✍ Kashif M ™
☎ +91 9994883085
✉ kashif7222@gmail.com


Sunday, 7 July 2013

Informatica - Mapping Level Performance

By on July 07, 2013

Optimizing Datatype Conversions

Forcing the Informatica Server to make unnecessary datatype conversions slows performance.

For example, if your mapping moves data from an Integer column to a Decimal column, then back to an Integer column, the unnecessary datatype conversion slows performance. Where possible, eliminate unnecessary datatype conversions from mappings.

Some datatype conversions can improve system performance. Use integer values in place of other datatypes when performing comparisons using Lookup and Filter transformations.

For example, many databases store U.S. zip code information as a Char or Varchar datatype. If you convert your zip code data to an Integer datatype, the lookup database stores the zip code 94303-1234 as 943031234. This helps increase the speed of the lookup comparisons based on zip code.

Optimizing Lookup Transformations
If a mapping contains a Lookup transformation, you can optimize the lookup. Some of the things you can do to increase performance include caching the lookup table, optimizing the lookup condition, or indexing the lookup table.

Caching Lookups
If a mapping contains Lookup transformations, you might want to enable lookup caching. In general, you want to cache lookup tables that need less than 300MB.
When you enable caching, the Informatica Server caches the lookup table and queries the lookup cache during the session. When this option is not enabled, the Informatica Server queries the lookup table on a row-by-row basis. You can increase performance using a shared or persistent cache:

Shared cache. You can share the lookup cache between multiple transformations. You can share an unnamed cache between transformations in the same mapping. You can share a named cache between transformations in the same or different mappings.
Persistent cache. If you want to save and reuse the cache files, you can configure the transformation to use a persistent cache. Use this feature when you know the lookup table does not change between session runs. Using a persistent cache can improve performance because the Informatica Server builds the memory cache from the cache files instead of from the database.

Reducing the Number of Cached Rows
Use the Lookup SQL Override option to add a WHERE clause to the default SQL statement. This allows you to reduce the number of rows included in the cache.

Optimizing the Lookup Condition
If you include more than one lookup condition, place the conditions with an equal sign first to optimize lookup performance.

Indexing the Lookup Table
The Informatica Server needs to query, sort, and compare values in the lookup condition columns. The index needs to include every column used in a lookup condition. You can improve performance for both cached and uncached lookups:
Cached lookups. You can improve performance by indexing the columns in the lookup ORDER BY. The session log contains the ORDER BY statement.

Uncached lookups. Because the Informatica Server issues a SELECT statement for each row passing into the Lookup transformation, you can improve performance by indexing the columns in the lookup condition. 

mkashu: Informatica - Mapping Level Performance
Review : Kashif | Kashif
Update: July 07, 2013 | Rating: 4.5

Comment for "Informatica - Mapping Level Performance"


Post a Comment

Blog Archive