Oracle Data Integrator 11g performance bottlenecks

This blog post aims to help Oracle® Data Integrator (ODI) designers, administrators, and system teams address performance bottlenecks in ODI execution plans. Following the steps outlined here will result in a speedier experience for your end users.


Performance bottlenecks in ODI execution plans occur for eight primary reasons. The following list is ordered by the priority in which they should be addressed:

  1. Network latency
  2. Temporary memory allocation
  3. Application memory allocation
  4. Sequential vs. parallel processing of fact groups
  5. Source database connection type
  6. Optimal usage of indexes
  7. Reduce data movement
  8. Execution location of transformations

Network latency

The Batch Update/Array Fetch configuration is a compromise between network and agent overhead. With a highly available network, you can keep low values (such as those below 30). With a poor network, you can use larger values (100 and higher).

The following parameters help tune this data flow:

  1. Array Fetch in the source data server definition
  2. Batch Update in the target data server definition

Temporary memory allocation

Temporary spaces are used during aggregation of data, during lookup queries, or during sessions that are running in parallel.

For optimal performance, the temporary space should equal the table space.

Application memory allocation

Application memory allocation refers to Oracle WebLogic memory. If the agent needs to have data flowing through it, it should not be installed on a machine that doesn’t have spare resources. In ODI 11g, you can adjust the ODI_INIT_HEAP and ODI_MAX_HEAP parameters in the odiparams configuration file to define the agent’s Java┬« virtual machine (JVM) initial and maximum heap size.

The ODI_INIT_HEAP and ODI_MAX_HEAP parameters highlighted in theodiparams configuration file

Default values:

The configuration file has the following default values:

  • ODI_MAX_HEAP: 256 MB
  • Max No of Connections: 10

Recommended values:

The following values are recommended for the best possible performance:

  • ODI_MAX_HEAP: 4096 MB
  • Max no of connections: 20

The ODI_MAX_HEAP value can be increased to approximately half the size of the RAM available on the server on which the ODI Agent is running.

Sequential vs. parallel processing of fact groups

When you’re running a load plan with the serial steps for fact groups, individual task run times are less. However, the total time for load plan completion is higher. If you execute your load plan with out-of-the-box (OOTB) parallel steps for fact groups instead, the total time is less, but individual task run times are higher. If you want to increase the number of concurrent connections to user schema, consider that the total load time is less with parallel steps.

Source database connection type

ODI has two options for connecting to the source database for source-dependent extract (SDE) loads: Java Database Connectivity (JDBC) connections and DB Links. DB Links provide better performance optimization, according to a study of SDE load execution times for these two options.

Optimal usage of indexes

Optimizing source indexes might help enhance load performance. Use the following steps to optimize source indexes:

  1. Analyze the Explain Plan for SDE queries.
  2. Check if indexes are used on sources tables.
  3. Look for the optimization scope (for example, creation and modification of indexes).

Reduce data movement

If you’re aggregating a large amount of source data to generate a small data flow to a remote target, then you should consider locating the staging area on the source side.

An architectural model of the source, staging area, andtarget

Execution location of transformations

When you’re filtering source data, execute the filters on the source servers to reduce the data flow from the source to the staging area.

When you’re joining source tables, complete the following steps:

  1. If the expected source set resulting from the join is smaller than the sum of the two sources, perform the join on the source.

  2. If the expected source set after the join is larger than the sum of the two sources, perform the join on the staging area. One example of this result would be when there is a cross-join.

Other performance tuning tips

Be aware while you’re setting up the physical architecture. If the staging and target areas are on the same server, then ensure that you select the correct data schema and work schema for the tasks.

Enable archival logging on the system and regularly purge logs and archive logs. Taking these steps will help clean up the metadata tables that use the most storage, thereby increasing the start time and execution time of scenarios.


We hope that you find these techniques helpful for eliminating ODI performance bottlenecks. We’d love to hear your own ideas and suggestions. Use the Feedback tab to make any comments or ask questions.


The following documents were used as references for this blog post:

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Sivaram Kolla

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