Presented by Ryan Wright, Founder, thatdot.com Abstract

This talk will introduce Quine: a brand new open source “streaming graph interpreter” meant as a new fundamental infrastructure component to address major challenges in data engineering and simplify enterprise data pipelines.

Quine fits in between the world of databases and stream processing systems. As data streams in from Kafka, Kinesis, etc., Quine builds it into a graph. Then using “standing queries”—queries that live inside the graph and efficiently propagate—it finds matches to complex patterns in the graph and streams the results out right away. Quine maintains a stateful representation of all data streamed through (like a database) so that complex results are built from the combination of new streaming data and potentially very old data—all without having to manage any time windows. Since the graph is fully versioned, you can always query for what the data used to be, at any historical moment. Quine is meant to be a complete package of everything that lives between two Kafka topics: high-volume events stream in, and highly-meaningful interpreted results stream out.

In this talk, we will explain the how Quine works under the hood, discuss some of the interesting and brain-bending challenges we had to confront in order to create it, and show some uses cases to illustrate why it’s important for modern data pipelines. Quine implements a property-graph data model on top of an asynchronous graph computational model. It’s like Pregel with Actors. Each node is capable of performing arbitrary computation, so we can bake in some powerful capabilities deep in the graph; and then package it up for easy use into user-contributed “recipes” available in the Github repo. Quine is free and open and the repo will be publicly available in February, and actively supported by thatDot and the community.

What You Will Learn This talk will introduce Quine: a brand new open source “streaming graph interpreter” meant as a new fundamental infrastructure component to address major challenges in data engineering and simplify enterprise data pipelines.

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Cost Free! (suggested donation $5-15 for non-members)

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Where

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Date – Thursday, March 10th

Time – 4:30pm – 5:30pm

Bio: Semih Salihoğlu is an Associate Professor and a David R. Cheriton Faculty Fellow at University of Waterloo. His research focuses on developing systems for managing, querying, or doing analytics on graph-structured data. His main on-going systems project is Kùzu, which is a new graph database management system that integrates novel storage, indexing and query processing techniques. He holds a PhD from Stanford University and is a recipient of the VLDB 2018 Best Paper and the VLDB 2022 Best Experiments and Analysis Paper awards.

Abstract: In this talk, I will present the Kùzu graph database management system (GDBMS) that we are developing at University of Waterloo. Datasets and workloads of popular applications that use GDBMSs require a set of storage and query processing features that relational DBMSs (RDBMSs) do not traditionally optimize for. These include optimizations for: (i) many-to-many (m-n) joins; (ii) cyclic joins; (iii) recursive joins; (iv) semi-structured data storage; and (v) support for universal resource identifiers. Kùzu aims to integrate state-of-art storage, indexing, and query processing techniques to highly optimize for this feature set. I will start by presenting the overall vision of Kùzu and then talk about the novel join operators in the system that performs joins using compressed factorized representations of intermediate tables. Kùzu is actively being developed to be a fully functional open-source DBMS with the goal of wide user adoption and under a permissible license.