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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

Performing a seek rate test

In this recipe, we will be discussing how to benchmark the disk seek rate speed using open source tools.

Getting ready

A file can be read from the disk in two ways: sequentially and at random. Reading a file in sequential order requires less effort than reading a file in random order. In PostgreSQL and other database systems, a file needs to be scanned in random order as per the index scans. During the index scans, as per the index lookups, the relation file needs to fetch the data randomly, by moving its file pointer backward and forward, which needs an additional mechanical overhead in spinning the disk in the normal HDD. In SSD, this overhead is lower as it uses the flash memory. This is one of the reasons why we define that random_page_cost as always higher than seq_page_cost in postgresql.conf. In the previous bonnie++ example, we have random seeks, which were measured per second as 201.0 and used 7% of the CPU.

How to do it...

We can use the same bonnie++ utility command to measure the random seek rate, or we can also use another disk latency benchmarking tool called ioping:

# ioping -R /dev/sda3 -s 8k -w 30
--- /dev/sda3 (block device 65.8 GiB) ioping statistics ---
2.23 k requests completed in 29.2 s, 17.5 MiB read, 76 iops, 613.4 KiB/s
generated 2.24 k requests in 30.0 s, 17.5 MiB, 74 iops, 596.2 KiB/s
min/avg/max/mdev = 170.6 us / 13.0 ms / 73.5 ms / 5.76 ms

How it works...

Ioping is a disk latency benchmarking tool that produces an output similar to the network utility command ping. This tool also provides no cache or with cache disk benchmarking as bonnie++ and also includes synchronous and asynchronous I/O latency benchmarking. You can install this tool using yum or apt-get in the respective Linux distributions. The preceding results were generated based on PostgreSQL's default block size of 8 KB, which ran for 30 seconds. Ioping provides another useful feature called ping-pong mode for read/write. This mode displays the instant read/write speed of the disk as shown in the following screenshot:

$ ioping -G /tmp/ -D -s 8k
8 KiB >>> /tmp/ (xfs /dev/sda3): request=1 time=1.50 ms (warmup)
8 KiB <<< /tmp/ (xfs /dev/sda3): request=2 time=9.73 ms
8 KiB >>> /tmp/ (xfs /dev/sda3): request=3 time=2.00 ms
8 KiB <<< /tmp/ (xfs /dev/sda3): request=4 time=1.02 ms
8 KiB >>> /tmp/ (xfs /dev/sda3): request=5 time=1.95 ms

In the preceding example, we ran ioping in ping-pong mode (-G) and used the direct I/O (-D) with a block size of 8 KB. We can also run the same ping-pong mode in pure cache mode using the (-C) option.

You have been reading a chapter from
PostgreSQL High Performance Cookbook
Published in: Mar 2017
Publisher: Packt
ISBN-13: 9781785284335
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