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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