Database Reference
In-Depth Information
Shared nothing data architecture
Though an old paradigm, shared nothing data architecture is gaining traction in the
context of Big Data. Here the data is distributed across the nodes in the cluster and
every processor operates on the data local to itself. The location where data resides
is referred to as data node and where the processing logic resides is called compute
node. It can happen that both nodes, compute and data, are physically one. These
nodes within the cluster are connected using high-speed interconnects.
The following figure depicts two aspects of the architecture, the one on the left rep-
resents data and computes decoupled processes and the other to the right repres-
ents data and computes processes co-located:
One of the most important aspects of shared nothing data architecture is the fact that
there will not be any contention or locks that would need to be addressed. Data is
distributed across the nodes within the cluster using a distribution plan that is defined
as a part of the schema definition. Additionally, for higher query efficiency, partition-
ing can be done at the node level. Any requirement for a distributed lock would bring
in complexity and an efficient distribution and partitioning strategy would be a key
success factor.
Reads are usually the most efficient relative to shared disk databases. Again, the ef-
ficiency is determined by the distribution policy, if a query needs to join data across
the nodes in the cluster, users would see a temporary redistribution step that would
bring required data elements together into another node before the query result is
returned.
Shared nothing data architecture thus supports massive parallel processing capabil-
ities. Some of the features of shared nothing data architecture are as follows:
• It can scale extremely well on general purpose systems
• It provides automatic parallelization in loading and querying any database
• It has optimized I/O and can scan and process nodes in parallel
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