Using Spark to speed up the diagnosis performance for big data applications
Who is this presentation for?Engineers, Pruduct Managers, DevOps
Cosmos is Microsoft’s internal big data analysis platform. Everyday, it processes huge numbers of data from Microsoft services like Bing, Office, Windows, Xbox, Dynamics etc. The DevOps team is responsible to keep the service reliability as we committed to customers. For each live site issue, the on-call engineer has a hard deadline to mitigate the problem. Since couple years ago we have been working on bringing IDE style diagnosis experience to large scale applications. However we observed several challenges for on-call engineers to use our IDE diagnosis tools:
• It’s slow to process complex jobs with large profiles, the IDE may crash for jobs with profile larger than 10G.
• We provide auto diagnosis wizard for common issues but on-call engineers still need to digger deeper into various logging systems case by case.
• It requires extra effort for on-call engineers to document their troubleshooting steps.
To solve these challenges, we run experiment to replace the diagnosis engine with Spark and use Jupyter notebook as frontend. Experiment result indicates the Spark based solution has improved the diagnosis performance significantly especially for complex job with large profile. Jupyter notebook also bring the benefit of fast iteration and easy knowledge share. In this session we are going to share our learnings along the journey.
Prerequisite knowledgeDistributed Compting Concept, Debugging, Spark, Jupyter notebook
What you'll learn
Ruixin Xu is a Senior Program Manager from Microsoft Azure Big Data Tools team. Her focus areas are product design and project management, development experience in Big Data platforms, software development tool-chain, Software as a Service (SaaS) offerings.
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