Streaming Graph Search with Quine


Got streaming event data? Need to join events together? That's a streaming graph search problem: find the pattern of related events to join with the next event in the stream. Joining events from streams used to require a combination of: lots of key-value caches, handling out-of-order data, time windows, accepting data loss, and changing your architecture every time the pattern changes. Quine is designed to make streaming graph search just a matter of writing a database query—and scalable to millions of events per second. Voted the "Best Open Source Project of 2022" by HackerNoon, Quine is essentially an event-triggered streaming graph database. It's most novel feature is the "standing query": graph queries that stay active, propagating efficiently and automatically as the data changes. This talk will explore how Quine works and how to use it to find complex patterns in real-time, even when they are spread out in high-volume event streams.


Ryan Wright is the creator of Quine, and has been leading software teams focused on data infrastructure and data science for two decades. He has served as principal engineer, director of engineering, principal investigator on DARPA-funded research programs, and is currently the founder and CEO of thatDot—the company supporting Quine. Ryan particularly enjoys taking the philosophical ends of computer science—usually problems related to language, meaning, and data—and making them more practical.

Open Data Science




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