1. Informatica PowerCenter Performance Tuning Tips
Pittsburgh Informatica User Group
March 22nd 2016
Gregory Reynolds

2. Topics General Tips Source Qualifiers Lookups
Sorters / Aggregators / Joiners
Targets
Other Options

3. General Tips Remove “Dead Weight” Set Proper Attributes on All Ports
Filter unneeded records in the source qualifier
Turn off or remove unused ports
Filter and aggregate as soon as possible
Trim strings immediately
Set Proper Attributes on All Ports
Improper data types require behind the scenes conversions or cause loss of data
Incorrect data sizes require conversions and may truncate data unintentionally
Watch Your Logs
Don’t ignore warnings and non-critical errors. It requires a significant amount of resources to write to a log file
Choose terse level logging in production to save space and processing

4. Source Qualifier Balance Work Effort Local Files Use Built-In Options
Consider the time it takes the source to do work instead
PowerCenter is faster at many things / DBMS is faster at others
Local Files
When reading from files it is almost always faster to ship the file to the local server before reading it then it is to try and read it with a remote connection
Large data files likely would be better off
staged before trying to do any
complex transformations
Use Built-In Options
A sort is usually faster here
than anywhere in the mapping
Same thing for distinct
Choose Deterministic Output if possible

5. Lookup Unconnected Lookups Cache Type Size Matters Filter The Data
Using an unconnected lookup multiple times saves all of the cost associated with building the cache except the first time.
Cache Type
Static cache is the default for a reason. Best for a large amount of lookup records
Disabling the cache sends every lookup to the database as an individual request. Works well for a very small amount of lookup requests versus a large table.
Persistent cache allows you to reuse a cache across multiple sessions. Warning: it may require a lot of drive space
Dynamic cache keeps records in the lookup up to date automatically as new data is sent to the target
Size Matters
Too small a cache allotment will force large lookups to go to disk
Too large a cache will waste available memory that other transformations could use
Filter The Data
Source data can be filtered
without using an override
More records filtered mean less time to
retrieve and smaller more efficient caches

6. Sorter, Aggregator, and Joiner
Maximum Cost
No data will pass through until every record has been read and sorted
Large cache usage
Session will fail if cache grows larger than capacity
Waste Not Want Not
Filter out as many unneeded records as is possible before hand
Sorted Input
Whenever possible use sorted input to save the majority of processing and cache cost from joiners as well as aggregators. Data will flow in incremental chunks if the input is sorted versus waiting until the end, releasing memory and cache requirements as it does so.
Simplified Group Bys
Grouping by a simple port may save an aggregator having to complete all the grouping before making any final decisions.
Smaller Master Set
Designate the master data in a sorter as the side that is expected to have fewer rows.
Two large data flows will not join as quickly

7. Targets Rejected Rows Update Else Insert Commit Not… Bulk Loading
Rejecting rows from the target database will slow down normal operations by potentially several seconds per record! Look out for unexpected NULL values, bad dates, and invalid keys.
Update Else Insert
With this option selected, every insert is approximately twice as slow as an update
Other options include using a lookup to determine the appropriate action before hand
Commit Not…
Setting too low a threshold for database commits will require transactions to wait more often while the commit is completing. Setting it too high will cause the database to use an increasing amount of temp space and may cause it to slow down transaction speeds
Bulk Loading
Bulk loading is a fast “cheat” to be able to push large amount of data into some types of databases at high speed but there can’t any indices on the target table

8. Other Tuning Options System Competition Workflow Parallelization
Session Partitioning
Push-down Optimization
Data Partitions
Buffer Size
Indices and Primary Keys
System Competition
Redesign slow mappings to find a better way