Fixing Serverless Observability Test Failures: A Deep Dive

by Rajiv Sharma 59 views

Introduction

Hey guys! Today, we're diving deep into a failing test within the Serverless Observability realm, specifically focusing on Deployment-Agnostic API Integration Tests. This issue popped up in our Synthetics API tests, and we're going to break down what happened, why it matters, and how we can get things back on track. So, buckle up and let's get started!

The specific test that failed is located in x-pack/solutions/observability/test/api_integration_deployment_agnostic/apis/synthetics/get_filters.ts. It's part of the Synthetics API integration tests, and the function in question is getMonitorFilters. The goal of this test is to ensure we can retrieve a list of filters with specific monitor types. However, we encountered an Internal Server Error, which indicates something went wrong on the server side. Let's delve deeper into the error message and trace the root cause.

Understanding the Error

The error message we received is quite informative:

Error: {"statusCode":500,"error":"Internal Server Error","message":"Error installing synthetics 1.4.2: Saved object [ingest-agent-policies/3e189a70-a8dd-45fd-8a54-a66aa22ec089] not found, monitor name: Sample name"}
 at Assertion.assert (expect.js:100:11)
 at Assertion.eql (expect.js:244:8)
 at addMonitorAPIHelper (create_monitor.ts:47:29)
 at processTicksAndRejections (node:internal/process/task_queues:105:5)
 at Context.<anonymous> (get_filters.ts:74:7)
 at Object.apply (wrap_function.js:74:16) {
 actual: '500',
 expected: '200',
 showDiff: true
}

Breaking it down, we see a 500 Internal Server Error. The crucial part of the message is: "Error installing synthetics 1.4.2: Saved object [ingest-agent-policies/3e189a70-a8dd-45fd-8a54-a66aa22ec089] not found, monitor name: Sample name". This tells us that the system failed to install synthetics version 1.4.2 because a saved object, specifically an ingest agent policy with the ID 3e189a70-a8dd-45fd-8a54-a66aa22ec089, could not be found. This missing ingest agent policy is preventing the creation of a monitor named "Sample name."

The stack trace provides further clues. It points us to create_monitor.ts at line 47, where the addMonitorAPIHelper function is being called. This function likely handles the creation of monitors and the associated ingest agent policies. The error occurs during this process, suggesting a problem with either the creation or retrieval of these policies.

Why This Matters

This failure is significant because it directly impacts the Serverless Observability feature. Our ability to monitor applications and services in a serverless environment relies heavily on the Synthetics API. If we can't create monitors due to missing ingest agent policies, we lose critical visibility into the performance and health of our systems. This can lead to undetected issues, performance degradation, and ultimately, a poor user experience. Observability is key to maintaining a stable and efficient serverless infrastructure, so resolving this issue is paramount.

Furthermore, the "Deployment-Agnostic" aspect of these tests is crucial. It means we want our monitoring solutions to work seamlessly across different deployment environments. If a specific environment is missing a required ingest agent policy, it indicates a configuration issue or a potential gap in our deployment process. Fixing this ensures our observability solutions are truly deployment-agnostic.

Potential Causes and Troubleshooting Steps

So, what could be causing this issue? Here are a few potential culprits:

  1. Missing Ingest Agent Policy: The most straightforward explanation is that the ingest agent policy with the specified ID (3e189a70-a8dd-45fd-8a54-a66aa22ec089) simply doesn't exist in the Kibana instance where the tests are running. This could be due to a misconfiguration, a failed migration, or accidental deletion.

  2. Incorrect Environment Setup: The test environment might not be properly set up to include the necessary ingest agent policies. This could happen if the environment was not provisioned correctly or if there's a discrepancy between the test environment and production.

  3. Version Incompatibility: Although the error message mentions synthetics version 1.4.2, there might be an incompatibility issue between this version and the current Kibana version or other dependencies. It's worth checking the compatibility matrix to ensure everything is aligned.

  4. Database Corruption: In rare cases, data corruption in the Kibana database could lead to missing saved objects. This is less likely but still a possibility to consider.

