Скачать или смотреть Understanding Data Distribution in Teradata: Optimizing Performance with the Right Primary Index

Discover how to improve your Teradata views performance by understanding data distribution. Learn the impact of choosing the right primary index on your tables and get tips for effective query optimization.
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Understanding Data Distribution in Teradata

When working with Teradata, one of the frequent challenges that database administrators encounter is ensuring that the performance of their views is optimized. A key aspect contributing to performance involves understanding data distribution, especially when choosing the right primary index for tables.

In this post, we will explore how to analyze data distribution in Teradata and examine how it impacts your query performance.

The Importance of Data Distribution

Teradata operates on a Distributed Database architecture, utilizing massive parallelism. This means that data is spread across multiple hardware nodes, with each node containing approximately 20 to 50 Access Module Processors (AMPs). Each AMP functions as an instance of a database server.

Why Does Data Distribution Matter?

Parallel Processing Efficiency: The efficiency of parallel processing relies on equal distribution of data among AMPs. A well-distributed table can lead to faster scans and improved performance.

Join and Filtering Performance: The primary index (PI) is not only crucial for data distribution; it is also utilized for filtering data in WHERE clauses and in joins.

Criteria for Choosing the Right Primary Index (PI)

When selecting a primary index, the following criteria should be considered:

Access: Prioritize columns used for equi-joins and equality-based WHERE conditions.

Distribution: Aim for a balanced distribution of rows across AMPs. Ideally, there shouldn't be too many rows per value (generally, a few hundred or thousand is acceptable). A common rule is to keep skewness to not more than 50%.

Volatility: Choose stable PI columns; they shouldn't be updated frequently.

Evaluating Your Options

When you have multiple potential columns for a primary index, it’s valuable to compare them. For instance, if choosing one index (Product ID) yields distribution across 190 AMPs with each having up to 83 rows, while choosing two indices (Product ID, Date) results in 476 AMPs with each having up to 24 rows, how do you decide which is better?

Analysis of an Example

Single Index (Product ID):

190 AMPs

Up to 83 rows per AMP

Dual Index (Product ID, Date):

476 AMPs

Up to 24 rows per AMP

In this example, the two-index distribution results in a finer partitioning of data across more AMPs. However, the trade-off is the potential impact on how effectively the join operations occur, as there may be fewer stable references for the joins.

Best Practices for Small Tables

Given that your table is relatively small (only 13,000 rows), you may approach the PI selection process differently compared to larger tables. Here are some tips:

Use Primary or Foreign Key Columns: For smaller tables, basing the PI on PK or FK columns typically yields good performance.

Avoid Adding Date/Timestamp: While adding a date or timestamp can be tempting for partitioning larger tables, it might compromise the column's effectiveness for joins.

Consider Base Tables: If your table is often joined with larger ones, check if there’s compatibility with their primary index for optimal performance.

Final Thoughts

Choosing the right primary index in Teradata is a nuanced process that requires careful consideration of how data will be accessed and distributed. By understanding the criteria for indexing, prioritizing good distribution, and acknowledging the unique aspects of your data and schemas, you can significantly improve your Teradata views' performance.

Stay engaged and open to adjusting your indexing strategy as data volume and access patterns evolve—this will ensure the longevity and efficiency of your database architecture.