We dive back into Designing Data-Intensive Applications to learn more about replication while Michael thinks cluster is a three syllable word, Allen doesn't understand how we roll, and Joe isn't even paying attention.
For those that like to read these show notes via their podcast player, we like to include a handy link to get to the full version of these notes so that you can participate in the conversation at https://www.codingblocks.net/episode160.
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The major difference between a thing that might go wrong and a thing that cannot possibly go wrong is that when a thing that cannot possibly go wrong goes wrong it usually turns out to be impossible to get at or repair Douglas Adams
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In this episode, we are discussing Data Replication, from chapter 5 of "Designing Data-Intensive Applications".Replication in Distributed Systems
- When we talk about replication, we are talking about keeping copies of the same data on multiple machines connected by a network
- For this episode, we're talking about data small enough that it can fit on a single machine
- Why would you want to replicate data?
- Keeping data close to where it's used
- Increase availability
- Increase throughput by allowing more access to the data
- Data that doesn't change is easy, you just copy it
- 3 popular algorithms
- Single Leader
- Multi-Leader
- Leaderless
- Well established (1970's!) algorithms for dealing with syncing data, but a lot data applications haven't needed replication so the practical applications are still evolving
- Cluster group of computers that make up our data system
- Node each computer in the cluster (whether it has data or not)
- Replica each node that has a copy of the database
- Every write to the database needs to b
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