nmsilikon.blogg.se - Jprofiler 5.0 1

JPROFILER 5.0 1 HOW TO
JPROFILER 5.0 1 MAC OS X
JPROFILER 5.0 1 CODE
JPROFILER 5.0 1 LICENSE

# Eliminate the version-specific directory

JPROFILER 5.0 1 MAC OS X

Io.k8s.display-name="JProfiler from $" -H "User-Agent: Mozilla/5.0 (Macintosh Intel Mac OS X 10_12_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/.62 Safari/537.36" \ JPROFILER_DISTRO="jprofiler_linux_10_1_1.tar.gz" \

JPROFILER 5.0 1 LICENSE

If you don’t have a JProfiler image, here is a sample Dockerfile that can be used to build one (check if your JProfiler license agreement allows you to do that): FROM centos:7 This assumes that an application image and working deployment configuration for the Java application exist. This way, our JVM can reference at startup time the JProfiler agent from the shared volume. The approach here is to use an Init Container to copy the JProfiler installation to a volume shared between our Init Container and the other Containers that will be started in the Pod. For the most part, they are like regular Containers and have the property that once their execution completes successfully they are terminated only then the application Container(s) in the Pod are started. A Pod can have one or more Init Containers.

JPROFILER 5.0 1 HOW TO

Below is an example of how to accomplish just that with one such tool – JProfiler.Ī definitely not recommended approach is to “bake” JProfiler in the application’s image, which leads to tight coupling and larger application image.Ī better way is to use an Init Container. Overall, I’m really happy with this approach because it immediately shows me the performance pain points in our application.Running a JVM in Kubernetes Pod somewhat complicates things when it comes to connecting to it from an external profiler tool. If you can (and if you have something that’s called an architecture), it should be easier for you to just use class names to determine the entry points into your application (the same goes for determining the triggers). Identifying your own application just by the package name makes the Cypher queries a lot more complex.Additionally, jQAssistant isn’t able to load huge (> 2 GB) XML files out of the box, but there may be some tweaks to get that working, too.The other approach that we’re trying to do at work is to export just the Call Tree (because it’s not so much data) as a XML file, import it into Neo4j (by using jQAssistant, too) and calculate the HotSpots ourself.

I still have to ask the developers of JProfiler if this would be possible.

One approach would be the get direct access to the HotSpot data in JProfiler.

It simply doesn’t work when the XML data is getting too big (> 500 MB)

There is one big problem with this approach if you use want to export big hotspot call trees in JProfiler.

With these steps, you quickly drill down to the root cause of problems.

Retrieve the data for further analysis in Pandas.

Encode your problems/concepts in Cypher.

This approach for mining performance problem follows a simple pattern: HTTP: /petclinic/owners/1/pets/1/visits/new These points in the call tree should give us enough information that we can determine where to look for improvements in our application.

Request: The Call node with the information about the HTTP request (optional, but because JProfiler delivers this information as well, we use it here in this example).

JPROFILER 5.0 1 CODE

Entry: The first call of our own application code aka the Call node that starts also with our application’s package name.

Trigger: The executor of the SQL statement in our application aka the Call node with the last class/method call that starts with our application’s package name.

HotSpot: The executed SQL statement aka the HotSpot node.

Speaking in graph terms, we need some specific nodes of our call tree graph with the following information:

(optionally) the request that causes the problem (to be able to localize the problem).

where in our application does the problem happen) and

the information about the entry point (e.

the trigger in our application of the hotspot combined with.

Now comes the analysis part: As mentioned in the introduction, we don’t only want the hotspots that signal that something awkward happened, but also All the work before was just there to get a nice graph model that feels more natural.