Summary The PostgreSQL database is massively popular due to its flexibility and extensive ecosystem of extensions, but it is still not the first choice for high performance analytics. Swarm64 aims to change that by adding support for advanced hardware capabilities like FPGAs and optimized usage of modern SSDs. In this episode CEO and co-founder Thomas Richter discusses his motivation for creating an extension to optimize Postgres hardware usage, the benefits of running your analytics on the same platform as your application, and how it works under the hood. If you are trying to get more performance out of your database then this episode is for you! Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. 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Go to dataengineeringpodcast.com/tidydata today and get started for free with no credit card required. Your host is Tobias Macey and today I’m interviewing Thomas Richter about Swarm64, a PostgreSQL extension to improve parallelism and add support for FPGAs Interview Introduction How did you get involved in the area of data management? Can you start by explaining what Swarm64 is? How did the business get started and what keeps you motivated? What are some of the common bottlenecks that users of postgres run into? What are the use cases and workloads that gain the most benefit from increased parallelism in the database engine? By increasing the processing throughput of the database, how does that impact disk I/O and what are some options for avoiding bottlenecks in the persistence layer? Can you describe how Swarm64 is implemented? How has the product evolved since you first began working on it? How has the evolution of postgres impacted your product direction? What are some of the notable challenges that you have dealt with as a result of upstream changes in postgres? How has the hardware landscape evolved and how does that affect your prioritization of features and improvements? What are some of the other extensions in the postgres ecosystem that are most commonly used alongside Swarm64? Which extensions conflict with yours and how does that impact potential adoption? In addition to your work to optimize performance of the postres engine, you also provide support for using an FPGA as a co-processor. What are the benefits that an FPGA provides over and above a CPU or GPU architecture? What are the available options for provisioning hardware in a datacenter or the cloud that has access to an FPGA? Most people are familiar with the relevant attributes for selecting a CPU or GPU, what are the specifications that they should be looking at when selecting an FPGA? For users who are adopting Swarm64, how does it impact the way they should be thinking of their data models? What is involved in migrating an existing database to use Swarm64? What are some of the most interesting, unexpected, or challenging lessons that you have learned while building and growing the product and business of Swarm64? When is Swarm64 the wrong choice? What do you have planned for the future of Swarm64? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Links Swarm64 Lufthansa Cargo IBM Cognos Analytics OLAP Cube PostgreSQL Geospatial Data TimescaleDB Podcast Episode FPGA == Field Programmable Gate Array Greenplum Foreign Data Tables PostgreSQL Table Storage API EnterpriseDB Xilinx OVH Cloud Nimbix Azure Tableau The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast