How the shoe and clothes giant manufacturer's IT tamed an out-of-control proliferation of third party tools in their global websites which was killing performance. Furthermore, this led to a blame culture setting in between business and IT. A new third party governance process focusing on performance data and user experience validation was key to stop the bleeding.
Google Cloud Platform has released an open source Zipkin server that allows Zipkin-compatible clients to send traces to Google’s own Stackdriver Trace distributed tracing service for analysis. This Zipkin/Stackdriver Trace integration is aimed at developers whose applications and services are written in a language or framework that Stackdriver Trace doesn’t officially support.
At the AWS re:Invent 2016 conference, held in Las Vegas, USA, a distributed tracing service named AWS X-Ray was released in preview within all 12 public AWS Regions. In a similar fashion to Google’s Dapper, Twitter’s Zipkin and the OpenTracing API, AWS X-Ray helps developers analyse and debug distributed applications, such as those built using a microservices architectural style.
Logz.io offers a hosted service which performs intelligent log analysis by using machine learning to derive insights from human interactions with log data that includes discussions on tech forums and public code repositories.
Honeycomb is a tool for observing and correlating events in distributed systems. It provides a different approach from existing tools like Zipkin in that it moves away from the single-request-tracing model to a more free-form model of collecting and querying data across layers and dimensions.
Netflix has recently made available the source code of the Chaos Monkey 2.0. The latest iteration of the resilience tool is fully integrated with Spinnaker and event tracking systems, but the SSH support has been removed.
Day 1 of JavaOne 2016 topics: learning about Java 8&9 features, Docker for Java developers, and development tools for Java EE 8. InfoQ highlights a few of the day's interesting sessions.
Continuous deployment results in a higher sense of responsibility and better quality of deployments, argues Paul de Raaij, technical pathfinder at Coolblue. Coding standards prevent your code base from becoming a mess, automated inspections are great for tedious and boring checks, and manual checks are great for checking if the logic or use of code actually makes sense.
At QCon New York 2016, Etsy software engineer Stefanie Schirmer told how her company successfully transitioned to an API-first architecture that supports multiple devices, addresses server-side performance problems, and was quickly adopted by development teams.
Stefan Thies, DevOps Evangelist at Sematext, in a recent post discusses ten important container monitoring metrics and their implications on operating Docker containers, specifically when running many containers per host. Combined in a single correlated view these metrics provide a starting point for monitoring Docker-based environments.
Windows Management Instrumentation (WMI) is a primary source of data when monitoring Windows systems. Given that the performance counters available vary from machine to machine, a tool is needed to list all counters available. The WMI type provider is one possible option to explore WMI performance counters.
Android N introduces a hybrid runtime using compilation + interpretation + JIT to obtain the best compromise between installation time, memory footprint, battery consumption and performance.
This post describes the applications of the shooting target in kanban board introduced by Tomas Rybing.
Censum, the Java garbage collection analysis tool by jClarity, has reached version 3.0. The main new features of the new version include the ability to analyse Safepoint logs, new graphs showcasing the behaviour of the G1 garbage collector, and a set of analytics to highlight whenever applications force to much OS activity.
At the microXchg conference, held in Berlin, Adrian Cockcroft presented “Analyzing Response Time Distributions for Microservices”. Cockcroft demonstrated how the combination of his Spigo microservice architecture simulation tool and the online Guesstimate Monte Carlo method tool can be used to visualise and experimentally simulate request response times within a complicated microservice system.
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