In a previous post, I described a sharding system to scale throughput and performance for query and ingest workloads. In this post, I will introduce another common technique, partitioning, that provides further advantages in performance and management for a sharding database. I will also describe how to handle partitions efficiently for both query and ingest workloads, and how to manage cold (old) partitions where the read requirements are quite different from the hot (recent) partitions.
[ Also on InfoWorld: How to choose a cloud database ]
Sharding vs. partitioning
Sharding is a way to split data in a distributed database system. Data in each shard does not have to share resources such as CPU or memory, and can be read or written in parallel.
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