HadoopMapReduceTaskFailures #

There is an unusually high number of MapReduce task failures.

alert: HadoopMapReduceTaskFailures
annotations:
  description: |-
    There is an unusually high number of MapReduce task failures.
      VALUE = {{ $value }}
      LABELS = {{ $labels }}    
  runbook: https://srerun.github.io/prometheus-alerts/runbooks/jmx_exporter/hadoopmapreducetaskfailures/
  summary: Hadoop Map Reduce Task Failures (instance {{ $labels.instance }})
expr: hadoop_mapreduce_task_failures_total > 100
for: 10m
labels:
  severity: critical

Here is the runbook for the HadoopMapReduceTaskFailures alert rule:

Meaning #

The HadoopMapReduceTaskFailures alert is triggered when the total number of MapReduce task failures exceeds 100 within a 10-minute window. This indicates that there is an unusually high number of failed tasks in the Hadoop cluster, which can impact the overall performance and reliability of the system.

Impact #

The impact of this alert can be significant, as failed MapReduce tasks can lead to:

• Data loss or inconsistencies
• Job failures or timeouts
• Increased latency and decreased throughput
• Increased load on the cluster, leading to resource exhaustion
• Potential cascading failures or errors in dependent systems

Diagnosis #

To diagnose the root cause of the HadoopMapReduceTaskFailures alert, follow these steps:

1. Check the Hadoop logs for errors or exceptions related to task failures.
2. Investigate the job history to identify the specific jobs and tasks that are failing.
3. Review the resource utilization of the cluster, including CPU, memory, and disk usage.
4. Verify that the Hadoop configuration is correct and up-to-date.
5. Check for any network connectivity issues or failures.
6. Review the task attempts and retries to identify any patterns or trends.

Mitigation #

To mitigate the HadoopMapReduceTaskFailures alert, follow these steps:

1. Identify and fix the root cause of the task failures, based on the diagnosis steps above.
2. Retry failed tasks or jobs to recover from the failures.
3. Adjust the Hadoop configuration to optimize resource utilization and job scheduling.
4. Implement additional monitoring and logging to detect and respond to future task failures.
5. Consider increasing the capacity or scalability of the Hadoop cluster to handle increased load.
6. Communicate with stakeholders and dependent teams about the issue and resolution.