt as failed and frees the container running it [1]. The NodeManager detects JVM crashes or sudden exits and reports failures to the ApplicationMaster, which triggers task retries on other nodes to ensure completion [1].
The ApplicationMaster manages retries and job recovery by scheduling the restarted tasks until they complete successfully or the job fails definitively. Logs are automatically generated for failed tasks to aid debugging [1].
Configuring Task Retries and Failure Tolerance
The limits of task retries can be configured using and . By default, when a Map or Reduce task fails due to temporary issues, the ApplicationMaster reschedules the task on a different node up to 4 times [1].
Allowing Task Failures without Job Failure
Task failures can be allowed without failing the entire job by configuring and . This allows a certain percentage of tasks to fail while the job is still considered successful [1].
Summary
- Marking Job Success: ApplicationMaster marks job SUCCESSFUL after all map and reduce tasks finish successfully [1].
- Committing Output: Output is written to temporary locations during execution; committed to final output only after successful task completion to avoid corruption [1].
- Handling Failures: Task attempts failing due to errors or JVM crashes are detected by NodeManager and ApplicationMaster; failed tasks are retried until success or job failure [1].
- Logs: Automatic logs are created on task failures for debugging [1].
This process enables Hadoop to maintain the integrity of output while maximizing fault tolerance and efficiency in distributed job execution. A MapReduce job may fail if a task fails all retry attempts, or if core components like the ApplicationMaster, NodeManager, or ResourceManager crash or become unresponsive.
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