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.
Danny Yuan discusses how Uber uses stream processing to solve a wide range of problems, including real-time aggregation and prediction on geospatial time series, and much more.
Nicolas Frankel demoes some of the many important Non-Functional Requirements out-of-the-box that come with Spring Boot: monitoring, metrics, exposing those over HTTP.
Emad Benjamin covers various GC tuning techniques and how to best build platform engineered systems; in particular the focus is on tuning large scale JVM deployments.
Atlassian Hybrid Cloud/On-Premise Software Delivery and the Journey to 300,000 Applications in the Cloud
George Barnett discusses techniques for building the supporting infrastructure for a hybrid model, and how to make monitoring, deployment tools, and shared services work effectively.
Scott Seighman discusses causes of common performance issues in Big Data environments, heap size, garbage collection, JVM reuse tuning guidelines and Big Data performance analysis tools.
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