Composite Data Virtualization Composite Performance

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1 Composite Data Virtualization Composite Performance Composite Software January 2010

2 TABLE OF CONTENTS INTRODUCTION... 3 COMPOSITE PLATFORM ARCHITECTURE... 4 QUERY EXECUTION OPTIMIZATION... 5 PERFORMANCE FACTORS... 6 SAMPLE BENCHMARK CASES... 7 CONCLUSION... 9 Composite Software 2

3 INTRODUCTION This white paper provides a brief overview of the Composite Information Server platform and Query Processing Engine. It explains how various factors affect performance of applications using data services-based integration. The paper also describes a simple benchmarking test with Composite Information Server, which tests various aspects of the platform performance with single and multiple heterogeneous data sources. The performance results are presented in a summary table at the end of the paper. The information presented in this document is intended to help developers and IT personnel plan Composite Information Server deployments. Composite Software 3

4 COMPOSITE PLATFORM ARCHITECTURE Figure One below shows an overview of Composite Information Server platform architecture. The Information Server receives client requests through supported protocols such as : ODBC, JDBC, and ADO.NET for SQL ad hoc queries and HTTP, REST, SOAP, etc for data service functions. Requests are authenticated using either Composite Domain Authentication or one of the standard external authentication providers such as LDAP. The query is then passed to the Query Processing Engine, which executes it against a range of underlying data sources and combines pulled data into a single SQL or XML result set which is returned to the client. Several caching options also exist. Composite Information Server platform provides two rich graphical modeling tool, Composite Studio and Composite Designer, which help data architects and developers construct and test data services, models and queries. Additionally, Composite includes a Web-based Manager and Monitor for administration, management and monitoring. Active Cluster provides additional support for high availability. Composite Discovery discovers data and relationship in support of data modeling. Figure One Composite Information Server Architecture Composite Software 4

5 QUERY EXECUTION OPTIMIZATION At the execution time, the Query Processing Engine translates requests (Web Service procedure calls and ad hoc SQL queries) into a distribution plan. During this stage the engine parses and analyzes queries, optimizing the execution plan to determine the best set of intermediate steps and data source requests that will help compute the final query result. The engine then executes the sequence of queries against relevant data sources and pulls the data in real time to combine it into a result set that is returned to the client application. The engine is designed to minimize any overhead and aims to leverage efficient join methods that on average outperform hand-coded queries written by an average developer. In doing so, the engine employs a number of techniques, including: SQL Pushdown The Query Processing Engine tries to offload as much query processing as possible by pushing down select query operations such as string searches, comparisons, local joins, sorting, aggregating, grouping into the underlying relational data sources. This allows the engine to take advantage of native data source capabilities and limit the amount of intermediate data returned from each data source. Parallel Processing The engine tries to optimize query execution by employing parallel and asynchronous request processing. After building an optimized query plan, the engine executes data service calls asynchronously on separate threads, reducing idle time and data source response latency. Caching The Composite Information Server can be configured to cache results for query, procedure and web service calls on a per view/per query basis. When enabled, the caching engine stores result sets either in a local file-based cache or in a relational data base. The Query Engine will verify whether results of a query it is about to execute are already stored in the caching system and will use the cached data as appropriate. While caching can improve the overall execution time, its impact is most pronounced when used on frequently invoked queries that are executed against rarely changing date or high latency sources such as Web Service based data providers. Distributed joins The Query Processing Engine detects when a query being executed involves data consumed from different data sources and tries to employ distributed query optimization techniques to improve overall performance and minimize amount of data moved over the network. The engine optimizes queries and takes advantage of sort-merge, semi, hash and nested-loop joins depending on the nature of the query and types of data sources. Clustering The Composite Active Cluster option (sold separately) allows organizations to easily scale their Composite Information Server deployments to provide additional, on-demand capacity for handling increased client load. Composite Software 5

6 PERFORMANCE FACTORS A number of factors influence the overall performance of the Composite Information Server platform, including: The server hardware characteristics and overall server load The number and nature of the underlying data sources (i.e. relational or functional) The data source load level and the location of data sources (i.e. local or across the globe) The complexity of the data services and queries used by client applications The volume of data that needs processing by the request The volume of data that needs to be sent across the network The number of concurrent client applications sending requests These factors affect performance of the Composite Information Server platform in different ways and can be mitigated or controlled through different techniques. For example, moving from a 32bit operating system to 64bit native platform would allow Composite Information Server access memory beyond the 2Gb memory pool available under 32bit platform, and thus will reduce the need to use file paging when dealing with large volumes of query data. Or if the query involves a local relational data source and a remote Web Service based source, then the processing speed will be limited to the performance of the slowest data source, in this case the Web Service based application. Composite Software 6

7 SAMPLE BENCHMARK CASES Configuration To conduct the tests, two relational databases were used: MS SQL Server 2000 Oracle 10g Both databases were loaded with identical sets of data, a Customers table containing 75,000 entries and Orders table containing 750,000 entries. The Customers table consists of 8 columns, while the Orders table consists of 9 columns, with average length of 10 bytes across all columns. The following table summarizes the amounts of data retrieved from data sources for each of the queries. Query Type Total Columns Total Rows Total Data Select * from Orders 9 750,000 ~177Mb Select * from Orders join Customers on ID ,000 ~270Mb The two databases were deployed on two identical 2Ghz Dual Pentium Xeon servers with 2Gb of RAM running Microsoft Windows Server Instances of Composite Information Server were deployed on two 3Ghz Pentium Xeon servers with 2Gb or RAM also running Microsoft Windows Server The test cases outlined in the following section cover various performance scenarios from the simplest single data source access that demonstrates overhead of Composite Information Server platform to more general use case covering distributed heterogeneous join across databases. All single server tests were performed using a direct client to server connection, whereby multiserver cluster tests were performed using a hardware load balancer. The load balancer introduces additional latency, the magnitude of which can be approximately estimated from the following table: Direct client to server vs. client to load balancer to server tests Query Total Data Rows fetched Direct LB Select * from MSSQL.Orders 750,000 62sec 80sec Select * from Oracle.Orders 750,000 52sec 57sec Performance Results Case #1: Single thread, single query, single server instance Query Rows fetched CIS v4.5 Direct Select * from MSSQL.Orders 750,000 62sec 57sec Composite Software 7

8 Select * from Oracle.Orders 750,000 52sec 46sec Select * from MSSQL.Customers join MSSQL.Orders 750, sec 109sec Select * from Oracle.Customers join Oracle.Orders 750, sec 91sec Select * from MSSQL.Customers join Oracle.Orders 750, sec N/A Select * from Oracle.Customers join MSSQL.Orders 750, sec N/A Case #2: Multiple threads, single query, single server instance Query Number of Concurrent Requests Select * from MSSQL.Orders 102sec 145sec 226sec Select * from Oracle.Orders 97sec 123sec 170sec Select * from MSSQL.Customers join MSSQL.Orders 147sec 196sec 398sec Select * from Oracle.Customers join Oracle.Orders 207sec 272sec 394sec Select * from MSSQL.Customers join Oracle.Orders 283sec 532sec 462sec Select * from Oracle.Customers join MSSQL.Orders 211sec 334sec 448sec Case #3: Multiple threads, single query, 2-node cluster, load balancer in round robin mode Query Number of Concurrent Requests Select * from MSSQL.Orders 248sec 379sec 546sec Select * from Oracle.Orders 225sec 369ssec 522sec Select * from MSSQL.Customers join MSSQL.Orders 446sec sec Select * from Oracle.Customers join Oracle.Orders 437sec 824sec 1172sec Select * from MSSQL.Customers join Oracle.Orders 478sec 851sec 1211sec Select * from Oracle.Customers join MSSQL.Orders 480sec 809sec 1097sec Composite Software 8

9 CONCLUSION This white paper reviews the architecture, key performance factors and simple benchmark cases associated with the Composite Information Server. The paper presents experimental results from several sample applications that illustrate platform performance. The sample applications involve a mix of relational data sources and are designed to measure various factors that affect platform performance, such as workload intensity (number of clients calling the platform), complexity of the executed queries, and amount of processed data. For the sample tests conducted, the key findings can be summarized as follows: Composite Information Server platform scales nearly linearly with the increase in the number of concurrent client applications. The overall platform capacity scales nearly linearly with the addition of new server nodes under Composite Active Cluster product option. Composite Software 9

10 ABOUT COMPOSITE SOFTWARE Composite Software, Inc. is the only company that focuses solely on data virtualization. Global organizations faced with disparate, complex data environments, including ten of the top 20 banks, six of the top ten pharmaceutical companies, four of the top five energy firms, major media and technology organizations as well as government agencies, have chosen Composite s proven data virtualization platform to fulfill critical information needs, faster with fewer resources. Scaling from project to enterprise, Composite s middleware enables data federation, data warehouse extension, enterprise data sharing, real-time and cloud computing data integration. Founded in 2002, Composite Software is a privately held, venture-funded corporation based in Silicon Valley. For more information, please visit

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