opentelemetry-java-instrumentation VS jmh

Java

wget https://github.com/open-telemetry/opentelemetry-java-instrumentation/releases/latest/download/opentelemetry-javaagent.jar

We are actually able to handle the long tail of tracing by leveraging the amazing open source community. For languages like Java we use the automatic instrumentation created by the OpenTelemetry community which is really great and support ton of libraries, you can see a list of supported libraries here: https://github.com/open-telemetry/opentelemetry-java-instrum...

As in Python, it creates spans for every method call and HTTP entry point. It also instruments JDBC calls, but we have a Reactive stack and thus use R2DBC. For the record, a GitHub issue is open for adding support.

Download the latest Java agent ‘JAR’ from the official repo on GitHub and copy the opentelemetry-javaagent-all.jar file to your project.

Java OpenTelemetry MongoDB library for Java applications

Download the latest version of the Java JAR agent, and copy jar agent file in your application code. We have placed the agent under the folder named agents.

OpenTelemetry and Java Flight Recorder (JFR) cover most bases. Use the OpenTelemetry Java agent if you want auto-instrumentation or just the APIs if you want to do your own instrumentation.

Download the latest Java JAR agent.

OpenTelemetry provides three repositories to instrument applications. The opentelemetry-java-instrumentation repo contains the code for auto-instrumentation of Java applications.

JMH for benchmarks

I would recommend using JMH instead.

Reflection is probably very roughly between 10 and 1000 times slower. Why don't you measure it yourself using JMH?

Aleksey Shipilev is another such leader, whom is especially knowledgeable about the internals of the JVM. His writings are invaluable. He is (was) the lead of the Java microbenchmark framework (JMH} which is how one would write small performance experiments in Java, and learn what really makes a difference or now.

In the last episode we implemented a parallelMap operation using streams, raw threads, a threadpool with futures, and coroutines. At first glance the raw threads was quickest, followed by futures, coroutines and then streams. In this, part 56 of an exploration of where a Test Driven Development implementation of the Gilded Rose stock control system might take us in Kotlin, we investigate the performance of the different functions further, in particular digging down into why coroutines seem to be slow and finding a way to speed them up. We also find a way to use a particular ForkJoinPool to run the streams code, making it as fast as the others (bar the raw threads). Frankly we only use very rough benchmarks here, with no statistical testing except 'it looks like'. That's OK for gross differences, but is highly suspect when deciding which of two similarly performant approaches is faster. For that check out JMH and you could watch my video from KotlinConf 2017

A few months ago, we started to use JMH in our project to test and find performance issues. The tool provides multiple modes and profilers, and we found this useful for our purposes.