Databases Reference
In-Depth Information
NoSQL pros:
Loading test data can be done with drag-and-drop tools before
ER
modeling is
complete.
Modular architecture allows components to be exchanged.
Linear scaling takes place as new processing nodes are added to the cluster.
Lower operational costs are obtained by autosharding.
Integrated search functions provide high-quality ranked search results.
There's no need for an object-relational mapping layer.
It's easy to store high-variability data.
NoSQL cons:
ACID
transactions can be done only within a document at the database level.
Other transactions must be done at the application level.
Document stores don't provide fine-grained security at the element level.
NoSQL systems are new to many staff members and additional training may be
required.
The document store has its own proprietary nonstandard query language,
which prohibits portability.
The document store won't work with existing reporting and
OLAP
tools.
Understanding the role of placing functions within an application tier is important to
understanding how an application will perform. Another important factor to consider
is how memory such as
RAM
,
SSD
, and disk will impact your system.
Terminology of database clusters
The NoSQL industry frequently refers to the concept of
processing nodes in a data-
base cluster
. In general, each cluster consists of racks filled with commodity com-
puter hardware, as shown in figure 2.4.
Database cluster
Rack 1
Rack 2
Node
Node
RAM
RAM
CPU
CPU
Disk
Disk
Figure 2.4
Some of the terminology used in
distributed database clusters. A cluster is
composed of a set of processors, called
nodes, grouped together in racks. Nodes are
commodity processors, each of which has its
own local CPU, RAM, and disk.
Node
Node
RAM
RAM
CPU
CPU
Disk
Disk
