Eliminate The Bottlenecks In Your Key/Value Storage With SpeeDB

Summary At the foundational layer many databases and data processing engines rely on key/value storage for managing the layout of information on the disk. RocksDB is one of the most popular choices for this component and has been incorporated into popular systems such as ksqlDB. As these systems are scaled to larger volumes of data and higher throughputs the RocksDB engine can become a bottleneck for performance. In this episode Adi Gelvan shares the work that he and his team at SpeeDB have put into building a drop-in replacement for RocksDB that eliminates that bottleneck. He explains how they redesigned the core algorithms and storage management features to deliver ten times faster throughput, how the lower latencies work to reduce the burden on platform engineers, and how they are working toward an open source offering so that you can try it yourself with no friction. 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. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days or even weeks. By the time errors have made their way into production, it’s often too late and damage is done. Datafold built automated regression testing to help data and analytics engineers deal with data quality in their pull requests. Datafold shows how a change in SQL code affects your data, both on a statistical level and down to individual rows and values before it gets merged to production. No more shipping and praying, you can now know exactly what will change in your database! Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold. TimescaleDB, from your friends at Timescale, is the leading open-source relational database with support for time-series data. Time-series data is time stamped so you can measure how a system is changing. Time-series data is relentless and requires a database like TimescaleDB with speed and petabyte-scale. Understand the past, monitor the present, and predict the future. That’s Timescale. Visit them today at dataengineeringpodcast.com/timescale Your host is Tobias Macey and today I’m interviewing Adi Gelvan about his work on SpeeDB, the "next generation data engine" Interview Introduction How did you get involved in the area of data management? Can you describe what SpeeDB is and the story behind it? What is your target market and customer? What are some of the shortcomings of RocksDB that these organizations are running into and how do they manifest? What are the characteristics of RocksDB that have led so many database engines to embed it or build on top of it? Which of the systems that rely on RocksDB do you most commonly see running into its limitations? How does the work you have done at SpeeDB compare to the efforts of the Terark project? Can you describe how you approached the work of identifying areas for improvement in RocksDB? What are some of the optimizations that you introduced? What are some tradeoffs that you deemed acceptable in the process of optimizing for speed and scale? What is the integration process for adopting SpeeDB? In the event that an organization has a system with data resident in RocksDB, what is the migration process? What are the most interesting, innovative, or unexpected ways that you have seen SpeeDB used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on SpeeDB? When is SpeeDB the wrong choice? What do you have planned for the future of SpeeDB? 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 [email protected]) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links SpeeDB RocksDB TerarkDB EMC Infinidat LSM == Log-Structured Merge Tree B+ Tree LevelDB LMDB Bloom Filter Badger The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast