{"id":2116,"date":"2026-03-22T12:27:56","date_gmt":"2026-03-22T11:27:56","guid":{"rendered":"https:\/\/askem.eu\/?p=2116"},"modified":"2026-03-22T12:27:59","modified_gmt":"2026-03-22T11:27:59","slug":"optimiser-postgresql-pour-une-stack-open-source-tuning-maintenance-et-haute-disponibilite","status":"publish","type":"post","link":"https:\/\/askem.eu\/en\/2026\/03\/22\/optimiser-postgresql-pour-une-stack-open-source-tuning-maintenance-et-haute-disponibilite\/","title":{"rendered":"Optimiser PostgreSQL pour une stack open source : tuning, maintenance et haute disponibilit\u00e9"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Optimiser PostgreSQL pour une stack open source&nbsp;: tuning, maintenance et haute disponibilit\u00e9<\/h2>\n\n\n\n<p>PostgreSQL est le socle de donn\u00e9es de la plupart des briques open source auto-h\u00e9berg\u00e9es&nbsp;: CKAN, Keycloak, Nextcloud, Gitea, Superset, Drupal. Par d\u00e9faut, sa configuration est volontairement conservatrice pour fonctionner sur n&rsquo;importe quelle machine. R\u00e9sultat&nbsp;: sur un serveur de production avec 16 ou 32 Go de RAM, PostgreSQL n&rsquo;exploite qu&rsquo;une fraction de ses capacit\u00e9s. Cet article pr\u00e9sente les leviers concrets pour optimiser ses performances, automatiser sa maintenance et pr\u00e9parer la haute disponibilit\u00e9.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Les param\u00e8tres critiques \u00e0 ajuster<\/h2>\n\n\n\n<p>La configuration par d\u00e9faut de PostgreSQL (<code>postgresql.conf<\/code>) m\u00e9rite d&rsquo;\u00eatre revue d\u00e8s la mise en production. Voici les param\u00e8tres les plus impactants&nbsp;:<\/p>\n\n\n\n<p><strong>shared_buffers<\/strong>&nbsp;: la m\u00e9moire partag\u00e9e allou\u00e9e au cache de donn\u00e9es. La valeur par d\u00e9faut (128 Mo) est d\u00e9risoire. La recommandation courante est de la fixer \u00e0 25&nbsp;% de la RAM totale du serveur, soit 4 Go pour un serveur de 16 Go. Au-del\u00e0 de 8 Go, les gains deviennent marginaux sur la plupart des charges de travail.<\/p>\n\n\n\n<p><strong>effective_cache_size<\/strong>&nbsp;: ce param\u00e8tre n&rsquo;alloue pas de m\u00e9moire, il indique au planificateur de requ\u00eates la quantit\u00e9 de cache disponible (OS + PostgreSQL). Le fixer \u00e0 75&nbsp;% de la RAM permet au planificateur de choisir des plans de requ\u00eates plus agressifs, notamment l&rsquo;utilisation d&rsquo;index plut\u00f4t que des scans s\u00e9quentiels.<\/p>\n\n\n\n<p><strong>work_mem<\/strong>&nbsp;: la m\u00e9moire allou\u00e9e par op\u00e9ration de tri ou de jointure. Avec la valeur par d\u00e9faut de 4 Mo, les tris complexes d\u00e9bordent sur disque. Pour une stack open data avec des requ\u00eates analytiques, 64 Mo \u00e0 128 Mo est un bon point de d\u00e9part. Attention&nbsp;: cette valeur est multipli\u00e9e par le nombre de connexions actives et d&rsquo;op\u00e9rations parall\u00e8les.<\/p>\n\n\n\n<p><strong>maintenance_work_mem<\/strong>&nbsp;: la m\u00e9moire d\u00e9di\u00e9e aux op\u00e9rations de maintenance (VACUUM, CREATE INDEX, ALTER TABLE). La porter \u00e0 512 Mo ou 1 Go acc\u00e9l\u00e8re consid\u00e9rablement ces op\u00e9rations, notamment le reindexage des grosses tables CKAN.<\/p>\n\n\n\n<p><strong>wal_buffers<\/strong>&nbsp;: le tampon pour les Write-Ahead Logs. Le fixer \u00e0 64 Mo est suffisant pour la plupart des charges de travail. Au-del\u00e0, l&rsquo;impact est n\u00e9gligeable.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Exemple de configuration pour un serveur 16 Go \/ 4 vCPU<\/h2>\n\n\n\n<pre class=\"wp-block-code\"><code># postgresql.conf \u2014 serveur 16 Go RAM, 4 vCPU, SSD\nshared_buffers = 4GB\neffective_cache_size = 12GB\nwork_mem = 128MB\nmaintenance_work_mem = 1GB\nwal_buffers = 64MB\n\n# Parall\u00e9lisme\nmax_parallel_workers_per_gather = 2\nmax_parallel_workers = 4\nmax_worker_processes = 8\n\n# Planificateur\nrandom_page_cost = 1.1          # SSD (d\u00e9faut 4.0 pour HDD)\neffective_io_concurrency = 200  # SSD (d\u00e9faut 1 pour HDD)\n\n# WAL et checkpoints\ncheckpoint_completion_target = 0.9\nwal_compression = on\nmax_wal_size = 2GB\nmin_wal_size = 512MB\n\n# Logging\nlog_min_duration_statement = 500  # Log les requ\u00eates &gt; 500ms\nlog_checkpoints = on\nlog_lock_waits = on<\/code><\/pre>\n\n\n\n<p>L&rsquo;outil <a href=\"https:\/\/pgtune.leopard.in.ua\/\" target=\"_blank\" rel=\"noreferrer noopener\">PGTune<\/a> permet de g\u00e9n\u00e9rer une configuration de d\u00e9part adapt\u00e9e \u00e0 votre mat\u00e9riel. C&rsquo;est un excellent point de d\u00e9part, \u00e0 affiner ensuite en fonction de la charge r\u00e9elle.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Automatiser la maintenance avec pg_cron<\/h2>\n\n\n\n<p>PostgreSQL n\u00e9cessite une maintenance r\u00e9guli\u00e8re pour rester performant. Le processus autovacuum s&rsquo;en charge partiellement, mais il est souvent insuffisant sur des bases tr\u00e8s actives comme celles de CKAN ou Nextcloud. L&rsquo;extension <strong>pg_cron<\/strong> permet de planifier des t\u00e2ches de maintenance directement dans PostgreSQL&nbsp;:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>-- Installer l'extension\nCREATE EXTENSION pg_cron;\n\n-- VACUUM ANALYZE hebdomadaire sur la base CKAN\nSELECT cron.schedule('vacuum-ckan', '0 3 * * 0',\n  'VACUUM (VERBOSE, ANALYZE) ckan_default');\n\n-- REINDEX mensuel des tables volumineuses\nSELECT cron.schedule('reindex-ckan', '0 4 1 * *',\n  'REINDEX DATABASE ckan_default');\n\n-- Nettoyage des connexions inactives depuis plus de 30 min\nSELECT cron.schedule('kill-idle', '*\/10 * * * *',\n  $$SELECT pg_terminate_backend(pid)\n    FROM pg_stat_activity\n    WHERE state = ''idle''\n    AND state_change &lt; now() - interval ''30 minutes''\n    AND pid &lt;&gt; pg_backend_pid()$$);<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\">Surveiller les performances avec pg_stat_statements<\/h2>\n\n\n\n<p>L&rsquo;extension <strong>pg_stat_statements<\/strong> est indispensable pour identifier les requ\u00eates lentes et les goulets d&rsquo;\u00e9tranglement. Elle collecte des statistiques sur toutes les requ\u00eates ex\u00e9cut\u00e9es&nbsp;:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>-- Activer dans postgresql.conf\n-- shared_preload_libraries = 'pg_stat_statements'\nCREATE EXTENSION pg_stat_statements;\n\n-- Top 10 des requ\u00eates les plus lentes (temps total cumul\u00e9)\nSELECT\n  round(total_exec_time::numeric, 2) AS total_ms,\n  calls,\n  round(mean_exec_time::numeric, 2) AS avg_ms,\n  round((100 * total_exec_time \/\n    sum(total_exec_time) OVER ())::numeric, 2) AS pct,\n  left(query, 120) AS query_preview\nFROM pg_stat_statements\nORDER BY total_exec_time DESC\nLIMIT 10;<\/code><\/pre>\n\n\n\n<p>Ces statistiques se couplent naturellement avec Grafana et le dashboard <a href=\"https:\/\/grafana.com\/grafana\/dashboards\/9628-postgresql-database\/\" target=\"_blank\" rel=\"noreferrer noopener\">PostgreSQL Database<\/a> pour une visualisation en temps r\u00e9el. Si vous avez d\u00e9j\u00e0 d\u00e9ploy\u00e9 la stack Prometheus\/Grafana\/Loki (voir notre article sur l&rsquo;observabilit\u00e9), l&rsquo;exporter <strong>postgres_exporter<\/strong> fait le pont entre les deux.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Pooling de connexions avec PgBouncer<\/h2>\n\n\n\n<p>Chaque connexion PostgreSQL consomme environ 10 Mo de RAM. Quand CKAN, Keycloak, Nextcloud et Gitea ouvrent chacun 20 \u00e0 50 connexions, le serveur peut rapidement saturer. <strong>PgBouncer<\/strong> s&rsquo;interpose comme un pool de connexions l\u00e9ger&nbsp;:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># pgbouncer.ini<\/code><\/pre>\n\n\n<p>[databases]<\/p>\n\n\n\n<p>ckan = host=127.0.0.1 port=5432 dbname=ckan_default keycloak = host=127.0.0.1 port=5432 dbname=keycloak nextcloud = host=127.0.0.1 port=5432 dbname=nextcloud<\/p>\n\n\n<p>[pgbouncer]<\/p>\n\n\n\n<p>listen_port = 6432 listen_addr = 0.0.0.0 auth_type = md5 auth_file = \/etc\/pgbouncer\/userlist.txt pool_mode = transaction # Le plus efficace pour les applications web default_pool_size = 20 max_client_conn = 200 max_db_connections = 50<\/p>\n\n\n\n<p>Le mode <code>transaction<\/code> est le plus adapt\u00e9 aux applications web&nbsp;: la connexion au serveur PostgreSQL est lib\u00e9r\u00e9e d\u00e8s que la transaction se termine, ce qui permet de servir 200 clients avec seulement 50 connexions r\u00e9elles.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Pr\u00e9parer la haute disponibilit\u00e9 avec la r\u00e9plication streaming<\/h2>\n\n\n\n<p>Pour les environnements de production critiques, PostgreSQL propose la r\u00e9plication streaming native. Un serveur secondaire (replica) re\u00e7oit les WAL en temps r\u00e9el et peut prendre le relais en cas de panne du primaire&nbsp;:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># Sur le primaire \u2014 postgresql.conf\nwal_level = replica\nmax_wal_senders = 3\nwal_keep_size = 1GB\n\n# Sur le primaire \u2014 pg_hba.conf\nhost replication replicator 10.0.0.0\/24 scram-sha-256\n\n# Sur le secondaire \u2014 initialisation\npg_basebackup -h primary.local -D \/var\/lib\/postgresql\/16\/main \\\n  -U replicator -Fp -Xs -P -R<\/code><\/pre>\n\n\n\n<p>Le flag <code>-R<\/code> cr\u00e9e automatiquement le fichier <code>standby.signal<\/code> et configure la connexion au primaire. Pour le basculement automatique (failover), des outils comme <strong>Patroni<\/strong> (avec etcd ou Consul) ou <strong>repmgr<\/strong> ajoutent une couche d&rsquo;orchestration qui d\u00e9tecte les pannes et promeut automatiquement le secondaire.