This blog is about table partitioning in NDB Cluster. We will see
how this feature has been enhanced from version to version. Also
we will see which partitioning user should use under which
scenario. Here I will assume that the user has some knowledge on
NDB cluster.
Data distribution and table partitioning are usually coupled
together. In NDB, when we talk about table partitioning, we mean
‘data distribution’ mainly as NDB doesn’t fully support RANGE,
HASH or LIST partitioning. Most of the enhancements made to
partitioning over the years are about ‘data distribution’ rather
than supporting various partitioning schemes. The main goals of
data distribution are:
- Balance: Avoid premature bottlenecks of memory, storage, cpu or
network
- Scaling: Make use of all resources, add capacity with new
resources
- Efficiency: Locality of access and minimal unnecessary data
transfer
To …
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