Tuning Mappings for Better Performance

Challenge

In general, mapping-level optimization takes time to implement, but can significantly boost performance. Sometimes the mapping is the biggest bottleneck in the load process because business rules determine the number and complexity of transformations in a mapping.
Before deciding on the best route to optimize the mapping architecture, you need to resolve some basic issues. Tuning mappings is a tiered process. The first tier can be of assistance almost universally, bringing about a performance increase in all scenarios. The second tier of tuning processes may yield only small performance increase, or can be of significant value, depending on the situation.
Some factors to consider when choosing tuning processes at the mapping level include the specific environment, software/ hardware limitations, and the number of records going through a mapping. This Best Practice offers some guidelines for tuning mappings.

Description

Analyze mappings for tuning only after you have tuned the system, source, and target for peak performance. To optimize mappings, you generally reduce the number of transformations in the mapping and delete unnecessary links between transformations.
For transformations that use data cache (such as Aggregator, Joiner, Rank, and Lookup transformations), limit connected input/output or output ports. Doing so can reduce the amount of data the transformations store in the data cache. Too many Lookups and Aggregators encumber performance because each requires index cache and data cache. Since both are fighting for memory space, decreasing the number of these transformations in a mapping can help improve speed. Splitting them up into different mappings is another option.
Limit the number of Aggregators in a mapping. A high number of Aggregators can increase I/O activity on the cache directory. Unless the seek/access time is fast on the directory itself, having too many Aggregators can cause a bottleneck. Similarly, too many Lookups in a mapping causes contention of disk and memory, which can lead to thrashing, leaving insufficient memory to run a mapping efficiently.
Consider Single-Pass Reading
If several mappings use the same data source, consider a single-pass reading. Consolidate separate mappings into one mapping with either a single Source Qualifier Transformation or one set of Source Qualifier Transformations as the data source for the separate data flows.
Similarly, if a function is used in several mappings, a single-pass reading will reduce the number of times that function will be called in the session.
Optimize SQL Overrides
When SQL overrides are required in a Source Qualifier, Lookup Transformation, or in the update override of a target object, be sure the SQL statement is tuned. The extent to which and how SQL can be tuned depends on the underlying source or target database system.  See the section Tuning SQL Overrides and Environment for Better Performance  for more information.
Scrutinize Datatype Conversions
PowerCenter Server automatically makes conversions between compatible datatypes. When these conversions are performed unnecessarily performance slows. For example, if a mapping moves data from an Integer port to a Decimal port, then back to an Integer port, the conversion may be unnecessary.
In some instances however, datatype conversions can help improve performance. This is especially true when integer values are used in place of other datatypes for performing comparisons using Lookup and Filter transformations.
Eliminate Transformation Errors
Large numbers of evaluation errors significantly slow performance of the PowerCenter Server. During transformation errors, the PowerCenter Server engine pauses to determine the cause of the error, removes the row causing the error from the data flow, and logs the error in the session log.
Transformation errors can be caused by many things including: conversion errors, conflicting mapping logic, any condition that is specifically set up as an error, and so on. The session log can help point out the cause of these errors. If errors recur consistently for certain transformations, re-evaluate the constraints for these transformation. Any source of errors should be traced and eliminated.
Optimize Lookup Transformations
There are a number of ways to optimize lookup transformations that are setup in a mapping.

When to Cache Lookups

When caching is enabled, the PowerCenter Server caches the lookup table and queries the lookup cache during the session. When this option is not enabled, the PowerCenter Server queries the lookup table on a row-by-row basis.
NOTE: All the tuning options mentioned in this Best Practice assume that memory and cache sizing for lookups are sufficient to ensure that caches will not page to disks. Practices regarding memory and cache sizing for Lookup transformations are covered in Best Practice: Tuning Sessions for Better Performance.
In general, if the lookup table needs less than 300MB of memory, lookup caching should be enabled.
A better rule of thumb than memory size is to determine the size of the potential lookup cache with regard to the number of rows expected to be processed. For example, consider the following example.
In Mapping X, the source and lookup contain the following number of records:

ITEMS (source):	5000 records
MANUFACTURER:	200 records
DIM_ITEMS:	100000 records

Number of Disk Reads

	Cached Lookup	Un-cached Lookup
LKP_Manufacturer
Build Cache	200	0
Read Source Records	5000	5000
Execute Lookup	0	5000
Total # of Disk Reads	5200	10000
LKP_DIM_ITEMS
Build Cache	100000	0
Read Source Records	5000	5000
Execute Lookup	0	5000
Total # of Disk Reads	105000	10000

Consider the case where MANUFACTURER is the lookup table. If the lookup table is cached, it will take a total of 5200 disk reads to build the cache and execute the lookup. If the lookup table is not cached, then it will take a total of 10,000 total disk reads to execute the lookup. In this case, the number of records in the lookup table is small in comparison with the number of times the lookup is executed. So this lookup should be cached. This is the more likely scenario.
Consider the case where DIM_ITEMS is the lookup table. If the lookup table is cached, it will result in 105,000 total disk reads to build and execute the lookup. If the lookup table is not cached, then the disk reads would total 10,000. In this case the number of records in the lookup table is not small in comparison with the number of times the lookup will be executed. Thus the lookup should not be cached.
Use the following eight step method to determine if a lookup should be cached:

1. Code the lookup into the mapping.
2. Select a standard set of data from the source. For example, add a where clause on a relational source to load a sample 10,000 rows.
3. Run the mapping with caching turned off and save the log.
4. Run the mapping with caching turned on and save the log to a different name than the log created in step 3.
5. Look in the cached lookup log and determine how long it takes to cache the lookup object. Note this time in seconds: LOOKUP TIME IN SECONDS = LS.
6. In the non-cached log, take the time from the last lookup cache to the end of the load in seconds and divide it into the number or rows being processed: NON-CACHED ROWS PER SECOND = NRS.
7. In the cached log, take the time from the last lookup cache to the end of the load in seconds and divide it into number or rows being processed: CACHED ROWS PER SECOND = CRS.
8. Use the following formula to find the breakeven row point:
   
   (LS*NRS*CRS)/(CRS-NRS)  = X
   
   Where X is the breakeven point. If your expected source records is less than X, it is better to not cache the lookup. If your expected source records is more than X, it is better to cache the lookup.
   
   For example:
   
   Assume the lookup takes 166 seconds to cache (LS=166).
   Assume with a cached lookup the load is 232 rows per second (CRS=232).
   Assume with a non-cached lookup the load is 147 rows per second (NRS = 147).
   
   The formula would result in: (166*147*232)/(232-147) = 66,603.
   
   Thus, if the source has less than 66,603 records, the lookup should not be cached. If it has more than 66,603 records, then the lookup should be cached.

Sharing Lookup Caches

There are a number of methods for sharing lookup caches.

• Within a specific session run for a mapping, if the same lookup is used multiple times in a mapping, the PowerCenter Server will re-use the cache for the multiple instances of the lookup. Using the same lookup multiple times in the mapping will be more resource intensive with each successive instance.  If multiple cached lookups are from the same table but are expected to return different columns of data, it may be better to setup the multiple lookups to bring back the same columns even though not all return ports are used in all lookups. Bringing back a common set of columns may reduce the number of disk reads.
• Across sessions of the same mapping, the use of an unnamed persistent cache allows multiple runs to use an existing cache file stored on the PowerCenter Server. If the option of creating a persistent cache is set in the lookup properties, the memory cache created for the lookup during the initial run is saved to the PowerCenter Server. This can improve performance because the Server builds the memory cache from cache files instead of the database. This feature should only be used when the lookup table is not expected to change between session runs.
• Across different mappings and sessions, the use of a named persistent cache allows sharing of an existing cache file.

Reducing the Number of Cached Rows

There is an option to use a SQL override in the creation of a lookup cache. Options can be added to the WHERE clause to reduce the set of records included in the resulting cache.
NOTE: If you use a SQL override in a lookup, the lookup must be cached.

Optimizing the Lookup Condition

In the case where a lookup uses more than one lookup condition, set the conditions with an equal sign first in order to optimize lookup performance.

Indexing the Lookup Table

The PowerCenter Server must query, sort and compare values in the lookup condition columns. As a result, indexes on the database table should include every column used in a lookup condition. This can improve performance for both cached and un-cached lookups.
¨ In the case of a cached lookup, an ORDER BY condition is issued in the SQL statement used to create the cache. Columns used in the ORDER BY condition should be indexed. The session log will contain the ORDER BY statement.
¨ In the case of an un-cached lookup, since a SQL statement created for each row passing into the lookup transformation, performance can be helped by indexing columns in the lookup condition.
Optimize Filter and Router Transformations
Filtering data as early as possible in the data flow improves the efficiency of a mapping. Instead of using a Filter Transformation to remove a sizeable number of rows in the middle or end of a mapping, use a filter on the Source Qualifier or a Filter Transformation immediately after the source qualifier to improve performance.
Avoid complex expressions when creating the filter condition. Filter transformations are most effective when a simple integer or TRUE/FALSE expression is used in the filter condition.
Filters or routers should also be used to drop rejected rows from an Update Strategy transformation if rejected rows do not need to be saved.
Replace multiple filter transformations with a router transformation. This reduces the number of transformations in the mapping and makes the mapping easier to follow.
Optimize Aggregator Transformations
Aggregator Transformations often slow performance because they must group data before processing it.
Use simple columns in the group by condition to make the Aggregator Transformation more efficient. When possible, use numbers instead of strings or dates in the GROUP BY columns. Also avoid complex expressions in the Aggregator expressions, especially in GROUP BY ports.
Use the Sorted Input option in the aggregator. This option requires that data sent to the aggregator be sorted in the order in which the ports are used in the aggregators group by. The Sorted Input option decreases the use of aggregate caches. When it is used, the PowerCenter Server assumes all data is sorted by group and, as a group is passed through an aggregator, calculations can be performed and information passed on to the next transformation. Without sorted input, the Server must wait for all rows of data before processing aggregate calculations. Use of the Sorted Inputs option is usually accompanied by a Source Qualifier which uses the Number of Sorted Ports option.
Use an Expression and Update Strategy instead of an Aggregator Transformation. This technique can only be used if the source data can be sorted. Further, using this option assumes that a mapping is using an Aggregator with Sorted Input option. In the Expression Transformation, the use of variable ports is required to hold data from the previous row of data processed. The premise is to use the previous row of data to determine whether the current row is a part of the current group or is the beginning of a new group. Thus, if the row is a part of the current group, then its data would be used to continue calculating the current group function. An Update Strategy Transformation would follow the Expression Transformation and set the first row of a new group to insert and the following rows to update.
Optimize Joiner Transformations
Joiner transformations can slow performance because they need additional space in memory at run time to hold intermediate results.
Define the rows from the smaller set of data in the joiner as the Master rows. The Master rows are cached to memory and the detail records are then compared to rows in the cache of the Master rows. In order to minimize memory requirements, the smaller set of data should be cached and thus set as Master.
Use Normal joins whenever possible. Normal joins are faster than outer joins and the resulting set of data is also smaller.
Use the database to do the join when sourcing data from the same database schema. Database systems usually can perform the join more quickly than the Informatica Server, so a SQL override or a join condition should be used when joining multiple tables from the same database schema.
Optimize Sequence Generator Transformations
Sequence Generator transformations need to determine the next available sequence number, thus increasing the Number of Cached Values property can increase performance. This property determines the number of values the Informatica Server caches at one time. If it is set to cache no values then the Informatica Server must query the Informatica repository each time to determine what is the next number which can be used. Configuring the Number of Cached Values to a value greater than 1000 should be considered. It should be noted any cached values not used in the course of a session are lost since the sequence generator value in the repository is set, when it is called next time, to give the next set of cache values.
Avoid External Procedure Transformations
For the most part, making calls to external procedures slows down a session. If possible, avoid the use of these Transformations, which include Stored Procedures, External Procedures and Advanced External Procedures.
Field Level Transformation Optimization
As a final step in the tuning process, expressions used in transformations can be tuned. When examining expressions, focus on complex expressions for possible simplification.
To help isolate slow expressions, do the following:
1. Time the session with the original expression.
2. Copy the mapping and replace half the complex expressions with a constant.
3. Run and time the edited session.
4. Make another copy of the mapping and replace the other half of the complex expressions with a constant.
5. Run and time the edited session.
