The Optimizer typically excels in utilizing statistics, yet examining the execution plan and cardinality estimations can sometimes be beneficial. Since Teradata 14.10, I have habitually included the SHOW STATISTICS statement in my considerations. The resulting metrics can aid in identifying statistical issues. Teradata Statistics – Avoid the Heuristics This case study demonstrates how the Optimizer …
When do you need a fire brigade most? When your house is on fire! When is the best time to establish the fire brigade? No, not when your house is on fire, but before the city the house is located in is founded. Finally, when should there be a fire safety inspection? Before and while you …
As a Teradata SQL specialist, skew is a common challenge. In fact, approximately 90% of SQL performance difficulties stem from skew, based on my experience. However, this article focuses on the potential for minor modifications to yield significant improvements. This article highlights the potential consequences of selecting inappropriate character sets on SQL performance, a commonly …
Learn how specialization in Teradata development can lead to the development of huge SQL statements and how to reduce the risk of bad performance.
Welcome to part two of our Teradata performance optimization series. In this article, we will delve into how a good data model can prevent future performance issues in data warehousing projects. We will also discuss the drawbacks of taking over operational systems’ source definitions directly and the importance of surrogate keys. Stay tuned for part three on fixing a broken data model.
Teradata 16.10 introduces Sparse Maps, a feature to place small table rows on one AMP. Learn more about how it can optimize your system here.
Teradata data access is categorized based on the number of AMPs involved and the technique employed to search rows within a data block. In our article about SQL Tuning, we mentioned that one of the main goals in performance optimization is to distribute the workload evenly across all AMPs. It is best to avoid confusion …
Teradata 16 introduces a new option for handling sets of rows combined with “UNION ALL” in views and derived tables, reducing resource usage. The optimizer can apply aggregation steps and join operations to each branch of the “UNION ALL” separately, resulting in smaller spool sizes and improved performance.
Learn how query rewriting can improve performance in row partition elimination. Follow the test setup to optimize the retrieval of rows from a table.
Learn how to optimize Teradata performance with these general tuning ideas. From changing one parameter at a time to testing on the target system.
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