Distribution of data among servers, replication, locality

Distribution

• + Capacity
• + Throughput
  • + Avoid hot spots
• - Consistency

Approaches

• B-Tree
  • Supports range queries on totally ordered keys
  • Allows several records from the same table to be returned with a single request
  • May cause a server to become a hot spot
• Hashing
  • Simple
  • Likely to spread the load better
  • If a single request needs multiple records from a table, it's likely to require separate requests to multiple servers, which adds overhead

B-Trees

• RP*

Hashing

• Extensible
• Linear
• Consistent

Replication

• + Throughput
  • + Mitgates hot spots
• + Latency
  • Eliminates cross data center requests
• - Consistency

• Is replication needed for performance reasons?
  • If a single server can handle 1M requests/second, is there any need to replicate?
  • If the load gets too high, perhaps reduce the load by reducing the amount of data stored on a server, rather than replicating the data
  • If there is a hot spot, data reduction may not solve overloading problems
  • A system without replication would be much easier to manage
• Perhaps replicas are needed to reduce latency (e.g., East Coast datacenter and West Coast datacenter)

Locality

• + Network traffic
• + Latency for serial requests
• + Performance isolation in multi-tenant environments
• + Economy of metadata
  • For example, only access control information for data which resides on a host must be replicated to that host

• Is there any locality in interesting database applications?
• The most interesting form of locality is locality within a request: would like to satisfy each request with a single call to a single server, if possible