We have just published a new best practices paper for IBM Smart Analytics System and IBM PureData System for Operational Analytics customers: Performance monitoring in a data wartehouse.
This best practices paper covers real-time monitoring of the IBM Smart Analytics System and IBM PureData System for Operational Analytics. You can apply most of the content to other types of clusters of servers running a data warehouse system with DB2 software and database partitioning under AIX and Linux operating systems. The focus of this paper is finding the reasons for performance problems. These can be bottlenecks that are in the operating system, are in the DB2 database software, or are related to a single query. The focus is on data warehouse systems with long-running queries rather than transactional systems with mostly short queries.
A main goal of this paper is to provide a set of key performance indicators (KPIs) or metrics for the operating system and DB2 software, along with a methodology for analyzing performance problems in a distributed DB2 environment. This paper describes scenarios to help you gather the right information depending on the symptoms of the performance problem.
This paper first provides an overview of the approach and what to consider in general when monitoring the performance of a data warehouse. It then describes the most important operating system and DB2 metrics for multiserver data warehouse systems. The last section describes in detail several performance problem scenarios that are related to data warehouse or BI workloads and explains how to use the metrics for analyzing the problems.
Most of the information about KPIs that are described in the paper has sample commands that extract actual values. However, these examples are not intended to provide comprehensive tooling. You can use this best practices paper as a guideline for selecting the metrics to focus on when using monitoring tools such as IBM InfoSphere® Optim™ Performance Manager.