To troubleshoot this, we can take the following steps:

  • Verify Ingest Agent Policy Existence: First, we need to check if the ingest agent policy with the ID 3e189a70-a8dd-45fd-8a54-a66aa22ec089 exists in the Kibana instance. We can use the Kibana API or the Saved Objects management UI to search for it. If it's missing, we'll need to figure out how it was supposed to be created and why it's not there.
  • Inspect Environment Configuration: We should review the environment configuration to ensure all necessary components and settings are in place. This includes checking for any scripts or processes that are responsible for creating ingest agent policies.
  • Check Kibana Logs: The Kibana server logs might contain additional information about the error. We can look for any related error messages or warnings that could shed light on the root cause.
  • Recreate the Environment: If the environment is easily reproducible, we can try recreating it from scratch to see if the issue persists. This can help rule out environment-specific problems.
  • Test with Different Versions: If version incompatibility is suspected, we can try running the tests with different versions of Synthetics and Kibana to see if the issue is resolved.

Digging Deeper into Serverless Observability

Now, let’s zoom out a bit and talk more broadly about serverless observability. In the world of serverless computing, where applications are built using ephemeral functions and services, observability becomes even more critical than in traditional environments. Why? Because the dynamic and distributed nature of serverless architectures makes it challenging to monitor and troubleshoot issues.

With serverless, you don’t have long-running servers that you can SSH into and poke around. Instead, you have functions that spin up and down on demand, often executing in environments you don’t directly control. This means traditional monitoring techniques, like looking at server CPU usage or memory consumption, are no longer sufficient. You need a more holistic approach that focuses on understanding the behavior of your application as a whole.

Observability in a serverless context encompasses three key pillars:

  1. Metrics: These are numerical measurements that provide insights into the performance and health of your application. Examples include function invocation counts, execution duration, error rates, and resource utilization. Metrics help you identify trends and anomalies, but they often don’t tell you why something is happening.

  2. Logs: Logs are textual records of events that occur within your application. They can provide valuable context for understanding the behavior of your code and identifying the root cause of issues. However, in a serverless environment, logs can be scattered across multiple functions and services, making them difficult to correlate and analyze.

  3. Traces: Traces provide a complete view of a transaction as it flows through your distributed system. They show you the path a request takes, the services it interacts with, and the time spent in each service. Traces are essential for understanding the dependencies between your functions and identifying performance bottlenecks.

Synthetics, the component implicated in our failing test, plays a crucial role in serverless observability. Synthetics allows you to proactively monitor your applications by simulating user interactions and validating that your services are behaving as expected. By running synthetic tests, you can detect issues before they impact real users and ensure your serverless applications are always available and performant.

The Significance of Deployment-Agnostic Testing

Let's circle back to the "Deployment-Agnostic" aspect of our failing test. This is a critical concept in modern software development, especially when dealing with cloud-native and serverless architectures. Being deployment-agnostic means that your applications and monitoring solutions can function seamlessly across different environments, whether it's development, staging, production, or even different cloud providers.

Why is this so important? There are several key benefits:

  • Reduced Risk: Deployment-agnostic testing helps you identify environment-specific issues early in the development lifecycle. By testing your observability solutions across different environments, you can catch configuration errors or inconsistencies before they make their way into production.

  • Increased Agility: When your applications and monitoring tools are deployment-agnostic, you can move them between environments more easily. This gives you greater flexibility to adapt to changing business needs and take advantage of new opportunities.

  • Improved Consistency: Deployment-agnostic testing ensures that your observability solutions behave consistently across all environments. This makes it easier to compare performance data and identify issues, regardless of where your application is running.

  • Simplified Operations: By standardizing your deployment process and using deployment-agnostic tools, you can simplify your operations and reduce the risk of human error.

In the context of our failing test, the fact that the ingest agent policy was missing in one environment but not others highlights the importance of deployment-agnostic testing. It suggests that there's an inconsistency in the way our environments are being provisioned or configured. Addressing this inconsistency will make our observability solutions more robust and reliable.