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Int\u00e9gration Docker Compose<\/h2>\n\n\n\n<p>Si votre PostgreSQL tourne dans un conteneur Docker (ce qui est courant dans une stack auto-h\u00e9berg\u00e9e), la configuration se monte via un volume&nbsp;:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># docker-compose.yml (extrait)\nservices:\n  postgres:\n    image: postgres:16-alpine\n    volumes:\n      - pgdata:\/var\/lib\/postgresql\/data\n      - .\/postgresql.conf:\/etc\/postgresql\/postgresql.conf:ro\n    command: postgres -c config_file=\/etc\/postgresql\/postgresql.conf\n    shm_size: '4g'   # Aligner avec shared_buffers\n    deploy:\n      resources:\n        limits:\n          memory: 16g\n        reservations:\n          memory: 8g\n    healthcheck:\n      test: &#91;\"CMD-SHELL\", \"pg_isready -U postgres\"]\n      interval: 10s\n      timeout: 5s\n      retries: 5<\/code><\/pre>\n\n\n\n<p>Le param\u00e8tre <code>shm_size<\/code> est crucial&nbsp;: Docker limite par d\u00e9faut la m\u00e9moire partag\u00e9e \u00e0 64 Mo, ce qui fait \u00e9chouer un <code>shared_buffers<\/code> de 4 Go. Alignez-le avec la valeur de <code>shared_buffers<\/code>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">En r\u00e9sum\u00e9<\/h2>\n\n\n\n<p>PostgreSQL est un moteur de base de donn\u00e9es remarquable, mais ses performances par d\u00e9faut sont brid\u00e9es. En ajustant une dizaine de param\u00e8tres, en mettant en place PgBouncer, en automatisant la maintenance et en activant le monitoring avec pg_stat_statements, on obtient un socle de donn\u00e9es solide, performant et observable \u2014 exactement ce qu&rsquo;exige une stack open source en production. La r\u00e9plication streaming et Patroni permettent ensuite de passer \u00e0 la haute disponibilit\u00e9 quand le contexte l&rsquo;exige.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Optimiser PostgreSQL pour une stack open source&nbsp;: tuning, maintenance et haute disponibilit\u00e9 PostgreSQL est le socle de donn\u00e9es de la plupart des briques open source auto-h\u00e9berg\u00e9es&nbsp;: CKAN, Keycloak, Nextcloud, Gitea, Superset, Drupal. Par d\u00e9faut, sa configuration est volontairement conservatrice pour fonctionner sur n&rsquo;importe quelle machine. R\u00e9sultat&nbsp;: sur un serveur de production avec 16 ou 32 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":2117,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"ocean_post_layout":"","ocean_both_sidebars_style":"","ocean_both_sidebars_content_width":0,"ocean_both_sidebars_sidebars_width":0,"ocean_sidebar":"","ocean_second_sidebar":"","ocean_disable_margins":"enable","ocean_add_body_class":"","ocean_shortcode_before_top_bar":"","ocean_shortcode_after_top_bar":"","ocean_shortcode_before_header":"","ocean_shortcode_after_header":"","ocean_has_shortcode":"","ocean_shortcode_after_title":"","ocean_shortcode_before_footer_widgets":"","ocean_shortcode_after_footer_widgets":"","ocean_shortcode_before_footer_bottom":"","ocean_shortcode_after_footer_bottom":"","ocean_display_top_bar":"default","ocean_display_header":"default","ocean_header_style":"","ocean_center_header_left_menu":"","ocean_custom_header_template":"","ocean_custom_logo":0,"ocean_custom_retina_logo":0,"ocean_custom_logo_max_width":0,"ocean_custom_logo_tablet_max_width":0,"ocean