Kibana CCS Test Timeout: Discover Search Issue

This article delves into a specific test failure encountered in Kibana, a popular data visualization and exploration tool. The failure, titled "discover/ccs_compatible discover search CCS timeout bfetch enabled timeout on single shard shows warning and results with bfetch enabled," indicates an issue related to Cross-Cluster Search (CCS) functionality within Kibana's Discover application. Specifically, the test failure occurs when a timeout is triggered during a CCS search with the bfetch option enabled, and the search is performed on a single shard. This situation highlights potential problems in how Kibana handles timeouts in CCS scenarios, especially when bfetch is involved. Understanding the root cause of this failure is crucial for ensuring the stability and reliability of Kibana's CCS capabilities.

Understanding the Error: NoSuchElementError

The error message NoSuchElementError: no such element: Unable to locate element: {"method":"css selector","selector":".euiToast:nth-child(1)"} provides a critical clue about the nature of the failure. This error arises because the test is unable to find a specific element on the page, identified by the CSS selector .euiToast:nth-child(1). The .euiToast class typically corresponds to a notification or alert message displayed in Kibana's user interface. In this context, the test likely expects a warning message (a toast) to appear when the CCS search times out. The fact that this element cannot be located suggests that the expected warning message is not being displayed under the specified conditions. This could indicate a problem with the timing of the message, the conditions under which it is triggered, or the rendering of the toast notification itself. Further investigation is needed to determine why the expected warning is not visible and whether it represents a genuine issue in Kibana's error handling.

Decoding the Stack Trace

The stack trace provides a detailed call sequence leading up to the error, offering valuable insights into the code execution path. While the raw stack trace can appear cryptic, it essentially shows the series of function calls that resulted in the NoSuchElementError. Analyzing the stack trace helps pinpoint the specific area of code where the error originated. In this case, the stack trace includes references to Selenium WebDriver, a tool used for automating web browser interactions. This confirms that the test is an automated UI test that interacts with Kibana through a web browser. The presence of node_modules/selenium-webdriver in the stack trace indicates that the error occurred within the Selenium WebDriver framework while attempting to locate the specified element. The other lines in the stack trace, such as those starting with #0, #1, etc., represent addresses within the compiled code and may require furtherSymbolication to fully understand the involved functions and modules. However, the overall stack trace confirms that the error stems from the UI automation process and the inability to find the expected toast element.

Implications of CCS Timeout with Bfetch Enabled

The specific scenario described in the test failure—CCS timeout with bfetch enabled on a single shard—is important because it highlights a potential performance bottleneck or misconfiguration in Kibana's CCS implementation. Cross-Cluster Search allows users to query data across multiple Elasticsearch clusters, enabling unified analysis and reporting. The bfetch option (Background Fetch) is designed to improve the performance of CCS queries by fetching data in the background, allowing the user interface to remain responsive. However, when a timeout occurs in this context, it suggests that the background data fetching process is taking longer than expected. This could be due to various factors, including network latency, resource limitations on the target cluster, or inefficiencies in the bfetch implementation itself. The fact that the timeout occurs on a single shard suggests that the issue may not be directly related to the distribution of data across multiple shards, but rather to the performance of fetching data from a single shard in a CCS environment. This scenario warrants further investigation to identify and address the root cause of the timeout and ensure optimal performance of CCS queries.

Potential Causes and Troubleshooting Steps

Several potential causes could contribute to the observed test failure. One possibility is that the timeout value configured for the CCS search is too low, especially when bfetch is enabled. The background fetching process may require more time to complete, particularly if the target cluster is under heavy load or network conditions are suboptimal. In this case, increasing the timeout value might resolve the issue. However, it's important to strike a balance, as setting the timeout too high could mask underlying performance problems. Another potential cause is resource contention on the target Elasticsearch cluster. If the cluster is experiencing high CPU utilization, disk I/O, or memory pressure, it could slow down the data fetching process and lead to timeouts. Monitoring the resource utilization of the target cluster during CCS queries can help identify this issue. Furthermore, the bfetch implementation itself might have performance bottlenecks or bugs that cause it to timeout under certain conditions. Analyzing Kibana's logs and metrics, as well as Elasticsearch's logs, can provide valuable insights into the behavior of bfetch and help pinpoint any performance issues. Additionally, network latency between the Kibana instance and the target Elasticsearch cluster could contribute to the timeout. Testing network connectivity and latency can help rule out this possibility. Finally, it's possible that the test itself has a flaw or is not correctly configured to handle timeouts in the CCS scenario. Reviewing the test code and configuration can help identify any issues in the test setup.

