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  1. Abstract
  2. Supported Versions
  3. Tuning and Sizing Guidelines for Data Engineering Integration (10.4.x)

Tuning and Sizing Guidelines for Data Engineering Integration (10.4.x)

Tuning and Sizing Guidelines for Data Engineering Integration (10.4.x)

Case Study: Data Integration Service Application Load and Start-Up

Case Study: Data Integration Service Application Load and Start-Up

The following case study observes the load and start-up of deployed applications on the Data Integration Service.

Test Setup

The study compares the following types of applications:
  • 40-object applications. Each application contains 40 application objects that include 32 mappings and 8 physical data objects.
  • 100-object applications. Each application contains 100 application objects that include 50 mappings and 50 physical data objects.
The applications are deployed in sets of 100, 500, or 1,000 applications to the Data Integration Service.
The database used by the Model Repository Service is deployed on the same machine as the domain node.

Environment

Chipset
Intel® Xeon® CPU E5-2680 v3 @ 2.50GHz
Cores
12 cores
Memory
132 GB
Operating system
Red Hat Enterprise Linux server release 7.6

Performance Chart

The following chart shows the time taken to start up the Data Integration Service based on the number of applications deployed to the Data Integration Service and the number of objects in each application:

Conclusions

The total number of objects in an application has little to no effect on application load/startup time. To reduce the number of deployed applications on a Data Integration Service, group related entities to place 40 - 60 application objects that include mappings and workflows in one application.

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