_custom_logo_mobile_max_width":0,"ocean_custom_logo_max_height":0,"ocean_custom_logo_tablet_max_height":0,"ocean_custom_logo_mobile_max_height":0,"ocean_header_custom_menu":"","ocean_menu_typo_font_family":"","ocean_menu_typo_font_subset":"","ocean_menu_typo_font_size":0,"ocean_menu_typo_font_size_tablet":0,"ocean_menu_typo_font_size_mobile":0,"ocean_menu_typo_font_size_unit":"px","ocean_menu_typo_font_weight":"","ocean_menu_typo_font_weight_tablet":"","ocean_menu_typo_font_weight_mobile":"","ocean_menu_typo_transform":"","ocean_menu_typo_transform_tablet":"","ocean_menu_typo_transform_mobile":"","ocean_menu_typo_line_height":0,"ocean_menu_typo_line_height_tablet":0,"ocean_menu_typo_line_height_mobile":0,"ocean_menu_typo_line_height_unit":"","ocean_menu_typo_spacing":0,"ocean_menu_typo_spacing_tablet":0,"ocean_menu_typo_spacing_mobile":0,"ocean_menu_typo_spacing_unit":"","ocean_menu_link_color":"","ocean_menu_link_color_hover":"","ocean_menu_link_color_active":"","ocean_menu_link_background":"","ocean_menu_link_hover_background":"","ocean_menu_link_active_background":"","ocean_menu_social_links_bg":"","ocean_menu_social_hover_links_bg":"","ocean_menu_social_links_color":"","ocean_menu_social_hover_links_color":"","ocean_disable_title":"default","ocean_disable_heading":"default","ocean_post_title":"","ocean_post_subheading":"","ocean_post_title_style":"","ocean_post_title_background_color":"","ocean_post_title_background":0,"ocean_post_title_bg_image_position":"","ocean_post_title_bg_image_attachment":"","ocean_post_title_bg_image_repeat":"","ocean_post_title_bg_image_size":"","ocean_post_title_height":0,"ocean_post_title_bg_overlay":0.5,"ocean_post_title_bg_overlay_color":"","ocean_disable_breadcrumbs":"default","ocean_breadcrumbs_color":"","ocean_breadcrumbs_separator_color":"","ocean_breadcrumbs_links_color":"","ocean_breadcrumbs_links_hover_color":"","ocean_display_footer_widgets":"default","ocean_display_footer_bottom":"default","ocean_custom_footer_template":"","osh_disable_topbar_sticky":"default","osh_disable_header_sticky":"default","osh_sticky_header_style":"default","osh_sticky_header_effect":"","osh_custom_sticky_logo":0,"osh_custom_retina_sticky_logo":0,"osh_custom_sticky_logo_height":0,"osh_background_color":"","osh_links_color":"","osh_links_hover_color":"","osh_links_active_color":"","osh_links_bg_color":"","osh_links_hover_bg_color":"","osh_links_active_bg_color":"","osh_menu_social_links_color":"","osh_menu_social_hover_links_color":"","ocean_post_oembed":"","ocean_post_self_hosted_media":"","ocean_post_video_embed":"","ocean_link_format":"","ocean_link_format_target":"self","ocean_quote_format":"","ocean_quote_format_link":"post","ocean_gallery_link_images":"on","ocean_gallery_id":[],"footnotes":""},"categories":[17,18],"tags":[],"class_list":["post-2116","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data","category-devops","entry","has-media"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Optimiser PostgreSQL pour une stack open source : tuning, maintenance et haute disponibilit\u00e9 - askem<\/title>\n<meta name=\"description\" content=\"ASKEM BUREAU D&#039;\u00c9TUDES ET DE FORMATION NUM\u00c9RIQUE. 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