The likes of LinkedIn and Uber use Pinot to power some astonishingly high-scale queries against realtime data. The numbers alone would make an impressive case-study. But behind the headline lies a fascinating set of architectural decisions and constraints to get there. So how does Pinot work? How does it process queries? How are the various roles split across a cluster? And equally important – what does it *not* try to achieve.
Joining me to go through the nuts and bolts of how Pinot handles SQL queries is Tim Berglund, veteran technology explainer of the realtime-data world. He takes us through Pinot step-by-step, covering the roles of brokers, servers, controllers and minions as we build up the picture of a query engine that’s interesting in theory and massively performant in practice.
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Apache Pinot:
Apache Pinot Docs:
StarTree:
Event Driven Design episode with Bobby Calderwood:
Tim on Twitter:
Kris on Mastodon:
Kris on LinkedIn:
Kris on Twitter:
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#podcast #softwaredevelopment #apachepinot #database #dataengineering #sql
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