Troubleshooting Kibana Lens App Field List Test Failures

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Hey guys! Ever run into a test failure that just makes you scratch your head? We've all been there. Today, we're diving deep into a specific Kibana test failure related to the Lens app, focusing on issues with the field list. This can be a tricky area, so let's break it down in a way that's easy to understand and, most importantly, easy to troubleshoot.

Understanding the Error

So, what exactly went wrong? The error message we're looking at is:

Error: expected '3' to sort of equal 52
    at Assertion.assert (expect.js:100:11)
    at Assertion.eql (expect.js:244:8)
    at Context.<anonymous> (fields_list.ts:227:18)
    at processTicksAndRejections (node:internal/process/task_queues:95:5)
    at Object.apply (wrap_function.js:74:16) {
  actual: '3',
  expected: 52,
  showDiff: true
}

This error popped up during a test in the fields_list.ts file within the Lens app's group 2 tests. Specifically, the test "lens app - group 2 lens fields list tests form-based datasource should move some fields as empty when the time range excludes them" failed. The core issue? The test expected 52 fields to be moved as empty when the time range excluded them, but it only found 3. This is a classic mismatch between what we expect and what actually happens, which is the bread and butter of debugging!

Breaking Down the Error Message

Let's dissect this error message a bit further:

  • expected '3' to sort of equal 52: This is the heart of the problem. The test expected a certain number of fields (52) to be treated as empty, but the actual count was only 3. The "sort of equal" suggests we're likely dealing with some kind of comparison that's not a strict equality check.
  • at Assertion.assert (expect.js:100:11) and at Assertion.eql (expect.js:244:8): These lines point to the assertion library being used (likely Expect.js or a similar library). The assert and eql methods are used for making assertions about expected values. In this case, the eql method (which checks for deep equality) failed.
  • at Context.<anonymous> (fields_list.ts:227:18): This is crucial! It tells us the exact location of the failure within the test file: line 227 in fields_list.ts. This is where we'll need to focus our investigation.
  • at processTicksAndRejections (node:internal/process/task_queues:95:5) and at Object.apply (wrap_function.js:74:16): These lines are related to the asynchronous nature of the test. They indicate that the error occurred within a promise or asynchronous operation.
  • actual: '3', expected: 52, showDiff: true: This provides the concrete values that caused the failure. actual is the number of fields found (3), expected is the number of fields the test anticipated (52), and showDiff: true suggests that a diff might be available to further analyze the discrepancy.

Understanding the Test's Intent

Before we jump into potential solutions, let's clarify what this test is trying to achieve. The test name, "lens app - group 2 lens fields list tests form-based datasource should move some fields as empty when the time range excludes them," gives us a big clue. Here's what it means:

  • Lens App: We're dealing with the Lens visualization tool in Kibana. Lens allows users to create visualizations by dragging and dropping fields.
  • Group 2 Lens Fields List Tests: This narrows down the scope to a specific group of tests related to the field list in Lens.
  • Form-Based Datasource: This indicates that the data source being used in the test is form-based, meaning it's likely a data view or index pattern defined through Kibana's management UI.
  • Should Move Some Fields as Empty When the Time Range Excludes Them: This is the core behavior being tested. When the time range selected in Lens excludes data for certain fields, those fields should be treated as empty or unavailable for visualization.

In essence, this test is ensuring that Lens correctly handles scenarios where data fields become irrelevant due to time-based filtering. This is crucial for accurate and meaningful visualizations.

Potential Causes and Troubleshooting Steps

Okay, now that we understand the error and the test's purpose, let's brainstorm some potential causes and outline a plan for troubleshooting. Think of this as our detective work – we're gathering clues and following leads!

1. Time Range Issues

This is the most likely culprit, given the test's focus on time ranges. The test expects fields to be treated as empty when the time range excludes them, so anything that interferes with time range filtering could cause this failure.

  • Incorrect Time Range Configuration: Double-check the time range being used in the test. Is it correctly configured to exclude data for the expected fields? A slight misconfiguration here can lead to a significant discrepancy in the field count.
  • Data Ingestion Problems: Are the data fields being correctly indexed with time-based information? If the time field is missing or improperly formatted, Kibana won't be able to filter data based on time ranges.
  • Timezone Mismatches: Timezone issues can be surprisingly sneaky! If the data is indexed in one timezone and Kibana is configured to use a different timezone, time-based filtering can produce unexpected results.

2. Data View or Index Pattern Configuration

The test mentions a "form-based datasource," which likely refers to a Data View (formerly known as Index Pattern) in Kibana. Issues with the Data View configuration can definitely impact the fields available to Lens.

  • Incorrect Field Mappings: Are the fields correctly mapped in the Data View? If a field's data type is incorrectly configured (e.g., a date field is mapped as text), Kibana won't be able to perform time-based filtering on it.
  • Field Filters: Are there any filters applied to the Data View that might be inadvertently excluding fields? Filters can be powerful, but they can also cause unexpected behavior if not configured carefully.
  • Missing Fields: Is the Data View configured to include all the necessary fields? It's possible that some fields are missing from the Data View, leading to a lower field count in Lens.

