Presented by:

Frank mcsherry

Frank McSherry

Materialize

Frank McSherry is Chief Scientist at Materialize, where he (and others) convert SQL into scale-out, streaming, and interactive dataflows. Before this, he developed the timely and differential dataflow Rust libraries (with colleagues at ETHZ), and led the Naiad research project and co-invented differential privacy while at MSR Silicon Valley. He has a PhD in computer science from the University of Washington.

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PostgreSQL's materialized views are a great tool to provide low-latency answers to complex queries – but did you know they could offer serializable consistency if you used cutting-edge streaming engines, many millions of dollars, and countless hours of work from talented systems engineers and academics? Oh, and I guess you could then also join them with arbitrarily complex queries over Kafka topics…

In this talk, we'll take a look at what Materialize does from a few different points of views: how it differs from PostgreSQL, and how that fits into the modern data landscape. We'll then explore the architectural considerations we made to bring PostgreSQL data into an operational data warehouse. Lastly, we'll dive into the implementation details and the first correctness bug any of us have ever uncovered in PG.

Date:
2024 April 17 11:10 PDT
Duration:
50 min
Room:
Almaden
Conference:
Postgres Conference 2024
Language:
English
Track:
Dev
Difficulty:
Intermediate