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.
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.
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.
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.
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.
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.
Cliff Click takes a look at Java vs C performance. He discusses both languages' strong and weak points and the programming context surrounding language choices.
Yves Reynhout discusses models, how they're created and tested against scenarios, how they're useful, what distinguishes them from others, how they're visualized and communicated, etc.
Itamar Syn-Hershko shows using various technologies -Storm, Node.js, Riemann, collectd, D3.js, ELK, PagerDuty, Slack - to power Forter’s service and keep it highly available and under control.
Nik Molnar discusses how to use client and server side profiling tools to improve the performance of a web application, providing solutions to the most common performance problems.
Tal Weiss shows how you can easily write your own JVM agent to capture accurate performance data for virtually any type of application from Java microservices to reactive actor systems in Scala.
Rick Hudson discusses the motivation, performance, and technical challenges of Go's low latency concurrent GC and why the approach fits Go well.