�ݺ�ߣshows by User: JaroslavBachorik

�ݺ�ߣshows by User: JaroslavBachorik / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: JaroslavBachorik / Thu, 07 Dec 2023 15:08:36 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: JaroslavBachorik JMX 2.0 - the Forgotten Source.pptx /slideshow/jmx-20-the-forgotten-sourcepptx/264421026 jmx2-231207150836-d7846673
A necromancy session on a few JMX API enhancements.]]>
A necromancy session on a few JMX API enhancements.]]> Thu, 07 Dec 2023 15:08:36 GMT /slideshow/jmx-20-the-forgotten-sourcepptx/264421026 JaroslavBachorik@slideshare.net(JaroslavBachorik) JMX 2.0 - the Forgotten Source.pptx JaroslavBachorik A necromancy session on a few JMX API enhancements. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jmx2-231207150836-d7846673-thumbnail.jpg?width=120&height=120&fit=bounds" /> A necromancy session on a few JMX API enhancements.

JMX 2.0 - the Forgotten Source.pptx from Jaroslav Bachorik

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0 Java Profiling Future /slideshow/java-profiling-future/259802156 jvmlsjavaprofilingfuture-230811160057-945b3182
OpenJDK Committer Worhsop 08/2023 session]]>
OpenJDK Committer Worhsop 08/2023 session]]> Fri, 11 Aug 2023 16:00:57 GMT /slideshow/java-profiling-future/259802156 JaroslavBachorik@slideshare.net(JaroslavBachorik) Java Profiling Future JaroslavBachorik OpenJDK Committer Worhsop 08/2023 session <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jvmlsjavaprofilingfuture-230811160057-945b3182-thumbnail.jpg?width=120&height=120&fit=bounds" /> OpenJDK Committer Worhsop 08/2023 session

Java Profiling Future from Jaroslav Bachorik

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0 ContextualContinuous Profilng /slideshow/contextualcontinuous-profilng/257542871 contextualprofilng-230424153916-738a4309
Gathering all the profiling information unconditionally for all processing comprising a distributed service can be quite overwhelming and costly. Especially when most of that information might not be relevant or actionable. The idea of contextual profiling is to utilize the context captured by tracers to drive the collection and representation of the profiling data in a way that relates directly to the customer's application and business processes. This talk will shed more light on how the contextual profiling is implemented in Datadog Java Profiler and muse the idea of bringing the concept to OpenJDK JFR such it may be used by all Java users.]]>
Gathering all the profiling information unconditionally for all processing comprising a distributed service can be quite overwhelming and costly. Especially when most of that information might not be relevant or actionable. The idea of contextual profiling is to utilize the context captured by tracers to drive the collection and representation of the profiling data in a way that relates directly to the customer's application and business processes. This talk will shed more light on how the contextual profiling is implemented in Datadog Java Profiler and muse the idea of bringing the concept to OpenJDK JFR such it may be used by all Java users.]]> Mon, 24 Apr 2023 15:39:16 GMT /slideshow/contextualcontinuous-profilng/257542871 JaroslavBachorik@slideshare.net(JaroslavBachorik) ContextualContinuous Profilng JaroslavBachorik Gathering all the profiling information unconditionally for all processing comprising a distributed service can be quite overwhelming and costly. Especially when most of that information might not be relevant or actionable. The idea of contextual profiling is to utilize the context captured by tracers to drive the collection and representation of the profiling data in a way that relates directly to the customer's application and business processes. This talk will shed more light on how the contextual profiling is implemented in Datadog Java Profiler and muse the idea of bringing the concept to OpenJDK JFR such it may be used by all Java users. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/contextualprofilng-230424153916-738a4309-thumbnail.jpg?width=120&height=120&fit=bounds" /> Gathering all the profiling information unconditionally for all processing comprising a distributed service can be quite overwhelming and costly. Especially when most of that information might not be relevant or actionable. The idea of contextual profiling is to utilize the context captured by tracers to drive the collection and representation of the profiling data in a way that relates directly to the customer's application and business processes. This talk will shed more light on how the contextual profiling is implemented in Datadog Java Profiler and muse the idea of bringing the concept to OpenJDK JFR such it may be used by all Java users.

