We wrap up our replication discussion of Designing Data-Intensive Applications, this time discussing leaderless replication strategies and issues, while Allen missed his calling, Joe doesn't read the gray boxes, and Michael lives in a future where we use apps.
If you're reading this via your podcast player, you can find this episode's full show notes at https://www.codingblocks.net/episode162. As Joe would say, check it out and join in on the conversation.
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Single Leader to Multi-Leader to Leaderless
- When you have leaders and followers, the leader is responsible for making sure the followers get operations in the correct order
- Dynamo brought the trend to the modern era (all are Dynamo inspired) but also…
- Riak
- Cassandra
- Voldemort
- We talked about NoSQL Databases before:
- What exactly is NewSQL? https://en.wikipedia.org/wiki/NewSQL
- What if we just let every replica take writes? Couple ways to do this…
- You can write to several replicas
- You can use a coordinator node to pass on the writes
- But how do you keep these operations in order? You don't!
- Thought exercise, how can you make sure operation order not matter?
- Couple ideas: No partial updates, increments, version numbers
Multiple Writes, Multiple Reads
- What do you do if your client (or coordinator) try to write to multiple nodes…and some are down?
- Well, it's an implementation detail, you can choose to enforce a "quorom". Some number of nodes have to acknowledge the write.
- This ratio can be configurable, making it so some % is required for a write to be accepted
- What about nodes that are out of date?
- The trick to mitigating stale data…the replicas keep a version number, and you only use the latest data – potentially by querying multiple nodes at the same time for the requested data
- We've talked about logical clocks before, it's a way of tracking time via observed changes…like the total number of changes to a collection/table…no timezone or nanosecond differences
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