Using Java Mission Control & Java Flight RecorderIsuru Perera
This presentation explains the Java Mission Control (JMC) and how to use it.
Java Mission Control has two main tools: JMX Console & Java Flight Recorder (JFR). These are very powerful tools provided by the Oracle JDK.
Using Java Mission Control & Java Flight RecorderIsuru Perera
This presentation explains the Java Mission Control (JMC) and how to use it.
Java Mission Control has two main tools: JMX Console & Java Flight Recorder (JFR). These are very powerful tools provided by the Oracle JDK.
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This slide is a trail CHT analysis for relatively complex bodies with chtMultiRegionFoam which is an solver of OpenFOAM. Two methods to make mesh are explained.
Apache Spark's Built-in File Sources in DepthDatabricks
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This tutorial is intended for verification engineers that must validate algorithmic designs. It presents the detailed steps for implementing a SystemVerilog verification environment that interfaces with a GNU Octave mathematical model. It describes the SystemVerilog – C++ communication layer with its challenges, like proper creation and activation or piped algorithm synchronization handling. The implementation is illustrated for Ncsim, VCS and Questa.
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Spinning up new instances fast, and effective JIT compilation, may be game changers these days, but they are just a part of the story. What about leaks in the code taking all gained speed away? Java Flight Recorder is an event based tracing framework. It is built directly into the Java runtime and provides access to all internal data, while allowing additional custom enhancements. The goal is to present the value of JFR and how it is able to achieve low overhead (cca 1%). We explain fundamental elements and the performance. We also explore newly added features in current and upcoming releases of JDK Mission Control 8.x.
FPGA-Based Acceleration Architecture for Spark SQL Qi Xie and Quanfu Wang Spark Summit
In this session we will present a Configurable FPGA-Based Spark SQL Acceleration Architecture. It is target to leverage FPGA highly parallel computing capability to accelerate Spark SQL Query and for FPGA’s higher power efficiency than CPU we can lower the power consumption at the same time. The Architecture consists of SQL query decomposition algorithms, fine-grained FPGA based Engine Units which perform basic computation of sub string, arithmetic and logic operations. Using SQL query decomposition algorithm, we are able to decompose a complex SQL query into basic operations and according to their patterns each is fed into an Engine Unit. SQL Engine Units are highly configurable and can be chained together to perform complex Spark SQL queries, finally one SQL query is transformed into a Hardware Pipeline. We will present the performance benchmark results comparing the queries with FGPA-Based Spark SQL Acceleration Architecture on XEON E5 and FPGA to the ones with Spark SQL Query on XEON E5 with 10X ~ 100X improvement and we will demonstrate one SQL query workload from a real customer.
This slide is a trail CHT analysis for relatively complex bodies with chtMultiRegionFoam which is an solver of OpenFOAM. Two methods to make mesh are explained.
Apache Spark's Built-in File Sources in DepthDatabricks
In Spark 3.0 releases, all the built-in file source connectors [including Parquet, ORC, JSON, Avro, CSV, Text] are re-implemented using the new data source API V2. We will give a technical overview of how Spark reads and writes these file formats based on the user-specified data layouts. The talk will also explain the differences between Hive Serde and native connectors, and share the experiences of how to tune the connectors and choose the best data layouts for achieving the best performance.
This tutorial is intended for verification engineers that must validate algorithmic designs. It presents the detailed steps for implementing a SystemVerilog verification environment that interfaces with a GNU Octave mathematical model. It describes the SystemVerilog – C++ communication layer with its challenges, like proper creation and activation or piped algorithm synchronization handling. The implementation is illustrated for Ncsim, VCS and Questa.
Linux Performance Analysis: New Tools and Old SecretsBrendan Gregg
Talk for USENIX/LISA2014 by Brendan Gregg, Netflix. At Netflix performance is crucial, and we use many high to low level tools to analyze our stack in different ways. In this talk, I will introduce new system observability tools we are using at Netflix, which I've ported from my DTraceToolkit, and are intended for our Linux 3.2 cloud instances. These show that Linux can do more than you may think, by using creative hacks and workarounds with existing kernel features (ftrace, perf_events). While these are solving issues on current versions of Linux, I'll also briefly summarize the future in this space: eBPF, ktap, SystemTap, sysdig, etc.
Spinning up new instances fast, and effective JIT compilation, may be game changers these days, but they are just a part of the story. What about leaks in the code taking all gained speed away? Java Flight Recorder is an event based tracing framework. It is built directly into the Java runtime and provides access to all internal data, while allowing additional custom enhancements. The goal is to present the value of JFR and how it is able to achieve low overhead (cca 1%). We explain fundamental elements and the performance. We also explore newly added features in current and upcoming releases of JDK Mission Control 8.x.
This slides show
1. How to obtain code coverage information for Java code
2. What kind of code coverage it is possible to get
3. Is 100% block coverage feasible, is it useful
4. How the code coverage could be used for more than discovering a percentage of uncovered code
This is an overview about Java Mission Control and Java Flight Recorder which is part of the Oracle JDK since JDK 7u40. The purpose of JFR is to have a continuous recording about the behavior of the JVM and the Java application at the same time. You can walk back in time and find out whats going on, to discover a specific problem situation in history
Monitoring Java Application Security with JDK Tools and JFR Events.pdfAna-Maria Mihalceanu
Learn how JDK Flight Recorder, JDK Mission Control and JFR Security Events can help monitoring security of your Java application so that you can detect potential safety risks.
Performance Monitoring with Java Flight Recorder on OpenJDK [DEV2406]Hiroaki NAKADA
JDK Flight Recorder (JFR) is a mechanism for collecting diagnostic and profiling data from the JVM. A lot of information can be obtained, with low overhead, about CPU, memory, GC, I/O, method calls, thread dumps, events, and more. And now, as JFR is integrated into OpenJDK, JFR is not only free but also supports convenient new APIs. For example, you can add and record your own metrics and analyze the JFR log files directly, using Java code. These features extend the use case of JFR. This session explains how to program custom events and visualize them for monitoring with, for example, Kibana or Grafana. JFR provides you with a next-generation standard JVM monitoring solution.
Presentation slides used at the 17th Java Colombo Meetup.
http://www.meetup.com/java-colombo/events/218658123/
This presentation explains the JMX Console & Java Flight Recorder (JFR) tools in Java Mission Control (JMC)
OSMC 2021 | inspectIT Ocelot: Dynamic OpenTelemetry Instrumentation at RuntimeNETWAYS
If you want to trace or extract specific data from a Java application with OpenTelemetry, you usually have to modify the application’s code. However, this is often not possible, especially with bought-in software. We would like to show, how the open source inspectIT Ocelot Java agent can be used to dynamically inject OpenTelemetry code at runtime for extracting specific application and business data – and all this without having to adapt the application itself.
Best Practices for performance evaluation and diagnosis of Java Applications ...IndicThreads
Session Presented at 5th IndicThreads.com Conference On Java held on 10-11 December 2010 in Pune, India
WEB: http://J10.IndicThreads.com
------------
Enterprise applications typically comprise of multi layered stacks including the application modules, application servers, the Java Virtual Machine and the underlying Operating System. Consequently the performance of these applications are a factor of these different layers. In the eventuality of a performance problem, it is often difficult to determine the starting point for diagnosis. The Java Virtual Machine is the ‘engine’ for most of the applications. It is responsible broadly for efficient execution and memory management of applications. End users have difficulty attributing the effect of the JVM on the performance of the application. This is because usually JVM is viewed as a ‘black box’.
This talk provides an insight into the key subsystems of the JVM by looking under the hood of a high performance JVM. It ventures onto talk about approaches and techniques for analyzing performance issues. It concludes by introducing the audience to a tool called the “Health Center” which is useful for evaluating and comprehending the JVM behavior of a running application in an unobtrusive, lightweight manner.
Takeaways for the Audience A better understanding of key JVM components, approaches and techniques to diagnose performance issues and performance evaluation using the Health Center
Is it tuning the garbage collector? Writing clean(er) code?
No, the first step is understanding what’s going on in your application!
Performance tuning starts with analysis, and JDK tools can help you gain insights on classes and threads and can perform live GC analysis or heap dump postprocessing: jcmd, jconsole, jstat, jmap and jfr.
We’ll examine the functional visibility areas essential to Java and how these tools provide that information. Moreover, will discuss options on how to integrate information gathered from these tools with widespread monitoring systems like Prometheus.
