This year we are back at Velocity Conference San Jose – You can visit us at booth #812 we will be showing off the latest FusionReactor developments and features to help developers fix problems faster! To celebrate this we are giving away a free ticket for Velocity Conference, San Jose, California.
The Prize is for one Bronze 2-Day Pass which Includes:
– All sessions (Wed & Thu)
– All keynote and plenary presentations
– Sponsor Pavilion and all events held there– All on-site networking events
– Lunch (Wed & Thu)
To enter the draw:
1) Start a trial of FusionReactor and send us a screenshot of your activation Menu / About Page. Send the screenshot to firstname.lastname@example.org, the version must be FusionReactor 7.2.3 or higher.
We will be announcing the winner on, Friday 1st June, this will give the winner enough time to organize flights and accommodation.
If you do not win, please do not worry you can still save 25% on the entry price by using our Discount Code: FusionReactor25
In one of my first jobs, one of the tasks I had was to fix a bug which used to occur from time to time in a very complex production system. That is easy! – I thought. I will reproduce the same situation in my development environment, find the broken line, implement a quick fix and it’s done! However, it turned out that reproducing exactly the same scenario which occurred in production, was not possible – so after some time, I gave up this idea. I was forced to spend days analyzing logs and trying to correlate many different events, to come up with an idea of what might have happened. Soon, I realized that it’s as tedious as looking for a needle in a haystack. A couple of fruitless days later, I came to the conclusion that I would need to add more logging here and there and wait a couple of days or even months to see if the bug occurred again. Then I thought that hunting bugs in production is somehow crude, compared to the sophisticated tools we have when developing an application. You’re implementing a new feature and seeing that the result of what your service returned is not what you had expected? You just put a few breakpoints in and click the Debug button! A few moments later, you know exactly what happened. Wouldn’t it be awesome to do the same in a production environment?
Wait a second! – you might have thought. But don’t we have the remote debugging features in most of the modern IDEs? Couldn’t we just connect to the running production application and debug it as we do from our local environment? While it’s possible, another problem arises: most of our business applications handle many requests per second. There is no easy way to control breakpoints firing everywhere when your application is being remotely debugged. As you can imagine, we don’t want to block all of our users from using our application when we decide to debug it. More often than not, we also can’t just force our application to reproduce the bug which happened yesterday – sometimes the only way to do it is to wait until it occurs again to one of our users. Thus, keeping a remote debug session in production, without a strict control of how breakpoints fire, is like putting landmines in the forest and inviting our users to run through it.
FusionReactor is an Application Performance Monitor, which comes with many advanced capabilities which you wouldn’t normally expect to find in monitoring solution. One of these, is the production debugger, designed to allow you to get low-level debug information from your production runtime environment – without affecting performance and without the issues and obstacles you would have with a traditional debugger.
One of the main issues, you would be faced with, using a traditional debugger – is that, once a breakpoint is set, it would fire for any thread which crosses that point in the code. FusionReactor overcomes this, by having a range of techniques to control the way a breakpoint should fire. For example, it can limit the number of times (threads) that a given breakpoint will trigger – which solves the problem of impacting too many users. Need more ways to control it ? We can even configure a breakpoint to fire for a user from a specific IP address (session), or when a specific variable matches a value or when a specific exception takes place ! However, what if a breakpoint triggers at night when nobody from our team is watching? The folks at FusionReactor have thought of this and the product allows you to define thread pause timeouts so if you would not intercept a paused thread within a specific time then the debugger will release the lock and allow thread execution to continue. When used with the thread limits this reduces the possible impact to one thread only – and only for n seconds, minutes or hours…
Another great benefit, is that FusionReactor can send out an email with the stack-trace and variables at the point that the trigger fires. This gives you a very flexible and unobtrusive way to get notified with plenty of information to make debugging easier than ever before. It’s a LOT better and simpler than adding debug/log file code into your production applications! For one thing – you don’t have to remember to remove the debug code in your production application !
If that’s not enough to convince you that debugging in production doesn’t have to be cumbersome, FusionReactor is shipped with a fully integrated IDE-style debugger which runs directly in your browser – no need to install additional fat clients to start remote debugging. Everything is built in and ready to go.
What I described here is not a vision of the future it’s available now and it’s safe, secure and simple to use. The alternative is (of course) good old log files, which we all know have their limitations. I don’t see any reason to spend countless hours digging through log files hoping to find the needle in the haystack – using logs are “archaic techniques” and I think maybe it’s time to finally start using tools for the 21st century 😉
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The buffer pool space is located outside of the garbage collector-managed memory. It’s a way to allocate native off-heap memory. What’s the benefit of using buffer pools? To answer this question, let’s firstly learn what byte buffers are.
java.nio package comes with the Bytebuffer class. It allows us to allocate both direct and non-direct byte buffers. Their is nothing special about non-direct byte buffers – they are an implementation of HeapByteBuffer created by ByteBuffer.allocate() and ByteBuffer.wrap() factory methods. As the name of the class suggests, these are on-heap byte buffers. Wouldn’t it be easier to allocate all the buffers on the Java heap space then? Why would anyone need to allocate something in a native memory? To answer this question, we need to understand how operating systems perform I/O operations. Any read or write instructions are executed on memory areas which are contiguous sequence of bytes. So does byte occupy a contiguous space on the heap? While technically it makes sense, the JVM specification does not have such guarantees. What’s more interesting, the specification doesn’t even guarantee that heap space will be contiguous itself! Although it seems to be rather unlikely that JVM will place a one-dimensional array of primitives in different places in memory, byte array from Java heap space cannot be used in native I/O operations directly. It has to be copied to a native memory before every I/O, which of course, leads to obvious inefficiencies. For this reason, a direct buffer was introduced.