3. Lens App Bugs

While less likely than configuration issues, it's always possible that there's a bug in the Lens app itself. This is especially true if you're working with a recent or development version of Kibana.

  • Lens Filtering Logic: There might be an issue in Lens's filtering logic that prevents it from correctly identifying and treating fields as empty when the time range excludes them.
  • Field List Rendering: The problem could also be in how Lens renders the field list. Perhaps the field count is incorrect due to a rendering bug.
  • Recent Code Changes: If the test started failing after a recent code change in Lens, it's a strong indicator that the change introduced a bug.

4. Test Environment Issues

Finally, let's not forget the possibility of issues with the test environment itself.

  • Inconsistent Data: Is the data used in the test consistent and predictable? If the data changes frequently or contains unexpected values, it can lead to test failures.
  • Test Setup: Are the test setup steps correctly configuring the environment? For example, is the Data View being created with the correct settings before the test runs?
  • Concurrency Issues: In a multi-threaded test environment, concurrency issues can sometimes cause unexpected failures. This is less likely in this specific case, but it's worth considering.

A Step-by-Step Troubleshooting Guide

Okay, armed with our list of potential causes, let's create a step-by-step guide to tackle this issue:

  1. Reproduce the Error Locally: The first step is always to try and reproduce the error in a local development environment. This will give you the freedom to experiment and debug without affecting other developers or the production environment. Use the link provided in the error report (kibana-on-merge - 9.0) to get more context about the build and test run. This link could provide a hint about the Kibana version that failing or other context. You can execute the test locally using the command line tools that Kibana provides for developers. Often, this involves commands like yarn test functional or similar, with additional flags to filter to the specific test.
  2. Inspect the Test Code (fields_list.ts:227): Go directly to the line of code where the error occurred (line 227 in fields_list.ts). Use a debugger or console.log statements to inspect the values of relevant variables. What are the actual fields being considered? What is the time range being used? Is the test correctly setting up the conditions it expects?
  3. Verify the Time Range: Carefully examine the time range configuration in the test. Is it correctly set to exclude data for the expected fields? Try manually setting the same time range in the Lens UI and see if the field list behaves as expected. This can help isolate whether the issue is in the test setup or in Lens's time range handling.
  4. Examine the Data View: Check the Data View configuration in Kibana's Management UI. Are the field mappings correct? Are there any filters that might be interfering? Make sure the Data View includes all the necessary fields and that their data types are correctly configured. You might also try creating a new, simplified Data View with only the fields involved in the test to see if that resolves the issue. This can help rule out problems with the Data View configuration itself.
  5. Debug Lens Filtering Logic: If the time range and Data View configurations seem correct, the next step is to dive into the Lens code. Use a debugger to step through the filtering logic in Lens. How does Lens determine which fields to treat as empty based on the time range? Are there any conditional statements or calculations that might be producing incorrect results? This might involve setting breakpoints in the Lens code and examining the state of variables as the code executes.
  6. Check Data Consistency: Ensure that the data used in the test is consistent and predictable. If the data changes frequently, it can lead to flaky test results. If possible, use a fixed dataset for the test to eliminate data variability as a potential cause. Examine the data in Elasticsearch directly to verify its structure and contents. Are the time-based fields correctly formatted and populated? Are there any unexpected values or inconsistencies in the data?
  7. Review Recent Code Changes: If the test started failing after a recent code change in Lens, carefully review the changes. Did any changes affect time range handling, field filtering, or field list rendering? Use Git history or other version control tools to compare the code before and after the change. Pay close attention to any code that interacts with the time range, Data View, or field list. It's possible that the change introduced a bug or inadvertently broke existing functionality.
  8. Consult Kibana Logs: Check the Kibana server logs for any error messages or warnings that might provide clues. Look for log entries related to Lens, time range filtering, or Data View access. The logs might contain stack traces or other diagnostic information that can help pinpoint the source of the problem. Pay attention to the timestamps in the logs to correlate them with the test execution time.

Fixing the Issue

Once you've identified the root cause, the fix will depend on the specific problem. Here are some common solutions:

  • Correct Time Range Configuration: If the time range is misconfigured, update the test code to use the correct time range.
  • Adjust Data View Settings: If the Data View is incorrectly configured, modify the field mappings or filters as needed.
  • Fix Lens Bug: If there's a bug in Lens, implement a code fix and submit a pull request.
  • Update Test Setup: If the test setup is incorrect, adjust the setup steps to ensure the environment is properly configured.

No matter the solution, always write a new test case or modify the existing one to cover the bug that you fix. This will help prevent regressions in the future. This may involve creating a new test case that specifically targets the scenario where the fields are expected to be empty based on the time range, or modifying the existing test to be more robust and handle different time range configurations.

Conclusion

Troubleshooting test failures can be frustrating, but it's also a valuable learning experience. By systematically investigating the error, understanding the test's intent, and considering potential causes, you can effectively diagnose and resolve even the most challenging issues. Remember, the key is to break down the problem into smaller parts, gather clues, and follow the evidence. And hey, don't be afraid to ask for help! Kibana has a fantastic community, and there are plenty of experienced developers who are willing to lend a hand. Happy debugging, guys!