Fetching and Building Mission Control 8+

As described in a previous post, Mission Control is now on GitHub. Since this alters how to fetch and build OpenJDK Mission Control, this is an updated version of my old post on how to fetch and build JMC from version 8 and up.

Getting Git

First step is to get Git, the SCM used for OpenJDK Mission Control. Installing Git is different for different platforms, but here is a link to how to get started:

https://git-scm.com/book/en/v2/Getting-Started-Installing-Git

Installing the Skara Tooling (Optional)

This is an optional step, making it easier if you want to contribute to Mission Control:

http://hirt.se/blog/?p=1186

Cloning the Source

Once Git is installed properly, getting the source is as easy as cloning the jmc repo. First change into the directory where you want to check out jmc. Then run:

git clone https://github.com/openjdk/jmc.git

Getting Maven

Since you probably have some Java experience, you probably already have Maven installed on your system. If you do not, you now need to install it. Simply follow the instructions here:

https://maven.apache.org/install.html

Building Mission Control

First we need to ensure that Java 8 is on our path. Some of the build components still use JDK 8, so this is important.

java –version

This will show the Java version in use. If this is not a Java 8 JDK, change your path. Once done, we are now ready to build Mission Control. Open up two terminals. Yep, two!

In the first one, go to where your cloned JMC resides and type in and execute the following commands (for Windows, replace the dash (/) with a backslash (\)):

cd releng/third-party
mvn p2:site
mvn jetty:run

Now, leave that terminal open with the jetty running. Do not touch.

In the second terminal, go to your cloned jmc directory. First we will need to build and install the core libraries:

cd core
mvn install

Next run maven in the jmc root:

mvn clean package

JMC should now be building. The first time you build Maven will download all of the third party dependencies. This will take some time. Subsequent builds will be less painful. On my system, the first build took 6:01 min. The subsequent clean package build took 2:38.

Running Mission Control

To start your recently built Mission Control, run:

Windows

target\products\org.openjdk.jmc\win32\win32\x86_64\jmc.exe -vm %JAVA_HOME%\bin

Mac OS X

target/products/org.openjdk.jmc/macosx/cocoa/x86_64/JDK\ Mission\ Control.app/Contents/MacOS/jmc -vm $JAVA_HOME/bin

Contributing to JDK Mission Control

To contribute to JDK Mission Control, you need to have signed an Oracle Contributor Agreement. More information can be found here:

http://openjdk.java.net/contribute/

Don’t forget to join the dev list:

http://mail.openjdk.java.net/mailman/listinfo/jmc-dev

We also have a Slack (for contributors), which you can join here:

https://join.slack.com/t/jdkmissioncontrol/signup

More Info

For more information on how to run tests, use APIs etc, there is a README.md file in the root of the repo. Let me know in the comments section if there is something you think I should add to this blog post and/or the README!

Using the Skara Tooling

I’m writing this for myself as much as I’m writing this to share. After only a day of using JMC with Skara, I’ve fallen in love with it. I spend less time painstakingly putting together review e-mails, copying and pasting code to comment on certain lines of code, cloning separate repos to do parallel work efficiently, setting up new workspaces for the these repos etc. Props to the Skara team for saving me time by cutting out a big chunk of the stuff not related to coding and a whole lot of ceremony.

Note that the Skara tooling can be used outside of the scope of OpenJDK – git sync alone is a good reason for why everyone who wants to reduce ceremony can benefit from the Skara tooling.

So, here are a few tips on how to get started:

  1. Clone Skara:
    git clone https://github.com/openjdk/skara
  2. Build it:
    gradlew (win) or sh gradlew (mac/linux)
  3. Install it:
    git config --global include.path "%CD%/skara.gitconfig" (win) or git config --global include.path "$PWD/skara.gitconfig" (mac/linux)
  4. Set where to sync your forks from:
    git config --global sync.from upstream

For folks on Red Hat distros, 2 and 3 can be replaced by make install. For more information on the installation, see the Skara wiki.

