r3 - 2015-07-13 - 12:57:05 - Main.wellskYou are here: TWiki >  Deployment Web > DeploymentPlanningAndDesign > LifecycleQueryEnginePerformanceReportV6

Lifecycle Query Engine 6.0 Performance Report for Query Scalability todo.png

Authors: KeithWells
Build basis: Lifecycle Query Engine 6.0


This article presents the results of Lifecycle Query Engine (LQE) performance testing for the Rational Collaborative Lifecycle Managment (CLM) 6.0 release. JRS and LQE provide an integrated view of artifacts across data sources allowing the capability to generate reports across tools and project areas.

The Lifecycle Query Engine (LQE) implements a Linked Lifecycle Data Index over data provided by one or more lifecycle tools. A lifecycle tool makes its data available for indexing by exposing its Linked Lifecycle Data via a Tracked Resource Set, whose members MUST be retrievable resources with RDF representations, called Index Resources.

An LQE Index built from one or more Tracked Resource Sets allows SPARQL queries to be run against the RDF dataset that aggregates the RDF graphs of the Index Resources. This permits data from multiple lifecycle tools to be queried together, including cross-tool links between resources. Changes that happen to Index Resources in a lifecycle tool are made discoverable via the Tracked Resource Set's Change Log, allowing the changes to be propagated to the Lifecycle Index to keep it up to date.

JRS provides a Report Builder to guide users through the intricacies of building SPARQL ueries to view data in a report format. This article will show performance benchmarks from LQE tests providing guidance to customers in planning their LQE deployment and server configurations.


The information in this document is distributed AS IS. The use of this information or the implementation of any of these techniques is a customer responsibility and depends on the customer’s ability to evaluate and integrate them into the customer’s operational environment. While each item may have been reviewed by IBM for accuracy in a specific situation, there is no guarantee that the same or similar results will be obtained elsewhere. Customers attempting to adapt these techniques to their own environments do so at their own risk. Any pointers in this publication to external Web sites are provided for convenience only and do not in any manner serve as an endorsement of these Web sites. Any performance data contained in this document was determined in a controlled environment, and therefore, the results that may be obtained in other operating environments may vary significantly. Users of this document should verify the applicable data for their specific environment.

Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multi-programming in the user’s job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here.

This testing was done as a way to compare and characterize the differences in performance between different versions of the product. The results shown here should thus be looked at as a comparison of the contrasting performance between different versions, and not as an absolute benchmark of performance.

What Our Tests Measure

Automated tooling such as Apache Jmeter and Rational Performance Tester (RPT) are widely used to simulate a workload normally generated by client software such as the Eclipse client or web browsers. All response times listed are those measured by our automated tooling and not a client.

The diagram below describes at a very high level which aspects of the entire end-to-end experience (human end-user to server and back again) that our performance tests simulate. The tests described in this article simulate a segment of the end-to-end transaction as indicated in the middle of the diagram. Performance tests are server-side and capture response times for this segment of the transaction.



LQE used the JMeter performance measurement tool from Apache to simulate clients as JMeter Threads. Each user is represented by 1 JMeter thread. For these tests, the load is incremented by 50 threads (users) between 50 and 650 (or higher).

The performance test models a dashboard refresh initiated by a user. When a user refreshes a dashboard, each widget that refreshes represents one or more queries which get executed in parallel.

Dashboard widgets can represent fast user-related queries, slower, larger scoped queries called report queries, or more encompassing project-level queries. In these tests, about 80% of the queries are classified as user-related queries, and the rest are report-based queries.

Each thread (user) refreshes their dashboard resulting in 6 or more queries executed at the same time. The total number of threads are ramped up over a period of 5 minutes. Then each thread will continue to refresh their dashboard 15 times with a think time of 80 seconds between refreshes.

Between concurrency tests there is 2.67 minute pause to allow the system to quiesce. Queries are tested against two different 100M triple datasets:

  • Reslqe – the reslqe dataset is a generated dataset which was seeded by a real LQE indexTdb which had indexed 15 internal IBM RTC projects. The dataset was profiled and then increased in size to 100M triples by an internal data generator utility.
  • SSE – The SSE dataset started as a RELM LQE indexTdb which was profiled and grown in size to 100M triples by an internal data generator utility.