Processing field level transformations takes time. If the transformation expressions are complex, then processing will be slower. Its often possible to get a 10- 20% performance improvement by optimizing complex field level transformations. Use the target table mapping reports or the Metadata Reporter to examine the transformations. Likely candidates for optimization are the fields with the most complex expressions. Keep in mind that there may be more than one field causing performance problems.
Factoring out Common Logic
This can reduce the number of times a mapping performs the same logic. If a mapping performs the same logic multiple times in a mapping, moving the task upstream in the mapping may allow the logic to be done just once. For example, a mapping has five target tables. Each target requires a Social Security Number lookup. Instead of performing the lookup right before each target, move the lookup to a position before the data flow splits.
Minimize Function Calls
Anytime a function is called it takes resources to process. There are several common examples where function calls can be reduced or eliminated.
Aggregate function calls can sometime be reduced. In the case of each aggregate function call, the Informatica Server must search and group the data.
Thus the following expression:
SUM(Column A) + SUM(Column B)
Can be optimized to:
SUM(Column A + Column B)
In general, operators are faster than functions, so operators should be used whenever possible.
For example if you have an expression which involves a CONCAT function such as:
CONCAT(CONCAT(FIRST_NAME, ), LAST_NAME)
It can be optimized to:
FIRST_NAME || || LAST_NAME
Remember that IIF() is a function that returns a value, not just a logical test. This allows many logical statements to be written in a more compact fashion.
For example:
IIF(FLG_A=Y and FLG_B=Y and FLG_C=Y, VAL_A+VAL_B+VAL_C,
 IIF(FLG_A=Y and FLG_B=Y and FLG_C=N, VAL_A+VAL_B,
  IIF(FLG_A=Y and FLG_B=N and FLG_C=Y, VAL_A+VAL_C,
   IIF(FLG_A=Y and FLG_B=N and FLG_C=N, VAL_A,
    IIF(FLG_A=N and FLG_B=Y and FLG_C=Y, VAL_B+VAL_C,
      IIF(FLG_A=N and FLG_B=Y and FLG_C=N, VAL_B,
       IIF(FLG_A=N and FLG_B=N and FLG_C=Y, VAL_C,
        IIF(FLG_A=N and FLG_B=N and FLG_C=N, 0.0))))))))
Can be optimized to:
IIF(FLG_A=Y, VAL_A, 0.0) + IIF(FLG_B=Y, VAL_B, 0.0) + IIF(FLG_C=Y, VAL_C, 0.0)
The original expression had 8 IIFs, 16 ANDs and 24 comparisons. The optimized expression results in 3 IIFs, 3 comparisons and two additions.
Be creative in making expressions more efficient. The following is an example of rework of an expression which eliminates three comparisons down to one:
For example:
IIF(X=1 OR X=5 OR X=9, 'yes', 'no')
Can be optimized to:
IIF(MOD(X, 4) = 1, 'yes', 'no')
Calculate Once, Use Many Times
Avoid calculating or testing the same value multiple times. If the same sub-expression is used several times in a transformation, consider making the sub-expression a local variable. The local variable can be used only within the transformation but by calculating the variable only once can speed performance.
Choose Numeric versus String Operations
The Informatica Server processes numeric operations faster than string operations. For example, if a lookup is done on a large amount of data on two columns, EMPLOYEE_NAME and EMPLOYEE_ID, configuring the lookup around EMPLOYEE_ID improves performance.
Optimizing Char-Char and Char-Varchar Comparisons
When the Informatica Server performs comparisons between CHAR and VARCHAR columns, it slows each time it finds trailing blank spaces in the row.  The Treat CHAR as CHAR On Read option can be set in the Informatica Server setup so that the Informatica Server does not trim trailing spaces from the end of CHAR source fields.
Use DECODE instead of LOOKUP
When a LOOKUP function is used, the Informatica Server must lookup a table in the database. When a DECODE function is used, the lookup values are incorporated into the expression itself so the Informatica Server does not need to lookup a separate table. Thus, when looking up a small set of unchanging values, using DECODE may improve performance.
Reduce the Number of Transformations in a Mapping
Whenever possible the number of transformations should be reduced. As there is always overhead involved in moving data between transformations. Along the same lines, unnecessary links between transformations should be removed to minimize the amount of data moved. This is especially important with data being pulled from the Source Qualifier Transformation.
Use Pre- and Post-Session SQL Commands
You can specify pre- and post-session SQL commands in the Properties tab of the Source Qualifier transformation and in the Properties tab of the target instance in a mapping. To increase the load speed, use these commands to drop indexes on the target before the session runs, then recreate them when the session completes.
Apply the following guidelines when using the SQL statements:

• You can use any command that is valid for the database type. However, the PowerCenter Server does not allow nested comments, even though the database might.
• You can use mapping parameters and variables in SQL executed against the source, but not against the target.
• Use a semi-colon (;) to separate multiple statements.
• The PowerCenter Server ignores semi-colons within single quotes, double quotes, or within /* ...*/.
• If you need to use a semi-colon outside of quotes or comments, you can escape it with a back slash (\).
• The Workflow Manager does not validate the SQL.

Use Environmental SQL
For relational databases, you can execute SQL commands in the database environment when connecting to the database. You can use this for source, target, lookup, and stored procedure connection. For instance, you can set isolation levels on the source and target systems to avoid deadlocks. Follow the guidelines mentioned above for using the SQL statements.