Unveiling Performance Bottlenecks With Flamegraphs using async-profiler
Welcome to the first article of ‘The Month of Profiling,’ a series I will be presenting throughout November. We’ll explore a range of technologies that are crucial for identifying and resolving performance issues, as well as for fine-tuning your systems. The series will conclude with a detailed walkthrough on how to integrate these technologies for effective performance optimization.
Introduction
Optimizing the performance of a complex application like a database requires a deep understanding of multiple components; its resource utilization, the behavior of the JVM, and how nodes interact with each other. There are several useful approaches to understanding an application’s behavior, either when performance tuning or dealing with an outage. In most circumstances, teams will rely on a set of dashboards based on various metrics emitted from the database and the underlying operating system. However, the picture is only as complete as the metrics that are available, and in my experience, there can never be enough metrics get a complete understanding of an application’s behavior. In an ideal world, we’d know exactly where our time is spent and what is allocating memory, two things that have an enormous impact on performance. By understanding what’s happening in our database and the tuning that are available, we can often tweak some settings for a significant improvement in performance.