{"id":5686,"date":"2024-05-15T06:53:36","date_gmt":"2024-05-15T06:53:36","guid":{"rendered":"https:\/\/test.opensource-db.in\/wp1\/?p=5686"},"modified":"2024-05-29T18:55:57","modified_gmt":"2024-05-29T18:55:57","slug":"mastering-pgbench-for-database-performance-tuning-part-%e2%85%b1","status":"publish","type":"post","link":"https:\/\/test.opensource-db.in\/wp1\/mastering-pgbench-for-database-performance-tuning-part-%e2%85%b1\/","title":{"rendered":"Mastering pgbench for Database Performance Tuning &#8211; Part \u2161"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Introduction<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">In the realm of database performance tuning, the pursuit of optimal throughput and responsiveness is a never-ending journey. In our previous <a href=\"https:\/\/test.opensource-db.in\/wp1\/mastering-pgbench-for-database-performance-tuning\/\">blog<\/a> , we discussed about <code>pgbench<\/code> , setting up and first run of <code>pgbench<\/code> , tips, tricks  and common pitfalls of benchmarking. Building upon that foundation, we now venture into the realm of advanced optimization strategies, where we delve deeper into the intricacies of <code>pgbench<\/code> with more options in it. Additionally, we will explore ways to interpret pgbench results effectively, transforming raw metrics into actionable insights that drive tangible performance enhancements.<br><br>If you haven&#8217;t read our previous blog , read <a href=\"https:\/\/test.opensource-db.in\/wp1\/mastering-pgbench-for-database-performance-tuning\/\">here<\/a>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Exploring pgbench with more options<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">In our previous blog , we explored the basic options of <code>pgbench<\/code> like <code>-c<\/code> for number of clients,<code>-j<\/code>\u00a0for number of threads, <code>-t<\/code>\u00a0for number of transactions,\u00a0<code>-T<\/code>\u00a0for time limit . But, there are lot more options that the pgbench utility offers. Let&#8217;s explore a few more of these options and see how they can be used.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>No vacuum <code>-n<\/code><\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">It is used to avoid the vacuuming before running the test . Usually , this option is used in a custom test run using a script which does not include the default tables.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>#Running test run without -n\n&#91;postgres@localhost bin]$ .\/pgbench db\npgbench (16.3)\nstarting vacuum...end.\ntransaction type: &lt;builtin: TPC-B (sort of)&gt;\n\n#Running test run with -n\n&#91;postgres@localhost bin]$ .\/pgbench -n db\npgbench (16.3)\ntransaction type: &lt;builtin: TPC-B (sort of)&gt;\n<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">Analyzing the difference , without option <code>-n<\/code> it is running vacuum. You can see avoiding vacuum with option <code>-n<\/code> <\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Connect<\/strong> <code>-C<\/code><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This option will establish a new connection for each transaction instead of once per client sessions. It is used to measure the client overhead.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>#Using -C\n&#91;postgres@localhost bin]$ .\/pgbench -C db\npgbench (16.3)\nstarting vacuum...end.\ntransaction type: &lt;builtin: TPC-B (sort of)&gt;\nscaling factor: 1\nquery mode: simple\nnumber of clients: 1\nnumber of threads: 1\nmaximum number of tries: 1\nnumber of transactions per client: 10\nnumber of transactions actually processed: 10\/10\nnumber of failed transactions: 0 (0.000%)\nlatency average = 5.255 ms\naverage connection time = 2.524 ms\ntps = 190.284094 (including reconnection times)\n<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">Observing the <code>tps<\/code> , it is including the time taken for reconnection . If you don&#8217;t use this option , you will see tps <code>without initial connection time<\/code>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Rate <\/strong><code>-R<\/code><strong> &amp; Latency limit<\/strong> <code>-L<\/code><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">These two options are mostly used together. Rate <code>-R<\/code> used to specify the rate\/speed of transactions and Latency limit -L used to count the transaction over the specified limit. These two are calculated in milliseconds.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>&#91;postgres@localhost bin]$ .\/pgbench -R 10 -L 10 db\npgbench (16.3)\nstarting vacuum...end.\ntransaction type: &lt;builtin: TPC-B (sort of)&gt;\nscaling factor: 1\nquery mode: simple\nnumber of clients: 1\nnumber of threads: 1\nmaximum number of tries: 1\nnumber of transactions per client: 10\nnumber of transactions actually processed: 10\/10\nnumber of failed transactions: 0 (0.000%)\nnumber of transactions skipped: 0 (0.000%)\nnumber of transactions above the 10.0 ms latency limit: 0\/10 (0.000%)\nlatency average = 3.153 ms\nlatency stddev = 0.480 ms\nrate limit schedule lag: avg 0.311 (max 0.518) ms\ninitial connection time = 2.973 ms\ntps = 6.476235 (without initial connection time)<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">Here, we can see the number of transactions above the specified latency limit and number of transactions skipped. In  this case, there are no transactions that crossed the given inputs.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Report per command<\/strong> <code>-r<\/code><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This option gives us the stats of each command after the benchmark finishes.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>&#91;postgres@localhost bin]$ .\/pgbench -r db\npgbench (16.3)\nstarting vacuum...end.\ntransaction type: &lt;builtin: TPC-B (sort of)&gt;\nscaling factor: 1\nquery mode: simple\nnumber of clients: 1\nnumber of threads: 1\nmaximum number of tries: 1\nnumber of transactions per client: 10\nnumber of transactions actually processed: 10\/10\nnumber of failed transactions: 0 (0.000%)\nlatency average = 1.020 ms\ninitial connection time = 2.315 ms\ntps = 980.199961 (without initial connection time)\nstatement latencies in milliseconds and failures:\n         0.001           0  \\set aid random(1, 100000 * :scale)\n         0.000           0  \\set bid random(1, 1 * :scale)\n         0.000           0  \\set tid random(1, 10 * :scale)\n         0.000           0  \\set delta random(-5000, 5000)\n         0.029           0  BEGIN;\n         0.169           0  UPDATE pgbench_accounts SET abalance = abalance + :delta WHERE aid = :aid;\n         0.053           0  SELECT abalance FROM pgbench_accounts WHERE aid = :aid;\n         0.056           0  UPDATE pgbench_tellers SET tbalance = tbalance + :delta WHERE tid = :tid;\n         0.043           0  UPDATE pgbench_branches SET bbalance = bbalance + :delta WHERE bid = :bid;\n         0.054           0  INSERT INTO pgbench_history (tid, bid, aid, delta, mtime) VALUES (:tid, :bid, :aid, :delta, CURRENT_TIMESTAMP);\n\t 0.611           0  END;<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">It gives us the latency and failures of the default script used by <code>pgbench<\/code>. <\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Summary<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">To summarize, effective benchmarking is crucial for the optimal performance and scalability of databases.By leveraging benchmarking tools such as\u00a0<code>pgbench<\/code>, database administrators and developers can simulate real-world workloads, measure performance metrics, and identify areas for optimization. However, successful benchmarking requires careful planning, execution, and analysis. In this blog , we have explored few more options in leveraging the <code>pgbench<\/code> utility. If you haven&#8217;t checked our previous blog on <code>pgbench<\/code> , here is the <a href=\"https:\/\/test.opensource-db.in\/wp1\/mastering-pgbench-for-database-performance-tuning\/\">blog<\/a>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Thank you and stay tuned for more\u2026<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Introduction In the realm of database performance tuning, the pursuit of optimal throughput and responsiveness is a never-ending journey. In [&hellip;]<\/p>\n","protected":false},"author":9,"featured_media":5693,"comment_status":"open","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[52,1,23,42,89],"tags":[],"class_list":["post-5686","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-customer-success","category-others","category-postgresql-14","category-postgresql-15","category-postgresql-16"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Mastering pgbench for Database Performance Tuning - Part \u2161 - OpenSource DB<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Mastering pgbench for Database Performance Tuning - Part \u2161 - OpenSource DB\" \/>\n<meta property=\"og:description\" content=\"Introduction In the realm of database performance tuning, the pursuit of optimal throughput and responsiveness is a never-ending journey. In [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/test.opensource-db.in\/wp1\/mastering-pgbench-for-database-performance-tuning-part-\u2171\/\" \/>\n<meta property=\"og:site_name\" content=\"OpenSource DB\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/people\/OpenSource-DB\/100072970755470\/\" \/>\n<meta property=\"article:published_time\" content=\"2024-05-15T06:53:36+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-05-29T18:55:57+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/test.opensource-db.in\/wp1\/wp-content\/uploads\/2024\/05\/image-1-1.png\" \/>\n\t<meta property=\"og:image:width\" content=\"3600\" \/>\n\t<meta property=\"og:image:height\" content=\"1890\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Venkat Akhil\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@opensource_db\" \/>\n<meta name=\"twitter:site\" content=\"@opensource_db\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Venkat Akhil\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/test.opensource-db.in\/wp1\/mastering-pgbench-for-database-performance-tuning-part-%e2%85%b1\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/test.opensource-db.in\/wp1\/mastering-pgbench-for-database-performance-tuning-part-%e2%85%b1\/\"},\"author\":{\"name\":\"Venkat Akhil\",\"@id\":\"https:\/\/test.opensource-db.in\/wp1\/#\/schema\/person\/a37b142ecbf953189a4f9209b0b8d328\"},\"headline\":\"Mastering pgbench for Database Performance Tuning &#8211; 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