Teradata Workload Analyzer enables DBAs
to save time and improve system management.
DBAs often spend a lot of time setting up workloads, which can involve a user-intensive trial-and-error process. But there’s an easier way: Teradata Workload Analyzer. This solution helps DBAs fully understand normal workloads, peak surges and abnormal workloads in multiple time periods so they can design the correct workload definitions. This leads to higher throughput and more satisfied business users, and it ensures that users only add more server capacity when they need it. In short, Teradata Workload Analyzer:
- Improves workload accuracy and quality
- Enables improved response time consistency
- Increases predictability of workloads
- Enhances system management capabilities
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Drill for Answers
Teradata Workload Analyzer provides the capabilities to continuously drill down on a workload with various “Who,” “What,” “Where” and “Exceptions” parameters. It can visualize clusters of requests within the workload, each with distinct service time patterns and other characteristics. For example, an initial workload defined on just one account may include distinct users who execute tactical requests requiring higher priority, while the remaining users do not. Workload Analyzer can be used to identify these clusters by providing reports, correlation and distribution graphs on different dimensions.
Workload Analyzer guides DBAs toward appropriate classification criteria. (See figure.) At any point, a visual drill down workload cluster analysis can be performed where correlation and distribution parameters can be chosen and then analyzed for associated usage patterns. Drilling deeper within a chosen cluster can determine which parameters most effectively identify the request group to isolate. This trial-and-error approach streamlines the process by providing users with distinct count and distribution range insight and can eliminate ineffective analysis on a single user or a tight distribution.
Set New Parameters
Drilling down is a recursive process for deeper analysis on correlation and distribution parameters. If DBAs are not satisfied with the current analysis parameters, they can select more appropriate ones, as guided by reviewing distinct values and ranges for those other parameters. For example, with respect to the distinct value counts, one particular workload could display:
- User Name (24)
- Applications (1)
- Account Name (1)
- Client Addresses (2)
- Queryband (3)
- Function (3)
- Urgency (1)
- AggLevel (8)
- Estimated Processing Time (0-1000 secs)
- AMP Count (0-1)
In this instance, DBAs find that only one distinct application or account exists and all queries are run at the same urgency. With this information, trying to identify a correlation against different Application, Account or Urgency values would be a wasted effort.
However, the opportunity for correlation does exist with Users, Function and AggLevel, so those correlation options can be pursued. Similarly, for the distribution parameter ranges, an estimated processing time range from 0 to 1,000 seconds suggests that a large variation of requests is included in this workload. The opportunity for identifying clusters is higher than if the estimated processing time range were simply 0 to 1 second.
DBAs can add clusters to the current workload for deeper analysis, or clusters can be split into a new workload. By repeating this process, a good set of workloads can be defined or all unassigned clusters can be assigned to workloads.
DBA Time Saver
Workload Analyzer will reduce DBAs’ efforts to set up Teradata Active System Management workloads and increase their quality with fewer correlation attempts. Improved response time consistency and predictability of workloads will also be realized as well as improvements in system management capabilities.
Workload Analyzer provides a better solution than other methods by establishing and refining workloads while cutting the time and effort spent on unsuccessful attempts.