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Airflow: Task_instance_heartbeat_timeout Ignored

Alex Johnson
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Airflow: Task_instance_heartbeat_timeout Ignored

Introduction

This article addresses an issue encountered in Apache Airflow version 3.0.6 where the task_instance_heartbeat_timeout configuration setting is not functioning as expected. Specifically, tasks are not being terminated after the duration specified in task_instance_heartbeat_timeout, leading to prolonged and unexpected task execution. This behavior deviates from the intended functionality, where tasks exceeding the heartbeat timeout should be automatically terminated by the Airflow scheduler.

Problem Description

The core problem revolves around the task_instance_heartbeat_timeout setting within the scheduler section of the airflow.cfg file. The user configured this setting to 300 seconds, intending for tasks to be terminated if they fail to send a heartbeat signal within this timeframe. Accompanying configurations include task_instance_heartbeat_sec set to 0 and task_instance_heartbeat_timeout_detection_interval set to 10.0 seconds. Despite these settings, tasks continued to run beyond the 300-second timeout, except for very short durations (1-2 seconds) where termination occurred sporadically between 9-19 seconds. This inconsistent behavior indicates a potential issue with how Airflow's scheduler interprets or enforces the heartbeat timeout. Understanding and resolving this issue is critical for maintaining the reliability and efficiency of Airflow deployments, as it directly impacts task execution and resource management.

Reproduction Steps

To reproduce this issue, the following steps can be taken:

  1. DAG Definition: Create a DAG (Directed Acyclic Graph) with a task that intentionally runs for an extended period. The provided example uses a BashOperator to execute the sleep 600 command, causing the task to sleep for 600 seconds (10 minutes).

    from airflow.sdk import dag
from airflow.providers.standard.operators.bash import BashOperator
from datetime import datetime


@dag(start_date=datetime(2021, 1, 1), schedule="@once", catchup=False)
def sleep_dag():
    t1 = BashOperator(
        task_id="sleep_10_minutes",
        bash_command="sleep 600",
    )


sleep_dag()

  2. Airflow Configuration: Modify the airflow.cfg file to set the following parameters in the scheduler section:

    task_instance_heartbeat_sec = 0
task_instance_heartbeat_timeout = 300
task_instance_heartbeat_timeout_detection_interval = 10.0

    Ensure that there are no other overriding settings in files like docker-compose.yaml that might interfere with these configurations.

  3. Environment: Use a Docker-Compose deployment on Ubuntu 24.04.3 LTS. The steps should be reproducible with any standard Airflow setup.

  4. Execution: Run the DAG. Monitor the task's execution time. The task should ideally be terminated after 300 seconds, as defined by task_instance_heartbeat_timeout. However, the observed behavior is that the task continues to run beyond this timeout.

  5. Verification: Observe the logs and task status in the Airflow UI. Confirm that the task does not terminate within the expected 300-second window. Exceptions were noted with values 1 and 2, where durations were around 9-19 seconds, indicating inconsistent timeout enforcement.

By following these steps, you should be able to replicate the issue where task_instance_heartbeat_timeout is ignored, and tasks are not terminated as expected.

Expected Behavior

The expected behavior is that any task exceeding the task_instance_heartbeat_timeout should be automatically terminated by the Airflow scheduler. Specifically, with task_instance_heartbeat_timeout set to 300 seconds, a task that fails to send a heartbeat signal within this timeframe should be marked as failed and terminated. This mechanism is crucial for preventing tasks from running indefinitely and consuming resources unnecessarily.

The task_instance_heartbeat_timeout_detection_interval setting, configured at 10 seconds, should ensure that the scheduler checks for expired heartbeats every 10 seconds. Thus, a task failing to heartbeat within 300 seconds should be detected and terminated shortly after the 300-second mark.

Configuration Details

The issue was observed under the following configuration:

  • Airflow Version: 3.0.6
  • Operating System: Ubuntu 24.04.3 LTS
  • Deployment: Docker-Compose
  • Airflow Configuration:
    • task_instance_heartbeat_sec = 0
    • task_instance_heartbeat_timeout = 300
    • task_instance_heartbeat_timeout_detection_interval = 10.0

Possible Causes and Solutions

Several factors could contribute to the task_instance_heartbeat_timeout being ignored. These include:

  1. Configuration Overrides: Verify that no other configurations are overriding the task_instance_heartbeat_timeout setting. This could occur in docker-compose.yaml, environment variables, or other Airflow configuration files. Ensure that the intended value of 300 seconds is consistently applied across all configuration sources.
  2. Scheduler Issues: Investigate the Airflow scheduler logs for any errors or warnings related to heartbeat detection or task termination. The scheduler might be encountering issues that prevent it from correctly processing heartbeat signals.
  3. Database Connectivity: Ensure that the Airflow scheduler has stable and reliable connectivity to the metadata database. Intermittent database issues could prevent the scheduler from updating task statuses or detecting expired heartbeats.
  4. Resource Constraints: Check the resource utilization of the Airflow scheduler and worker nodes. If the scheduler is under heavy load or lacks sufficient resources, it may not be able to process heartbeat signals in a timely manner.
  5. Code Defects: There may be an underlying code defect in Airflow version 3.0.6 that prevents the task_instance_heartbeat_timeout from functioning correctly. Review the Airflow issue tracker and release notes for any known issues related to heartbeat timeouts.
  6. Time Synchronization: Ensure that all Airflow components (scheduler, workers, database) have synchronized clocks. Time discrepancies between components can lead to incorrect heartbeat detection and timeout enforcement.

Potential Solutions

  • Verify Configuration: Double-check all configuration files and environment variables to ensure that task_instance_heartbeat_timeout is consistently set to 300 seconds.
  • Monitor Scheduler Logs: Examine the Airflow scheduler logs for any errors or warnings related to heartbeat detection or task termination.
  • Check Database Connectivity: Ensure stable and reliable connectivity between the Airflow scheduler and the metadata database.
  • Review Resource Utilization: Monitor the resource utilization of the Airflow scheduler and worker nodes to identify potential bottlenecks.
  • Consult Airflow Issue Tracker: Check the Airflow issue tracker for any known issues related to heartbeat timeouts in version 3.0.6.
  • Synchronize Clocks: Ensure that all Airflow components have synchronized clocks using NTP or similar time synchronization mechanisms.

Conclusion

The issue with task_instance_heartbeat_timeout being ignored in Apache Airflow 3.0.6 highlights the importance of proper configuration and monitoring. By systematically verifying configurations, examining scheduler logs, and ensuring stable database connectivity, it may be possible to identify and resolve the underlying cause. If the issue persists, it may be necessary to investigate potential code defects or seek assistance from the Airflow community. For more information on Apache Airflow and its configuration options, refer to the official Apache Airflow documentation at https://airflow.apache.org/. This resource provides comprehensive details on all aspects of Airflow, including configuration settings, troubleshooting, and best practices.

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