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Bending pause times to your will with Generational ZGC

The Netflix TechBlog

If you’re interested in how we use Java at Netflix, Paul Bakker’s talk How Netflix Really Uses Java , is a great place to start. Each of these errors is a canceled request resulting in a retry so this reduction further reduces overall service traffic by this rate: Errors rates per second.

Latency 228
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TCP: Out of Memory — Consider Tuning TCP_Mem

DZone

You may also like: Java Out of Memory Heap Analysis. The application was running on a GNU/Linux OS, Java 8, Tomcat 8 application server. All other application instances were handling the traffic properly. What happens when you're out of memory? Recently we experienced an interesting production problem.

Tuning 164
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Trending Sources

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Dynatrace Application Security detects and blocks attacks automatically in real-time

Dynatrace

WAFs protect the network perimeter and monitor, filter, or block HTTP traffic. Compared to intrusion detection systems (IDS/IPS), WAFs are focused on the application traffic. RASP solutions sit in or near applications and analyze application behavior and traffic. How to get started.

Traffic 237
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Building Netflix’s Distributed Tracing Infrastructure

The Netflix TechBlog

Our tactical approach was to use Netflix-specific libraries for collecting traces from Java-based streaming services until open source tracer libraries matured. We chose Open-Zipkin because it had better integrations with our Spring Boot based Java runtime environment.

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Unlock end-to-end observability insights with Dynatrace PurePath 4 seamless integration of OpenTracing for Java

Dynatrace

Therefore, we’re happy to announce support for OpenTracing data that’s emitted by auto- and custom-instrumentation of Java source code with Dynatrace PurePath 4, our distributed tracing and code-level analysis technology. With Dynatrace OneAgent you also benefit from support for traffic routing and traffic control.

Java 238
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Migrating Netflix to GraphQL Safely

The Netflix TechBlog

A single API team maintained both the Java implementation of the Falcor framework and the API Server. The control group’s traffic utilized the legacy Falcor stack, while the experiment population leveraged the new GraphQL client and was directed to the GraphQL Shim. The Replay Tester tool samples raw traffic streams from Mantis.

Traffic 353
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Achieving observability in async workflows

The Netflix TechBlog

Prodicle Distribution Our service is required to be elastic and handle bursty traffic. In late 2019, our team started integrating it with the rest of the ecosystem by writing a thin Java Domain graph service (DGS) to wrap the asynchronous watermarking functionality that was then in Ruby on Rails.

Traffic 160