InfoQ Homepage Performance Content on InfoQ
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Understanding Parallel Stream Performance in Java SE 8
Brian Goetz explores tools and techniques involved in parallelism, and how to analyze a computation for potential parallelism, with specific attention to the parallel stream library in Java 8.
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Large-Scale Stream Processing with Apache Kafka
Neha Narkhede explains how Apache Kafka was designed to support capturing and processing distributed data streams by building up the basic primitives needed for a stream processing system.
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Faster Index for Java, or CDT Pays Its Debt to JDT
Stefan Xenos and Sergey Prigogin present how the JDT new index was made to be an order of magnitude faster than what it was before.
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React.js Reconciliation
Jim Sproch describes how reconciliation works within React, and how to use it to enhance both performance and user experience.
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An Erlang-Based Philosophy for Service Reliability
Jamshid Mahdavi explains how WhatsApp has developed their server components, the deployment processes, and how they monitor, alert, and repair the inevitable failures in a billion-users service.
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The Dark Art of Container Monitoring
Luca Marturana covers the current state of the art for container monitoring and visibility, including real use cases with pros/cons of each and focuses on advanced container visibility techniques.
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Much Faster Networking
David Riddoch talks about the technologies that make high performance networking possible on commodity servers, with a special focus on direct access to the network adapter by bypassing the kernel.
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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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