Informatica Data Director Performance

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1 Informatica Data Director Performance 2011 Informatica

2 Abstract A variety of performance and stress tests are run on the Informatica Data Director to ensure performance and scalability for a wide variety of business scenarios. This article describes the performance and stress tests conducted on the Informatica Data Director, and provides guidance in performance and capacity planning for the deployments. It also describes the IDD architecture and the 3-tier architecture in a typical IDD deployment and provides the sample model and the methodology for sizing the IDD environments. Supported Versions Informatica Data Director Table of Contents Introduction Informatica Data Director Architecture Testing Methodology Test Environments Performance Test Results Scalability Test Results IDD Capacity Planning Conclusion Introduction Informatica conducts extensive performance and stress tests on Informatica Data Director (IDD) for various MDM Hub scenarios. The results of these tests help in performance and capacity planning for the deployments. The methodology and the test environments chosen, ensure that all the important IDD performance characteristics are measured. You can use the guidance provided in this article, along with other tools and documents for architecting, and performance and capacity planning for the deployments. This article includes an overview of the IDD architecture and the 3-tier architecture in a typical IDD deployment. It also includes the sample model and the methodology for sizing the IDD environments based on the measured performance characteristics and the industry standard usage patterns for sizing the enterprise applications. The tests described in this article are based on the typical IDD usage patterns and should be used to understand the deployment architecture and the performance characteristics of IDD and selecting the appropriate hardware for the expected user loads. Actual configuration performance may vary from these estimates based on the network loads and bandwidth availability, differences between the assumptions made in the sample model and the actual usage patterns, as well as other factors. Informatica Data Director Architecture IDD is a web application that uses the MDM Hub infrastructure for all of its metadata and configuration storage needs and leverages the MDM Hub APIs for all data operations. It is best to view the full IDD stack as a three-tier architecture. These tiers consist of the Informatica MDM Hub Store, the Informatica MDM Hub Server(s) (includes Cleanse-Match Servers) and the Informatica Data Director runtime. 2

3 The Hub Store is where business data is stored and consolidated. The Hub Store contains common information about all of the databases that are part of an Informatica MDM Hub implementation. It resides in a supported database server environment -- either Oracle (10g or 11g) or DB2 UDB (v8.1). The Hub Server is the run-time component that manages core and common services for the Informatica MDM Hub. The Hub Server is a J2EE application, deployed on a supported application server that orchestrates the data processing within the Hub Store. Supported application servers are JBoss 4.03, WebLogic 9.2 and 10.0, and Websphere x. The Data Director is a J2EE based Web application, which is deployed in the same J2EE application archive with the Hub Server to simplify configuration and provide the optimal configuration for the Hub API performance. Data Director supports clients with Firefox 3.5.x, Internet Explorer 7.0 and Safari 4.0. Data Director represents the latest generation of highly interactive browser based applications and uses AJAX extensively for enhancing user experience. Data Director also utilizes Adobe Flash for several of its modules that require graphical representation of data. Testing Methodology Informatica MDM Hub can be used in a variety of scenarios and use cases, and provides both batch and real time APIs. Informatica conducts extensive tests as part of its release process, to ensure adequate performance for all of the MDM Hub scenarios. The results of these tests and the performance benchmarks are available through Informatica support. The Data Director testing methodology augments on the MDM Hub test results and is intended to test the performance characteristics specific to IDD, such as web sessions, AJAX operations, and client (browser) side script execution. The tests are run on a small development data set to simplify the setup and reduce the environment costs. What allows us to do this is that the IDD uses MDM Hub API for all data access and manipulation. IDD performance does not directly depend on the size of the data set and the performance characteristics on the large data sets can be derived from the MDM Hub API tests that are run independently from the IDD tests. We define 3 types of automated tests that are run regularly in the IDD release cycle: Performance Tests The goal of the performance tests is to measure the end-to-end (glass-back) response times as experienced by users. The performance tests are performed using the WatiN scripting library using Internet Explorer and represent around 70 different user interaction scenarios in IDD. The response time is measured in the browser using JavaScript and browser event instrumentation. The measurements include the browser XHTML/XML rendering time and JavaScript execution for all user and AJAX interactions. Performance tests are typically run in a single test client configuration because of the high load that the HTML rendering and the script execution put on the client. The main metric tracked for these tests is the response latency. The performance tests are used for improving the functional performance and user interaction experience. Stress Tests The goal of the stress tests is to measure the performance characteristics of IDD Server runtime under load. These tests are performed by simulating the basic IDD user scenarios to create a representative user load profile with the 80% to 20% ratio of the read to write operations. The scalability tests are done using the WatiN library and the JMeter. The tests are executed outside of the browser to effectively simulate the load with both the latency and the throughput measured. The tests are run with gradually increasing the number of simulated users over the ramp-up period (typically 1 hour) to measure the latency and response times under different loads. The WatiN tests are run with no-wait users to achieve the maximum load with the minimum number of clients. The JMeter tests are run using a large number of threads simulating the user think time. Survival Tests The goal of the survival tests is to ensure that IDD can sustain significant user loads for extended periods of time. The tests are run at 80% of the full capacity and are run with a large number of test threads using the fixed throughput timers to simulate a large number of occasional users with significant think time, which would create a large number of server sessions. 3

