Kibana Streams Crash: ILM Policy Bug In Retention Page

by Alex Johnson 55 views

Experiencing crashes on your Kibana Streams retention page? You're not alone. This article dives deep into a bug affecting Kibana version 9.2.0+ that causes the page to crash when specific Index Lifecycle Management (ILM) policies are in place. We'll break down the bug, explain the cause, provide steps to reproduce it, and discuss the expected behavior.

Understanding the Kibana Streams Retention Page Crash

If you're encountering an "Unable to load page" error when navigating to Streams and selecting a stream with a particular ILM policy, you've likely run into this bug. This issue stems from an invalid grow prop being passed into EuiFlexItem, leading to an internal Kibana crash. The root cause lies in ILM policies where all phases have a minimum age of 0, specifically those with only hot and cold phases. The ilm_summary.tsx file incorrectly assumes the last phase will have a non-zero min_age. When both phases have min_age = 0, a division by zero occurs, resulting in a NaN value. This NaN is then passed to the grow property, triggering a fatal React crash, preventing users from viewing stream retention and disrupting the overall Streams management experience.

Diving Deeper into the Bug

To truly grasp the issue, let's examine the technical details. The error message, Error: Prop grow passed to EuiFlexItem must be a boolean or an integer between 0 and 10, received NaN, clearly indicates that the grow property of the EuiFlexItem component is receiving an invalid value. This property is designed to control how much a flex item will grow relative to the other flex items in the same container. It expects a number between 0 and 10 or a boolean value. However, in this case, it's receiving NaN (Not a Number), which is a result of a mathematical operation that doesn't produce a meaningful numerical result – specifically, division by zero.

The problematic ILM policy configuration is the key to understanding why this happens. When an ILM policy defines both the hot and cold phases with a min_age of "0ms" and "0d" respectively, the ilm_summary.tsx component's logic falters. This component calculates a totalDuration based on the minimum ages of the ILM phases. When all phases have a minimum age of zero, the totalDuration becomes zero. Subsequently, a calculation within the component attempts to divide by this zero totalDuration, leading to the NaN value. This NaN is then mistakenly passed as the grow prop to the EuiFlexItem component, causing the crash.

Why is this significant? This bug doesn't just prevent users from viewing retention information; it effectively breaks the default Streams management tab. Users rely on this tab to understand and manage their data lifecycle, and this crash severely hinders their ability to do so. The inability to properly manage data retention can lead to compliance issues, increased storage costs, and potential data loss. Therefore, understanding and addressing this bug is crucial for maintaining a stable and efficient Kibana environment.

The Impact on User Experience

Imagine a scenario where a user needs to quickly assess the retention policy applied to a specific stream. They navigate to the Streams management page, select the desired stream, and are immediately met with an "Unable to load page" error. This not only disrupts their workflow but also instills frustration and uncertainty. They may be left wondering if there's a problem with their data, their configuration, or Kibana itself. This negative experience can erode trust in the platform and its ability to reliably manage data.

Furthermore, the lack of access to retention information can have serious operational consequences. Without a clear understanding of how long data is being retained, users may struggle to comply with data governance policies or optimize their storage usage. They might inadvertently retain data for longer than necessary, leading to increased costs, or delete data prematurely, potentially resulting in data loss. This highlights the critical importance of addressing this bug to ensure a smooth and reliable user experience.

Steps to Reproduce the Kibana Streams Crash

Here's a step-by-step guide to reproduce the bug, allowing you to see the issue firsthand and confirm if you're affected:

  1. Create a Custom ILM Policy: The first step is to create an ILM policy with the specific configuration that triggers the bug. This policy should include only hot and cold phases, both with a minimum age of 0. You can achieve this through Kibana's Management UI or by directly interacting with the Elasticsearch API.

