INTERGRAL SOFTWARE END USER LICENSE AGREEMENT (“EULA”)
THE FOLLOWING EULA APPLIES TO FUSIONREACTOR VERSION 7 AND ITS MINOR/MICRO UPDATES ONLY
IMPORTANT: CAREFULLY READ THE FOLLOWING LICENSE AGREEMENT. THIS
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Use of Decompiler
The Software includes decompiling functionality (“Decompiler”)
that enables reproducing source code from the original binary
code. You hereby acknowledge that the binary code and source code
may be protected by copyright, trademark and other laws
which may prohibit you from decompiling them and/or using
Decompiler. Before using Decompiler, you should make sure that
the decompilation is not prohibited by any applicable license
agreement of the application and/or original binary code
(except to the extent that you may be expressly permitted
under applicable law) or that you have obtained permission to
decompile the code from the copyright owner. Using Decompiler is
entirely optional. INTERGRAL does neither encourage nor condone
the use of the Decompiler and disclaims any liability for your
use of Decompiler in violation of applicable laws.
Use of Debugger
The Software includes debugging functionality (“Debugger”)
that enables debugging programs that may contain binary code,
source code and intellectual property protected by copyright,
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you should make sure that debugging is not prohibited by any
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INTERGRAL does neither encourage nor condone the use of the
Debugger and disclaims any liability for your use of Debugger
in violation of applicable laws.
FusionReactor Cloud Service
Use of the FusionReactor Cloud Service is governed by the terms
and conditions that accompanies or is included with the
FusionReactor Cloud Service. By accepting this EULA, you are also
accepting the additional terms and conditions when using the
Software with the FusionReactor Cloud Service.
Third Party Software
Any software provided along with the Software that is associated
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terms of that license agreement. This license does not apply to
those portions of the Software. Copies of these third party
licenses are included in all copies of the Software. By accepting
this EULA, you are also accepting the additional terms.
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subject to U.S. export jurisdiction and agrees to comply with all
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as well as end-user, end- use, and destination restrictions
issued by U.S. and other governments and notwithstanding the
above; and (b) the provisions of the USA Uniform Computer
Information Transaction Act do not apply to this EULA.
CONSENT TO COLLECT AND USE OF PERSONAL INFORMATION
You acknowledge and agree that we may collect, transfer, process
and store certain information (“Information”) collected by the
Software, including but not limited to:
(i) IP addresses of the system on which Software is installed;
(ii) MAC Addresses of the system on which Software is installed;
(iii) Email addresses configured for use with the Software;
(iv) Information about the system on which Software is installed,
for example: OS version; processor architecture;
(v) Information about the application software on which Software
is installed, for example: application software version;
application software configuration; JVM configuration;
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for example: version of the Software; statistical information
about the Software; configuration of the Software.
INTERGRAL and its Data Processors may use Information subject to
applicable laws in order to license and bill for the Software and
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not limited to:
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(b) administering the functionality of the Software and services;
(c) to improve, update or upgrade the Software and services;
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services of INTERGRAL;
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(f) complying with applicable laws or regulations.
INTERGRAL will not intentionally use Information to personally
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Any use of Information will be in accordance with the privacy
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Information may be processed, stored or transferred to INTERGRAL
and its Data Processors which may be located in countries
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INTERGRAL will use reasonable efforts to take appropriate
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will eliminate all risk of misuse of Information.
General: The export of the Software from the country of original
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Governing Law, Jurisdiction and Costs
This EULA is governed by the laws of Baden-Wuerttemberg, Germany
without regard to Baden-Wuerttemberg’s conflict or choice of law
provisions. Exclusive jurisdiction and place of performance is
Boeblingen, Germany, as long as permitted by applicable law. The
United Nations Convention for the International Sale of Goods
shall not apply.
Changes to the License Agreement
INTERGRAL may make changes to the License Agreement as it
distributes new versions of the Software. When these changes are
made, INTERGRAL will make a new version of the License Agreement
available on the website where the Software is made available.
Entire Agreement and Severability
This EULA is the entire agreement between INTERGRAL and you, and
supersedes all prior or contemporaneous agreements or
understandings, whether oral or written any other communications
or advertising with respect to the Software; this EULA may be
modified only by written agreement signed by authorized
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agent is authorized to make any amendment to this EULA.
If any provision of this EULA shall be held to be invalid or
unenforceable, the remainder of this EULA shall remain in full
force and effect and an enforceable term will be substituted
reflecting our intent as closely as possible. All rights not
expressly granted in this agreement are retained by INTERGRAL.
To the extent any express or implied restrictions are not
permitted by applicable laws, these express or implied
restrictions shall remain in force and effect to the maximum
extent permitted by such applicable laws. The failure or delay of
INTERGRAL to exercise any of its rights under this EULA or upon
any breach of this EULA shall not be deemed a waiver of those
rights or of the breach. You agree that any varying or additional
terms contained in any purchase order or other written notification
or document issued by you in relation to the Software licensed
hereunder shall be of no effect.
INTERGRAL and other trademarks contained in the Software are
trademarks or registered trademarks of INTERGRAL Information
Solutions GmbH. Third party trademarks, trade names, product
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their respective owners. You may not remove or alter any
trademark, trade names, product names, logo, copyright or other
proprietary notices, legends, symbols or labels in the Software.
This EULA does not authorize you to use INTERGRAL’s or its
licensors’ names or any of their respective trademarks.
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
New in FusionReactor 7.0.0, we added the ability to monitor your applications usage of Apache Kafka.
Firstly, what is Apache Kafka. Apache Kafka is a publish-subscribe service that allows for multiple system to publish to a given topic and for multiple system to create from a topic. It is commonly used to build real time data pipelines because it is reliable, fast and scalable. To find out more about Apache Kafka visit there website: https://kafka.apache.org/
There are two major aspects of using Kafka the consumers and producers. In this post we are going to talk about how FusionReactor can monitor consumer applications.
When you start your application with FusionReactor (see Install Guide) we will automatically detect and start tracking calls to the Kafka brokers. These calls are then tracked into transactions and displayed in the Transaction History page.
These transaction track the topic that is being polled and the partition that was polled, as well as the number of records returned and the consumer group used. From these transactions we also graph the activity and execution time into the normal transaction graphs in FusionReactor.
In addition to the standard graphs in FusionReactor if you are using an Enterprise or Ultimate license you will also have access to the ‘Framework Source’ page this page will aggregate the Kafka metrics into a selection of graphs to show how the Kafka usage breaks down by, Broker, Topic and Partition.
When you have a Java application connected to Kafka, there are various metrics available from the driver library. FusionReactor is able to track these metrics and display them over time in a collection of graphs. FusionReactor provides two pages of these graphs Kafka Metrics and Kafka Node Metrics, each page is then displayed based on the selected consumer.
The Kafka Metrics page is intended to give a general overview of the state of the applications use of Kafka, so on this page you will see metrics including:
The Kafka Node Metrics include metrics for each consumer that is running in the JVM, split down to each node that is providing the data. These metrics include:
For more information about what these metrics are please visit the Apache Kafka documentation
DZone have just published the 2017 Guide to Java: Development and Evolution and FusionReactor are featured in it. The 2017 Guide to Java explores upcoming features of Java 9, how to make your apps backwards-compatible, a look into whether Microservices are right for you, and using the Futures API in Java.
You can download it for free from the Dzone website.