Refreshed JProfiler IDEA plugin for the new UI

Kotlin 2.0 features a total rewrite of the Kotlin compiler known as "Kotlin K2". IntelliJ IDEA will remove support for the old compiler in version 2024.3 impacting all plugins that depend on the Kotlin plugin, such as the JProfiler IDEA plugin.

The newest version of the JProfiler IDEA plugin for IntelliJ IDEA now includes support for Kotlin K2 mode. This means that the JProfiler plugin no longer prevents switching to K2 mode in IntelliJ IDEA 2024.2 and is now ready for IntelliJ IDEA 2024.3 where K2 mode will be the default and plugins depending on the old API will no longer be loaded.

We took this opportunity to align the JProfiler plugin with another recent major change in IntelliJ IDEA: The new UI, which became the default in IntelliJ IDEA 2024.2. It is impractical for IDEA plugins to support both the old and the new UI, so we decided to change the UI once the new UI became widely used.

Migrating to install4j 11.0

In most cases, migrating to install4j 11 involves simply opening and saving your project with the install4j 11 IDE. Nevertheless, there are some considerations with respect to backwards compatibility and a couple of behavioral changes.

Namespace awareness of XML actions in install4j

This post explains an exceptional backward-incompatibility in the install4j 10.0.9 release. This was necessary due to a change in install4j 10.0.8 that was intended to fix the corruption of namespaced XML documents by modifying XML actions.

Why JVMTI sampling is better than async sampling on modern JVMs

In recent years, "async sampling" has been hyped as a better way of CPU profiling on the JVM. While this has been true for some time, it is no longer the case. This blog post explains the history of sampling and the current state of the art.

How invokedynamic makes lambdas fast

Recently, we have been at work rewriting our website in Kotlin. Instead of a view technology that uses string templates with embedded logic, we now use the Kotlin HTML builder to develop views as pure Kotlin code. This has a number of advantages, like being able to easily refactor common code. Also, the performance of such views is much better than that of string templates, which contain interpreted code snippets.

When measuring the performance, we noticed that a lot of anonymous classes were created for our views and their loading time was significant. Code that uses the Kotlin HTML builder is very lambda-heavy and as of Kotlin 1.9, lambdas are implemented as anonymous classes. The JVM has a sophisticated mechanism to avoid creating classes at compile time that was introduced in Java 8 - the LambdaMetafactory and invokedynamic. The JVM developers also claimed that the performance would be better than anonymous classes. So why does Kotlin not use that?

Garbage collector analysis in JProfiler

This screencast shows how to use the garbage collector probe in JProfiler. Having access to detailed information about the overall activity of the GC, as well as the single garbage collections, is crucial for tuning the garbage collector and achieving an optimal performance for your application.

Recording JFR snapshots with JProfiler

Recording JFR snapshots with JProfiler

This screencast shows JProfiler's versatile functionality as a JFR recording controller. As an example, a JFR recording on a Kubernetes cluster is recorded and the resulting snapshot is shown in JProfiler. In this context, you can see the wizard for configuring JFR recording settings. In addition, JFR recordings of terminated JVMs and the handling of externally started JFR recordings are demonstrated.

Enhanced JFR snapshot analysis with JProfiler

JProfiler has excellent support for viewing JFR snapshots. This screencast focuses on the event browser, which is specific to JFR snapshots, and also gives an overview of the other view sections that offer some of the same views as regular profiling sessions.

Working with probe events in JProfiler

Probe events are of great help in debugging specific performance problems. To find events of interest, JProfiler gives you a lot of tools to narrow down the set of displayed events.

This screencast shows the HTTP server and HTTP client probes, the JDBC and JPA/Hibernate probes as well as the socket probe when profiling a real-world application. The various ways of filtering probe events as well as duration and throughput histograms are explained.

The profiled application is the CommaFeed RSS reader.

Customizing telemetries in JProfiler

Telemetries are an essential feature for a profiler, they help you get an idea about when things happen in the profiled JVM, and how various subsystems are correlated.

This screencast shows how to customize the telemetries section in JProfiler by adding probe telemetries. It discusses bookmarks, recording actions and setting time range filters for probe events in probe telemetries.