{"id":34207,"date":"2024-10-10T10:39:14","date_gmt":"2024-10-10T08:39:14","guid":{"rendered":"https:\/\/www.dbi-services.com\/blog\/?p=34207"},"modified":"2024-10-10T10:39:17","modified_gmt":"2024-10-10T08:39:17","slug":"enhancing-postgresql-performance-with-index-advisor-and-hypopg","status":"publish","type":"post","link":"https:\/\/www.dbi-services.com\/blog\/enhancing-postgresql-performance-with-index-advisor-and-hypopg\/","title":{"rendered":"Enhancing PostgreSQL Performance with Index Advisor and HypoPG"},"content":{"rendered":"\n<p>In database management, query performance is critical for efficient data retrieval. PostgreSQL, a powerful open-source database, offers various extensions to optimize and tune query performance. Two such extensions are <strong>Index Advisor<\/strong> and <strong>HypoPG<\/strong>. These tools help database administrators (DBAs) identify and test potential indexes to improve query performance without the overhead of creating and dropping indexes repeatedly. This blog will guide you through the installation and usage of these extensions to enhance your PostgreSQL performance.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-1-installing-index-advisor-and-hypopg-extensions\">1. Installing Index Advisor and HypoPG Extensions<\/h2>\n\n\n\n<p>Before diving into the usage of these extensions, let&#8217;s start with the installation process.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-1-1-postgresql-index-advisor\">1.1. PostgreSQL Index Advisor<\/h3>\n\n\n\n<p>Index Advisor is a PostgreSQL extension that recommends indexes based on query analysis. One nice point is that the extension code is still maintained. index_advisor  requires Postgres with&nbsp;<a href=\"https:\/\/github.com\/HypoPG\/hypopg\">hypopg<\/a>&nbsp;installed. Here&#8217;s how to install it:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Installation:<\/li>\n<\/ul>\n\n\n\n<pre class=\"wp-block-code\"><code>--- Install hypopg\n\npostgres@server&gt; sudo apt install postgresql-XY-hypopg\n\n--- Install index_adviser\n\npostgres@server&gt; git clone https:\/\/github.com\/supabase\/index_advisor.git\npostgres@server&gt; cd index_advisor\npostgres@server&gt; sudo make install<\/code><\/pre>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Enable the Extension in PostgreSQL:<\/li>\n<\/ul>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: sql; title: ; notranslate\" title=\"\">\npostgres# CREATE EXTENSION hypopg;\npostgres# create extension if not exists index_advisor cascade;\n<\/pre><\/div>\n\n\n<p>Now that the extensions are installed, let&#8217;s explore how to use them to analyze and optimize query performance.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-2-using-index-advisor-to-recommend-indexes\">2. Using Index Advisor to Recommend Indexes<\/h2>\n\n\n\n<p>Index Advisor analyzes your queries and suggests indexes that can improve performance. Let&#8217;s see it in action with a sample query.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-2-1-example-query\">2.1. Example Query<\/h3>\n\n\n\n<p>Consider the following query that retrieves data from <code>dbi_technologies<\/code>, <code>dbi_techres<\/code>, and <code>dbi_techlevels<\/code> tables:<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: sql; title: ; notranslate\" title=\"\">\nSELECT tech.id techid, tech.label techlabel, reslevel.techlevel techlevel, reslevel.techlvlid\nFROM dbi_technologies tech\nLEFT JOIN\n  (SELECT techlvl.label techlevel, techlvl.id techlvlid, techres.tech_id tech_id\n   FROM dbi_techres techres,\n   dbi_techlevels techlvl\n   WHERE techres.res_id = $2\n   AND techlvl.id = techres.techlevel_id) AS reslevel\nON tech.id = reslevel.tech_id\nWHERE tech.cat_id = $1\nORDER BY techlabel;\n<\/pre><\/div>\n\n\n<h3 class=\"wp-block-heading\" id=\"h-2-2-running-index-advisor\">2.2. Running Index Advisor<\/h3>\n\n\n\n<p>To get index recommendations for the above query, use the following command:<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: sql; title: ; notranslate\" title=\"\">\nSELECT * FROM index_advisor(&#039;YOUR QUERY HERE&#039;);\n<\/pre><\/div>\n\n\n<h3 class=\"wp-block-heading\" id=\"h-2-3-sample-output\">2.3. Sample Output<\/h3>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: sql; title: ; notranslate\" title=\"\">\n-&#x5B; RECORD 1 ]-------------------------\nstartup_cost_before | 70.97\nstartup_cost_after  | 66.76\ntotal_cost_before   | 71.00\ntotal_cost_after    | 66.79\nindex_statements    | &#x5B;&#039;CREATE INDEX ON dbicc.dbi_technologies USING btree (cat_id)&#039;, &#039;CREATE INDEX ON dbicc.dbi_techres USING btree (res_id)&#039;]\nerrors              | &#x5B;]\n<\/pre><\/div>\n\n\n<h3 class=\"wp-block-heading\" id=\"h-2-4-output-explanation\">2.4. Output Explanation<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>startup_cost_before<\/strong> and <strong>startup_cost_after<\/strong>: These show the estimated cost until the query can return the first row of the result before and after applying the recommended indexes. For example, with a hash join, this includes the cost to build the internal table. For a sequential scan, the startup cost is always zero, as rows can be returned immediately.<\/li>\n\n\n\n<li><strong>total_cost_before<\/strong> and <strong>total_cost_after<\/strong>: Estimated total cost of executing the query before and after applying the recommended indexes.<\/li>\n\n\n\n<li><strong>index_statements<\/strong>: Suggested SQL statements to create the indexes. In this case, we got two suggestions.<\/li>\n\n\n\n<li><strong>errors<\/strong>: Any errors encountered during the analysis (empty in this case).<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-3-using-hypopg-to-test-hypothetical-indexes\">3. Using HypoPG to Test Hypothetical Indexes<\/h2>\n\n\n\n<p>HypoPG allows you to test the impact of hypothetical indexes without actually creating them. This is useful for evaluating potential indexes before committing to their creation. The hypothetical indexes created by HypoPG are stored in your connection&#8217;s private memory, not in any catalog. This means they won&#8217;t increase the size of any table or affect other connections.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-3-1-analyzing-query-performance-without-hypothetical-indexes\">3.1. Analyzing Query Performance without Hypothetical Indexes<\/h3>\n\n\n\n<p>Let&#8217;s start by testing the query performance without any hypothetical indexes.<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: sql; title: ; notranslate\" title=\"\">\npostgres# EXPLAIN SELECT tech.id techid, tech.label techlabel, reslevel.techlevel techlevel, reslevel.techlvlid\nFROM dbi_technologies tech\nLEFT JOIN (\n    SELECT techlvl.label techlevel, techlvl.id techlvlid, techres.tech_id tech_id\n    FROM dbi_techres techres, dbi_techlevels techlvl\n    WHERE techres.res_id = 60\n    AND techlvl.id = techres.techlevel_id\n) AS reslevel\nON tech.id = reslevel.tech_id\nWHERE tech.cat_id = 10\nORDER BY techlabel;\n\n+-------------------------------------------------------------------------------------------------------------------------+\n| QUERY PLAN                                                                                                              |\n|-------------------------------------------------------------------------------------------------------------------------|\n| Sort  (cost=72.10..72.13 rows=14 width=241)                                                                             |\n|   Sort Key: tech.label                                                                                                  |\n|   -&gt;  Nested Loop Left Join  (cost=0.41..71.83 rows=14 width=241)                                                       |\n|         -&gt;  Seq Scan on dbi_technologies tech  (cost=0.00..10.01 rows=14 width=19)                                      |\n|               Filter: (cat_id = 10)                                                                                     |\n|         -&gt;  Nested Loop  (cost=0.41..4.41 rows=1 width=226)                                                             |\n|               -&gt;  Index Only Scan using idx_tech_res_level on dbi_techres techres  (cost=0.28..4.02 rows=1 width=8)     |\n|                     Index Cond: ((tech_id = tech.id) AND (res_id = 60))                                                 |\n|               -&gt;  Index Scan using dbi_techlevels_id_pkey on dbi_techlevels techlvl  (cost=0.13..0.33 rows=1 width=222) |\n|                     Index Cond: (id = techres.techlevel_id)                                                             |\n+-------------------------------------------------------------------------------------------------------------------------+\n<\/pre><\/div>\n\n\n<h3 