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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

Grouping

In this recipe, we will be discussing the optimizer node type, which will be chosen during the group by operation.

Getting ready

As we discussed group or aggregate operations in the previous recipe, grouping operations will have performed based on the group key list. The PostgreSQL optimizer chooses hash aggregate, when it finds enough memory and if not, group aggregate will be the option. Unlike hash aggregate, the group aggregate operation needs data to be sorted. If the group columns have a sorted index already, then group aggregate will choose over the hash aggregate as to reduce the memory usage.

How to do it…

  1. To demonstrate group aggregate, let's run the query in the benchmarksql database to get the count of customers, grouped by their city:
     benchmarksql=# EXPLAIN SELECT COUNT(*), c_city FROM
               bmsql_customer GROUP BY c_city;
                                                QUERY PLAN                                             
    -----------------------------...
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