Explain Data Partition & Sharding

Similar Posts:
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 |
Q: Data Lookup workflow after data partition?
A: TODO
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