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PostgreSQL High Performance Cookbook
PostgreSQL High Performance Cookbook

PostgreSQL High Performance Cookbook: Mastering query optimization, database monitoring, and performance-tuning for PostgreSQL

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Profile Icon Chauhan Profile Icon Dinesh Kumar
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PostgreSQL High Performance Cookbook

Chapter 1. Database Benchmarking

In this chapter, we will cover the following recipes:

  • CPU benchmarking
  • Memory benchmarking
  • Disk benchmarking
  • Performing a seek rate test
  • Working with the fysnc commit rate
  • Checking IOPS
  • Storage sizing
  • Discussing RAID levels
  • Configuring pgbench
  • Running read/write pgbench tests

Introduction

PostgreSQL is renowned in the database management system world. With every PostgreSQL release, it's gaining in popularity due to its advanced features and performance. This cookbook is especially designed to give more information about most of the major features in PostgreSQL, and also how to achieve good performance with the help of proper hardware/software benchmarking tools. This cookbook is also designed to discuss, all the high availability options we can achieve with PostgreSQL, and also give some details about how to migrate your database from other commercial databases.

To benchmark the database server, we need to benchmark several hardware/software components. In this chapter, we will discuss major tools that are especially designed to benchmark a certain component.

I would like to say thanks to the Phoronix Test Suite team, for allowing me to discuss their benchmarking tool. Phoronix is an open source benchmarking framework, which, by default, provides test cases for several hardware/software components, thanks to its extensible architecture, where we can write our own test suite with the set of benchmarking test cases. Phoronix also supports to upload your benchmarking results to http://openbenchmarking.org/, which is a public/private benchmark results repository, where we can compare our your benchmarking results with others.

Note

Go to the following URL for installation instructions for Phoronix Test Suite: http://www.phoronix-test-suite.com/?k=downloads.

CPU benchmarking

In this recipe, let's discuss how to benchmark the CPU speed using various open source benchmarking tools.

Getting ready

One of the ways to benchmark CPU power is by measuring the wall clock time for the submitted task. The task can be like calculating the factorial of the given number, or calculating the nth Fibonacci number, or some other CPU-intensive task.

How to do it...

Let us discuss about how to configure phoronix and sysbench tools to benchmark the CPU:

Phoronix

Phoronix supports a set of CPU tests in a test suite called CPU. This test suite covers multiple CPU-intensive tasks, which are mentioned at the following URL: https://openbenchmarking.org/suite/pts/CPU.

If you want to run this CPU test suite, then you need to execute the Phoronix Test Suite benchmark CPU command as a root user. We can also run a specific test by mentioning its test name. For example, let's run a sample CPU benchmarking test as follows:

$ phoronix-test-suite benchmark pts/himeno 
Phoronix Test Suite v6.8.0 
    To Install: pts/himeno-1.2.0 
    ... 
    1 Test To Install 
    pts/himeno-1.2.0: 
        Test Installation 1 of 1 
        1 File Needed 
        Downloading: himenobmtxpa.tar.bz2 
 Started Run 2 @ 05:53:40 
 Started Run 3 @ 05:54:35  [Std. Dev: 1.66%] 
Test Results: 
 1503.636072
 1512.166077
 1550.985494
Average: 1522.26 MFLOPS

Phoronix also provides a way to observe the detailed test results via HTML file. Also, it supports the offline generation of PDF, JSON, CSV, and text format outputs. To open these test results in the browser, we need to execute the following command:

$ phoronix-test-suite show-result <Test Name>

The following is a sample screenshot of the results of the preceding command:

Phoronix

sysbench

The sysbench tool provides a CPU task, which calculates the number of prime numbers within a given range and provides the CPU-elapsed time. Let's execute the sysbench command as shown in the following screenshot, to retrieve the CPU measurements:

[root@localhost ~]# sysbench --test=CPU --CPU-max-prime=10000 --num-threads=4 run
Doing CPU performance benchmark
Threads started!
Done.
Maximum prime number checked in CPU test: 10000
Test execution summary:


        total time:                          3.2531s
      

    total number of events:              10000
    total time taken by event execution: 13.0040
    per-request statistics:
         min:                                  1.10ms
         avg:                                  1.30ms
         max:                                  8.60ms
         approx.  95 percentile:               1.43ms
Threads fairness:


        events (avg/stddev):           2500.0000/8.46
      

    execution time (avg/stddev):   3.2510/0.00

How it works...

The preceding results are collected from CentOS 7, which was running virtually on a Windows 10 machine. The virtual machine has four processing units (CPU cores) of Intel Core i7-4510U of CPU family six.

Phoronix

The URL http://openbenchmarking.org/ provides a detailed description of each test detail along with its implementation, and would encourage you to read more information about the himeno test case.

sysbench

From the previous results, the system takes 3.2531 seconds to compute the 10,000 prime numbers, with the help of four background threads.

Memory benchmarking

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

Getting ready

As with the CPU test suite, phoronix supports one another memory test suite, which covers RAM benchmarking. Otherwise, we can also use a dedicated memtest86 benchmarking tool, which performs memory benchmarking during a server bootup phase. Another neat trick would be to create a tmpfs mount point in the RAM and then create a tablespace on it in PostgreSQL. Once we create the tablespace, we can then create in-memory tables, where we can benchmark the table read/write operations. We can also use the dd command to measure the memory read/write operations.