ContextualContinuous Profilng from Jaroslav Bachorik

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0 Extending Spark With Java Agent (handout) /slideshow/extending-spark-with-java-agent-handout/67735714 57afcef1-84a3-45c7-837d-4844ed418ac3-161027152539
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]]> Thu, 27 Oct 2016 15:25:38 GMT /slideshow/extending-spark-with-java-agent-handout/67735714 JaroslavBachorik@slideshare.net(JaroslavBachorik) Extending Spark With Java Agent (handout) JaroslavBachorik <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/57afcef1-84a3-45c7-837d-4844ed418ac3-161027152539-thumbnail.jpg?width=120&height=120&fit=bounds" />

Extending Spark With Java Agent (handout) from Jaroslav Bachorik

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0 GeeCon2016- High Performance Instrumentation (handout) /slideshow/geecon2016-high-performance-instrumentation-handout/67455160 dfc1f80e-2f7d-4d79-9e9d-4846a9b3e9ac-161020130903
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]]> Thu, 20 Oct 2016 13:09:03 GMT /slideshow/geecon2016-high-performance-instrumentation-handout/67455160 JaroslavBachorik@slideshare.net(JaroslavBachorik) GeeCon2016- High Performance Instrumentation (handout) JaroslavBachorik <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dfc1f80e-2f7d-4d79-9e9d-4846a9b3e9ac-161020130903-thumbnail.jpg?width=120&height=120&fit=bounds" />

GeeCon2016- High Performance Instrumentation (handout) from Jaroslav Bachorik

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0 Make Java Profilers Lie Less /slideshow/make-java-profilers-lie-less/54257982 profilersgeecon-151022115934-lva1-app6891
Java profilers lie. Some more, some less. Deal with it. The information you get from a profiler is usually just about right - but with no guarantees. This might become rather frustrating especially when you picked all the low hanging fruits and need to squeeze the last few tens of nanoseconds from critical parts. Well, you first need to identify the critical parts. This talk will provide an overview of the common profiler lies, show you why microbenchmarking is "The Good Thing"(tm) and what would it take form JVM to change this.]]>
Java profilers lie. Some more, some less. Deal with it. The information you get from a profiler is usually just about right - but with no guarantees. This might become rather frustrating especially when you picked all the low hanging fruits and need to squeeze the last few tens of nanoseconds from critical parts. Well, you first need to identify the critical parts. This talk will provide an overview of the common profiler lies, show you why microbenchmarking is "The Good Thing"(tm) and what would it take form JVM to change this.]]> Thu, 22 Oct 2015 11:59:33 GMT /slideshow/make-java-profilers-lie-less/54257982 JaroslavBachorik@slideshare.net(JaroslavBachorik) Make Java Profilers Lie Less JaroslavBachorik Java profilers lie. Some more, some less. Deal with it. The information you get from a profiler is usually just about right - but with no guarantees. This might become rather frustrating especially when you picked all the low hanging fruits and need to squeeze the last few tens of nanoseconds from critical parts. Well, you first need to identify the critical parts. This talk will provide an overview of the common profiler lies, show you why microbenchmarking is "The Good Thing"(tm) and what would it take form JVM to change this. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/profilersgeecon-151022115934-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> Java profilers lie. Some more, some less. Deal with it. The information you get from a profiler is usually just about right - but with no guarantees. This might become rather frustrating especially when you picked all the low hanging fruits and need to squeeze the last few tens of nanoseconds from critical parts. Well, you first need to identify the critical parts. This talk will provide an overview of the common profiler lies, show you why microbenchmarking is "The Good Thing"(tm) and what would it take form JVM to change this.

Make Java Profilers Lie Less from Jaroslav Bachorik

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0 https://cdn.slidesharecdn.com/profile-photo-JaroslavBachorik-48x48.jpg?cb=1701961670 Specialties: Java Performance, Java Bytecode, Instrumentation, JVM, NetBeans, JMX https://cdn.slidesharecdn.com/ss_thumbnails/jmx2-231207150836-d7846673-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/jmx-20-the-forgotten-sourcepptx/264421026 JMX 2.0 - the Forgotte... https://cdn.slidesharecdn.com/ss_thumbnails/jvmlsjavaprofilingfuture-230811160057-945b3182-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/java-profiling-future/259802156 Java Profiling Future https://cdn.slidesharecdn.com/ss_thumbnails/contextualprofilng-230424153916-738a4309-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/contextualcontinuous-profilng/257542871 ContextualContinuous P...