A direct buffer is a chunk of native memory shared with Java from which you can perform a direct read.
An instance of DirectByteBuffer can be created using the ByteBuffer.allocateDirect() factory method. Byte buffers are the most efficient way to perform I/O operations and thus, they are used in many libraries and frameworks – for example in Netty.
A direct byte buffer may also be created by mapping a region of a file directly into memory. In other words, we can load a region of a file to a particular native memory region that can be accessed later. As you can imagine, it can give a significant performance boost if we have the requirement to read the content of a file multiple times. Thanks to memory mapped files, subsequent reads will use the content of the file from the memory, instead of loading the data from the disc every time it’s needed. MappedByteBuffer can be created via the FileChannel.map() method.
An additional advantage of memory mapped files, is that the OS can flush the buffer directly to the disk when the system is shutting down. Moreover, the OS can lock a mapped portion of the file from other processes on the machine.
One of the problems with direct buffers is that it’s expensive to allocate them. Regardless of the size of the buffer, calling Buffer.allocateDirect() is a relatively slow operation. It is therefore more efficient to either use direct buffers for large and long-lived buffers or create one large buffer, slice off portions on demand, and return them to be re-used when they are no longer needed. A potential problem with slicing may occur when slices are not always the same size. The initial large byte buffer can become fragmented when allocating and freeing objects of different size. Unlike Java heap, direct byte buffer cannot be compacted, because it’s not a target for the garbage collector.
If you’re interested in the amount of direct or mapped byte buffers used by your application, then you can easily monitor them using FusionReactor. FusionReactor provides a break-down of all the different memory spaces. Simply navigate to Resources and then Direct – Buffer Pools.
By default, the Direct Buffer Pool graph is displayed. You can switch to the Mapped Buffer Pool by clicking on a drop-down in the top right corner. Java will grow those pools as required so the fact that Direct Memory Used covers Direct Capacity on the graph above, means that all buffer memory allocated so far is in use.
Please note – you can limit the amount of direct byte buffer space that an application can allocate, by using -XX:MaxDirectMemorySize=N flag. Although this is possible, you would need a very good reason to do so.
If you ever had some serious issues with the performance of your Java application, most probably you know how valuable thread profiling can be. But do you know which profiler you should use?
There are two basic techniques used by profilers – sampling and instrumentation.
A sampling profiler involves periodically asking the JVM for the current point of execution of all currently alive (active) threads. This type of profiler carries the least amount of overhead. This is important because introducing heavy measurement/instrumentation into the application can change the performance characteristics significantly. Using a sampling technique, we get a snapshot of the next stack trace when the timer fires. So the profiler looks at each thread and determines which method the thread is executing at that moment. As there are gaps between consecutive measurements, sampling profiler achieves a trade-off between the level of accuracy obtained vs the overhead involved in actually taking the measurement, This is illustrated in the following figure:
As you can see, the thread spent most of its time in save method and a little bit in read method. If the sampling happens only when the thread is in a save method (more probable as this method dominates), the profiler will report that the thread spent 100% of its time in save method, which is of course not accurate.
A rather logical way to minimize this sampling error is to reduce the time interval between sampling and increase the profiling time. However, as we discussed earlier, this solution might impact the performance characteristics of the application, so a balance is the key.
Instrumented profilers introduce a much larger performance overhead into the application. This method usually involves injecting bytecode into the classes for the purpose of profiling. This approach involves a higher performance impact, but generates a more accurate measurement when compared to the result from the sampling profiler. Another problem which may arise as a result of the way an instrumented profiler modifies the bytecode is the following; as you may know, JIT compiler inlines small methods. Because the instrumentation introduced by the profiler, some small methods might not be eligible to be in-lined anymore. It can have a serious impact on application performance. If you decide to use instrumented profilers, make sure that you instrument only a small section of the code.
Profiling in a development environment is easy. However, it might not be enough. When dealing with production data, we are exposed to a different scale and thus, we might observe different bottlenecks in our application. That’s why profiling in production is so important. As already mentioned, both sampling and instrumented profilers have their pros and cons. If you want to profile in a production environment, a low overhead sampling profiler seems to be a better choice. FusionReactor’s Production Code Profiler will help you identify bottlenecks in your production environment with a very low sampling overhead. The really cool thing about this profiler is that it can be configured in a way that it will automatically profile your application if it detects a long-running request or thread. What is a long-running request? It’s up to you to define, but 3 seconds is the default value. If you monitor some sort of latency-sensitive application, then you might want to decrease this value. Similarly, if your application performs some time-consuming computations, then most probably you don’t want to be notified all the time and increasing Minimum Transaction Time will be necessary. Taking a stacktrace snapshot takes as little as 1 ms per sample. With a default sampling rate of 200 ms, you can usually get some meaningful results from just 5 samples.
