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I am trying to create a table with both row and column partitioning to see if it will help speed up my queries.
The original table has 500 million rows of data.
When I try to create a partitioned table like this, it runs out of CPU time and does not complete:
 CREATE TABLE rtl.SpeedTest4 AS 




We have created a row partitioned table with any nulls, this table have done a compress null and it have decreased 4GB of 80GB but it hasn´t any null.
But if i don´t partitioned the table, it not compress any. i know if i don´t have null, compress null does´t decreased the size in a table.

Hi All


May be I have posted this question before in this forum, but I am reposting it because that time I did not get any reply. Now I have been asked in a technical meeting to suggest a solution. But I am still not sure how to decide this, as my understanding is still in grey area.


Let's start by understanding what a primary index does.  A primary index (PI) is used to distribute the rows of a table to the AMPs and on each AMP to order the rows by hash value within the combined partitions defined by a PARTITION BY clause (if this clause is not specified, there is just one partition, that is, the entire tabl

Teradata 14.0 introduces Teradata Columnar – a new option to organize the data of a user-defined table or join index on disk.

Teradata Columnar offers the ability to partition a table or join index by column.  It introduces column-storage as an alternative choice to row-storage for a column partition and autocompression.  Column partitioning can be used alone in a single-level partitioning definition or with row partitioning in a multilevel partitioning definition.