How does Teradata handle Skew in Joins? The skewed workload is one of the two most important problems we have to solve in Performance Tuning. This is especially important for join steps, as large amounts of data may be copied between the AMPs. The optimizer has techniques to execute joins separately for skewed primary index

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Teradata can choose from various join methods. The most common ones are Merge Join, Hash Join, Product Join, or for PPI tables, the Merge Join variants Rowkey Based Merge Join, or Sliding Window Merge Join. Less often, however, we see the Teradata nested join, since specific prerequisites must be met before it can be used.

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Sometimes natural skew can become a huge problem, causing bad join performance. The Partial Duplication & Partial Redistribution (PDPR)  feature on Teradata 14 (and above) helps to reduce this issue, but will not always be able to detect all possible applications (for example, if statistics are not revealing the skewed values). If you are stuck

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Functions applied to join columns are always the result of a bad physical data model. It is often the case that not even the first normal form was adhered to in the data model. But apart from causing the usual abnormalities and problems, in Teradata, it also hurts query performance. Here is a summary: In

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