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PostgreSQL High Performance Cookbook

You're reading from   PostgreSQL High Performance Cookbook Mastering query optimization, database monitoring, and performance-tuning for PostgreSQL

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781785284335
Length 360 pages
Edition 1st Edition
Languages
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Authors (2):
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Chitij Chauhan Chitij Chauhan
Author Profile Icon Chitij Chauhan
Chitij Chauhan
Dinesh Kumar Dinesh Kumar
Author Profile Icon Dinesh Kumar
Dinesh Kumar
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Toc

Table of Contents (13) Chapters Close

Preface 1. Database Benchmarking FREE CHAPTER 2. Server Configuration and Control 3. Device Optimization 4. Monitoring Server Performance 5. Connection Pooling and Database Partitioning 6. High Availability and Replication 7. Working with Third-Party Replication Management Utilities 8. Database Monitoring and Performance 9. Vacuum Internals 10. Data Migration from Other Databases to PostgreSQL and Upgrading the PostgreSQL Cluster 11. Query Optimization 12. Database Indexing

Partial indexes

In this recipe, we will be discussing how to create an index for the required data sample set.

Getting ready

Using partial indexes, we can reduce the size of an index by adding a predicate in the index definition. That is, only the entries that match the predicate will only be indexed instead of all of them. This partial index will be utilized when the index predicate satisfies the submitted SQL predicate.

How to do it...

For example, let's say that our application does a frequent query on bmsql_item as to list all the items that have a price between $5 to $10, then it is a candidate predicate to create a partial index as follows:

benchmarksql=# CREATE INDEX CONCURRENTLY part_idx ON bmsql_item(i_price) WHERE i_price BETWEEN 5 AND 10; 
CREATE INDEX

Let's query the table so as to return the items that have a cost between 5 and 10:

benchmarksql=# EXPLAIN SELECT * FROM bmsql_item WHERE i_price BETWEEN 5 AND 10; 
                                   QUERY PLAN             ...
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