Optimizing the Bottleneck’s

If the source is a flat file, ensure that the flat file is local to the Informatica server.

If source is a relational table, then try not to use synonyms or aliases.

If the source is a flat file, reduce the number of bytes (By default it is 1024 bytes per line) the Informatica reads per line. If we do this, we can decrease the Line Sequential Buffer Length setting of the session properties.

If possible, give a conditional query in the source qualifier so that the records are filtered off as soon as possible in the process.

In the source qualifier, if the query has ORDER BY or GROUP BY, then create an index on the source table and order by the index field of the source table.

PERFORMANCE TUNING OF TARGETS

If the target is a flat file, ensure that the flat file is local to the Informatica server. If target is a relational table, then try not to use synonyms or aliases.

Use bulk load whenever possible.

Increase the commit level.

Drop constraints and indexes of the table before loading.

PERFORMANCE TUNING OF MAPPINGS

Mapping helps to channel the flow of data from source to target with all the transformations in between. Mapping is the skeleton of Informatica loading process.

Avoid executing major sql queries from mapplets or mappings.

Use optimized queries when we are using them.

Reduce the number of transformations in the mapping. Active transformations like rank, joiner, filter, aggregator etc should be used as less as possible.

Remove all the unnecessary links between the transformations from mapping.

If a single mapping contains many targets, then dividing them into separate mappings can improve performance.

If we need to use a single source more than once in a mapping, then keep only one source and source qualifier in the mapping. Then create different data flows as required into different targets or same target.

If a session joins many source tables in one source qualifier, then an optimizing query will improve performance.

In the sql query that Informatica generates, ORDERBY will be present. Remove the ORDER BY clause if not needed or at least reduce the number of column names in that list. For better performance it is best to order by the index field of that table.

Combine the mappings that use same set of source data.

On a mapping, field with the same information should be given the same type and length throughout the mapping. Otherwise time will be spent on field conversions.

Instead of doing complex calculation in query, use an expression transformer and do the calculation in the mapping.

If data is passing through multiple staging areas, removing the staging area will increase performance.

Stored procedures reduce performance. Try to keep the stored procedures simple in the mappings.

Unnecessary data type conversions should be avoided since the data type conversions impact performance.

Transformation errors result in performance degradation. Try running the mapping after removing all transformations. If it is taking significantly less time than with the transformations, then we have to fine-tune the transformation.

Keep database interactions as less as possible.

PERFORMANCE TUNING OF SESSIONS

A session specifies the location from where the data is to be taken, where the transformations are done and where the data is to be loaded. It has various properties that help us to schedule and run the job in the way we want.

Partition the session:

This creates many connections to the source and target, and loads data in parallel pipelines. Each pipeline will be independent of the other. But the performance of the session will not improve if the number of records is less. Also the performance will not improve if it does updates and deletes. So session partitioning should be used only if the volume of data is huge and the job is mainly insertion of data.

Run the sessions in parallel rather than serial to gain time, if they are independent of each other.

Drop constraints and indexes before we run session. Rebuild them after the session run completes. Dropping can be done in pre session script and Rebuilding in post session script. But if data is too much, dropping indexes and then rebuilding them etc. will be not possible. In such cases, stage all data, pre-create the index, use a transportable table space and then load into database.

Use bulk loading, external loading etc. Bulk loading can be used only if the table does not have an index.

In a session we have options to ‘Treat rows as ‘Data Driven, Insert, Update and Delete’. If update strategies are used, then we have to keep it as ‘Data Driven’. But when the session does only insertion of rows into target table, it has to be kept as ‘Insert’ to improve performance.

Increase the database commit level (The point at which the Informatica server is set to commit data to the target table. For e.g. commit level can be set at every every 50,000 records)

By avoiding built in functions as much as possible, we can improve the performance. E.g. For concatenation, the operator ‘||’ is faster than the function CONCAT (). So use operators instead of functions, where possible. The functions like IS_SPACES (), IS_NUMBER (), IFF (), DECODE () etc. reduce the performance to a big extent in this order. Preference should be in the opposite order.

String functions like substring, ltrim, and rtrim reduce the performance. In the sources, use delimited strings in case the source flat files or use varchar data type.

Manipulating high precision data types will slow down Informatica server. So disable ‘high precision’.

Localize all source and target tables, stored procedures, views, sequences etc. Try not to connect across synonyms. Synonyms and aliases slow down the performance.

DATABASE OPTIMISATION

To gain the best Informatica performance, the database tables, stored procedures and queries used in Informatica should be tuned well.

If the source and target are flat files, then they should be present in the system in which the Informatica server is present.

Increase the network packet size.

The performance of the Informatica server is related to network connections.Data generally moves across a network at less than 1 MB per second, whereas a local disk moves data five to twenty times faster. Thus network connections often affect on session performance. So avoid network connections.