How to do it...

Let us discuss how to install phoronix and how to configure the tmpfs mount point in Linux:

Phoronix

Let's execute the following phoronix command, which will install the memory test suit and perform memory benchmarking. Once the benchmarking is completed, as aforementioned, observe the HTML report:

$ phoronix-test-suite benchmark pts/memory
Phoronix Test Suite v6.8.0
  Installed: pts/ramspeed-1.4.0
To Install: pts/stream-1.3.1
To Install: pts/cachebench-1.0.0

tmpfs

In Linux, tmpfs is a temporary filesystem, which uses the RAM rather than the disk storage. Anything we store in tmpfs will be cleared once we restart the system:

Note

Refer to the URL for more information about tmpfs: https://en.wikipedia.org/wiki/Tmpfs and https://www.jamescoyle.net/knowledge/1659-what-is-tmpfs.

Let's create a new mount point based on tmpfs using the following command:

# mkdir -p /memmount
# mount -t tmpfs -o size=1g tmpfs /memmount
# df -kh -t tmpfs
Filesystem      Size  Used Avail Use% Mounted on
tmpfs           1.9G   96K  1.9G   1% /dev/shm
tmpfs           1.9G  8.9M  1.9G   1% /run
tmpfs           1.9G     0  1.9G   0% /sys/fs/cgroup
tmpfs           1.0G     0  1.0G   0% /memmount

Let's create a new folder in memmount and assign it to the tablespace.

# mkdir -p /memmount/memtabspace
# chown -R postgres:postgres /memmount/memtabspace/
postgres=# CREATE TABLESPACE memtbs LOCATION '/memmount/memtabspace';
CREATE TABLESPACE
postgres=# CREATE TABLE memtable(t INT) TABLESPACE memtbs;
CREATE TABLE

Write test

postgres=# INSERT INTO memtable VALUES(generate_series(1, 1000000));

INSERT 0 1000000
Time: 1372.763 ms


postgres=# SELECT 
pg_size_pretty(pg_relation_size('memtable'::regclass));
 
 pg_size_pretty
----------------
 35 MB
(1 row)

From the preceding results, to insert 1 million records it took approximately 1 second with a writing speed of 35 MB per second.

Read test

postgres=# SELECT COUNT(*) FROM memtable;
count
---------
1000000
(1 row)
Time: 87.333 ms

From the preceding results, to read the 1 million records it took approximately 90 milliseconds with a reading speed of 385 MB per second, which is pretty fast for the local system configuration. The preceding read test was performed after clearing the system cache and by restarting the PostgreQSL instance, which avoids the system buffers.

How it works...

In the preceding tmpfs example, we created an in-memory table, and all the system calls PostgreQSL tries to perform to read/write the data will be directly affecting the memory rather than the disk, which gives a major performance boost. Also, we need to consider to drop these in-memory tablespace, tables after testing, since these objects will physically vanish after system reboot.

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Key benefits

  • Perform essential database tasks such as benchmarking the database and optimizing the server’s memory usage
  • Learn ways to improve query performance and optimize the PostgreSQL server
  • Explore a wide range of high availability and replication mechanisms to build robust, highly available, scalable, and fault-tolerant PostgreSQL databases

Description

PostgreSQL is one of the most powerful and easy to use database management systems. It has strong support from the community and is being actively developed with a new release every year. PostgreSQL supports the most advanced features included in SQL standards. It also provides NoSQL capabilities and very rich data types and extensions. All of this makes PostgreSQL a very attractive solution in software systems. If you run a database, you want it to perform well and you want to be able to secure it. As the world’s most advanced open source database, PostgreSQL has unique built-in ways to achieve these goals. This book will show you a multitude of ways to enhance your database’s performance and give you insights into measuring and optimizing a PostgreSQL database to achieve better performance. This book is your one-stop guide to elevate your PostgreSQL knowledge to the next level. First, you’ll get familiarized with essential developer/administrator concepts such as load balancing, connection pooling, and distributing connections to multiple nodes. Next, you will explore memory optimization techniques before exploring the security controls offered by PostgreSQL. Then, you will move on to the essential database/server monitoring and replication strategies with PostgreSQL. Finally, you will learn about query processing algorithms.

Who is this book for?

If you are a developer or administrator with limited PostgreSQL knowledge and want to develop your skills with this great open source database, then this book is ideal for you. Learning how to enhance the database performance is always an exciting topic to everyone, and this book will show you enough ways to enhance the database performance.

What you will learn

  • Build replication strategies for homogeneous and heterogeneous databases
  • Test and build a powerful machine with multiple bench marking techniques
  • Get to know a few SQL injection techniques
  • Find out how to manage the replication using multiple tools
  • Benchmark the database server using multiple strategies
  • Work with the query processing algorithms and their internal behaviors
  • Build a proper plan to upgrade or migrate to PostgreSQL from other databases
  • See the essential database load balancing techniques and the various partitioning approaches PostgreSQL provides
  • Learn memory optimization techniques and database server configurations
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Table of Contents

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