Download EJ Technologies JProfiler v11.1.2 Build 11147 (12 Mar 2020) + Keygen JProfiler's intuitive UI helps you resolve performance bottlenecks, pin down memory leaks and understand threading issues. EJ Applied sciences JProfiler Serial It’s a highly effective software program for profiling Java-based functions that allows functions to be analyzed to optimize their efficiency. Through the use of the options of the JProfiler utility, it’s attainable to profile a operating JVM, an utility server, a Java internet startup utility, and even browser-running app. The JProfiler plugin is developed by ej-technologies, so you should ask them about its status. As far as I can see, the latest available version of the plugin is marked as compatible with IntelliJ IDEA 11, but I don't know if there are any compatibility issues when using it. Downoad EJ Technologies JProfiler v11.1.2 Build 11147 + Keygen Torrent with Crack, Cracked FTUApps.Dev JProfiler's intuitive UI helps you resolve performance bottlenecks, pin down memory leaks and understand threading issues. When it comes to profiling, only the best tool is good enough. Check out below what makes JProfiler the top choice for profiling your applications on the JVM. EJ Technologies JProfiler 11.1.4 Build 11169 (x64) 110 Mb JProfiler is a powerful tool that you can use to profile Java based applications in a dynamic way and enables you to analyze them in hopes of optimizing performance. EXCEPTIONAL EASE OF USE When you profile, you need the most powerful.
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Published June 2002
JProfiler is a Java profiler combining CPU, Memory and Thread profiling in one application. Remotix 5 1 15. It is developed by ej-technologies and currently in version 1.2.
JProfiler offers two different kinds of sessions:
For the example used in this article, we will profile a GUI application using a local session. We have selected the Jclasslib class file browser as our application (available from http://jclasslib.sourceforge.net), a tool for displaying Java class files and bytecode. While Jclasslib provides a lot of insight into the Java class file format, the bytecode display seems to be quite memory intensive and fairly sluggish for displaying large methods. We'll use JProfiler to identify the bottleneck.
If we were starting Jclasslib from the command line, we would use To start Jclasslib using JProfiler, we fill in the same details into the JProfiler sessiondetails window.
First, we create a new session to start the application, then we fill in the same details we would use on the command line: main class; class path; VM parameters. The VM parameters field here is used to change the look and feel of the class file browser.
JProfiler lets you specify which packages you want included in the profile report. This useful feature allows you focus on the bottlenecks separately from the various components of an application. The Jclasslib package starts with org., so we disable the corresponding filter on the 'active filters' tab of the configuration dialog (see second screenshot on the right). This ensures that org.* classes will be reported in our profile.
Now we have configured our session with all the necessary information, we save the session and start it. A terminal window appears, capturing the stdout and stderr output of the profiled application (Jclasslib in this example) and the Jclasslib class file browser appears. During the startup phase JProfiler immediately starts displaying live information in all its views. If you'd rather have static snapshots on demand, there are buttons for freezing all views and fetching data manually in the toolbar.
To identify the bottlenecks in Jclasslib, we now select a very large method to display in the Jclasslib browser window: for our example we will browse the javax.swing.MetalLookAndFeel.initComponentDefaults method, which is 9 kB of bytecode. Before we select the method for display, we reset the CPU data in JProfiler to eliminate the profiling information collected during startup of Jclasslib. We are interested in profiling the Jclasslib display action, not the Jclasslib startup. Displaying the javax.swing.MetalLookAndFeel.initComponentDefaults method in the Jclasslib browser takes quite a long a time, and the heap size expands to a whopping 40 MB. This is the inefficient action we want to look at for our example. We freeze all the views in JProfiler and start working on the analysis.
First, we look at the performance problem. The key to finding out where all the time is spent is JProfiler's 'Hot spots view'. The top level entries are the most time consuming methods. We can see immediately that most of the time is spent in javax.swing.AbstractDocument.insertString (53%) and java.awt.CardLayout.show (32%). By expanding the top level nodes we get the backtraces showing the various ways the hot spot method was called.
