InfoQ Homepage Performance Content on InfoQ
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Compositional I/O Stream in Scala
Runar Bjarnason presents how to get started with the Scalaz-Stream library, shows some examples, and how we can combine functional streams into large distributed systems.
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Streaming Auto-scaling in Google Cloud Dataflow
Manuel Fahndrich describes how they tackled one particular resource allocation aspect of Google Cloud Dataflow pipelines - horizontal scaling of worker pools as a function of pipeline input rate.
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How Comcast Uses Data Science and ML to Improve the Customer Experience
Jan Neumann presents how Comcast uses machine learning and big data processing to facilitate search for users, for capacity planning, and predictive caching.
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Examining Low Pause Garbage Collection in Java
John Oliver takes a look at both G1 and Shenandoah, explaining how they work, what are their limitations, providing tuning advice. He also looks at recent and future changes to garbage collection.
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Java 9 - The (G1) GC Awakens!
Monica Beckwith talks about G1 pause (young and mixed) composition, G1's remembered sets and collection set and G1's concurrent marking algorithm, providing performance tuning advice.
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The Mechanics of Testing Large Data Pipelines
Mathieu Bastian explores the mechanics of unit, integration, data and performance testing for large, complex data workflows, along with the tools for Hadoop, Pig and Spark.
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Hot Code is Faster Code - Addressing JVM Warm-up
Mark Price explores the life cycle of Java code, and how the JVM evolves the runtime representation of code during program execution, providing tips to make sure Java code runs fast.
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Real World Experience Report on Running Docker
Ben Hall shares his experience working with Docket for development, testing and deployment into production, discussing scalability, resource management, security and other related issues.
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Understanding HotSpot JVM Performance with JITWatch
Chris Newland discusses performance-boosting techniques used by the JVM’s JIT and introduces JITWatch, a tool helping to get the best JVM performance for a code.
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How to Have Your Causality and Wall Clocks Too
Jon Moore talks about distributed monotonic clocks (DMC) whose timestamps can reflect causality but which have a component that stays close to wall clock time.
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Life of a Twitter JVM Engineer
Tony Printezis presents how services are deployed and monitored at Twitter, the benefits of using a custom-built JVM, and the challenges of the use of the JVM in an environment like Twitter.
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Broken Performance Tools
Brendan Gregg focuses on broken tools and metrics instead of the working ones. Metrics can be misleading, and counters can be counter-intuitive. He advises on how to approach new performance tools.
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