![]() ![]() It facilitates the profiling applications that run locally as well as in production.Detailed profiling and visualization: The profiler should be able to capture detailed metrics and allow for visualizations.īased on our evaluation, we chose Java Flight Recorder (JFR).Low TCO: We did not want to spend big bucks on it.Low overhead: The profiler should be suitable for continuous profiling in production.We evaluated a few options that were commonly used across the industry to perform JVM application profiling, a few notable mentions are: which help the developers in analyzing the applications. The second component uses the metrics/events from the reporter and generates relevant visualizations- these can be time trends, flame graphs, etc.In a JVM profiler, this component generally looks at the JVM-specific events such as thread samples (which show where the program spends its time), lock profiles, and garbage collection details. The first component gathers the metrics/events from a running application and logs them to a reporter (generally a file, stream, or downstream application).Profilers usually consist of two components: It then generates relevant metrics against the same and sends them to a reporter, the reporter can be either a file, stream or a downstream application. These constructs and operations include object creation, iterative executions (including recursive calls), method executions, thread executions, and garbage collections. The JVM Profiler monitors the java byte-code constructs and operations at the JVM level. JVM Profiling is the process of performing application diagnostics to figure out any performance, memory, or I/O-related bottlenecks that could be plaguing the applications that run on JVMs. In this post, we’ll discuss our approach to profiling JVM applications running in Kubernetes. Although some of them are suitable for production usage, getting them to work in a dynamic environment like Kubernetes is a real challenge. There are plenty of JVM profiling tools available and most of them are geared towards profiling local applications. This is where we need the detailed JVM profiling. We use NewRelic as our primary Application Performance Monitoring (APM) tool, and though NewRelic helps us find whether an application performance issue is actually caused by JVM, it falls short if we want to drill down to find the actual cause within JVM. We, at times, encounter performance issues with these workloads - be it issues around thread contention or frequent major GC cycles causing undesired autoscaling of applications and unnecessary resource usage. A lot of our applications are developed using Java or Kotlin. At OLX Autos, we run a lot of JVM workloads in our Kubernetes cluster. ![]()
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