InfoQ Homepage Presentations Streaming Auto-scaling in Google Cloud Dataflow
Streaming Auto-scaling in Google Cloud Dataflow
46:52
Summary
Manuel Fahndrich describes how they tackled one particular resource allocation aspect of Google Cloud Dataflow pipelines, namely, horizontal scaling of worker pools as a function of pipeline input rate. Managing the redistribution of key ranges across new pool sizes and the associated persistent data storage was particularly challenging.
Bio
Manuel Fahndrich earned his Ph.D. in C.S. from UC Berkeley in 1999. He spent the next 15 years as a Research Scientist at Microsoft, working on static and dynamic verification tools for object-oriented programs and system software. After joining Google in 2014 he has been working on data-parallel infrastructure, in particular auto-scaling for batch and streaming pipelines.
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May 01, 2016