After this talk, you will be ready to understand what your application spends time on and why so you can start improving its perform
Tracing the root cause of a performance issue requires a lot of patience, experience, and focus. It’s so hard that we sometimes attempt to guess by trying out tentative fixes, but that usually results in frustration, messy code, and a considerable waste of time and money. This talk explains how to correctly zoom in on a performance bottleneck using three levels of profiling: distributed tracing, metrics, and method profiling. After we learn to read the JVM profiler output as a flame graph, we explore a series of bottlenecks typical for backend systems, like connection/thread pool starvation, invisible aspects, blocking code, hot CPU methods, lock contention, and Virtual Thread pinning, and we learn to trace them even if they occur in library code you are not familiar with. Attend this talk and prepare for the performance issues that will eventually hit any successful system.
About authorWith two decades of experience, Victor is a Java Champion working as a trainer for top companies in Europe. Five thousands developers in 120 companies attended his workshops, so he gets to debate every week the challenges that various projects struggle with. In return, Victor summarizes key points from these workshops in conference talks and online meetups for the European Software Crafters, the world’s largest developer community around architecture, refactoring, and testing. Discover how Victor can help you on victorrentea.ro : company training catalog, consultancy and YouTube playlists.
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6. Agenda
• History in nutshell
• JMC and Flight Recorder in bullet points
• JFR under the hood
• JFR in Action and Examples
• Power of Data Visualisation
• What about JDK improvements and upcoming features?
• Q/A
7. History in nutshell
• 1998 Appeal Virtual Machines (AVM) - JRockit JVM
• 2002 AVM acquired by BEA
• 2008 acquired by Oracle
• 2012 JDK 7u4 update: Oracle integrated JFR into the HotSpot
• 2017 JDK 9 : Public APIs for creating and consuming data
• 2018 JDK 11: JMC/JFR announced to be fully Open-Sourced
8. JFR in bullets
• Part of Java Mission Control project
• Java Flight Recorder is an event based tracing framework
• Build directly into the Java Virtual Machine
• Provides access to all internal events
• Allows to create custom events
• Tries to achieve a goal 1% overhead
10. Profiling with JFR
• Observability allows to measure a system based on provided data metrics
(local, on-prem or cloud)
• each application may have di
ff
erent telemetry, di
ff
erent assumptions
• hardware, container, end-points, libraries….
• Monitoring -> keeping yes on sampled data
• Pro
fi
ling -> recording and analysing “real” data
https://www.hippopx.com/en/camera-security-monitoring-big-brother-control-surveillance-camera-video-surveillance-81117
11. Common Usage of JFR
• Micros-Services, Distributes and Stand-Alone applications
• helps in root-cause analysis
• application understanding, bottlenecks
• solution correctes
• testing
• monitoring
Let’s split
More Pods there…
12. Java Mission Control Project
Current release 8.3
Release Milestone Date
========== =========== ==========
8.2.0 GA 2022-01-19
8.2.1 GA 2022-06-16
8.3 EA 2022-10-11
8.1 : New Allocation Events for JDK 16,
• JMC Agent, JMC Agent Plugin
• Performance Improvements : Perser, Rules
8.2: Minor release
• JFR: Heat-Map View, WebSocket-API
8.3: Minor release
• JMC Core: Parser improvements
• JFR: Dependency view, Graph Pruning, JFR: Selectable attributes,
• Agent: bug
fi
xing
13. Java Mission Control Project
Current release 8.3
Dependency View
Graph pruning View
Flameview Selectable Attributes
14. Java Flight Recorder
under the hood
Java Event API
JVM Events
Thread Local
Buffers
Events
Global Bu
ff
ers
Repository
per 1s
JFR Event Streaming (JEP 349): Continual Monitoring
15. JFR under the hood: Event - fundamental element
• Import “jdk.jfr.Event”