The reslqe queries consisted of 8 queries: 2 report based queries and 6 user based queries.

Run Total Queries Executed
run_50 6000
run_100 12000
run_150 18000
run_200 24000
run_250 30000
run_300 36000
run_350 42000
run_400 48000
run_450 54000
run_500 60000
run_550 66000
run_600 72000
run_650 78000
Table 1 Total Number of reslqe queries per run

The SSE queries consisted of 10 queries: 4 report based queries and 6 user based queries,

Run Total Queries Executed
run_50 7500
run_100 15000
run_150 22500
run_200 30000
run_250 37500
run_300 45000
run_350 52500
run_400 60000
run_450 67500
run_500 75000
run_550 82500
run_600 90000
run_650 97500
Table 2 Total Number of SSE queries per run

LQE Test Dataset Characteristics

Condition Reslqe SSE
Size of the dataset on disk, on the LQE server 22.2GB 19.9GB
Number of resources 3,050,000 4,953,453
Number of triples 99,389,595 99,919,403

Performance goals

The goal of this performance testing was to determine how many users LQE could support in a query-only based scenario when the users refreshed their dashboards throughout the day.

Customer expectations for LQE query and indexing performance should be analyzed with attention to server hardware, dataset size, data model complexity, current system load, and query optimization.

  • Larger indexes requires more RAM native memory.
  • Heavier query loads should allocate more CPU cores.
  • Indexing can perform faster with faster disk I/O.

These tests were performed on a Redhat 7.0 server system with 96GB RAM, 250 GB SSD, 2 Intel Xeon E7-2830 8-core 2133 MHz CPUs, 100M triple datasets are based on CLM resources, and a set of dashboard-like queries.


The topology under test is based on Standard Topology (E1) Enterprise - Distributed / Linux / DB2.


You can run several instances of Lifecycle Query Engine (LQE) and each instance must use the same external data source (in this case DB2). The group of LQE nodes behave like a single logical unit. By deploying LQE across a set of servers, you can distribute the query workload and improve performance and scalability. Each LQE node contains its own independent triple store index, which indexes the same tracked resource set (TRS) data providers that you specified on the LQE Administration page.

The specifications of machines under test are listed in the table below. Server tuning details are listed in Appendix A

Role Model Processor Type Number of Processors Memory(GB) Disk Disk Capacity OS
LQE 1 Server IBM X3690-X5 2 x Intel Xeon E7-2830 8 core 2.13GHz 32 vCPU 96GB 400GB SSD 3 x 146 GB 15k RPM SAS RAID-0 4 x 500 GB 7200 RPM SAS Raid-5 2.78TB Redhat Enterprise 7 .0
LQE 2 Server IBM X3690-X5 2 x Intel Xeon E7-2830 8 core 2133 MHz 32 vCPU 96 GB 1 x 250GB SSD 4 x 300GBV SAS RAID-5 Configuration 1300GB Redhat Enterprise 7.0
Test Framework/DB2 Server IBM X3690-X5 2 x Intel Xeon E7-2830 8 core 2133 MHz 32 vCPU 96 GB 1 x 250GB SSD 4 x 300GBV SAS RAID-5 Configuration 1072GB Windows Server 2008 R2 Enterprise

Heading 1

Related topics: Deployment web home, Deployment web home

External links:

Additional contributors: TWikiUser, TWikiUser

Topic attachments
I Attachment Action Size Date Who Comment
Gifgif TestDisclaimer.gif manage 40.1 K 2015-06-15 - 13:23 UnknownUser  
Pngpng Topology.png manage 13.3 K 2015-07-13 - 12:46 UnknownUser  
Edit | Attach | Printable | Raw View | Backlinks: Web, All Webs | History: r5 < r4 < r3 < r2 < r1 | More topic actions...
This site is powered by the TWiki collaboration platformCopyright © by the contributing authors. All material on this collaboration platform is the property of the contributing authors.
Contributions are governed by our Terms of Use. Please read the following disclaimer.