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Scaling Uber's Elasticsearch Clusters
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| by Danny Yuan Follow 3 Followers on Apr 11, 2018 | NOTICE: The next QCon is in San Francisco Nov 5 - 9, 2018. Save an extra $100 with INFOQSF18!
48:18

Summary
Danny Yuan talks about how Uber scaled its Elasticsearch clusters as well as its ingestion pipelines for ingestions, queries, data storage, and operations by a three-person team. He covers topics like federation, query optimization, caching, failure recovery, data fidelity, transition from Lambda architecture to Kappa architecture, and improvements on Elasticsearch internals.

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Bio

Danny Yuan is a software engineer in Uber. He’s currently working on streaming systems for Uber’s marketplace platform. Prior to joining Uber, he worked on building Netflix’s cloud platform. His work includes predictive autoscaling, distributed tracing service, real-time data pipeline that scaled to process hundreds of billions of events every day, and Netflix’s low-latency crypto services.

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