Bottleneck is the reason by
which the performance of the Informatica ETL process gets slower. There are
different types of Bottlenecks in Informatica. It can happen either while
writing to the target or while reading from source and many more. Let’s discuss
them in one by one.
Target
Bottlenecks
Commonly we find target
Bottlenecks in Informatica. This occurs when the Integration Service writes to
a target database. It can be due to heavy load in the target or some other
connection problems with the target.
Source
Bottlenecks
Next is Source Bottlenecks.
This occurs when the Integration Service reads from a source database. This can
be due to the source query which is incorrect or source connection problems
Mapping
Bottlenecks
When we do not have a
source or target bottleneck, you may have a mapping bottleneck. Below are the
steps to identify mapping bottlenecks
- Read the thread statistics and work time statistics in the session log. When the Integration Service spends more time on the transformation thread than the writer or reader threads, you have a transformation bottleneck.
- When the Integration Service spends more time on one transformation, it is the bottleneck in the transformation thread.
- Add a Filter transformation before each target definition. Set the filter condition to false so that no data is loaded into the target tables. If the time it takes to run the new session is the same as the original session, you have a mapping bottleneck.
Session
Bottlenecks
If we don’t find any
source, target, or mapping bottleneck, then we may have a session bottleneck.
Small cache size, low buffer memory, and small commit intervals can cause
session bottlenecks.
System
Bottlenecks
Once we check for the
source, target, mapping, and session bottlenecks then we need to tune the system
to prevent system bottlenecks.
The Integration Service
uses system resources to process transformations, run sessions, and read and
write data. The Integration Service also uses system memory to create cache
files for transformations, such as Aggregator, Joiner, Lookup, Sorter, XML, and
Rank.
very easy to understand.definitions and examples are good
ReplyDeleteeasy to understand
ReplyDeleteBest material ever
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