Some examples:

To sync your fork with upstream and pull the changes:

git sync --pull

To list the open PRs:

git pr list

To create a PR:

git pr create

To push your committed changes in your branch to your fork, creating the remote branch:

git publish

JMC Workflow

Below is the typical work-flow for JMC.

First ensure that you have a fork of JMC. Either fork it on github.com, or on the command line:
git fork https://github.com/openjdk/jmc jmc

You typically just create that one fork and stick with it.

  1. (Optional) Sync up your fork with upstream:
    git sync --pull
  2. Create a branch to work on, with a name you pick, typically related to the work you plan on doing:
    git checkout –b <branchname>
  3. Make your changes / fix your bug / add amazing stuff
  4. (Optional) Run jcheck locally:
    git jcheck local
  5. Push your changes to the new branch on your fork:
    git publish (which is pretty much git push --set-upstream origin <branchname>)
  6. Create the PR, either on GitHub, or from the command line:
    git pr create

Summary / TL;DR

  • I ❤️ Skara

Mission Control is Now Officially on GitHub!

Since this morning, the JDK Mission Control (JMC) project has gone full Skara! mc_512x512This means that the next version (JMC 8.0) will be developed over at GitHub.

To contribute to JDK Mission Control, you (or the company you work for) need to have signed an OCA, like for any other OpenJDK-project. If you already have an OpenJDK username, you can associate your GitHub account with it.

Just after we open sourced JMC, I created a temporary mirror on GitHub to experiment with working with JMC at GitHub. That mirror is now closed for business. Please use the official OpenJDK one from now on:

https://github.com/openjdk/jmc

If you forked or stared the old repo, please feel free to fork and/or star the new one!

Compressing Flight Recordings

Flight recordings are nifty binary recordings of what is going on in the runtime and the application running on it. A flight recording contains a wide variety of information, such as various kinds of profiling information, threat stall information and a whole host of other information. All adhering to a common event model and with the ability to dynamically add new event types.

In the versions of JFR since JDK 9, some care was taken to reduce the memory footprint by LEB 128 encoding integers, noting that many things, like constant pool indices, usually occupy relatively low numbers. The memory footprint was cut in about half, compared to previous versions of JFR.

Now, sometimes you may want to compress the JFR data even further. The question then is – how much can you save if you compress the recordings further, and what algorithms would be best suited for doing the compression? What if you want the compression activity to use as little CPU as possible?

My friend and colleague at Datadog, Jaroslav Bachorik, set out to answer that question for some typical recording shapes that we see at Datadog, using a set of compression algorithms from Apache Commons Compress (bzip2, LZMA, LZ4), the built in GZip, a dedicated LZ4 library, XZ, and Snappy.

Below is a table of his findings for “small” (~1.5 MiB) and “large” (~5 MiB) recordings from one of our services. The benchmark was run on a MacBook Pro 2019. Now, you’d have to test on your own recordings to truly know, but I suspect that these results will hold up pretty well with other kinds of loads as well.

Algorithm Recording Size Throughput Compression Ratio Utility
Gzip small 24.299 3.28 79.647
Gzip large 5.762 3.54 20.436
BZip2 small 6.616 3.51 23.198
BZip2 large 1.518 3.84 5.826
LZ4 small 133.115 2.40 319.394
LZ4 large 38.901 2.57 100.009
LZ4 (Apache) small 0.055 2.74 0.152
LZ4 (Apache) large 0.013 3.00 0.039
LZMA small 1.828 4.31 7.882
LZMA large 0.351 4.37 1.533
Snappy small 134.598 2.27 305.494
Snappy large 35.986 2.49 89.692
XZ small 1.847 4.31 7.964
XZ large 0.349 4.37 1.523

Throughput is recordings/s. Utility is throughput * compression ratio, and meant to capture the combination of compression strength and performance. Note that the numbers are not normalized – only compare numbers in the same size category.

