Databases Reference
In-Depth Information
The column-store databases were designed to address performance issues around
query workloads that accessed large volumes of data or large analytical queries, as
opposed to row-based databases, which were primarily focused on making sure the
transactions, were recorded correctly and quickly in the databases. The biggest push for
adoption of column-store databases came from business intelligence applications and
analytics applications.
The Scale-Out Architecture
As the data volumes grew exponentially and increasingly there was a need to integrate
and leverage a vast array of data sources, a new generation of database products began
to emerge. These were labeled as
Not Only SQL
(NoSQL) products. These products were
designed to cater to the distributed architecture styles enabling high concurrency and
partition tolerance to manage data volumes up to the petabyte range.
Figure
4-1
illustrates scale-out database architecture. You can see the design
philosophy where data from several sources are acquired and then distributed across
multiple nodes. The full database is spread across multiple computers. In the earlier
versions of NoSQL databases there was a constraint that the data for a transaction or
query be limited to a single node.
Figure 4-1.
Scale-out database architecture
The concept of a multi-node database with transactions or queries isolated to individual
nodes was a design consideration to support transactional workloads of large websites.
Due to this limitation, the back-end database infrastructure of these nodes required manual
partitioning of data in identical schemas across nodes. The local database running on each
