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Managing Apache Spark Workload and Automatic Optimizing

Managing Apache Spark Workload and Automatic Optimizing

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eBay is highly using Spark as one of most significant data engines. In data warehouse domain, there are millions of batch queries running every day against 6000+ key DW tables, which contains over 22PB data (compressed) and still keeps booming every year. In machine learning domain, it is playing a more and more significant role. We have introduced our great achievement in migration work from MPP database to Apache Spark last year in Europe Summit. Furthermore, from the vision of the entire infrastructure, it is still a big challenge on managing workload and efficiency for all Spark jobs upon our data center. Our team is leading the whole infrastructure of big data platform and the management tools upon it, helping our customers — not only DW engineers and data scientists, but also AI engineers — to leverage on the same page. In this session, we will introduce how to benefit all of them within a self-service workload management portal/system. First, we will share the basic architecture of this system to illustrate how it collects metrics from multiple data centers and how it detects the abnormal workload real-time. We develop a component called Profiler which is to enhance the current Spark core to support customized metric collection. Next, we will demonstrate some real user stories in eBay to show how the self-service system reduces the efforts both in customer side and infra-team side. That’s the highlight part about Spark job analysis and diagnosis. Finally, some incoming advanced features will be introduced to describe an automatic optimizing workflow rather than just alerting.
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