Data Engineering Podcast CRDTs and Distributed Consensus with Christopher Meiklejohn - Episode 14
Summary
As we scale our systems to handle larger volumes of data, geographically distributed users, and varied data sources the requirement to distribute the computational resources for managing that information becomes more pronounced. In order to ensure that all of the distributed nodes in our systems agree with each other we need to build mechanisms to properly handle replication of data and conflict resolution. In this episode Christopher Meiklejohn discusses the research he is doing with Conflict-Free Replicated Data Types (CRDTs) and how they fit in with existing methods for sharing and sharding data. He also shares resources for systems that leverage CRDTs, how you can incorporate them into your systems, and when they might not be the right solution. It is a fascinating and informative treatment of a topic that is becoming increasingly relevant in a data driven world.
Preamble
- Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure
- When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your data pipelines or trying out the tools you hear about on the show.
- Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
- You can help support the show by checking out the Patreon page which is linked from the site.
- To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers
- Your host is Tobias Macey and today I’m interviewing Christopher Meiklejohn about establishing consensus in distributed systems
Interview
- Introduction
- How did you get involved in the area of data management?
- You have dealt with CRDTs with your work in industry, as well as in your research. Can you start by explaining what a CRDT is, how you first began working with them, and some of their current manifestations?
- Other than CRDTs, what are some of the methods for establishing consensus across nodes in a system and how does increased scale affect their relative effectiveness?
- One of the projects that you have been involved in which relies on CRDTs is LASP. Can you describe what LASP is and what your role in the project has been?
- Can you provide examples of some production systems or available tools that are leveraging CRDTs?
- If someone wants to take advantage of CRDTs in their applications or data processing, what are the available off-the-shelf options, and what would be involved in implementing custom data types?
- What areas of research are you most excited about right now?
- Given that you are currently working on your PhD, do you have any thoughts on the projects or industries that you would like to be involved in once your degree is completed?
Contact Info
- Website
- cmeiklejohn on GitHub
- Google Scholar Citations
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- Basho
- Riak
- Syncfree
- LASP
- CRDT
- Mesosphere
- CAP Theorem
- Cassandra
- DynamoDB
- Bayou System (Xerox PARC)
- Multivalue Register
- Paxos
- RAFT
- Byzantine Fault Tolerance
- Two Phase Commit
- Spanner
- ReactiveX
- Tensorflow
- Erlang
- Docker
- Kubernetes
- Erleans
- Orleans
- Atom Editor
- Automerge
- Martin Klepman
- Akka
- Delta CRDTs
- Antidote DB
- Kops
- Eventual Consistency
- Causal Consistency
- ACID Transactions
- Joe Hellerstein
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA