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.
Go to the following URL for installation instructions for Phoronix Test Suite: http://www.phoronix-test-suite.com/?k=downloads.
In this recipe, let's discuss how to benchmark the CPU speed using various open source benchmarking tools.
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.
Let us discuss about how to configure phoronix and sysbench tools to benchmark the CPU:
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:
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
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.
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.
In this recipe, we will be discussing how to benchmark the memory speed using open source tools.
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.
Let us discuss how to install phoronix and how to configure the
tmpfs mount point in Linux:
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 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:
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
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.
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.
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.
In this recipe, we will be discussing how to benchmark the disk speed using open source tools.
The well-known command to perform disk I/O benchmarking is dd. We all use the dd command to measure read/write operations by specifying the required block size, and we also measure the direct I/O by skipping the system write buffers. Similarly, phoronix supports a complete test suite for the disk as CPU and memory that perform different storage-related tests. Another famous disk benchmarking tool is bonnie++, which provides more flexibility in measuring the disk I/O.
Let us discuss how to run the disk benchmarking using phoronix and using bonnie++ testing tools:
To run the complete disk test suite on the system, run the following command:
$ phoronix-test-suite benchmark pts/disk
Phoronix also supports a quick I/O test case, where you can perform an instant disk performance test using the following command test, which is interactive and collects the input, and then runs the test cases:
$ phoronix-test-suite benchmark pts/iozone Phoronix Test Suite v6.8.0 Installed: pts/iozone-1.8.0 Disk Test Configuration 1: 4Kb 2: 64Kb 3: 1MB 4: Test All Options Record Size: 1 1: 512MB 2: 2GB 3: 4GB 4: 8GB 5: Test All Options File Size: 1 1: Write Performance 2: Read Performance 3: Test All Options Disk Test: 3
bonnie++ is a filesystem and disk-level benchmarking tool and can perform the same test multiple times. You can install this tool using either
apt-get install or installing it via the source code. Let's run the bulk I/O test case using the following arguments, where it tries to create 8 GB files:
$ /usr/local/sbin/bonnie++ -D -d /tmp/ -s 8G -b Writing with putc()...done Writing intelligently...done ... localhost.localdomain,8G,68996,106,14151,53,46772,15,95343,93,123633,16,201.0,7,16,795,58,+++++,+++,733,46,757,57,+++++,+++,592,38
Let us discuss how the bonnie++ performs the benchmarking, and what are all the tools bonnie++ offers to understand the benchmarking results:
From the preceding test case, we provided the results the bonnie++ as to use only direct I/O using the
-D option. Also, we asked to create 8 GB random files in the
/tmp/ location to measure the disk speed. As the final output from bonnie++, we will get CSV values, which we need to feed to the
bon_csv2html command, which provides some detailed information about the test results, as shown in the following screenshot:
$ echo "localhost.localdomain,8G,68996,106,14151,53,46772,15,95343,93,123633,16,201.0,7,16,795,58,+++++,+++,733,46,757,57,+++++,+++,592,38"|bon_csv2html > ~/Desktop/bonresults.html
bonnie++ performs three different tests for disk benchmarking. They are read, write and then seek speed. We will be discussing the seek rate in the further topics. The bonnie++ do always recommend to have high number in
/sec section in the preceding table, and lower % CPU values for better disk performance. Also,
++++ shows that the test was not performed accurately by bonnie++, as the test was incomplete with the provided arguments. To get the complete results, we need to rerun the same test multiple times using the
-n option, where bonnie will get enough time/resources to complete the job.
In this recipe, we will be discussing how to benchmark the disk seek rate speed using open source tools.
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
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.
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
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 (
In this recipe, we will be discussing how to benchmark the fsync speed using open source tools.
Fsync is a system call that flushes the data from system buffers into physical files. In PostgreSQL, whenever a CHECKPOINT operation occurs, it internally initiates the fsync, to flush all the modified system buffers into the respective files. The fsync benchmarking defines the transfer ratio of data from memory to the disk.
fsync benchmarking, we can use a dedicated benchmark test called
fs-mark from Phoronix. This
fs-mark test was built based on a filesystem benchmarking tool called
fio, which supports several
fsync test cases. We can run this
fs-mark test case using the following command:
$ phoronix-test-suite benchmark fs-mark FS-Mark 3.3: pts/fs-mark-1.0.1 Disk Test Configuration 1: 1000 Files, 1MB Size 2: 1000 Files, 1MB Size, No Sync/FSync 3: 5000 Files, 1MB Size, 4 Threads 4: 4000 Files, 32 Sub Dirs, 1MB Size 5: Test All Options Test:
Phoronix installs all the binaries on the local machine when we start benchmarking the corresponding test. In the preceding command, we are benchmarking the test
fs-mark, where it installs the tool at
~/.phoronix-test-suite/installed-tests/pts/fs-mark-1.0.1/fs_mark-3.3. Let's go to the location, and let's see what fsync tests it supports:
./fs_mark -help Usage: fs_mark -S Sync Method ( 0:No Sync, 1:fsyncBeforeClose, 2:sync/1_fsync, 3:PostReverseFsync, 4:syncPostReverseFsync, 5:PostFsync, 6:syncPostFsync)
I would encourage you to read the readme file, which exists in the same location, for detailed information about the sync methods. Let's run a simple
fs_mark benchmarking by choosing one sync method as shown in the following here:
./fs_mark -w 8096 -S 1 -s 102400 -d /tmp/ -L 3 -n 500 # ./fs_mark -w 8096 -S 1 -s 102400 -d /tmp/ -L 3 -n 500 # Version 3.3, 1 thread(s) starting at Fri Dec 30 04:26:28 2016 # Sync method: INBAND FSYNC: fsync() per file in write loop. # Directories: no subdirectories used # File names: 40 bytes long, (16 initial bytes of time stamp with 24 random bytes at end of name) # Files info: size 102400 bytes, written with an IO size of 8096 bytes per write # App overhead is time in microseconds spent in the test not doing file writing related system calls. FSUse% Count Size Files/sec App Overhead 39 500 102400 156.4 17903 39 1000 102400 78.9 22906 39 1500 102400 116.2 24269
We ran the preceding test with write files of size 102,400 and block size of 8,096. The number of files it needs to create is 500 and it needs to repeat the test three times by choosing sync method 1, which closes the file after writing the content to disk.
In this recipe, we will be discussing how to benchmark the disk IOPS using open source tools.
As mentioned previously, a disk can be read in either sequential or random orders. To measure the disk accurately, we need to perform more random read/write operations, which gives more stress to the disk. To calculate the IOPS (Input/Output Per Second) of a disk, we can either use fio or bonnie++ tools, which do sequential/random operations over the disk. In this chapter, let's use the fio (Flexible I/O) tool to calculate the IOPS for the disk.
Let's download the latest version of the
fio module from http://brick.kernel.dk/snaps/, also download
libaio-devel, which would be the
ioengine we will be using for the IOPS. This
ioengine defines, how the
fio module needs to submit the I/O requests to the kernel. There are multiple ioengines you can specify for the I/O requests such as
mmap, and so on. You can refer to the main page of fio for all the supported ioengines. After downloading the fio module, let's follow the regular Linux source installation method as
Let's run a sample sequential mixed read/write, as shown here:
$ ./fio --ioengine=libaio --direct=1 --name=test_seq_mix_rw --filename=test_seq --bs=8k --iodepth=32 --size=1G --readwrite=rw --rwmixread=50 test_seq_mix_rw: (g=0): rw=rw, bs=8K-8K/8K-8K/8K-8K, ioengine=libaio, iodepth=32 ... ... test_seq_mix_rw: (groupid=0, jobs=1): err= 0: pid=43596: Fri Dec 30 23:31:11 2016 read : io=525088KB, bw=1948.1KB/s, iops=243 , runt=269430msec ... bw (KB/s) : min= 15, max= 6183, per=100.00%, avg=2002.59, stdev=1253.68 write: io=523488KB, bw=1942.1KB/s, iops=242 , runt=269430msec ... bw (KB/s) : min= 192, max= 5888, per=100.00%, avg=2001.74, stdev=1246.19 ... Run status group 0 (all jobs): READ: io=525088KB, aggrb=1948KB/s, minb=1948KB/s, maxb=1948KB/s, mint=269430msec, maxt=269430msec WRITE: io=523488KB, aggrb=1942KB/s, minb=1942KB/s, maxb=1942KB/s, mint=269430msec, maxt=269430msec Disk stats (read/write): sda: ios=65608/65423, merge=0/5, ticks=869519/853644, in_queue=1723445, util=99.85%
Let's run a sample random mixed read/write, as shown here:
$ ./fio --ioengine=libaio --direct=1 --name=test_rand_mix_rw --filename=test_rand --bs=8k --iodepth=32 --size=1G --readwrite=randrw --rwmixread=50 test_rand_mix_rw: (g=0): rw=randrw, bs=8K-8K/8K-8K/8K-8K, ioengine=libaio, iodepth=32 ... ... test_rand_mix_rw: (groupid=0, jobs=1): err= 0: pid=43893: Fri Dec 30 23:49:19 2016 read : io=525088KB, bw=1018.9KB/s, iops=127 , runt=515375msec ... bw (KB/s) : min= 8, max= 6720, per=100.00%, avg=1124.47, stdev=964.38 write: io=523488KB, bw=1015.8KB/s, iops=126 , runt=515375msec ... bw (KB/s) : min= 8, max= 6904, per=100.00%, avg=1125.46, stdev=975.04 ... Run status group 0 (all jobs): READ: io=525088KB, aggrb=1018KB/s, minb=1018KB/s, maxb=1018KB/s, mint=515375msec, maxt=515375msec WRITE: io=523488KB, aggrb=1015KB/s, minb=1015KB/s, maxb=1015KB/s, mint=515375msec, maxt=515375msec Disk stats (read/write): sda: ios=65609/65456, merge=0/4, ticks=7382037/5520238, in_queue=12902772, util=100.00%
We ran the preceding test cases to work on 1 GB (
--size) file without any cache (
--direct), by doing 32 concurrent I/O requests (
--iodepth), with a block size of 8 KB (
--bs) as 50% read and 50% write operations (
--rwmixread). From the preceding sequential test results, the
bw (bandwidth), IOPS values are pretty high when compared with random test results. That is, in sequential test cases, we gain approximately 50% more IOPS (
read=242) than with the random IOPS (
Fio also provides more information such, as I/O submission latency and complete latency, along with CPU usage on the conducted test cases. I would encourage you to read more useful information about fio's features from its man pages.
In this recipe, we will be discussing how to estimate disk growth using the pgbench tool.
One of the best practices to predict the database disk storage capacity is by loading a set of sample data into the application's database, and simulating production kind of actions using pgbench over a long period. For a period of time (every 1 hour), let's collect the database size using
pg_database_size() or any native command, which returns the disk usage information. Once we get the periodic intervals for at least 24 hours, then we can find an average disk growth ratio by calculating the average of delta among each interval value.
Prepare the SQL script as follows, which simulates the live application behavior in the database:
Create connection; --- Create/Use pool connection. INSERT operation --- Initial write operation. SELECT pg_sleep(0.01); --- Some application code runs here, and waiting for the next query. UPDATE operation --- Update other tables for the newly inserted records. SELECT pg_sleep(0.1); --- Updating other services which shows the live graphs on the updated records. DELETE operation --- Delete or purge any unnecessary data. SELECT pg_sleep(0.01); --- Some application code overhead.
Let's run the following
pgbench test case, with the preceding test file for 24 hours:
$ pgbench -T 86400 -f <script location> -c <number of concurrent connections>
In parallel, let's schedule a job that collects the database size every hour using the
pg_database_size() function, also schedule another job to run for every 10 minutes, which run the VACUUM on the database. This VACUUM job takes care of reclaiming the dead tuples logically at database level. However, in production servers, we will not deploy the VACUUM job to run for every 10 minutes, as the autovacuum process takes care of the dead tuples. As this test is not for database performance benchmarking, we can also make autovacuum more aggressive on the database side as well.
Once we find the average disk growth per day, we can predict the database growth for the next 1 or 2 years. However, the database write rate also increases with the business growth. So, we need to deploy the database growth script or we need to analyze any disk storage trends from the monitoring tool to make a better prediction of the storage size.
In this recipe, we will be discussing about various RAID levels and their unique usage.
In this recipe, we will be discussing several RAID levels, which we configure for database requirements. RAID (Redundant Array of Interdependent Disks) has a dedicated hardware controller to deal with multiple disks, including a separate processor along with a battery backup cache, where data can be flushed to disk properly when a power failure occurs.
RAID levels can be differentiated as per their configurations. RAID supports configuration techniques such as striping, mirroring, and parity to improve the disk storage performance, or high availability. The most popular RAID levels are zero to six, and each level provides its own kind of disk storage capacity, read/write performance and high availability. The common RAID levels we configure for DBMS are 0, 1, 5, 6, or 10 (1 and 0).
Let us discuss about how the mostly used RAID level works:
This configuration only focuses on read/write performance by striping the data across multiple devices. With this configuration, we can allocate the complete disk storage for the applications data. The major drawback in this configuration is no high availability. In the case of any single disk failure, it will cause the remaining disks to be useless as they are missing the chunks from the failed disk. This is a not recommended RAID configuration for real-time database systems, but it is a recommended configuration for storing non-critical business data such as historical application logs, database logs, and so on.
This configuration is only to focus on high availability rather than on performance, by broadcasting the data among two disk drives. That is, a single copy of the data will be kept on two disks. If one disk is corrupted, then we can still use the other one for read/write operations. This is also not a recommended configuration for real-time database systems, as it is lacking the write performance. Also, in this configuration, we will be utilizing 50% of the disk to store the actual data, and the rest to keep its duplicated information for high availability. This is a recommended configuration where the durability of data matters when compared with write performance.
This configuration provides more storage and high availability on the disk, by storing the parity blocks across the disks. Unlike RAID 1, it offers more disk space to keep the actual data, as parity blocks are spread among the disks. In any case, if one disk is corrupted, then we can use the parity blocks from the other disk, to fetch the missing data. However, this is also not a recommended configuration, since every read/write operation on the disk needs to process the parity blocks, to get the actual data out of it.
This configuration provides more redundancy than RAID 5 by storing the two parity blocks information for each write operation. That is, if both disks become corrupted, RAID 6 can still get the data from the parity blocks, unlike RAID 5. This configuration is also not recommended for the database systems, as write performance is less as compared than previous RAID levels.
This configuration is the combination of RAID levels 0 and 1. That is, the data will be striped to multiple disks and will be replicated to another disk storage. It is the most recommended RAID level for real-time business applications, where we achieve a better performance than with RAID 1, and higher availability than RAID 0.
For more information about RAID levels, refer to the following URLs:
In this recipe, we will be discussing how to configure the pgbench to perform various test cases.
By default, PostgreSQL provides a tool, pgbench, which performs a default test suite based on TPC-B, which simulates the live database load on the servers. Using this tool, we can estimate the tps (transactions per second) capacity of the server, by conducting a dedicated read or read/write test cases. Before performing the pgbench test cases on the server, we need to fine-tune the PostgreSQL parameters and make them ready to fully utilize the server resources. Also, it's good practice to run pgbench from a remote machine, where the network latency is trivial among the nodes.
As aforementioned, pgbench simulates a TPC-B-like workload on the servers, by executing three update statements, followed by
INSERT statements into different pre-defined pgbench tables, and if we want to use those pre-defined tables, then we would need to initiate pgbench using the
--initialize options. Otherwise, we can write a customized SQL script.
To get effective results from pgbench, we need to fine-tune the PostgreSQL server with the following parameters:
This is the amount of memory for database operations
This improves the OS-level memory management
This is the amount of memory for each backend for data operations such as sort, join, and so on
This is the amount of memory for postgres internal process autovacuum
This is the maximum number of worker processes for the database
This is the number of worker processes to consider for a gather node type
This is the number of database connections
Do fsync, after this many bytes have been flushed to disk by each user process
This is used to set I/O usage ratio during the checkpoint
This is used to determine whether the database should run in the archive mode
This is used to log useful information about concurrent database locks
This is used to log information about the database's temporary files
This is used to set random page cost value for the index scans
You can also find other parameters at the following URL, which are also important before conducting any benchmarking on the database server: https://www.postgresql.org/docs/9.6/static/runtime-config.html.
Another good practice to get good performance is to keep the transaction logs (
pg_xlog) in another mount point, and also have unique tablespaces for tables and indexes. While performing the pgbench testing with predefined tables, we can specify these unique tablespaces using the
As we discussed earlier, pgbench is a TPC-B benchmarking tool for PostgreSQL, which simulates the live transactions load on the database server by collecting the required metrics such as
latency, and so on. Using pgbench, we can also increase the database size by choosing the test scale factor while using predefined tables. If you wanted to test multiple concurrent connections to the database and wanted to use the pooling mechanism, then it's good practice to configure the pgbouner/pgpool on the local database node to reuse the connections.
For more features and options with the pgbench tool, visit https://www.postgresql.org/docs/9.6/static/pgbench.html.
In this recipe, we will be discussing how to perform various tests using the pgbench tool.
Using pgbench options, we can benchmark the database for read/write operations. Using these measurements, we can estimate the disk read-write speed by including the system buffers. To perform a read-write-only test, then either we can go with pgbench arguments, or create a custom SQL script with the required
DELETE statements, then execute them with the required number of concurrent connections.
Let us discuss about read-only and write-only in brief:
To perform read-only benchmarking with pgbench predefined tables, we need to use the
-S option. Otherwise, as we discussed earlier, we need to prepare a SQL file with the required
While running read-only test cases, it's good practice to measure the database cache hit ratio, which defines the reduction in I/O usage. You can get the database hit ratio using the following SQL command:
postgres=# SELECT TRUNC(((blks_hit)/(blks_read+blks_hit)::numeric)*100, 2) hit_ratio FROM pg_stat_database WHERE datname = 'postgres'; hit_ratio ----------- 99.69 (1 row)
Also, if we enable
postgresql.conf, it will provide some information about disk blocks read/write operations by each backend process. We can get these disk I/O timing values from the
pg_stat_database catalog view.
Refer to the following URL, where pgbench supports various test suites, such as disk, CPU, memory, and so on: https://wiki.postgresql.org/wiki/Pgbenchtesting.