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Teradata Cookbook

You're reading from   Teradata Cookbook Over 85 recipes to implement efficient data warehousing solutions

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781787280786
Length 454 pages
Edition 1st Edition
Languages
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Authors (3):
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Abhinav Khandelwal Abhinav Khandelwal
Author Profile Icon Abhinav Khandelwal
Abhinav Khandelwal
Viswanath Kasi Viswanath Kasi
Author Profile Icon Viswanath Kasi
Viswanath Kasi
Rajsekhar Bhamidipati Rajsekhar Bhamidipati
Author Profile Icon Rajsekhar Bhamidipati
Rajsekhar Bhamidipati
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Toc

Table of Contents (14) Chapters Close

Preface 1. Installation 2. SQLs FREE CHAPTER 3. Advanced SQL with Backup and Restore 4. All about Indexes 5. Mixing Strategies – Joining of Tables 6. Building Loading Utility – Replication and Loading 7. Monitoring the better way 8. Collect Statistics the Better Way 9. Application and OPS DBA Insight 10. DBA Insight 11. Performance Tuning 12. Troubleshooting 13. Other Books You May Enjoy

Creating a partitioned primary index to improve performance

A PPI is a type of index that enables users to set up databases that provide performance benefits from a data locality, while retaining the benefits of scalability inherent in the hash architecture of the Teradata database. This is achieved by hashing rows to different virtual AMPs, as is done with a normal PI, but also by creating local partitions within each virtual AMP.

Normal PI access remains unchanged, but in the case of a range query for example, each virtual AMP is able to immediately focus its search on specific partitions within its workspace.

If the PPI column is specified in the join condition, the system knows the range of values in a query and it scans only the portions of the table that correspond to those dates. Table can be or have:

Non-partitioned primary index: A traditional non-partitioned...

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