Diving into Synthetics API Tests

Now, let's zoom in a bit more on Synthetics API tests. As we mentioned earlier, Synthetics is a powerful tool for proactively monitoring your applications. It allows you to create synthetic monitors that simulate user interactions and validate that your services are behaving as expected. These monitors can be configured to run on a schedule, alerting you to issues before they impact real users.

Synthetics API tests are specifically designed to test the APIs that underpin the Synthetics feature itself. This includes APIs for creating, updating, and deleting monitors, as well as APIs for retrieving monitor data and results. By testing these APIs, we can ensure that the Synthetics feature is functioning correctly and that we can reliably monitor our applications.

The failing test we're discussing today, getMonitorFilters, is an example of a Synthetics API test. It's designed to verify that we can retrieve a list of filters with specific monitor types. These filters are used to narrow down the list of monitors displayed in the Synthetics UI. If this test is failing, it indicates that there's a problem with the API endpoint that retrieves these filters, or with the underlying data store.

Synthetics API tests are crucial for maintaining the health and reliability of the Synthetics feature. They provide a safety net that helps us catch issues early and prevent them from impacting our users. By investing in comprehensive API testing, we can ensure that Synthetics remains a valuable tool for serverless observability.

Addressing the Root Cause and Preventing Future Failures

Okay, so we've dissected the error message, explored potential causes, and discussed the broader context of serverless observability and deployment-agnostic testing. Now, let's talk about how we can actually fix this issue and prevent similar failures from happening in the future.

The first step is to definitively identify the root cause. Based on the error message and our discussion, the most likely cause is a missing ingest agent policy in the test environment. To confirm this, we need to:

  1. Verify the Existence of the Ingest Agent Policy: As we mentioned earlier, we can use the Kibana API or the Saved Objects management UI to search for the policy with ID 3e189a70-a8dd-45fd-8a54-a66aa22ec089. If it's not there, we've confirmed our suspicion.

  2. Investigate Policy Creation: If the policy is missing, we need to figure out how it was supposed to be created in the first place. This might involve reviewing our deployment scripts, configuration files, or any other processes that are responsible for provisioning the environment.

  3. Recreate the Policy: Once we understand how the policy should be created, we can try to recreate it manually or through our automation scripts. This should resolve the immediate issue and allow the tests to pass.

However, fixing the immediate problem is only half the battle. To prevent similar failures in the future, we need to address the underlying cause. This might involve:

  • Improving Environment Provisioning: We need to ensure that our environment provisioning process is robust and reliable. This might involve adding checks to verify that all necessary components, including ingest agent policies, are created successfully.

  • Implementing Infrastructure as Code (IaC): IaC allows us to define our infrastructure using code, which makes it easier to automate and manage. By using IaC, we can ensure that our environments are consistent and that all necessary resources are created in a predictable way.

  • Enhancing Testing: We should consider adding more tests to cover different scenarios and edge cases. This might include tests that specifically verify the creation and retrieval of ingest agent policies.

  • Improving Error Handling: We can improve the error handling in our code to provide more informative error messages. This will make it easier to diagnose issues in the future.

By taking these steps, we can not only fix the current issue but also improve the overall reliability and robustness of our serverless observability solutions. Remember, observability is not just about monitoring; it's about being able to understand and troubleshoot your systems effectively.

Conclusion

Alright guys, we've covered a lot of ground in this article! We started with a failing test in our Serverless Observability suite, dug deep into the error message, explored potential causes, and discussed the importance of deployment-agnostic testing. We also zoomed out to talk about serverless observability in general and the role of Synthetics in that context. Finally, we outlined the steps we can take to address the root cause of the issue and prevent similar failures in the future.

This kind of in-depth analysis is crucial for maintaining the health and reliability of our systems. By understanding the underlying issues and addressing them proactively, we can ensure that our applications are always available, performant, and observable. Keep up the great work, and let's keep building awesome serverless solutions!