Remove Cache Remove Code Remove Java
article thumbnail

Seeing through hardware counters: a journey to threefold performance increase

The Netflix TechBlog

We decided to move one of our Java microservices?—?let’s We turned to JVM-specific profiling, starting with the basic hotspot stats, and then switching to more detailed JFR (Java Flight Recorder) captures to compare the distribution of the events. Cache line is a concept similar to memory page?—? let’s call it GS2?—?to

Hardware 363
article thumbnail

Progressive delivery at cloud scale: Optimizing CPU intensive code with Dynatrace

Dynatrace

And the code-level root cause information is what makes troubleshooting easy for developers. As Dynatrace automatically captures stack traces for all threads at all time the CPU Hotspot analysis makes it easy to identify which code is consuming all that CPU in that particular thread. Step 3: Identifying root-cause in code.

Code 246
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Radically speed up your code by fixing slow or frequent garbage collection

Dynatrace

Java Memory Management, with its built-in garbage collection, is one of the language’s finest achievements. However, garbage collection is one of the main sources of performance and scalability issues in any modern Java application. Optimize your code by finding and fixing the root cause of garbage collection problems.

Speed 214
article thumbnail

AI-driven analysis of Spring Micrometer metrics in context, with typology at scale

Dynatrace

One of these solutions is Micrometer which provides 17+ pre-instrumented JVM-based frameworks for data collection and enables instrumentation code with a vendor-neutral API. Spring Boot, on the other hand, is a Java framework for building cloud-native Java applications. That’s a large amount of data to handle.

Metrics 246
article thumbnail

Seamlessly Swapping the API backend of the Netflix Android app

The Netflix TechBlog

On the Android team, while most of our time is spent working on the app, we are also responsible for maintaining this backend that our app communicates with, and its orchestration code. Image taken from a previously published blog post As you can see, our code was just a part (#2 in the diagram) of this monolithic service. Java…Script?

Latency 241
article thumbnail

Announcing new and super fast Android auto-instrumentation (EAP)

Dynatrace

Moreover, features like Instant Run and the Gradle Build Cache weren’t supported. Out-of-the-box support for Instant Run and the Gradle Build Cache make the auto-instrumentation process barely noticeable. For bytecode instrumentation, we rely on a well-tested framework that’s also the foundation of the OneAgent Java module.

Cache 35
article thumbnail

Static Analysis of Java Enterprise Applications: Frameworks and Caches, the Elephants in the Room

The Morning Paper

Static analysis of Java enterprise applications: frameworks and caches, the elephants in the room , Antoniadis et al., Being static , it has the advantage that analysis results can be produced solely from source code without the need to execute the program. PLDI’20. Enterprise applications have (more than?)

Java 80