Join For Free. The first exposure of the dataset lists the number of times a particular root cause was the reason why end user either faced performance or availability issues: From the above it is, for example, visible that: Web service access over HTTP calls was the source for poor performance in Slow JDBC operations ranked third, just barely behind the locking issues. Opinions expressed by DZone contributors are their own.
Performance Partner Resources. Let's be friends:. Finally, an additional advantage of scaling with the help of a cluster, beyond pure Java performance — is that adding new nodes also leads to redundancy and better techniques of dealing with failure, leading to overall higher availability of the system. In this article, we explored a number of different concepts around Java performance testing. We started with load testing, APM tool based application and server monitoring — followed by some of the best practices around writing performant Java code.
Finally, we looked at JVM specific tuning tips, database side optimizations, and architectural changes to scale our application. Click here to read more about the acquisition. Try Our Free Code Profiler. Try Our Code Profiler. By Role. By Technology. By Language. Documentation Support Ideas Portal Menu. Start Free Trial.
Tip: Find application errors and performance problems instantly with Stackify Retrace. Troubleshooting and optimizing your code is easy with integrated errors, logs and code level performance insights. About the Author Latest Posts. Get In Touch. Facebook Twitter Youtube Linkedin. What is APM? Subscribe to Stackify's Developer Things Newsletter. Sign Up Today. Such a mechanism permits loading a module programmed by a user into the kernel to work with the kernel. In order to insert the prober into the operating system kernel, the following manner can be adopted: preprogramming a kernel monitoring module; loading the kernel monitoring module into the kernel to work; the helper thread transferring parameters to the kernel monitoring module and controlling the kernel monitoring module to insert the prober.
By doing this, in comparison with the manner in which the helper thread directly inserts the prober, the work of the helper thread is simplified and the insertion of the prober is achieved by using a module of the kernel level, thereby achieving higher speed and a smaller performance overhead of the present invention.
Specifically, for example, in Linux system, insmod command is executed to explicitly load the kernel module. The kernel monitoring module according to one embodiment of the present invention is loaded into the kernel by executing insmod command. After the kernel monitoring module is loaded into the kernel, it will keep working in the kernel unless rmmod command is executed. In this embodiment, the prober is inserted into the operating system scheduler by the user defined module loaded into the operating system kernel i.
After the helper thread is created, it registers with the loaded kernel monitoring module the ID of the Java process that is the monitored target and a native task ID corresponding to the helper thread.
Then, the kernel monitoring module inserts the callback function programmed according to the registered process ID and the helper thread ID into the scheduler. Specifically, for example, in Linux system, the insertion of the prober is achieved by the following codes:. In step , the prober monitors the states in the operating system kernel of Java threads in the Java process and sends a signal to the helper thread in response to detect that a Java thread is blocked.
These two parameters are registered by the helper thread to the kernel monitoring module. The following judging logic is achieved in the prober: if a native task scheduled out from the processor corresponds to a Java thread in the Java process when the processor performs a task context switching and the native task is in blocked state, the prober sends a signal to the helper thread. That is, a signal is sent to the thread indicated by the HTID only when the following two conditions are satisfied at the same time: 1 the native task scheduled out belongs to the process indicated by the PID; and 2 the native task scheduled out is in blocked state.
It is noted that a native task could be scheduled out from the processor for many reasons. It is possible for the native task to be scheduled out from the processor because it is in blocked state or the allocated time slice has expired. In these cases, the prober will be called. Because a signal is sent only when the condition 2 is also satisfied, a native task scheduled out due to the expiration of the allocated time slice does not trigger the sending of a signal to the helper thread, thereby significantly reducing the performance overhead.
The sending of the signal can be realized in various manners. The helper thread keeps waiting for the signal all the time, and is wakened when the signal is received. In another embodiment, it is possible to establish a communication channel between the user space and the kernel space. When the above conditions 1 and 2 are satisfied at the same time, the prober communicates with the helper thread through the communication channel to notify the detection of block.
Whichever manner is used, the signal sent to the helper thread contains the ID of the blocked native task. In step , the helper thread retrieves call stack information from the JVM in response to receive the signal from the operating system kernel, and locates a corresponding position in source code of the Java program by using the retrieved call stack information.
The step of retrieving call stack information from the JVM includes: retrieving call stack information of a Java thread corresponding to the native task from the JVM according to the native task ID and the mapping relationship. The process for FIG. First, in step 1 , the helper thread receives a signal from the kernel. The signal contains the ID of the blocked native task.
For better understanding, FIG. Here it is assumed that the received native task ID is Then, in step 2 , the helper thread queries a pre-built mapping database, e. In the case where the native task ID is , a corresponding Java thread ID is found from the mapping database the corresponding Java thread ID is 2 in the case of Table 1.
That is, the helper thread obtains a notification from the kernel: Java application thread 2 is blocked in the kernel. Then, in step 3 , the helper thread retrieves call stack information from the stack corresponding to Java application thread 2 in the JVM according to the found Java thread ID i. Specifically, it is possible to obtain the method name and position of the currently executed method of the stack of a specified thread by using the method GetFrameLocation provided by JVMTI.
