Flaky Test: Kafka Service Deletion Recovery In Strimzi

by Alex Johnson 55 views

Introduction

This article addresses a flaky test, RecoveryST.testRecoveryFromKafkaServiceDeletion(), encountered within the Strimzi Kafka Operator project. The issue manifests as intermittent failures during the recovery process after a Kafka service deletion. Specifically, clients sometimes fail to connect to the Kafka cluster despite the service being successfully recreated. This problem appears to stem from a delay in DNS entry propagation following the service recreation, leading to connectivity issues for the client applications. We will delve into the details of the bug, its reproduction steps, the observed behavior, and potential solutions to mitigate the flakiness.

Bug Description

The RecoveryST.testRecoveryFromKafkaServiceDeletion() test in Strimzi is designed to verify the system's ability to recover from the deletion of a Kafka service. However, it has been observed to fail intermittently in a pipeline environment. The core issue is that while the Kafka service is successfully recreated after deletion, client applications sometimes fail to connect to the cluster. Error logs indicate that the clients are unable to resolve the bootstrap URLs, suggesting a problem with DNS resolution. This flakiness is particularly noticeable, occurring approximately once in every 100 test runs on specific infrastructure.

This behavior suggests that even though the Kubernetes service object exists, the corresponding DNS entries required for clients to locate the service might not be immediately available. This delay can cause the client connection attempts to fail, leading to test failures. Two potential solutions are proposed: (1) implementing a mechanism to wait for the service to become resolvable before initiating client connections, or (2) adding a retry mechanism to the client jobs to handle temporary DNS resolution failures. The current configuration has backoffLimit set to 0, meaning no retries are attempted.

Addressing this flakiness is crucial for ensuring the reliability and stability of the Strimzi Kafka Operator. A stable test suite provides confidence in the operator's ability to manage and recover Kafka clusters effectively. By implementing one of the proposed solutions, the test can be made more robust and less prone to intermittent failures, improving the overall quality of the Strimzi project.

Steps to Reproduce

The flaky behavior of RecoveryST.testRecoveryFromKafkaServiceDeletion() can be reproduced by following these steps:

  1. Execute the Test Repeatedly: Run the RecoveryST.testRecoveryFromKafkaServiceDeletion() test multiple times in a loop or as part of an automated test suite.
  2. Observe Intermittent Failures: Monitor the test results for failures. The flakiness manifests as the test passing sometimes and failing at other times, even without any code changes.

On the infrastructure where this issue was observed, the failure rate was approximately 1 in 100 test runs. This indicates that the problem is not consistent but occurs often enough to be a concern.

Expected Behavior

The expected behavior of the RecoveryST.testRecoveryFromKafkaServiceDeletion() test is that it should consistently pass, indicating a successful recovery from the Kafka service deletion. Specifically:

  • The Kafka service should be successfully recreated after being deleted.
  • Client applications should be able to connect to the Kafka cluster without any issues.
  • Messages should be successfully produced and consumed by the client applications, verifying the end-to-end functionality of the Kafka cluster.

When the test passes, it confirms that the Strimzi Kafka Operator can automatically recover from service disruptions, ensuring the continuous availability of the Kafka cluster. A passing test provides confidence in the operator's ability to handle real-world scenarios where services might be unexpectedly terminated or need to be recreated.

Environment Details

  • Strimzi Version: 0.47
  • Kubernetes Version: Kubernetes 1.32.1
  • Installation Method: Not specified in the original bug report.
  • Infrastructure: K3s on EC2

Root Cause Analysis

The root cause of the flaky test appears to be related to the timing of DNS propagation after the Kafka service is recreated. When a service is deleted and then recreated in Kubernetes, there is a delay before the DNS entries for the service are updated and become resolvable by client applications. During this brief period, the client applications may attempt to connect to the Kafka cluster using the old DNS entries, which are no longer valid. This results in connection failures and the "No resolvable bootstrap urls given in bootstrap.servers" error.

The intermittent nature of the problem is likely due to variations in the DNS propagation time. In some cases, the DNS entries may be updated quickly enough for the client applications to connect successfully. However, in other cases, the delay may be longer, causing the connection attempts to fail.

The provided logs and Kubernetes dump further support this theory. The test log shows that the client jobs timed out while waiting for the clients to finish successfully. The producer job log shows the "No resolvable bootstrap urls given in bootstrap.servers" error, indicating a DNS resolution issue. The Kubernetes dump may contain additional information about the service configuration and DNS settings, which could help to further diagnose the problem.