Summary / TL;DR

  • The built-in GZip is doing a fairly good/balanced job of compressing flight recordings
  • You can get the best utility out of LZ4, closely followed by Snappy, but you sacrifice some compression
  • If you’re prepared to pay for it, LZMA and XZ give a good compression ratio
  • All credz to Jaroslav for his JMH-benchmark and all the data

JFR is Coming to OpenJDK 8!

I recently realized that this isn’t common knowledge, so I thought I’d take the opportunity to talk about the JDK Flight Recorder coming to OpenJDK 8! The backport is a collaboration between Red Hat, Alibaba, Azul and Datadog. These are exciting times for production time profiling nerds like me. Smile

The repository for the backport is available here:

http://hg.openjdk.java.net/jdk8u/jdk8u-jfr-incubator/

The proposed CSR is available here:

https://bugs.openjdk.java.net/browse/JDK-8230764

The backport is keeping the same interfaces and pretty much the same implementation as is available in OpenJDK 11, and is fully compatible. There were a few security fixes, due to there not being any module system to rely upon for isolation of the internals, also, some events will not be available (e.g. the Module related events) but other than that the API and tools work exactly the same.

JDK Mission Control will, of course, be updated to work flawlessly with the OpenJDK 8 version of JFR as well. The changes will be minute and are only necessary since Mission Control has some built-in assumptions that no longer hold true.

You can already build and try out OpenJDK 8 with JFR simply by building the JDK available in the repository mentioned above. Also, Aleksey Shipilev provide binaries – see here for details.

Have fun! Smile

Flight Recorder & Mission Control at Code One 2019

Code One is rapidly approaching (September 16-19). For fans of JDK Flight Recorder and JDK Mission Control, there will be a lot of relevant activities at Code One. This is an attempt to list them. If I missed something, please let me know!

Sessions

Here are the regular sessions:

Session Name

Presenters Day Time

Location

JDK Mission Control: Where We Are, Where We Are Going [DEV4284]

David Buck Monday 9:00 Moscone South
Room 301

Introduction to JDK Mission Control and JDK Flight Recorder [DEV2316]

Marcus Hirt
Klara Ward
Monday 16:00 Moscone South
Room 202
Improving Observability in Your Application with JFR and JMC [DEV3460] Marcus Hirt
Mario Torre
Tuesday 11:30 Moscone South
Room 201
Java Flight Recorder: Black Box of Java Applications[DEV3957] Poonam Parhar Wednesday 12:30

Moscone South
Room 203

Robotics on JDK 11? With Modules? Are You… [DEV2329] Marcus Hirt
Miro Wengner
Robert Savage
Wednesday 16:00

Moscone South
Room 313

Four Productive Ways to Use Open Source JFR and JMC Revisited [DEV3118] Sven Reimers
Martin Klähn
Thursday 11:15 Moscone South
Room 304
Enhanced Java Flight Recorder at Alibaba [DEV3667] Sanhong Li
Fangxi Yin
Guangyu Zhu
Thursday 12:15 Moscone South
Room 203

Performance Monitoring with Java Flight Recorder on OpenJDK [DEV2406]

Hirofumi Iwasaki
Hiroaki Nakada
Thursday 13:15 Moscone South
Room 201

Again, if I’ve missed one, please let me know!

Other Activities

  • There is going to be a hackergarten session around JMC and JFR, Wednesday at 14:30-16:00, inside of the Groundbreakers booth in the Exhibition Area.
  • On Friday a few JMC project members are planning to meet up for some coding between 10:00 and 12:00, and then have lunch together at 12:00. Ping me (Marcus) for an invite.
  • On Wednesday at 18:00 a few JMC project members are planning to go for dinner. Ping me (Marcus) for an invite.

Summary

  • Lots to do at Code One 2019 for fans of JFR and JMC.
  • Helpful links above. Winking smile

Using Dynamic Working Sets in Eclipse

JDK Mission Control is quite modular. To help navigate the source, working sets come in quite handy. And for a more flexible way to define working sets, Oomph provide a very nice plug-in for constructing dynamic working sets, using rules and regular expressions.

To use, first install the Oomph Dynamic Working Sets plug-in into your Eclipse:
https://wiki.eclipse.org/Dynamic_Working_Sets#Download.2FInstallation

Next either start creating your own working sets, or start out with the ones I use:
https://github.com/thegreystone/jmc-dev-helpers

To edit/create the working sets, go to Preferences | Oomph / Dynamic Working Sets, and press Edit…

Once satisfied with the working sets, you can switch the Package Explorer to using the Working Sets as Top Level Elements:

workingset

Good luck!

JDK 11 on the Raspberry Pi

This is a very short post on what I ended up doing to get an OpenJDK 11 build for Raspbian on my Raspberry Pi 3.


  1. Get the latest JDK 11 build of the Liberica JVM (Debian package for ARM v7 & v8, provided by Bell Soft)
    The java download page is here https://www.bell-sw.com/java.html.

    For example:
    wget https://github.com/bell-sw/Liberica/releases/download/11.0.2/bellsoft-jdk11.0.2-linux-arm32-vfp-hflt.deb
  2. Install it

    For example:
    sudo apt-get install ./bellsoft-jdk11.0.2-linux-arm32-vfp-hflt.deb
  3. Set the defaults (if you want to)
    sudo update-alternatives --config javac
    sudo update-alternatives --config java
    

Done!

Note that this gives you access to an open version of JDK Flight Recorder on your Raspberry Pi. Woho! 😉

You could, for example, use the flight recorder to record sensor information.

Another alternative would be using the Azul Zulu JVM, which also has a working Flight Recorder implementation in their JDK 11 arm32 builds.

Deep Distributed Tracing with OpenTracing and the JDK Flight Recorder

Recently I had a talk at Code One about using OpenTracing together with the JDK Flight Recorder to do deep tracing. Since the session wasn’t recorded, I though I’d do a blog about it instead. Here we go…

Distributed tracing has been of interest for a very long time. Multiple companies have sprung up around the idea over the years, and most APM (Application Performance Management) solutions are built around the idea. Google released a paper around their large scale distributed systems tracing infrastructure in 2010 – Dapper, and there are now several open source alternatives for distributed tracing available inspired by the paper, such as Jaeger and Zipkin.

In Java land, pretty much all of the APMs are doing pretty much the same thing: they use BCI (byte code instrumentation) for getting the data, and then they present that data to the end-user in various ways, oftentimes using some kind of analysis to recognize common problems and suggesting solutions to the end users of the APM. The real differentiation is knowing what data to get, and what to do with the data once captured.

Since there was no standard, one problem was for vendors to inject helpful, vendor specific, information into the distributed traces. The vendor of a software component may have a quite good idea about what information would be helpful to solve problems. Some vendors support APM specific APIs for contributing the data, but more often than not the instrumentation is done using BCI by scores of developers working for the various APM companies. The same is true for maintainers of open source components – either skip the problem entirely and let the APM vendors come up with good instrumentations points (if your component is popular enough), or pick a popular APM and integrate with it. That is, until OpenTracing came along…

Introduction to OpenTracing

OpenTracing is an open source, vendor neutral, distributed tracing API. In other words, library developers can interact with one API to support multiple APM/Tracer vendors. Also, customers can add contextual information to distributed traces without worrying about vendor lock-in. Contributors to OpenTracing include LightStep, Jaeger, Skywalking and Datadog, and the specification is available on GitHub:

https://github.com/opentracing

The core API concepts in OpenTracing are (from the slides of my talk, DEV5435):


Trace


– A distributed operation, potentially spanning multiple processes


– Implicitly defined by the individual Spans in the trace (more soon)


