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GC easy – Universal Java GC Log Analyser

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Case study

Overhead added by Garbage Collection Logging

Enabling Garbage collection logs on your application has certain advantages. In nutshell, Garbage collection logs will facilitate you to optimize Garbage Collection pause time, improve overall application’s response time, forecast production outages, reduce computing cost. Even though Garbage collection logs have these advantages, we weren’t sure what overhead does garbage collection logging add to the application. Thus we set out to conduct the below case study.

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DSquare trading App addressed GC Pauses using GCeasy

Dsquare is a highly specialized FX trading boutique. They are focused on consistently outperforming the market in short term trading. They use algorithmic trading models in the world’s biggest market-average daily volumes in foreign exchange to execute their strategies well in excess of $3 trillion.

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Uber optimizes Garbage collection performance using GCeasy

Recent times, Uber has been experiencing exponential growth in its traffic. Recent spike in traffic volume exposed several memory related performance bottlenecks in their platform: long garbage collection (GC) pauses, memory corruption, out-of-memory (OOM) exceptions, and memory leaks. In this brilliant article, the Uber engineering team summarizes their optimization journey: What all the challenges they faced, the tools they used, best practices they followed to fix their application’s performance bottlenecks. 

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DISAPPOINTING STORY ON MEMORY OPTIMIZATION

Not all stories need to be success stories. Reality is also not like that. We would like to share a true, disappointing story (but a phenomenal learning experience) that may be beneficial to you.

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QUESTIONING STATUS QUO – ‘SERIAL GC NOT FOR SERIOUS APPLICATIONS’

In several journals, articles, blogs and all throughout the internet there is one common theme echoed: “Serial GC is not meant for any serious applications. It should be used only for prototype application. It’s only meant for desktop application or applications that have few hundred MBs of heap size. It shouldn’t be used in production.” To question this status quo theme, we conducted the below study on a major travel web services application in North America (Kindly apologizes, I can’t disclose their name).

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A CASE STUDY: DIFFERENT GC ALGORITHMS BEHAVIOR IN PRODUCTION

On a major B2B application different GC algorithms behaviors were studied. This application is basically a web service provider servicing SOAP and REST requests from its clients.

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MAY BE BUG IN GOOGLE SEARCH?

In my personal dictionary, I have defined google as “Excellence in Engineering”. I can’t express my admiration for their products, engineering practices & innovation culture. In such admired Google, I uncovered a silly bug, that too in their core search engine product. Are you surprised? Me too.

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JAVA BATCH OPTIMIZATION

A major shipping monopoly in North America ported their Mainframe batch (25+) jobs to Java platform. This porting was done by one of the top 3 consulting companies of India. Because of the world-class architecture, selection of frameworks and code written by them, Jobs took 16:04 hours to complete. Batch jobs that were started at 6:00 pm PST completed around 10:00 am PST next day. Due to which business was suffering from couple of complications:

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JBOSS SHUNNING, UNLOADING CLASS SUN.REFLECT

I had this interesting problem with a major shipping/logistics company. Their application was running on Java 6, JBoss Cluster in RedHat Linux platform.

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