Here's where the 'filter sets' we earlier defined come into play. Of course, javax.swing.AbstractDocument.insertString calls a zillion other methods, but since javax. was selected as a filtered package, all these subsequent calls are summed up in the first method that was called from an unfiltered class, typically code written by yourself. What happens inside javax.swing.AbstractDocument.insertString is the internal mechanics of an external package which is outside your influence. If you were interested, you could always turn off the corresponding filter set.
From the CPU profile, we can see that all invocations to javax.swing.AbstractDocument.insertString originate in org.gjt.jclasslib.browser.detail.attributes.BytecodeDocument. This is the document that's used to display the method bytecode in the class file browser. The java.awt.CardLayout.show was then called when the document was actually displayed.
The 'Hot spots view' is a bottom-up view. JProfiler also offers a top-down view displaying the entire call tree. While the 'Hot spots view' cumulates times for all invocation paths, the 'Invocation tree' is perfect for finding single bottlenecks by expanding the tree along the large percentage values. It also gives you a feeling for the execution speed of various application components as well as a bird's eye view of the call flow. This view is useful for debugging as well as profiling.
Both through the 'Hot spots view' and the 'Invocation tree' we arrive at the same conclusion: javax.swing.AbstractDocument.insertString is extremely slow (here over 1 ms per call) and preparing the document for rendering is equally costly. Next, we turn to memory profiling to see whether the large heap size is connected to this performance bottleneck.
When we look at the garbage collector telemetry view, we can see that during the creation of the document a substantial number of short-lived objects are created on the heap. In the allocations monitor (second screen shot on the right), we can show the garbage collected objects by clicking on the trash can in the tool bar. By expanding the call tree along the large instance count values, we arrive at the same trouble spot as detected on the previous page. Below BytecodeDocument.setupDocument nearly 2 million objects have been allocated and garbage collected. This certainly constitutes a huge waste of resources completely out of proportion with respect to the performed task.
Now we'll use JProfiler's heap walker to examine this allocation problem from a close-up perspective. After taking a heap snapshot, we go to the classes view and find that more than half a million objects and 140000 arrays are still alive on the heap - mostly for displaying the 3000 lines in the bytecode document!
JProfiler's heap walker is quite different from the heap analysis views of competing products in that it operates on arbitrary object sets - and not on objects of a single class only. For every object set you can choose between six different views: classes, allocations, outgoing references, incoming references, class data and instance data. By selecting items in these views and using the navigation panel on the left, you can add new filter steps and modify your current object set. With the forward and back buttons in the tool bar you can move around in your selection history.
The classes view of the heap walker reveals three issues:
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Let's find out what these strange finalizer objects do. We add a filter step by selecting java.lang.ref.Finalizer, and choose the 'Outgoing references' in the navigation panel. The result is shown in the first screenshot on the right. Since java.lang.ref.Finalizer is derived from java.lang.ref.Reference it has a referent field which holds the content of the weak reference. Consequently, we select this referent field and choose the 'Classes' option in the navigation panel. As the second screenshot on the right shows, the overwhelming majority of weak references can be attributed to the javax.swing.text package.
Finally for object and int arrays we go back to the classes view and add filter steps by selecting the item in question and choosing 'Incoming references' from the navigation panel. As a result, the two screenshots on the right show who is referencing these arrays. Again, the javax.swing.text is responsible for allocating these resources.
To get a 10-day evaluation copy of JProfiler, go to the trial download page, enter your name and e-mail address and download the version for your platform (Windows, Linux X86, Mac OS X or Solaris SPARC). With the Windows version, you get a setup executable that installs JProfiler and launches it right away. When running JProfiler for the first time, a setup wizard comes up and collects the license information that has been mailed to you as well as some information about your runtime environment. When the 'open session' dialog appears, you're ready to go.
Last Updated: 2020-09-28Paintstorm studio 2 21 mac crack free download torrent. JProfiler’s intuitive UI helps you resolve performance bottlenecks,
pin down memory leaks and understand threading issues.
When it comes to profiling, only the best tool is good enough.
Check out below what makes JProfiler the top choice for profiling your applications on the JVM:
EXCEPTIONAL EASE OF USE Contexts 3 5 1 – fast window switcher.
