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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

Working with hash and merge join

In this recipe, we will be discussing merge and hash join mechanisms in PostgreSQL.

Getting ready

Merge join is another joining approach to perform the join operation between two datasets. PostgreSQL optimizer will generally choose this joining method for an equi joins or for union operations. To perform this join on two datasets, it is required to sort the two join key columns first and then it will run the join condition. The optimizer prefers this node type, while joining huge tables.

Hash join is another joining approach. In general, this approach is pretty fast if and only if the server has enough memory resources. To perform this join, PostgreSQL does not need any sorted results. Rather, it will take one table data to build a hash index, which it will be comparing with the other table tuples. PostgreSQL optimizer will generally choose this joining method for an equi joins or for union operations. The optimizer prefers this node type, while joining the...

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