FusionReactor’s thread profiling output looks like this:
On the left-hand side, you can see the percentage of time the particular method took. On the right -hand side, time in seconds is shown. As you can see in the output, logging in lowCostSearchEngineClient class can be a potential bottleneck in this application. Thanks to the bundled de-compiler, you can instantly check the method by simply clicking on it.
It’s not always easy to pinpoint a performance issue in a running application, but profilers are usually good estimators. Sampling profilers such as FusionReactor’s Profiler, are perfect tools to use them in a production environment.
We are thrilled to announce that for the second year in a row FusionReactor has been accredited with the “FeeFo Gold Trusted Merchant” 2018 award!
Feefo is an independent feedback service that are dedicated to collecting trusted reviews. The Gold Trusted Merchant award is only given to businesses that have an average positive Service Rating of between 95% and 100%.
The whole FusionReactor team would like to thank everyone who filled out feedback for us – we really appreciate it!
The award shows that FusionReactor is a leading tool for Java Application Performance monitoring & Degugging – designed to help Developers and Devops improve their applications and get to the root of the problem as fast as possible.
Using the Feefo feedback service has been a fantastic way for us to see how FusionReactor is being utilized by our customers. As well as highlighting any issues they may be facing. We always aim to provide the best possible service for our customers.
“The Trusted Service award is a recognized symbol of trust – helping customers click with confidence.”. From everyone on the FusionReactor team thank you and have a great year!
“We’ve made a new screencast that shows an introduction to FusionReactor Cloud, a new way to monitor any number of servers in a consolidated interface that can scale up and down with your infrastructure. Not only do new servers automatically register and deregister themselves with the Cloud dashboard, but it supports a pay-for-what-you-use model that prevents you from locking into a specific number of licenses.
This is a great fit for deployments like Docker Swarm where you can scale your service up and down from day to day. In this demo, I’ll show you how to add FusionReactor cloud easily into any CommandBox-based server and show how to deploy it to a Docker Swarm on our Ortus Docker image and then play around with it.” Brad Wood, Ortus Solutions (Follow him on Twitter)
The FusionTeam will once again be PLATINUM sponsors of this great event – make sure you join us and the Adobe ColdFusion Team in Las Vegas!
We will be showing off the new major release of FusionReactor, which is FR 7 as well as FusionReactor CLOUD.
To celebrate this, we are giving away a free ticket for the Adobe ColdFusion Summit.
The Prize is for one ColdFusion Summit registration which Includes:
– Admission to all keynote and breakout sessions
– Sponsor Pavilion.
– Attendee appreciation event
– Conference meals.
There are Two ways to enter the draw:
OR (You can do both to double your chances of winning).
The winner will be announced on : Friday 27th October
If you do not win, or you just want to sign up right away, you can get a reduced price ticket for just $249 by using our registration code: CF17OFFER249 when you purchase a ticket here
FR7 represents a massive step forward for FusionReactor, includes 20 completely new core features and around 100 major improvements and bug fixes. Our primary goals for this release have been to enhance the array of system, application and JVM metrics available, as well as build upon and extend the core functionality within FR Ultimate Edition.
Our mission is to provide developers, DevOps and I.T. professionals unparalleled depth of insight, transparency and interpretation into what applications are doing at the point where things are going wrong! Why? So, that they can isolate issues and performance problems faster than with any other tool.
FR7 has significantly increased the array of supported metrics available. Ranging from complete JMX MBean metrics, to enhanced framework (ColdFusion and Java) support to NoSQL data stores and current streaming platforms, such as Apache Kafka™.
The other major focus of this release has been to enhance FR Ultimate Edition – which adds a completely new definition to the meaning of application monitoring. FR Ultimate combines the most useful components and features from the most used error detection and performance analysis tools available to developers: the code debugger, application profiler and with the launch of FR7, the memory profiler. These tools can all be used safely, securely and most importantly with MINIMAL OVERHEAD in your production environment.
The complexity of today’s distributed, service based, containerized, ephemeral environments, combined with unprecedented quantities of data means that re-producing production issues is virtually impossible. The error detection features built into FR Ultimate completely change how issues would be investigated, removing the need to try and reproduce defects in a test environment or scouring through log files in the hope of finding a clue. FR Ultimate enables you to interact with issues as they unfold, directly in production. It is the only product on the market which offers such capabilities.
The whole team is extremely proud of FusionReactor 7 and we hope that you enjoy using it and that it continues to be your #1 tool to monitor, detect issues and protect your applications and servers.
CEO Intergral – makers of FusionReactor