When you profile, you need the most powerful tool you can get. At the same time, you do not want to spend time learning how to use the tool. JProfiler is just that: simple and powerful at the same time. Configuring sessions is straight-forward, third party integrations make getting started a breeze and profiling data is presented in a natural way. On all levels, JProfiler has been carefully designed to help you get started with solving your problems.
DATABASE PROFILING FOR JDBC, JPA AND NOSQL Wolf 1 41 – build responsive web sites.
Database calls are the top reasons for performance problems in business applications. JProfiler’s JDBC and JPA/Hibernate probes as well as the NoSQL probes for MongoDB, Cassandra and HBase show the reasons for slow database access and how slow statements are called by your code. From the JDBC timeline view that shows you all JDBC connections with their activities, through the hot spots view that shows you slow statements to various telemetry views and a list of single events, the database probes are an essential tool for getting insight into your database layer.
EXCELLENT SUPPORT FOR JAVA ENTERPRISE EDITION
Dedicated support for JEE is present in most views in JProfiler. For example, in the JEE aggregation level you see the call tree in terms of the JEE components in your application. In addition, the call tree is split up for each request URI. Also, JProfiler adds a semantic layer on top of the low-level profiling data, like JDBC, JPA/Hibernate, JMS and JNDI calls that are presented in the CPU profiling views. With its JEE support, JProfiler bridges the gap between a code profiler and a high-level JEE monitoring tool.
HIGHER LEVEL PROFILING DATA
JProfiler has a number of probes that show you higher level data from interesting subsystems in the JRE. In addition to the Java EE subsystems like JDBC, JPA/Hibernate, JSP/Servlets, JMS, web services and JNDI, JProfiler also presents high level information about RMI calls, files, sockets and processes. Each of these probes has its own set of useful views that gives you general insight, highlights performance problems and allows you to trace single events. And what’s more, all these views are also available for your own custom probes that you can configure on the fly within JProfiler.
STELLAR ANALYSIS OF MEMORY LEAKS
Finding a memory leak can be impossible without the right tool. JProfiler’s heap walker offers you an intuitive interface to solve both simple and complex memory problems. 5 different views and lots of inspections show different aspects of the current set of objects. Each view provides you with essential insights on the selected objects and lets you switch to different objects sets. Questions like why objects are not garbage collected are answered with a single click of the mouse.
EXTENSIVE QA CAPABILITIES
JProfiler is ideally suited as a QA tool, both during development as well as for dedicated QA teams. The rich functionality around snapshot comparisons makes it easy to track progress. JProfiler has strong support for command line operations. This includes the ability to profile, export snapshot data and create snapshots comparisons from the command line. The ant tasks bundled with JProfiler allow you to perform all command line operations from your build script.
BROADEST SUPPORT FOR PLATFORMS, IDES AND APPLICATION SERVERS
JProfiler integrates into your environment: We provide native agent libraries for a wide range of platforms, both for 32-bit and 64-bit JVMs. Integrations into all popular IDEs makes profiling during development as easy as running your application. And the large number of integrations wizards for nearly all application servers on the market ensures that you can get started with a few clicks and not with reading documentation.
LOW OVERHEAD
JProfiler records data only when you need it. In fact, you can start your application with the JProfiler agent and attach the JProfiler GUI at a later time. When you do not record any data, the overhead is extremely small. That’s what we call on demand profiling. Invariably, there are a lot of things you can adjust in an advanced profiler. JProfiler shows you how your profiling settings will impact performance and offers you templates to quickly select profiling settings for common use cases.
THE POWERFUL CPU PROFILER
Fixing performance bottlenecks is the most frequent use case for a profiler. However, CPU data can be overwhelming in its level of detail and the way data is collected can make a huge difference in usability. With JProfiler, you have a decisive advantage when trying to find the reason for a problem. Call tree view filters, aggregation levels and thread status selectors are just some examples of JProfiler’s versatility in this area.
THE INTEGRATED THREAD PROFILER
Problems related to threading are much more frequent than one might assume. Without a thread profiler, you only have a minimal chance to tackle such issues. A whole range of otherwise opaque problems can be solved when using JProfiler, such as increasing liveness in a multi-threaded application that uses too much locking. Thread profiling not only has a separate view section in JProfiler, it is also tightly integrated into the CPU profiling views.
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