We continue our discussion of Designing Data-Intensive Applications, this time focusing on multi-leader replication, while Joe is seriously tired, and Allen is on to Michael’s shenanigans.
For anyone reading this via their podcast player, this episode’s show notes can be at https://www.codingblocks.net/episode161, where you can join the conversation.
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- Thank you very much for the new reviews:
- iTunes: GubleReid, tbednarick, JJHinAsia, katie_crossing
- Audible: Anonymous User, Anonymous User … hmm
When One Leader Just Won’t Do
Talking about Multi-Leader ReplicationReplication Recap and Latency
- When you’re talking about single or multi-leader replication, remember all writes go through leaders
- If your application is read heavy, then you can add followers to increase your scalability
- That doesn’t work well with sync writes..the more followers, the higher the latency
- The more nodes the more likely there will be a problem with one or more
- The upside is that your data is consistent
- The problem is if you allow async writes, then your data can be stale. Potentially very stale (it does dial up the availability and perhaps performance)
- You have to design your app knowing that followers will eventually catch up – “eventual consistency“
- “Eventual” is purposely vague – could be a few seconds, could be an hour. There is no guarantee.
- Some common use cases make this particularly bad, like a user up
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