– Can be thought of as a directed acyclic graph (DAG) of Spans


– The span in the root of the DAG is called the root Span


– The edges between the Spans are called References


Span


– Has an operation name


– Has a start timestamp


– Has a finish timestamp


– Has a SpanContext


• Has Baggage Items (key/value pairs which cross process boundaries)


• Implementation specific state used to identify the span across process boundaries)


– Zero or more key/value Span Tags


– Zero or more Span Logs (key/value + timestamp)


Reference


– Defines a direct casual relationship between two spans


– ChildOf


• Parent depends on the child in some way


• Note that it is legal for a finish timestamp of a child to be after that of any parent


– FollowFrom


• Parent does not depend on the result of the child in any way


• Note that it is legal for a FollowsFrom child to be started after the end of any ancestor

Also worth noting is that a Scope is a thread local activation of a span.

The Example

As an example, we’ll be using a simple application consisting of three microservices. It is part of the back-end of a fictional robot store. Robots can be ordered at the Orders service, and they will be produced in a Factory. There is also a Customers service keeping track of the customers. Finally there is a load generator that can be used to exercise the services.

image 

The code is available under https://github.com/thegreystone/problematic-microservices. (Yes, as the name indicates, the services come pre-packaged with built-in problems. :))

The services, as well as the load generator, have built-in tracing support, so for a full systems run with the load generator, you would get a trace (a DAG of spans), looking something along the lines of:

image

Or, in Jaeger, where you have time on the X axis:

tracing

In this case I have scrolled down a bit to focus on the factory. As can be seen, there is great variability in the time it takes to create a chassis and/or paint a robot. We have multiple production lanes, and we’d expect times across the factory lanes to be more even, not to mention much faster. So what gives?

Well, we can expand the operation to see if there was some additional information:

tracing_details

Now, sometimes the tags may include crucial pieces information that may help you solve the problem without needing any additional information. In this particular case, though, knowing that we were building a pink BB-8 isn’t really doing the trick.

What would be the next step? All too often the next step would be to look at the code around the instrumentation point, trying to figure out what was going on at the time simply from analyzing the code. Sometimes that may be quite hard. The problem may be in third party code not expected to behave badly. There may even be some other piece of code not directly in the code path causing the problems, perhaps an agent misbehaving and causing long lasting safe points in the JVM.

So, we’re screwed then? Nah. What if you had a magic tool that could record what was going on in the JVM and the application at the time of the incident? Something providing not only method profiling information, but a deeper view, including information about vm operations, memory allocation profiling, events for the usual application caused thread halts and much, much more. Something that could be always on, with very low overhead. And let’s say you ran with a tracer that added some contextual information, such as information that could be used to identify traces, spans and thread local span activations in the recorded data, and which allowed you to use your favourite tracer too? Then things would get interesting indeed…

Running with the JFR Tracer

For Code One I wrote a little delegating JFR tracer, which allows you to record contextual information into the flight recorder. It was meant as an example on how to do deep distributed tracing. Deep enough to solve entire classes of problems that are hard to solve without more detailed knowledge.

The tracer works with Oracle JDK 7+ and OpenJDK 11+ (it is a multi-release jar, a.k.a. mrjar), and the source is available on GitHub here:

https://github.com/thegreystone/jfr-tracer

The bundle is available from Maven Central, and here is the dependency you need to add:

<dependency>
<groupId>se.hirt.jmc</groupId>
<artifactId>jfr-tracer</artifactId>
<version>0.0.3</version>
</dependency>

Next you need to initiate your tracer and pass it to the constructor of the DelegatingJfrTracer, like so:

GlobalTracer.register(new DelegatingJfrTracer(yourFavTracer));

That’s it. When the tracer is running you will get contextual information recorded into the flight recorder.