    • Using Kibana's Management UI:

      • Navigate to Stack Management -> Index Lifecycle Policies.
      • Click Create policy.
      • Name your policy (e.g., "ZeroAgePolicy").
      • In the Hot phase, set the minimum age to 0ms and configure other settings as needed (e.g., rollover conditions). It's important to set some rollover conditions, even if they're very high limits, otherwise the policy might not be valid. For instance, you can set max_age to 30d and max_primary_shard_size to 50gb.
      • In the Cold phase, set the minimum age to 0d and configure any desired actions (e.g., allocate, freeze). If you don't configure any actions in the cold phase, it can often simply be left empty. However, the min_age must be explicitly set.
      • Leave the other phases (Warm, Delete) disabled or configure them as needed.
      • Click Save policy.

    • Using Elasticsearch API:

      • You can use the following API request as a template, adjusting the policy name as needed:

        PUT _ilm/policy/zero_age_policy
{
  "policy": {
    "phases": {
      "hot": {
        "min_age": "0ms",
        "actions": {
          "rollover": {
            "max_age": "30d",
            "max_primary_shard_size": "50gb"
          }
        }
      },
      "cold": {
        "min_age": "0d",
        "actions": {}
      }
    }
  }
}

  2. Navigate to Any Stream: Once you've created the ILM policy, navigate to any stream within Kibana. This could be an existing stream or a newly created one.

  3. Edit the Retention Using the Created Policy: This is the crucial step that triggers the bug. Edit the stream's retention settings and apply the ILM policy you created in step 1.

    • Navigate to Streams within Kibana.
    • Select any stream.
    • Go to the Retention tab (or a similar tab that allows you to configure ILM policies).
    • Choose to use a custom policy and select the ILM policy you created (e.g., "ZeroAgePolicy").
    • Save the changes.

  4. Observe the Crash: After applying the ILM policy, attempt to view the stream's retention information. You should observe the "Unable to load page" error, indicating that the bug has been triggered.

By following these steps, you can reliably reproduce the Kibana Streams crash and confirm that the issue is related to the specific ILM policy configuration. This allows you to better understand the problem and take appropriate steps to mitigate it.

Visual Confirmation

The original bug report included images that visually demonstrate the steps to reproduce the issue. These images can be invaluable for confirming that you're following the correct procedures. If you have access to those images, refer to them as you work through the steps.

Expected Behavior

So, what should happen instead of a crash? The UI should load without errors and display all the phases of the ILM policy correctly. This means users should be able to view the hot and cold phases, their associated minimum ages, and any other configured actions. The retention visualization should accurately represent the data lifecycle as defined by the ILM policy.

In essence, the user should have a clear and intuitive understanding of how their data is being managed over time. This includes knowing when data will transition between phases, what actions will be performed in each phase (e.g., rollover, allocation, freezing), and ultimately, when data will be deleted. This transparency is crucial for effective data management and compliance.

Why is Correct Display Important?

The correct display of ILM policy phases is not just about aesthetics; it's about ensuring data integrity and compliance. If the UI crashes or displays incorrect information, users may make decisions based on flawed data, potentially leading to data loss, security breaches, or regulatory violations. For example, a user might inadvertently delete data prematurely if they misunderstand the retention policy, or they might retain data for longer than necessary, increasing storage costs and legal risks.

Furthermore, a clear and accurate representation of ILM policies fosters trust in the platform. Users are more likely to rely on a system that provides them with a transparent view of their data lifecycle. This trust is essential for the long-term adoption and success of the platform.

Conclusion

The Kibana Streams retention page crash caused by specific ILM policies is a significant issue that can disrupt user workflows and hinder effective data management. By understanding the bug, its cause, and the steps to reproduce it, users can better troubleshoot the problem and potentially implement workarounds until a fix is officially released. The expected behavior of the UI is to load without errors and display all ILM policy phases correctly, ensuring data integrity and compliance.

Remember to always test ILM policies thoroughly in a non-production environment before applying them to production data. This can help you identify potential issues like this bug and prevent unintended consequences.

For more information on Kibana and Elasticsearch, you can visit the official Elastic website: https://www.elastic.co/