Data Replication
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InfoSphere Data Replication for DB2 for z/OS and WebSphere Message Queue for z/OS: Performance Lessons
Understanding the impact of workload and database characteristics on the performance of both DB2®, MQ, and the replication process is useful for achieving optimal performance.Although existing applications cannot generally be modified, this knowledge is essential for properly tuning MQ and Q Replication and for developing best practices for future application development and database design. It also helps with estimating performance objectives that take these considerations into account. Performance metrics, such as rows per second, are useful but imperfect. How large is a row? It is intuitively, and correctly, obvious that replicating small DB2 rows, such as 100 bytes long, takes fewer resources and is more efficient than replicating DB2 rows that are tens of thousand bytes long. Larger rows create more work in each component of the replication process. The more bytes there are to read from the DB2 log, makes more bytes to transmit over the network and to update in DB2 at the target. Now, how complex is the table definition? Does DB2 have to maintain several unique indexes each time a row is changed in that table? The same argument applies to transaction size: committing each row change to DB2 as opposed to committing, say, every 500 rows also means more work in each component along the replication process. This RedpaperTM reports results and lessons learned from performance testing at the IBM® laboratories, and it provides configuration and tuning recommendations for DB2, Q Replication, and MQ. The application workload and database characteristics studied include transaction size, table schema complexity, and DB2 data type.
Smarter Business: Dynamic Information with IBM InfoSphere Data Replication CDC
To make better informed business decisions, better serve clients, and increase operational efficiencies, you must be aware of changes to key data as they occur. In addition, you must enable the immediate delivery of this information to the people and processes that need to act upon it. This ability to sense and respond to data changes is fundamental to dynamic warehousing, master data management, and many other key initiatives. A major challenge in providing this type of environment is determining how to tie all the independent systems together and process the immense data flow requirements. IBM® InfoSphere® Change Data Capture (InfoSphere CDC) can respond to that challenge, providing programming-free data integration, and eliminating redundant data transfer, to minimize the impact on production systems. In this IBM Redbooks® publication, we show you examples of how InfoSphere CDC can be used to implement integrated systems, to keep those systems updated immediately as changes occur, and to use your existing infrastructure and scale up as your workload grows. InfoSphere CDC can also enhance your investment in other software, such as IBM DataStage® and IBM QualityStage®, IBM InfoSphere Warehouse, and IBM InfoSphere Master Data Management Server, enabling real-time and event-driven processes. Enable the integration of your critical data and make it immediately available as your business needs it.
Database Performance Tuning and Optimization
Author: Sitansu S. Mittra
language: en
Publisher: Springer Science & Business Media
Release Date: 2006-04-18
Scope The book provides comprehensive coverage of database performance tuning and opti- zation using Oracle 8i as the RDBMS. The chapters contain both theoretical discussions dealing with principles and methodology as well as actual SQL scripts to implement the methodology. The book combines theory with practice so as to make it useful for DBAs and developers irrespective of whether they use Oracle 8i. Readers who do not use Oracle 8i can implement the principles via scripts of their own written for the particular RDBMS they use. I have tested each script for accuracy and have included the sample outputs generated from them. An operational database has three levels: conceptual, internal, and external. The c- ceptual level results from data modeling and logical database design. When it is imp- mented via an RDBMS such as Oracle, it is mapped onto the internal level. Database - jects of the conceptual level are associated with their physical counterparts in the internal level. An external level results from a query against the database and, as such, provides a window to the database. There are many external levels for a single conceptual level.