Communities
A collaborative platform to connect and grow with like-minded Informaticans across the globe
Product Communities
Connect and collaborate with Informatica experts and champions
Discussions
Have a question? Start a Discussion and get immediate answers you are looking for
User Groups
Customer-organized groups that meet online and in-person. Join today to network, share ideas, and get tips on how to get the most out of Informatica
Get Started
Community Guidelines
Knowledge Center
Troubleshooting documents, product guides, how to videos, best practices, and more
Knowledge Base
One-stop self-service portal for solutions, FAQs, Whitepapers, How Tos, Videos, and more
Support TV
Video channel for step-by-step instructions to use our products, best practices, troubleshooting tips, and much more
Documentation
Information library of the latest product documents
Velocity (Best Practices)
Best practices and use cases from the Implementation team
Learn
Rich resources to help you leverage full capabilities of our products
Trainings
Role-based training programs for the best ROI
Certifications
Get certified on Informatica products. Free, Foundation, or Professional
Product Learning Paths
Free and unlimited modules based on your expertise level and journey
Resources
Library of content to help you leverage the best of Informatica products
Tech Tuesdays Webinars
Most popular webinars on product architecture, best practices, and more
Product Availability Matrix
Product Availability Matrix statements of Informatica products
SupportFlash
Monthly support newsletter
Support Documents
Informatica Support Guide and Statements, Quick Start Guides, and Cloud Product Description Schedule
Product Lifecycle
End of Life statements of Informatica products
Ideas
Events
Change Request Tracking
Marketplace
English
  • Data Engineering Integration H2L
  • All Products

Table of Contents

Search

  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)

Back Next

Case Study: Data Integration Service Concurrency with Multiple HS2 Load Balancers

Case Study: Data Integration Service Concurrency with Multiple HS2 Load Balancers

The following case study shows the benefits of having multiple Hive Server2 Load Balancers for large concurrent mappings running on a Data Integration Service 4 node grid.
The mappings used TPC-DS benchmark queries of medium complexity with Hive sources and parameterized HDFS targets. During the test, peak CPU utilization was ~20 cores (80%) for less than 5 minutes. The average utilization was ~4 cores.

Environment

Cloudera Cluster
Data Integration Service
4 Node Grid
Chipset
Intel® Xeon® Processor X5675 @ 3.06 GHz
Intel® Xeon® Gold 6132 CPU @ 2.60GHz
Cores
4 x 6 cores
4 x 14 cores
Memory
32 GB
125 GB
Operating System
Red Hat Enterprise Linux 6.1
Red Hat Enterprise Linux 7.5 (Maipo)
Hadoop Distribution
Cloudera 5.15
-
Hadoop Cluster Size
25 nodes
-

Hive Server 2 Load Balancer Configuration

Test staff configured the HiveServer 2 load balancers using the following steps:
  • Install the HA Proxy package or another load balancer recommended by your IT team.
  • Configure the HA proxy service to listen on port 10000 and include the HS2 instances.
  • Configure the HA Proxy service to start on bootup.
  • In Cloudera Manager, include the Load Balancer server address in the HiveServer2 Load Balancer configuration properties.
  • Restart the Hive service.

Performance Chart

The following performance chart compares the dispatch times for 10K concurrent jobs on a Hadoop cluster. Cluster Dispatch time is the time taken by the Data Integration Service to submit all mappings to the cluster:

Conclusions

The test found that dispatch time improved ~40% with two HiveServer 2 instances.
0 COMMENTS
We’d like to hear from you!