Looking at the Recording

Dumping the flight recorder for the factory, and looking at the dump in the Threads view, might look something like this:

image

We can see that we have these long lasting monitor enter (Java Blocking) events, and looking at the stack traces directly by selecting individual events, or at the Lock Instances page, it is fairly obvious where the contention is:

image

We can, of course, create a custom OpenTracing view to make it easier to directly finding and homing in on long lasting traces (I’ll create a repo for a ready made one with some more flair at some point). Simply go the the Event Browser, and right click on the Open Tracing folder. Select “Create a new page using the selected event types”. You will now have a new page in the Outline. You can right click on the title on the page to rename it and switch icon.

Next select an arbitrary event, and right click on it. Select Group-By->Trace Id. In the new Group By table that appeared, select Visible Columns to enable (at least) the attribute showing the longest duration (the total duration of (wall clock) time the trace spent in the process that the recording came from). Next sort on the Longest Duration column.

In this case I’ve ran a few more (press enter in the single step load generator a few times, or let it just continuously add load):

image

You can, of course add additional tables with groupings that can be useful, for example, per thread. To quickly home in the entire user interface on a trace id of interest, just select a trace and choose “Store and Set as Focused Selection”:

SNAGHTML38aee12

Now you can go back to, for example, the Threads view, and click the Time Range: Set button in the upper right corner. Voila, you are in exactly the right place. You may also want to view concurrently occurring events in the same threads (see check boxes on top), and enable additional thread lanes:

SNAGHTML38f17fd

Summary

  • Distributed tracing is great, especially in today’s world of (very µ and plenty) µ-services.
  • For the Java platform, injecting trace/span-identifying information as contextual information into the JDK Flight Recorder is dynamite.
  • A simple example on how to do this automagically is available on my GitHub as a delegating Tracer, in an mrjar, supporting Oracle JDK 7+ and OpenJDK 11+:
        https://github.com/thegreystone/jfr-tracer
  • The slides for my Code One presentations can be found here:
        https://oracle.rainfocus.com/widget/oracle/oow18/catalogcodeone18?search=hirt
    (The relevant session for this blog is DEV5435.)
  • The JDK Flight Recorder r0xx0rz.
  • JDK Mission Control r0xx0rz.

Note that since the article was written, I have donated the tracer to OpenTracing.
See https://github.com/opentracing-contrib/java-jfr-tracer.

Dudes and Dudettes, Things Just Got Better!

Oh my god. The amount of FUD concerning the JDK licensing for JDK 11 is just amazing.

So, unless I’ve missed something, Oracle does the following:

  1. Contributes pretty much all of the closed source technologies (or what was originally to become closed source) of the Oracle JDK to OpenJDK, for example giving the community:
    • JDK Flight Recorder
    • JDK Mission Control
    • ZGC
    • …and probably more stuff I can’t think of right now
  2. Ensures the Oracle JDK and the OpenJDK builds are virtually indistinguishable, except for licensing
  3. Moves to, from what I’ve been told, a very competitively priced subscription model (as opposed to the rather, IMHO, highly priced Java SE Advanced licenses)
  4. Starts providing a free OpenJDK build (which includes all these donated technologies)
  5. Provides uncountable man hours of maintaining and innovating the Java platform
  6. Ensures that the community knows where to find the free bits by linking to them, and slaps on a bright yellow warning sign, so that everyone can see that the licensing has changed:

    image

And how does the community react, you wonder? Yep, that’s right. “Oracle is the Devil”, “This is a bait and switch operation” etc. Ad nauseum.

So, this is my personal take on open source: if I like a certain open source technology, and it helps me in my work, I support it. Either by contributing, or by paying (gasp) money for it. Especially if I would like the technology to thrive in the future. Technologies that are not supported, tend to die and be forgotten. I have personally, for a very long time, paid a yearly contribution of 35$ to Eclipse. And that is even though my team, and countless of other teams at Oracle, have contributed to various Eclipse projects over the years. And, no, Eclipse does not provide me support for it.

Summary

Oracle gives away countless of highly regarded technologies and starts releasing free OpenJDK builds. Parts of the Java community throws a fit.