Analyzing the Logs

The test log provides valuable information about the sequence of events leading to the test failure. The io.strimzi.test.WaitException indicates that the test timed out while waiting for the client jobs to complete successfully. This suggests that the client applications were unable to connect to the Kafka cluster within the allotted time.

The producer job log provides more specific information about the cause of the connection failure. The org.apache.kafka.common.config.ConfigException: No resolvable bootstrap urls given in bootstrap.servers error clearly indicates that the client application was unable to resolve the DNS entries for the Kafka brokers. This confirms the hypothesis that the DNS propagation delay is the root cause of the problem.

Examining the Kubernetes Dump

The Kubernetes dump may contain additional information about the service configuration, DNS settings, and network policies that could be relevant to the problem. For example, it may reveal whether the service is configured with a specific DNS name, whether there are any network policies that might be interfering with DNS resolution, or whether there are any issues with the DNS server itself.

By carefully examining the Kubernetes dump, it may be possible to identify additional factors that are contributing to the DNS propagation delay and the flakiness of the test.

Proposed Solutions

To address the flaky test, two potential solutions are proposed:

  1. Wait for Service to Be Resolvable:
    • Implement a mechanism to check whether the Kafka service is resolvable before starting the client jobs. This could involve using a DNS lookup tool or a simple network connectivity test to verify that the service's DNS entries are available.
    • If the service is not resolvable, the test should wait for a short period and then retry the check. This process should be repeated until the service becomes resolvable or a timeout is reached.
    • By ensuring that the service is resolvable before starting the client jobs, the test can avoid the connection failures caused by the DNS propagation delay.
  2. Add Retry Mechanism to Client Jobs:
    • Increase the backoffLimit for the client jobs to allow them to retry the connection attempts in case of failure.
    • The backoffLimit parameter specifies the number of times that a job will be retried before being considered failed. By increasing this value, the client jobs will be able to automatically retry the connection attempts if they initially fail due to the DNS propagation delay.
    • This approach can provide a more resilient solution to the problem, as it allows the client applications to recover from temporary DNS resolution failures without requiring any manual intervention.

Both solutions have their own advantages and disadvantages. The first solution (waiting for the service to be resolvable) may be more reliable, as it ensures that the client applications will only attempt to connect to the Kafka cluster when the DNS entries are known to be available. However, it may also add some overhead to the test execution time, as it requires waiting for the service to become resolvable. The second solution (adding a retry mechanism to the client jobs) may be simpler to implement, but it may not be as reliable, as it relies on the client applications to automatically recover from the DNS resolution failures.

Implementing a Retry Mechanism for Client Jobs

Implementing a retry mechanism within the client jobs offers a pragmatic approach to mitigating the DNS resolution delays. This involves modifying the job specifications to include a backoffLimit parameter. The backoffLimit dictates the number of retry attempts the job scheduler will undertake before marking the job as failed. By default, this value is often set to 0, meaning no retries occur. Increasing this value allows the client applications to automatically attempt reconnection in the event of initial failures caused by DNS propagation delays.

To implement this solution, the Kubernetes job definition for the client applications needs to be modified. The backoffLimit parameter should be added to the job specification, and its value should be set to a reasonable number of retries. The optimal value will depend on the typical DNS propagation time in the environment. A value of 3 to 5 retries may be sufficient to cover most cases.

In addition to setting the backoffLimit, it may also be helpful to configure a delay between retry attempts. This can be achieved by using the backoff parameter in the job specification. The backoff parameter specifies the amount of time to wait before retrying a failed job. This can help to avoid overwhelming the DNS server with repeated requests and give the DNS propagation process more time to complete.

By implementing these changes, the client jobs will be able to automatically recover from temporary DNS resolution failures, reducing the flakiness of the test and improving the overall reliability of the Strimzi Kafka Operator.

Conclusion

The flaky test RecoveryST.testRecoveryFromKafkaServiceDeletion() highlights a potential issue with DNS propagation delays during Kafka service recovery in Strimzi. The proposed solutions, either waiting for service resolvability or implementing a retry mechanism in client jobs, offer viable paths to mitigate this flakiness. Addressing this issue will contribute to a more robust and reliable Strimzi Kafka Operator, ensuring smoother Kafka cluster management and recovery.

For more in-depth information on Strimzi and Kafka, refer to the official Strimzi documentation. This external resource will provide further insights and context related to the concepts discussed in this article.