Explain Data Partition & Sharding
|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?
|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?
Q: What are the typical problem of data partition?
|Enforce data integrity|
|Data Rebalancing||Data distribution not uniform; Data visit not balanced with hot/cold data|
Q: Data Lookup workflow after data partition?