class=\"wp-block-heading\" id=\"h-3-2-creating-the-first-hypothetical-index\">3.2. Creating the First Hypothetical Index<\/h3>\n\n\n\n<p>We are going to create our first hypothetical index based on the output from the index_advisor statement. Use <code>hypopg_create_index<\/code> to create it:<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: sql; title: ; notranslate\" title=\"\">\nSELECT * FROM hypopg_create_index(&#039;CREATE INDEX ON dbicc.dbi_technologies USING btree (cat_id)&#039;);\n<\/pre><\/div>\n\n\n<h3 class=\"wp-block-heading\" id=\"h-3-3-analyzing-query-performance-with-hypothetical-index\">3.3. Analyzing Query Performance with Hypothetical Index<\/h3>\n\n\n\n<p>Run <code>EXPLAIN<\/code> on your query to see the impact of the hypothetical index:<\/p>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: sql; title: ; notranslate\" title=\"\">\npostgres# EXPLAIN SELECT tech.id techid, tech.label techlabel, reslevel.techlevel techlevel, reslevel.techlvlid\n            FROM dbi_technologies tech\n            LEFT JOIN\n            ( SELECT techlvl.label techlevel, techlvl.id techlvlid, techres.tech_id tech_id\n            FROM dbi_techres techres,\n            dbi_techlevels techlvl\n            WHERE techres.res_id = 60\n            AND techlvl.id = techres.techlevel_id) AS reslevel\n            ON tech.id = reslevel.tech_id\n            WHERE tech.cat_id = 10\n            ORDER BY techlabel;\n+-------------------------------------------------------------------------------------------------------------------------+\n| QUERY PLAN                                                                                                              |\n|-------------------------------------------------------------------------------------------------------------------------|\n| Sort  (cost=71.39..71.43 rows=14 width=241)                                                                             |\n|   Sort Key: tech.label                                                                                                  |\n|   -&gt;  Nested Loop Left Join  (cost=4.55..71.12 rows=14 width=241)                                                       |\n|         -&gt;  Bitmap Heap Scan on dbi_technologies tech  (cost=4.13..9.31 rows=14 width=19)                               |\n|               Recheck Cond: (cat_id = 10)                                                                               |\n|               -&gt;  Bitmap Index Scan on &quot;&lt;14454&gt;btree_dbicc_dbi_technologies_cat_id&quot;  (cost=0.00..4.13 rows=14 width=0)  |\n|                     Index Cond: (cat_id = 10)                                                                           |\n|         -&gt;  Nested Loop  (cost=0.41..4.41 rows=1 width=226)                                                             |\n|               -&gt;  Index Only Scan using idx_tech_res_level on dbi_techres techres  (cost=0.28..4.02 rows=1 width=8)     |\n|                     Index Cond: ((tech_id = tech.id) AND (res_id = 60))                                                 |\n|               -&gt;  Index Scan using dbi_techlevels_id_pkey on dbi_techlevels techlvl  (cost=0.13..0.33 rows=1 width=222) |\n|                     Index Cond: (id = techres.techlevel_id)                                                             |\n+-------------------------------------------------------------------------------------------------------------------------+\n<\/pre><\/div>\n\n\n<h3 class=\"wp-block-heading\" id=\"h-3-4-output-explanation\">3.4. Output Explanation<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Bitmap Heap Scan<\/strong> and <strong>Bitmap Index Scan<\/strong>: Show how the hypothetical index is used in scanning the table.<\/li>\n\n\n\n<li><strong>Cost Reduction<\/strong>: Reduced costs in the query plan indicate improved performance due to the hypothetical index.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-3-5-creating-our-second-hypothetical-index-and-check-the-output\">3.5. Creating our second Hypothetical Index and check the output<\/h3>\n\n\n<div class=\"wp-block-syntaxhighlighter-code \"><pre class=\"brush: sql; title: ; notranslate\" title=\"\">\npostgres# SELECT * FROM hypopg_create_index(&#039;CREATE INDEX ON dbicc.dbi_techres USING btree (res_id)&#039;);\n+------------+---------------------------------------+\n| indexrelid | indexname                             |\n|------------+---------------------------------------|\n| 14455      | &lt;14455&gt;btree_dbicc_dbi_techres_res_id |\n+------------+---------------------------------------+\nSELECT 1\nTime: 0.013s\n\npostgres# EXPLAIN SELECT tech.id techid, tech.label techlabel, reslevel.techlevel techlevel, reslevel.techlvlid\n             FROM dbi_technologies tech\n             LEFT JOIN\n             ( SELECT techlvl.label techlevel, techlvl.id techlvlid, techres.tech_id tech_id\n             FROM dbi_techres techres,\n             dbi_techlevels techlvl\n             WHERE techres.res_id = 60\n             AND techlvl.id = techres.techlevel_id) AS reslevel\n             ON tech.id = reslevel.tech_id\n             WHERE tech.cat_id = 10\n             ORDER BY techlabel;\n+---------------------------------------------------------------------------------------------------------------------------------------+\n| QUERY PLAN                                                                                                                            |\n|---------------------------------------------------------------------------------------------------------------------------------------|\n| Sort  (cost=62.47..62.51 rows=14 width=241)                                                                                           |\n|   Sort Key: tech.label                                                                                                                |\n|   -&gt;  Hash Right Join  (cost=10.65..62.21 rows=14 width=241)                                                                          |\n|         Hash Cond: (techres.tech_id = tech.id)                                                                                        |\n|         -&gt;  Hash Join  (cost=1.17..52.55 rows=66 width=226)                                                                           |\n|               Hash Cond: (techres.techlevel_id = techlvl.id)                                                                          |\n|               -&gt;  Index Scan using &quot;&lt;14455&gt;btree_dbicc_dbi_techres_res_id&quot; on dbi_techres techres  (cost=0.03..51.12 rows=66 width=8) |\n|                     Index Cond: (res_id = 60)                                                                                         |\n|               -&gt;  Hash  (cost=1.06..1.06 rows=6 width=222)                                                                            |\n|                     -&gt;  Seq Scan on dbi_techlevels techlvl  (cost=0.00..1.06 rows=6 width=222)                                        |\n|         -&gt;  Hash  (cost=9.31..9.31 rows=14 width=19)                                                                                  |\n|               -&gt;  Bitmap Heap Scan on dbi_technologies tech  (cost=4.13..9.31 rows=14 width=19)                                       |\n|                     Recheck Cond: (cat_id = 10)                                                                                       |\n|                     -&gt;  Bitmap Index Scan on &quot;&lt;14454&gt;btree_dbicc_dbi_technologies_cat_id&quot;  (cost=0.00..4.13 rows=14 width=0)          |\n|                           Index Cond: (cat_id = 10)                                                                                   |\n+---------------------------------------------------------------------------------------------------------------------------------------+\n<\/pre><\/div>\n\n\n<h3 class=\"wp-block-heading\" id=\"h-3-6-output-explanation\">3.6. Output Explanation<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Bitmap Heap Scan<\/strong> and <strong>Bitmap Index Scan<\/strong>: Show how the hypothetical index is used in scanning the table.<\/li>\n\n\n\n<li><strong>Cost Reduction<\/strong>: Reduced costs in the query plan indicate improved performance due to the hypothetical index.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-3-7-key-differences-and-benefits\">3.7. Key Differences and Benefits<\/h3>\n\n\n\n<p>After the creation of our HypoPG indexes, we can see some differences in our explain statements and notice some benefits:<\/p>\n\n\n\n<p><strong>Reduced Total Cost:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Without Indexes:<\/strong> The total cost was estimated at around 71.83.<\/li>\n\n\n\n<li><strong>With HypoPG Indexes:<\/strong> The total cost decreased to about 62.21.<\/li>\n<\/ul>\n\n\n\n<p><strong>Benefit:<\/strong> The overall cost of executing the query is lower, indicating improved efficiency.<\/p>\n\n\n\n<p><strong>Improved Join Strategies:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Without Indexes:<\/strong> The query used nested loop joins, which can be less efficient for large datasets.<\/li>\n\n\n\n<li><strong>With HypoPG Indexes:<\/strong> The query plan switched to hash joins, which are generally faster for joining large sets of data.<\/li>\n<\/ul>\n\n\n\n<p><strong>Benefit:<\/strong> Hash joins are more efficient and can handle larger datasets better than nested loops.<\/p>\n\n\n\n<p><strong>Better Index Usage:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Without Indexes:<\/strong> The plan didn&#8217;t use any specific indexes for <code>cat_id<\/code> or <code>res_id<\/code> in the <code>dbi_technologies<\/code> table.<\/li>\n\n\n\n<li><strong>With HypoPG Indexes:<\/strong> The plan utilized the hypothetical indexes for both <code>cat_id<\/code> and <code>res_id<\/code>.<\/li>\n<\/ul>\n\n\n\n<p><strong>Benefit:<\/strong> The use of these indexes speeds up data retrieval by directly accessing the relevant rows rather than scanning the entire table.<\/p>\n\n\n\n<p><strong>Change in Access Methods:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Without Indexes:<\/strong> The plan involved a sequence scan and index-only scans.<\/li>\n\n\n\n<li><strong>With HypoPG Indexes:<\/strong> The plan used bitmap heap scans and bitmap index scans.<\/li>\n<\/ul>\n\n\n\n<p><strong>Benefit:<\/strong> Bitmap scans are efficient for handling multiple rows and are typically faster when dealing with indexed columns.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-4-conclusion\">4. Conclusion<\/h2>\n\n\n\n<p>Index Advisor and HypoPG are powerful tools in the PostgreSQL ecosystem, allowing DBAs to fine-tune their database performance with minimal disruption (and without overheating your brain). By recommending and simulating indexes, these extensions help you make decisions about index creation, ensuring your queries run efficiently.<\/p>\n\n\n\n<p>Use these tools to analyze your queries, create hypothetical indexes, and measure their impact before making any permanent changes. I hope that this blog will help you make your PostgreSQL performances better thanks to Index Advisor and HypoPG.<\/p>\n\n\n\n<p>Happy optimizing!<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-useful-links\">Useful links<\/h2>\n\n\n\n<p><a href=\"https:\/\/github.com\/supabase\/index_advisor\">https:\/\/github.com\/supabase\/index_advisor<\/a><br><a href=\"https:\/\/hypopg.readthedocs.io\/en\/rel1_stable\/index.html\">https:\/\/hypopg.readthedocs.io\/en\/rel1_stable\/index.html<\/a><br><a href=\"https:\/\/github.com\/HypoPG\/hypopg\">https:\/\/github.com\/HypoPG\/hypopg<\/a><br>Don&#8217;t mind checking my previous blog about pgcli to make your life easier while using PostgreSQL: <a href=\"https:\/\/www.dbi-services.com\/blog\/simplifying-postgresql-management-a-guide-to-install-and-use-pgcli\/\">https:\/\/www.dbi-services.com\/blog\/simplifying-postgresql-management-a-guide-to-install-and-use-pgcli\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In database management, query performance is critical for efficient data retrieval. PostgreSQL, a powerful open-source database, offers various extensions to optimize and tune query performance. Two such extensions are Index Advisor and HypoPG. These tools help database administrators (DBAs) identify and test potential indexes to improve query performance without the overhead of creating and dropping [&hellip;]<\/p>\n","protected":false},"author":87,"featured_media":34228,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[229,83],"tags":[77],"type_dbi":[2749],"class_list":["post-34207","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-database-administration-monitoring","category-postgresql","tag-postgresql","type-postgresql"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.2 (Yoast SEO v27.4) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Enhancing PostgreSQL Performance with Index Advisor and HypoPG - dbi Blog<\/title>\n<meta name=\"description\" content=\"Discover how PostgreSQL Index Advisor and HypoPG can boost your database performance. 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