PgConf.Russia 2017

Citus allows you to distribute postgres tables across many servers. It extends postgres to transparently delegate or parallelise work across a set of worker nodes, enabling you to scale out the CPU and memory available for queries.

One year ago, we began a long journey to allow Citus to scale out another dimension: write throughput. With writes being routed through a single postgres node, write throughput in Citus was ultimately bottlenecked on the CPUs of a single node. Citus MX is a new edition of Citus which allows distributed tables to be used from from any of the nodes, enabling NoSQL-like write-scalability.

Using PostgreSQL since 2003, we went all the way from a database of a couple of GB to a cluster of more than 5TB. At the moment, we have more than 700 tables and about 1500 stored procedures. We are ready to share with you the following: - Problems encountered at different development stages and how we resolved them. - Best practices in database administration. - Our own extension to work with several closely related databases. - Best known methods and tools that enable our several teams to work together without interference. - How we set up test equipment of different types. And, of course, we'll talk about optimization, and how we identify bottlenecks and high-load use cases.

VIDEO

With big data threads, even the upload of data to a database can often be problematic – apart from the data upload itself, you need to create indexes, perform VACUUM after the upload for correct Index-only scans, etc. From this talk, you will learn how to avoid most of such problems (if not all of them).

VIDEO

Overview of the architecture of Greenplum MPP (Massively Parallel Processing) database. Explain the internals of GPDB. Show how to configure and setup GPDB. How to distribute data effectively for MPP

VIDEO