Citus Data: Distributed PostGreSQL for Big Data with Ozgun Erdogan and Craig Kerstiens - Episode 13
Data Engineering Podcast
Upcoming Features and Improvements for SQL and Big Data Analytics
In this chapter, they discuss new features and extensions for real-time analytics, including tap, percentile, and hyperlog log. They also talk about using sketch algorithms, Postgres as a column-oriented data store, and time partitioning for improved performance and scalability.
Summary
PostGreSQL has become one of the most popular and widely used databases, and for good reason. The level of extensibility that it supports has allowed it to be used in virtually every environment. At Citus Data they have built an extension to support running it in a distributed fashion across large volumes of data with parallelized queries for improved performance. In this episode Ozgun Erdogan, the CTO of Citus, and Craig Kerstiens, Citus Product Manager, discuss how the company got started, the work that they are doing to scale out PostGreSQL, and how you can start using it in your environment.
Preamble
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- Your host is Tobias Macey and today I’m interviewing Ozgun Erdogan and Craig Kerstiens about Citus, worry free PostGreSQL
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Citus is and how the project got started?
- Why did you start with Postgres vs. building something from the ground up?
- What was the reasoning behind converting Citus from a fork of PostGres to being an extension and releasing an open source version?
- How well does Citus work with other Postgres extensions, such as PostGIS, PipelineDB, or Timescale?
- How does Citus compare to options such as PostGres-XL or the Postgres compatible Aurora service from Amazon?
- How does Citus operate under the covers to enable clustering and replication across multiple hosts?
- What are the failure modes of Citus and how does it handle loss of nodes in the cluster?
- For someone who is interested in migrating to Citus, what is involved in getting it deployed and moving the data out of an existing system?
- How do the different options for leveraging Citus compare to each other and how do you determine which features to release or withhold in the open source version?
- Are there any use cases that Citus enables which would be impractical to attempt in native Postgres?
- What have been some of the most challenging aspects of building the Citus extension?
- What are the situations where you would advise against using Citus?
- What are some of the most interesting or impressive uses of Citus that you have seen?
- What are some of the features that you have planned for future releases of Citus?
Contact Info
- Citus Data
- citusdata.com
- @citusdata on Twitter
- citusdata on GitHub
- Craig
- Website
- @craigkerstiens on Twitter
- Ozgun
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- Citus Data
- PostGreSQL
- NoSQL
- Timescale SQL blog post
- PostGIS
- PostGreSQL Graph Database
- JSONB Data Type
- PipelineDB
- Timescale
- PostGres-XL
- Aurora PostGres
- Amazon RDS
- Streaming Replication
- CitusMX
- CTE (Common Table Expression)
- HipMunk Citus Sharding Blog Post
- Wal-e
- Wal-g
- Heap Analytics
- HyperLogLog
- C-Store
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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