Availability Guide for Application Design

Data Protection and Recovery
Availability Guide for Application Design—525637-004
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Eliminating Batch Windows
Eliminating Batch Windows
Traditionally, batch-mode applications capture transactions during hours of business
and update the database of record during the night. Each day starts with an up-to-date
database from which accurate historical data can be gleaned, statements can be
printed, and so on.
The Problem
As discussed in Section 1, What Is Application Availability?, businesses are staying
open longer. Consider a bank. Banks stay open much later now and need to provide
late-night banking services, if only through automated tellers and home banking.
Longer business hours have two significant impacts on traditional batch-mode
applications:
•
Each batch run contains more transactions.
•
The batch window is smaller, if it exists at all.
In other words, the batch application must do more work in a shorter time. The likely
effect is that the batch run will spill over into the next day’s operations. The effect of
this could be to:
•
Delay the start of operations for the day
•
Have no accurate record for the day
•
Cause possible data corruption
•
Upset customers
•
Upset employees
Since the chances of finding a batch window large enough to run the batch job are
getting smaller, you need to find a way of processing the batch data without using a
batch window.
Possible Solutions
Three approaches that can be used to solve this problem on HP NonStop systems are
listed below:
•
Using the features of the HP NonStop operating system to allow online operations
concurrent with batch processing using the NetBatch-Plus product
•
Using low-priority transaction processing to perform the batch function
•
Taking database snapshots
The most appropriate approach depends on your application. To determine which
approach works best, first establish whether your batch application performs read-only
access or read/write access to the database. Read-only tasks include:
•
Periodic summary reporting
•
Building historical data for decision support