4 Test Environments The following test environment is used for the performance tests: Server CPU OS Hub Config DB Server Intel Core 2 Duo, 1.86 GHz, 2GB RAM Windows Server 2003 OS, Service Pack 2 ORACLE 10.2 App Server Intel Core 2 Duo, 1.86 GHz, 2GB RAM Windows Server 2003 OS, Service Pack 2 JBoss AS Test Client Intel Core 2 Duo, 1.86 GHz, 2GB RAM Windows XP IE 7 The following test environment is used for the stress and survival tests: Server Hardware OS Hub Config DB Server App Server Intel Core 2 Quad, 2.4 GHz, 8GB RAM Intel Core 2 Quad, 2.4 GHz, 8GB RAM Linux Red Hat ORACLE 10.2 Linux Red Hat JBoss AS Test Client Intel Core 2 Quad, 2.4 GHz, 8GB RAM Windows Server 2003 OS, Service Pack 2 IE 7 Performance Test Results All performance tests presented in this documents have been demonstrated on the XU SP2 Patch B version of the MDM Hub. Here are the performance test results for the representative performance test scenarios. Test Scenarios Scenario Details Latency (sec) Creation of a new "Person" tab in data view Creates new 'Person' tab in 'Data view' 2.1 Removes tab 1.4 Saving and deleting 'Person' record in 'Data View' Saves 'Person' record with all fields filled and having 10 'Names', 10 'Phones' and 10 Addresses 7.4 Deletes person record 1.4 Switching between data view tabs operations Switching to search tab 2 Switching to existing record tab 2.3 Switching to copied record tab 2.6 Search and subject area record opening from search tab time Search for person record with specified Display Name 1.3 4

5 Test Scenarios Scenario Details Latency (sec) Opening record from search tab 3.2 Closing tab in Data View 2.2 Switching between child tabs in 'Person' record Switching to 'Phone' children tab 2.8 Switching to 'Names' children tab 1.6 Switching to 'BillAddresses' children tab 2.4 Basic search operation, navigation through search results Basic search 1.1 Basic search, search result navigation 1.6 UI operations on search tab Switch to 'Saved queries' tab 1.2 Switch to 'Active query' tab 0.7 Query builder open 1.5 Save Query As dialog opening time 0.9 Xrefs dialog opening/closing Open Xrefs dialog 5.1 Basic HM operations Open HM Tab for large graph 4.5 The performance test results are specific to the different types of user interactions in IDD. The tests show that all of the major user interactions can be completed within seconds depending on the complexity of the operation with a few of outliers that perform significant operations on the data. Scalability Test Results Stress tests have been performed in both the performance and the stress test environments. We have demonstrated near linear scalability across the environments with the 13.4 TPS achieved in the performance test environment and 26.0 TPS achieved in the stress test environment and achieving 6.5 TPS per CPU core throughput. Here is the stress test graph for the tests performed in the stress test environment. The graph shows that the latency quickly stabilizes after the initial system warm up and is very stable through the ramp up period when the application server CPU is saturated, as seen from the CPU Usage graph below. 5

6 IDD Capacity Planning In this section we will explain the methodology that we use for extrapolating the stress test results for capacity planning. For simplicity we assume that all users are in a single, or adjacent time zones. Similar model can be easily created for multiple time zones. This model is based on estimating the number of user sessions and throughput for each of the sessions. It allows for a small number of input parameters that are usually easy to estimate in any business environment. Please note that other approaches, such as estimating the total number of transactions can also be used. Here are the parameters that are used in the sizing model. Parameter Description Sample Value Number of frequent users Number of occasional users Percent of time active for the frequent users Percent of time active for the occasional users Work Day Peak load period Think time Number of users that rely on IDD to do their job and use it constantly throughout their working day. These are typically data stewards that are responsible for the data quality. Number of users that use IDD occasionally. These are typically business users that use IDD several times a day to access or manage the master data as part of the regular business processes. Typically we use 50%, which represents that total amount of time that the frequent users would be actively working with the system. Typically we use 10%, which represents that total amount of time that the frequent users would be actively working with the system. Duration of a normal workday for the business users. Period of the day where the majority of operations are performed and the % of operations that are performed during the peak load period. Amount of time that the user needs to review or correct the data. Typical think % 10% 10 hours 2 hours 30% 5 seconds 6

7 Parameter Description Sample Value time that we observed for IDD users averaged 5 seconds. The total number of active user sessions during the peak load period can be calculated as: Number of Sessions = [Frequent User Sessions] + [Occasional User Sessions] where Frequent User Sessions = [# frequent users] * [% of time active] and Occasional User Sessions = [# occasional users] * [% of time active] * [work load %] * [work day] / [peak load time] For our sample values the number of session can be calculated as: Number of sessions = 20 * *.1 *.3 * 10 / 2 = = 160 Given the assumptions on the think time and the typical response times demonstrated in the performance tests above we could calculate the throughput created by a single user as: Single User throughput = ([avg tx response time] + [think time]) / 60 sec Single User throughput = (1 sec + 5 sec) / 60 sec =.1 TPS Thus the 160 concurrent sessions would have produced ~ 16 TPS (Sustained TPS) if the requests were evenly distributed throughout the day. The load patterns however are random and such assumption is not valid. We typically recommend the factor of 3 to account for such discrepancies and to ensure that the system can provide adequate response at any point in time despite the load fluctuations. Peak TPS = 3 * [Sustained TPS] Using these assumptions and the throughput test results that were demonstrated, we can calculate the number of App Server cores that would be required for this configuration to support the IDD load: Number of Cores = CEILING([Peak TPS] / [Throughput per Core]) Number of Cores = 8 cores For our sample configuration we would recommend 2 Application servers with 4 CPU cores each to support the IDD load. Conclusion In this article we described the variety of the performance and stress tests that are run on the Informatica Data Directory to ensure the adequate performance and scalability for a wide variety of business scenarios and have shown that we were able to achieve high throughput and low latencies in the performance and scalability tests that have been conducted in out performance labs. We also included the framework for sizing the IDD environments using the achieved test results and recommended a simple sizing model that can be easily adapted to any specific scenario and modified to use the available data about the user activity profiles and the user load. Author Dmitri Korablev VP Strategy and Planning 7

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