Analyzing the Buildkite Link

The provided Buildkite link (https://buildkite.com/elastic/kibana-on-merge/builds/82758#019ac5fd-502c-454a-9c91-0d99f96b24f4) points to a specific build execution in the Kibana continuous integration (CI) pipeline. Buildkite is a popular CI/CD platform used by many software development teams, including the Kibana team, to automate the process of building, testing, and deploying software. By examining the Buildkite build, we can gain further insights into the context of the test failure. The link directs us to a specific build execution (builds/82758) and a particular step within that build (#019ac5fd-502c-454a-9c91-0d99f96b24f4). This step likely corresponds to the execution of the Kibana CCS tests, including the failing test in question. Within the Buildkite build logs, we can find detailed information about the test execution, including the specific commands that were run, the environment variables that were set, and any error messages or stack traces that were generated. Analyzing these logs can provide valuable context for understanding the cause of the test failure. For example, we might find information about the Elasticsearch cluster configuration, the Kibana settings, or the test data that was used. Additionally, the Buildkite build may include information about other tests that failed or passed in the same build, which can help us understand whether the failure is isolated to this specific test or part of a broader issue. By carefully examining the Buildkite build, we can gather valuable clues that aid in troubleshooting the test failure.

KibanaCiData and Test Metadata

The kibanaCiData block provides structured metadata about the test failure, which can be useful for automated analysis and reporting. This data is typically generated by the Kibana CI system and includes key information about the failed test. The failed-test section contains details about the specific test that failed, including the test class (Kibana CCS Tests.src/platform/test/functional/apps/discover/ccs_compatibility/_timeout_results·ts) and the test name (discover/ccs_compatible discover search CCS timeout bfetch enabled timeout on single shard shows warning and results with bfetch enabled). This information helps identify the exact test case that experienced the failure. The test.failCount field indicates the number of times the test failed, which in this case is 1. This metadata can be used to track test failures over time, identify flaky tests, and prioritize bug fixes. The kibanaCiData block is typically used by Kibana's CI infrastructure to collect and analyze test results, generate reports, and trigger alerts. This structured data makes it easier to automate the process of identifying and addressing test failures. For example, the CI system could automatically create a bug report based on the kibanaCiData, or it could assign the issue to the appropriate team based on the test class. By leveraging this metadata, the Kibana team can improve the efficiency and effectiveness of their testing process.

Steps to Resolve the Kibana CCS Test Failure

To effectively resolve this Kibana CCS test failure, a systematic approach is crucial. The initial step involves a thorough review of the test failure details, including the error message, stack trace, and Buildkite logs. This review aims to gain a comprehensive understanding of the failure context and identify potential causes. Next, examining the Kibana and Elasticsearch logs is essential to uncover any relevant error messages or warnings that may shed light on the issue. Following this, assessing the resource utilization of the Elasticsearch cluster during the test execution is necessary to rule out resource contention as a contributing factor. If resource constraints are identified, optimizing the cluster configuration or scaling resources may be required. Furthermore, analyzing the network connectivity between Kibana and Elasticsearch is important to ensure there are no network-related issues causing timeouts. If network latency is suspected, investigating network infrastructure and configurations may be necessary. Additionally, reviewing the Kibana CCS settings and configurations is crucial to verify that the timeout values and other settings are appropriately configured for the test environment. Incorrect or suboptimal settings can lead to unexpected timeouts. Moreover, evaluating the test code itself is important to ensure its correctness and robustness. The test logic should be thoroughly examined for any potential flaws or misconfigurations that could be triggering the failure. After implementing the necessary fixes or adjustments, re-running the test is vital to confirm that the issue has been successfully resolved. It is recommended to monitor the test results closely and iterate on the troubleshooting process if the failure persists. By diligently following these steps, the Kibana CCS test failure can be effectively addressed, ensuring the stability and reliability of Kibana's CCS functionality.

Conclusion

The Kibana CCS test failure described in this article highlights the complexities of testing distributed systems and the importance of robust error handling. The NoSuchElementError combined with the CCS timeout scenario with bfetch enabled points to a potential issue in how Kibana handles timeouts and displays warning messages in CCS environments. By systematically analyzing the error message, stack trace, Buildkite logs, and test metadata, developers can effectively pinpoint the root cause of the failure and implement appropriate solutions. Addressing this issue will improve the overall stability and reliability of Kibana's CCS functionality, ensuring a better user experience for those who rely on it for cross-cluster data analysis. Further exploration of Kibana testing and troubleshooting can be found on the Elasticsearch and Kibana official documentation.