Databases Reference
In-Depth Information
Database
Server
Database
Server
Client
Region 1
Client
Region n
Client
Region 1
Client
Region n
FIGURE 4.2
Client-server architecture.
●
Slow networks
●
RDBMS I/O
●
SAN architecture
●
Infrastructure cost
●
Complex data processing and transformation requirements
●
Minimal fault tolerance within infrastructure and expensive fault tolerance solutions
Due to the inherent complexities and the economies of scale, the world of data warehousing did
not adopt to the concept of large-scale distributed data processing. On the other hand, the world of
OLTP adopted and deployed distributed data processing architecture, using heterogeneous and pro-
prietary techniques, though this was largely confined to large enterprises, where latencies were not
the primary concern. The most popular implementation of this architecture is called client-server data
processing (
Figure 4.2
).
The client-server architecture had its own features and limitations, but it provided limited scal-
ability and flexibility:
●
Benefits:
●
Centralization of administration, security, and setup.
●
Back-up and recovery of data is inexpensive, as outages can occur at the server or a client and
can be restored.
●
Scalability of infrastructure by adding more server capacity or client capacity can be
accomplished. The scalability is not linear.
●
Accessibility of the server from heterogeneous platforms locally or remotely.
●
Clients can use servers for different types of processing.
●
Limitations:
●
The server is the central point of failure.
●
Very limited scalability.
●
Performance can degrade with network congestion.
●
Too many clients accessing a single server cannot process data in a quick time.
In the late 1980s and early 1990s there were several attempts at distributed data processing in
the OLTP world, with the emergence of object-oriented programming and object store databases. We
learned that with effective programing and nonrelational data stores, we could effectively scale up
