Tempo Panic: TopK() And Series Limit Bug

by Alex Johnson 41 views

Introduction

This article dives into a critical bug encountered in Grafana Tempo, specifically a panic that occurs when using the topK() function in metrics queries while hitting the internal series limit. This issue can disrupt monitoring and observability workflows, making it crucial to understand the root cause and potential solutions. We'll explore the bug's symptoms, reproduction steps, and the underlying code responsible for this behavior. For those unfamiliar, Grafana Tempo is a high-scale, cost-effective distributed tracing backend. It's designed to work seamlessly with Prometheus and Grafana, offering a robust solution for tracing and observability. Understanding Tempo's limitations and potential pitfalls like this bug is essential for maintaining a healthy monitoring ecosystem.

Understanding the Bug: Metrics Query Panics with topK() and Series Limit

The core of the issue lies in how Tempo handles metrics queries that attempt to retrieve the top N series using the topK() function when the query also exceeds the server-side series limit. This limit, designed to prevent overwhelming the system with excessively large result sets, can inadvertently trigger a panic when combined with topK(). The panic manifests as a runtime error: slice bounds out of range, indicating an attempt to access a slice beyond its allocated memory. This bug highlights a critical interaction between Tempo's series limit enforcement and the topK() function's logic. The series limit is a crucial mechanism for maintaining system stability, but its interaction with functions like topK() requires careful consideration to avoid unexpected behavior. In essence, the bug arises because Tempo attempts to truncate the result set to the maximum series limit after the topK() function has already reduced the number of series. This leads to an out-of-bounds slice access, causing the panic. Understanding this sequence of operations is key to grasping the bug's nature and potential solutions.

Reproducing the Panic: Steps to Trigger the Bug

To effectively address any bug, the ability to reliably reproduce it is paramount. In this case, the panic can be triggered by following these steps:

  1. Start Tempo: Begin by deploying and running a Grafana Tempo instance. The specific version or SHA is relevant, as the bug might be present in some versions and not others. Knowing the Tempo version helps in pinpointing the bug's introduction and potential fixes.
  2. Perform a High-Cardinality Query: Execute a metrics query that is likely to return a large number of series. A common example is a query that aggregates data by a high-cardinality label, such as a UUID ({ } | rate() by (some.uuid)). This step is crucial for exceeding the series limit, which is a prerequisite for triggering the panic.
  3. Apply topK(): Add the topK() function to the query to reduce the result set to the top N series. For instance, you might use { } | rate() by (some.uuid) | topk(10) to retrieve the top 10 series. This is the critical step that, when combined with the series limit, triggers the panic.

By following these steps, you can consistently reproduce the bug and verify any proposed fixes. The ability to reproduce the issue is also invaluable for creating automated tests that prevent regressions in future releases. Regression testing is a critical aspect of software development, ensuring that bug fixes remain effective over time.

Diving into the Code: The Root Cause of the Panic

The stack trace provided in the bug report points to a specific line of code within Tempo's codebase: resp.Series = resp.Series[:maxSeries]. This line, located in traces/vendor/github.com/grafana/tempo/modules/frontend/combiner/metrics_query_range.go, is responsible for truncating the query results to the maximum series limit. However, the bug arises because this truncation happens after the topK() function has already reduced the result set. The topK() function is designed to select the top N series based on a specified metric, effectively reducing the size of the result set. However, the subsequent truncation to maxSeries can lead to an out-of-bounds slice access if the topK() function has returned fewer than maxSeries results.

To illustrate, consider a scenario where the series limit (maxSeries) is 10,000, and the topK(10) function returns only 10 series. The line resp.Series = resp.Series[:maxSeries] will attempt to create a slice of the first 10,000 elements from a slice that only has 10 elements, leading to the