Databases Reference
In-Depth Information
Volume
Figure 1.1
In this figure, we see how the business drivers
volume, velocity, variability, and agility apply pressure to the
single CPU system, resulting in the cracks. Volume and
velocity refer to the ability to handle large datasets that
arrive quickly. Variability refers to how diverse data types
don't fit into structured tables, and agility refers to how
quickly an organization responds to business change.
Single-node
RDBMS
Velocity
Agility
Variability
1.2.1
Volume
Without a doubt, the key factor pushing organizations to look at alternatives to their
current
RDBMS
s is a need to query big data using clusters of commodity processors.
Until around 2005, performance concerns were resolved by purchasing faster proces-
sors. In time, the ability to increase processing speed was no longer an option. As chip
density increased, heat could no longer dissipate fast enough without chip overheat-
ing. This phenomenon, known as the power wall, forced systems designers to shift
their focus from increasing speed on a single chip to using more processors working
together. The need to scale out (also known as
horizontal scaling
), rather than scale up
(faster processors), moved organizations from serial to parallel processing where data
problems are split into separate paths and sent to separate processors to divide and
conquer the work.
1.2.2
Velocity
Though big data problems are a consideration for many organizations moving away
from
RDBMS
s, the ability of a single processor system to rapidly read and write data is
also key. Many single-processor
RDBMS
s are unable to keep up with the demands of
real-time inserts and online queries to the database made by public-facing websites.
RDBMS
s frequently index many columns of every new row, a process which decreases
system performance. When single-processor
RDBMS
s are used as a back end to a web
store front, the random bursts in web traffic slow down response for everyone, and tun-
ing these systems can be costly when both high read and write throughput is desired.
1.2.3
Variability
Companies that want to capture and report on exception data struggle when attempt-
ing to use rigid database schema structures imposed by
RDBMS
s. For example, if a
business unit wants to capture a few custom fields for a particular customer, all cus-
tomer rows within the database need to store this information even though it doesn't
apply. Adding new columns to an
RDBMS
requires the system be shut down and
ALTER
TABLE
commands to be run. When a database is large, this process can impact system
availability, costing time and money.
