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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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 updating some information…they often expect to see that change afterwards
- There are a couple techniques that can help with this problem
Techniques for mitigation replication lag
- Read You Writes Consistency refers to an attempt to read significant data from leader or in sync replicas by the user that submitted the data
- In general this ensures that the user who wrote the data will get the same data back – other users may get stale version of the data
- But how can you do that?
- Read important data from a leader if a change has been made OR if the data is known to only be changeable by that particular user (user profile)
- Read from a leader/In Sync Replica for some period of time after a change
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