Then, the obtained method name is used to call the method GetLineNumberTable provided by JVMTI so as to obtain a mapping table of the position and the line number of the currently executed method. It is possible to find out at which line of the method the thread currently runs by iterating the table, thereby locating the corresponding position in Java source code.
The corresponding position can be shown to those people that perform debugging or can be saved for a later bottleneck analysis. Lastly, the handling of a special case is described.
Those skilled in the art understand that, like ordinary Java application threads, the helper thread created in the present invention is also a Java thread and Java application threads and the helper thread are located within the same process, e.
Additionally, the helper thread also corresponds to a native task in the kernel space. On the other hand, in step , the target monitored in the prober i. As described above, this is achieved by checking whether the condition 1 is satisfied.
Therefore, when the helper thread itself is blocked, since it detects that the conditions 1 and 2 are satisfied at the same time in the prober, the prober will send a signal to the helper thread in this case. However, this signal is useless and is irrelevant to the bottleneck related part of the source code itself of the Java program to be monitored, and this signal will be ignored.
Various manners can be adopted to ignore the signal caused by the helper thread itself being blocked. For example, at least two methods can be used below. The first method is to conduct an extra judgment in the prober. In addition to the conditions of 1 the native task scheduled out belongs to the process indicated by the PID and 2 the native task scheduled out is in blocked state, a further condition 3 is set: the native task scheduled out is different from the ID of the native task corresponding to the helper thread, i.
Then, in the case where the three conditions are satisfied at the same time, a signal is sent to the helper thread. The second method is to judge in the helper thread. When the helper thread receives a signal containing the native task ID of the blocked native task from the operating system kernel step 1 in FIG.
In the case where it is assumed that the native task ID is , a corresponding Java thread ID is found from the mapping database the corresponding Java thread ID is 21 in the case of Table 1. When they match, it means the helper thread itself is blocked in the kernel. At this time, the helper thread ignores the signal and skips the execution of step 3 in FIG. In the above description, detailed description of the method flow according to an embodiment of the present invention is provided.
The method flow is applicable to the case of single-core processor. The method to detect and locate a bottleneck of Java program according to the present invention is applicable to the case of multi-core processor as well. In the case where the processor that executes the Java program is a multi-core processor, a plurality of helper threads is created. That is, in the case of a quad-core processor, four helper threads 1 to 4 are created. Then, each of the four helper threads is bound to one core of the multi-core processor, respectively.
There are two paths to follow. Blindly follow hunches. Get an application profiler. Blindly follow hunches This is: Low capital cost hunches are free Exceptionally easy to instrument there isn't any instrumentation Relies heavily upon your programming experience to see problems Not terribly accurate at finding issues Great at creating rabbit holes that consume amazing amounts of time but generally don't generate much measurable improvement. The better ones isolate methods that Consume the most amount of time Have the most amount of calls Require you to be able to instrument your application or at least manually step through use cases that are of concern.
Can flood you with too much information, so you need to understand what to look for Depending upon how large your application, you can get away with following blind hunches and relying upon wall-clock time or throwing some DateTime calls in your code to keep track of how long execution takes. Follow up to comments: Mason Wheeler correctly points out that some good profilers can be inexpensive.
You can create a lighter-weight profiler this way by being very specific about what's measured and logged. Some of the better profilers provide this ability too though. Improve this answer. That definitely depends on the language. For Delphi, for example, easily the most useful profiler is a freeware tool simply called Sampling Profiler. So profiling is something that is not done in production? ChocoDeveloper: there is an option between those 2 extremes, namely adding some time measuring functions to your application in critical areas on your own.
The advantage of that approach is that you have it full under your control, for example you could allow your application to switch on time-logging whenever you want also in production. The disadvantage is when you don't know exactly which processes to measure, you may end up adding that measuring function at a lot of places in your application.
ChocoDeveloper: on the other hand, it may be possible to find a third-party application profiler which can be used in production, but that depends heavily on your environment which you still did not describe.
One problem, however, which can occur with such a tool, is that full-profiling of your application may slow it down significantly, making production-use infeasible.
ChocoDeveloper, sometimes adding a standard profiler to a production environment is not acceptable, either due to performance degradation, security, or both. If you have the flexibility to do so, then you should because it's the simplest approach.
If you cannot, then you may need alternatives. Show 2 more comments. Community Bot 1. Aaronaught Aaronaught 43k 10 10 gold badges 91 91 silver badges bronze badges. Better late than never: So how do you find these kind of bottlenecks? That's the price you pay for all those samples. Mike Dunlavey Mike Dunlavey Interesting, thanks. I'm not sure I understood it though.
Can't you just generate more data samples for your profiler with the same script? Please also note that many corporate networks block youtube, making your answer useless for readers behind these — gnat. ChocoDeveloper: Revisiting this 2 years later. It's like interviewing 10 candidates for a job, versus With the small number you're going to pay close attention to each one.
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