Pan Gao - Application Practice of Distributed Graph Database across the KE Holdings

Application Practice of Distributed Graph Database across the KE Holdings

This talk answers three core questions: How do you choose the best graph database (one that is ideal for the unique production environment’s needs) from multiple options? How can a knowledge graph with ten billion nodes achieve a millisecond query? How are KE Holdings’s 48 billion ordered triple datasets stored in the database?

Learn why KE Holdings needed a graph database, and then model your selection process on how they chose the right graph database for their business needs. Pan Gao then explains how to deploy the graph database, as well as principles, optimizations, and trade-offs.

By the end of this talk, you’ll know if you need a graph database, how to choose your ideal solution, and what to do to get up and running with Dgraph.

About Pan

Pan Gao is the Chief Search Architect of KE Holdings. He manages a team of 20 Software Engineers at KE Holdings. He is a hands-on Technical Lead who is passionate about technology and problem solving. KE Holdings are reinventing how service providers and housing customers efficiently navigate and consummate housing transactions. They are a leading integrated online and offline platform for housing transactions and services in China (much like Zillow in the US). KE Holdings has a market capitalization of $80 billion (four times more than Zillow’s), and 200 million real estate listings.

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How has dgraph fit your OLAP needs? Are you able to report/analyze “hot” data, or does ETL move it elsewhere?

If dgraph is used for analytics, have you derived value/insight from traversals that would have been troublesome with a more traditional store?

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