JProfiler’s support for Netty covers many modern web frameworks that use Netty under hood, like Vert.x, Micronaut, Ktor or Spring WebFlux.
This screen cast shows how to profile a Netty server with customized URL splitting and gives an overview of the HTTP server probe.
This screencast shows how to use the “Inline async executions” call tree analysis to get a unified call tree across thread boundaries
This is made possible by JProfiler “request tracking” feature. In the screencast, a simple example with Spring WebFlux is profiled that creates permutations of a specified word.
Most reactive stream frameworks use the executor service JDK API for asynchronous execution. JProfiler can track tasks that are submitted to an executor service and stitch them back into a single call tree when the “Inline async executions” call tree analysis is invoked. This is essential when analyzing performance bottlenecks in asynchronous programming models.
This screen cast shows how to profile HTTP calls with the HTTP client probe and use the HTTP remote request tracking feature to follow HTTP calls from one JVM into another.
A simple Spring Boot project with two application is profiled: A gateway server that serves static files and proxies API request to a second server that handles REST calls.
You can see how JProfiler tracks calls from the browser including the JavaScript stack trace through the gateway server into the REST server. The HTTP client probe with its various views is shown and its profiling settings are discussed.
This screen cast shows how to analyze memory leaks in Java applications or reduce memory consumption in general if the objects of interest are not all referenced in the same way.
Analyzing a single object and finding the path to a garbage collector root is not enough in that case and the merged incoming references view as well as the merged dominating reference view are helpful in finding out about the distribution of the actual incoming references and how to make a maximum of objects eligible for garbage collection.
This screen cast shows how to configure a script probe for a Java profiling session in JProfiler.
Script probes are custom probes that intercept selected methods and construct payload strings with Java scripts that are entered directly in the JProfiler UI.
You then get a payload call tree, payload hot spots with back traces, telemetries for probe event counts and event durations as well as the ability to inspect single events with their threads and stack traces.
This screencast shows the JProfiler plugin for IntelliJ IDEA. A run configuration is profiled, source code navigation is discussed and the call graph data display in the IDE is shown.
This screencast explains a basic strategy for solving memory leaks with JProfiler.
There is an older version of this screencast from 2009 that is not accurate for the heap walker anymore but that shows other useful features in JProfiler.
Complexity analysis in JProfiler is a tool for experimentally determining the Big-O behavior of algorithms based on the execution times of single selected methods. A bubble chart with curve fits of common complexities visualizes the results of the analysis.
This screen cast shows how to attach to a remote JVM with zero configuration on the remote side. The only requirement is an SSH connection to the remote machine. Remote JVMs are listed in the JProfiler UI and a JVM can be selected for profiling.
This screen cast shows how the “Set root” action is used to analyze a specific part of your code. The “Set root” action in the call tree view is used to select the call stack of interest. The hot spot view and the call graph then only show data for the selected part of the call tree.