Tuning OTel Collector Performance Through Profiling - Braydon Kains, Google

Summary

In this insightful talk, Braden K, a contributor to OpenTelemetry, discusses performance tuning of the OpenTelemetry Collector through profiling. He introduces profiling as a means to measure program activity at specific locations, akin to time series data points. Using 'pprof', a built-in Go tool, he demonstrates how to analyze and improve the Collector's performance, addressing issues like memory leaks and inefficient process metric collection. The talk showcases the utility of profiling tools for developers and users alike, emphasizing their accessibility and the potential for continuous profiling to enhance understanding and optimization of applications.

Takeaways

• 🔧 The speaker, Braden K, discusses the use of profiling for performance tuning of the OpenTelemetry Collector and emphasizes the accessibility of profiling tools.
• 📈 Profiling is likened to taking measurements at different locations in a program over time, similar to how metrics are measured at different timestamps.
• 📊 Profiling formats such as PPR (which OpenTelemetry profiles are based on) support multiple types of signals and measurements, including CPU and memory profiling.
• 🛠️ The OpenTelemetry Collector is written in Go, which has built-in support for using PPR, simplifying the process of profiling.
• 🔬 Case studies are presented to demonstrate the use of profiling in identifying performance issues, such as a potential memory leak in the Prometheus receiver.
• 🔍 The use of flame graphs in PPR provides a visual representation of memory allocation and can help pinpoint areas of a program consuming the most resources.
• 🔄 The speaker identifies a potential cardinality leakage in the cumulative to Delta processor, suggesting a need for better cache eviction configuration.
• ⚙️ Process metrics collection on Windows is identified as inefficient due to the high cardinality and system call costs, leading to a quest for optimization.
• 💡 The Windows Management Interface (WMI) is explored as a more efficient method for retrieving parent process IDs, reducing the workload of a single scrape.
• 🚀 The speaker shares ongoing work to improve the OpenTelemetry Collector's process scraping efficiency on Windows, with a PR pending merge.
• 🌐 The talk concludes with an encouragement for OpenTelemetry developers and users to utilize profiling tools to understand and improve their collector's performance.

Q & A

What is the main topic of the talk given by Braden KES?

The main topic of the talk is about tuning the OpenTelemetry Collector's performance through profiling, specifically using the pprof built-in tool in Go to analyze performance problems.

What is the purpose of profiling in the context of the talk?

Profiling is used to measure and analyze the performance of a program at different locations, similar to how metrics are used to measure something over time. It helps to identify performance issues such as memory leaks or CPU usage inefficiencies.

What is the pprof format and why is it significant in the talk?

Pprof is a profiling format that supports multiple types of signals and measurements. It is significant because it is the format that OpenTelemetry profiles are based on, and it is built into Go, the language used for the OpenTelemetry Collector.

What is the difference between a metric and a profile in the context of performance monitoring?

A metric is a time series data point, a measurement of something at a specific time. A profile, on the other hand, is a measurement taken at a specific location in a program, and when aggregated, it provides insights into what the program was doing over the measured locations.

What is the role of Braden KES in the OpenTelemetry project?

Braden KES is a code owner on the host metric receiver and a member of the system metric semantic conventions working group. He mainly focuses on system metrics and works on the Google Cloud Ops agent.

What was the issue with the Prometheus receiver that Braden KES investigated?

The issue was a suspected memory leak in the Prometheus receiver, where the memory usage was constantly growing over time.

What is a flame graph and how is it used in the context of the talk?

A flame graph is a visualization tool used in profiling to show where different allocations are happening in the program. It helps identify areas of the program that are using more resources, which can be indicative of performance issues.

What was the conclusion from the memory profiling of the Prometheus receiver?

The conclusion was that the Prometheus receiver itself was not allocating a significant amount of memory. The growth in memory usage was attributed to the cumulative to Delta processor, suggesting a potential cardinality leakage issue.

What is the significance of the cumulative to Delta processor in the context of the memory leak investigation?

The cumulative to Delta processor was identified as the main consumer of memory, suggesting that it might be storing too much data without properly evicting old entries, leading to a memory leak.

What was the second case study presented in the talk?

The second case study focused on improving the efficiency of process metric collection, specifically addressing the high CPU usage when getting the parent process ID for every process on Windows using the host metrics receiver.

What was the solution proposed to reduce CPU usage in the process metric collection on Windows?

The solution proposed was to use the Windows Management Interface (WMI) to query and retrieve the parent process ID in one go, instead of using the win32 API which was found to be inefficient.

What are the potential benefits of using the OpenTelemetry profile format across different languages?

The potential benefits include a standardized way to profile applications, allowing for consistent tooling and analysis methods across different programming languages, making it easier to identify and solve performance issues.