• Basic Element that caries valuable information
EventID:
Timestamp : when event was taken
Duration: not always
Thread ID: Thread where event has occurred
StackTrace ID: It’s optional referst to the StackTrace, default depth 64
Payload: custom information
16. Import jdk.jfr.Event;
public class SampleEvent extends Event {
//internal logic
String message;
}
…
void someAdvanceLogic() {
SampleEvent e = new SampleEvent();
e.message = “Important Information”;
e.begin();
// advanced logic
e.end();
e.commit();
}
…
JFR under the hood: Event
17. JFR under the hood: : Tuning up an Event
Annotations: https://docs.oracle.com/en/java/javase/19/docs/api/jdk.jfr/jdk/jfr/class-use/MetadataDe
fi
nition.html
Annotation Description Default
@Name
Automatically Set, Recommended pattern: org.com…
Event
Full path with Name:
openvalue.SampleEvent
@Label Short descriptive information
@Category Category is present to the user
@Threshold
Default min duration fo the event to be included in the
recording
0ns
@StackTrace Include a stack-trace to the event TRUE
@Enabled Is Event enabled by default con
fi
guration
18. JFR under the hood: : Tuning up an Event
import jdk.jfr.Event
import jdk.jfr.Label
import jdk.jdf.Name
@Name(“com.openvalue.events.SampleEvent”)
@Label(“Sample Event”)
class SampleEvent extends Event {
@Label(“Message”)
String name;
@Label(“Value”)
int value;
}
Annotations: https://docs.oracle.com/en/java/javase/19/docs/api/jdk.jfr/jdk/jfr/class-use/MetadataDe
fi
nition.html
19. JFR under the hood: Performance
• Usage of Thread Local Bu
ff
ers
• Java Platform Optimisation
• Methods: INLINING, CODE ELIMINATION, SCALARIZATION
• What happens when event is enabled / disabled
-XX:StartFlightRecording=filename=<RECORDING_FILE>.jfr,dumponexit=true,settings=profile
20. void someAdvanceLogic() {
SampleEvent e = new SampleEvent();
e.message = “Important Information”;
e.begin();
// advanced logic
e.commit();
}
void commit() {
// IF it’s not enabled -> NOT INTERESTING
if(isEnabled()){
//now() reads CPU clock register, cheap check
long duration = now() - startTime;
if(duration > THRESHOLD) {
if (shouldCommit()) {
// Cheap - Thread local writes
actuallyCommit();
}
}
}
Mikeal Vidstedt presented quite neat pseudo-code that helps to understand to the commit()
JFR under the hood: Event in Action
21. void someAdvanceLogic(){
// allocating event
SampleEvent e = new SampleEvent();
e.begin(); -> INLINING => e.startTime = now(); -> e.startTime = <JVM intrinsic>
// advanced logic
// timestamp, likewise INLINING, implicit end()
e.commit();
// JFR ENABLED STATE
if(e.isEnabled()){
// perform additional checks and maybe actuallyCommit()
}
}
JFR under the hood: Enabled
22. JFR under the hood: Disabled - part 1
void someAdvanceLogic() {
SampleEvent e = new SampleEvent();
// INLINING from the previous slide
e.startTime = <JVM intrinsic>;
// advanced logic
//INLINING
if(false) { // result e.isEnabled()
//perform additional checks
//CODE ELIMINATION -> will be removed
}
}
23. JFR under the hood: Disabled - part 2
void someAdvanceLogic() {
SampleEvent e = new SampleEvent(); // SCALARIZATION -> REMOVAL
e.begin()
1. initial state: e.begin();
2. INLINING => e.startTime = <JVM intrinsic>;
3. INLINING => long startTime = <JVM intrisic>;
4. CODE ELIMINATION => long startTime = <JVM intrisic>; REMOVAL
//business logic
}
24. JFR under the hood: Disabled - part 3
void someAdvanceLogic() {
//business logic
}
25. JFR: Start Recording
# Starting a recording
$ java -XX:StartFlightRecording …
# Starting Recording and Storing into the file
$ java -XX:StartFlightRecording=filename=<FILE_NAME>.jfr
# Starting Recording and dump on exit
$ java -XX:StartFlightRecording=filename=<FILE_NAME>.jfr,dumponexit-true,settings=profile
# Using jcmd command via PID to start recording
$ jcmd <PID> JFR.start duration=30s filename=hotmethods_jcmd.jfr
26. JFR: Data Visualisation
• Command line tool avaliable from JDK 11 => jfr
$jfr summary <JFR_file>
$jfr print —json <JFR_FILE>
• JFR GUI. (DEMO)
• Automated analysis
• Java Application => Thread, Memory, etc.
• Event Browser