Explain Data Partition & Sharding



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Name Example
Parition horizontally vs vertically  
Partition algorithms in data sharding  
Join tables in data sharding  
Rebalancing in data sharding  

Q: When to scale up, when to scale out?

A: Scale out bring complexities. Scale up may make more sense when data volume is relatively small. After a certain scale point, it is cheaper and more feasible to scale horizontally by adding more machines than to grow it vertically by adding beefier servers.


Q: What common partition methods are?

Name Summary
Horizontal partitioning a.k.a Data Sharding
Vertical Partitioning Store different data with different approaches per features
Directory Based Partitioning create a lookup service to knows your current partitioning scheme

Q: Partition algorithms of data sharding?

Name Summary
Hash-based partitioning  
List partitioning  
Round-robin partitioning  

Q: What are the typical problem of data partition?

Name Summary
Table joins  
Enforce data integrity  
Data Rebalancing Data distribution not uniform; Data visit not balanced with hot/cold data


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