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-->\n","\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EpgInsights is a built-in Postgres extension for Snowflake Postgres that continuously collects performance and health metrics from your Snowflake Postgres instance. It samples about 34 different Postgres statistics views every 30 seconds and stores the results in queryable tables inside your database. No external agents, collectors, or time-series databases are required.\u003C/p\u003E\n","\u003Cp\u003EIn this guide you will enable pgInsights, explore the Snowsight Insights pages, and query the underlying data with plain SQL. By the end you will know how to use the "metric, interpretation, action" workflow to spot health issues, identify the queries causing them, and take action before they become application incidents.\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake Postgres instance running \u003Cstrong\u003EPostgres 18.6 or later\u003C/strong\u003E. If you don't have one, follow the \u003Ca href=\"https://quickstarts.snowflake.com/guide/getting-started-with-snowflake-postgres/\"\u003EGetting Started with Snowflake Postgres\u003C/a\u003E quickstart first.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003Epsql\u003C/code\u003E installed locally and connected to your instance\u003C/li\u003E\u003Cli\u003EAn instance that has been refreshed recently. If your instance has been running for a while, run a \u003Cstrong\u003ERefresh\u003C/strong\u003E so it picks up the latest minor version, OS patches, and Snowflake Postgres features (including pgInsights). Check your current version with \u003Ccode\u003ESELECT version();\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EAn existing database with some table activity (queries, inserts, updates) so pgInsights has metrics to collect\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERefresh your instance\u003C/h3\u003E\n","\u003Cp\u003EA Refresh replaces your instance without changing its configured resources. Your connection string stays the same, but there is a brief service interruption during the failover, so make sure your applications can reconnect.\u003C/p\u003E\n","\u003Cp\u003ETo refresh from Snowsight, go to \u003Cstrong\u003EPostgres\u003C/strong\u003E, select your instance, then choose \u003Cstrong\u003EManage\u003C/strong\u003E > \u003Cstrong\u003ERefresh\u003C/strong\u003E. Select \u003Cstrong\u003EBypass maintenance window and apply immediately\u003C/strong\u003E if you don't want to wait for your next maintenance window.\u003C/p\u003E\n","\u003Cp\u003ETo refresh with SQL, run \u003Ccode\u003EALTER POSTGRES INSTANCE\u003C/code\u003E with the instance's \u003Cstrong\u003Ecurrent\u003C/strong\u003E \u003Ccode\u003ECOMPUTE_FAMILY\u003C/code\u003E value:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EALTER POSTGRES INSTANCE my_instance\n  SET COMPUTE_FAMILY = STANDARD_M\n  APPLY IMMEDIATELY;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EUse \u003Ccode\u003EDESCRIBE POSTGRES INSTANCE my_instance;\u003C/code\u003E to track progress. The refresh is complete when the instance returns to the \u003Ccode\u003EReady\u003C/code\u003E state. For more detail, see \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-postgres/managing-instances\"\u003ESnowflake Postgres instance management\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to enable and verify pgInsights with two SQL commands\u003C/li\u003E\u003Cli\u003EHow to read the Snowsight Insights pages: health gauges, query rankings, wait events, and more\u003C/li\u003E\u003Cli\u003EHow to query pgInsights tables directly with SQL for ad-hoc troubleshooting and automation\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA Snowflake Postgres instance with pgInsights enabled and actively collecting metrics\u003C/li\u003E\u003Cli\u003EHands-on experience with the 8 Insights tabs in Snowsight\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EEnable pgInsights\u003C/h2\u003E\n","\u003Cp\u003EConnect to your Snowflake Postgres instance with \u003Ccode\u003Epsql\u003C/code\u003E and enable the two extensions:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE EXTENSION IF NOT EXISTS pg_stat_statements;\nCREATE EXTENSION insights CASCADE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Ccode\u003Epg_stat_statements\u003C/code\u003E is a standard Postgres contrib extension that tracks per-query execution statistics. The \u003Ccode\u003Einsights\u003C/code\u003E extension builds on top of it, adding continuous collection, history, rollups, baselines, and forecasts.\u003C/p\u003E\n","\u003Ch3\u003EVerify the installation\u003C/h3\u003E\n","\u003Cp\u003EConfirm the extension is installed and the background workers are running:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Check the extension version\nSELECT extname, extversion FROM pg_extension WHERE extname = 'insights';\n\n-- Confirm the background workers are active\nSELECT pid, application_name\nFROM   pg_stat_activity\nWHERE  application_name LIKE 'insights%';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see two workers: \u003Ccode\u003Einsights orchestrator\u003C/code\u003E and \u003Ccode\u003Einsights scheduler: postgres\u003C/code\u003E. These begin collecting metrics immediately, no restart or additional configuration is required.\u003C/p\u003E\n","\u003Ch3\u003EWhat gets created\u003C/h3\u003E\n","\u003Cp\u003EAll pgInsights objects live in the \u003Ccode\u003Einsights\u003C/code\u003E schema. Run \u003Ccode\u003E\\dt insights.*\u003C/code\u003E in psql to see the new tables. The most important ones:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETable\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPurpose\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Epgi_metric\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECurrent value of every metric (latest snapshot)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Epgi_metric_history\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERaw time-series at ~30-second granularity (30-day retention)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Epgi_sql\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENormalized query text keyed by query ID\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Epgi_sql_metric_history\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPer-statement execution stats per 1-minute snapshot\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Epgi_wait_event\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPer-session wait-event samples (1-day retention)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Epgi_metric_trend\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExpected value range per hour-of-week (anomaly baselines)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Epgi_metric_forecast\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELinear-regression projections, up to 180 days out\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003Epgi_collection\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EWhat is being collected, how often, and whether it is enabled\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EFor the complete table and metric reference, see the \u003Ca href=\"https://docs.snowflake.com/LIMITEDACCESS/user-guide/snowflake-postgres/postgres-insights\"\u003EpgInsights documentation\u003C/a\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EGenerate Traffic\u003C/h2\u003E\n","\u003Cp\u003EpgInsights needs real database activity to have something to show. Run your application's normal workload, replay production queries against a staging copy, or simply run a mix of queries by hand — SELECTs, INSERTs, UPDATEs, and JOINs all contribute useful metrics.\u003C/p\u003E\n","\u003Cp\u003ELet the workload run for at least 5 minutes so pgInsights has time to capture multiple snapshots.\u003C/p\u003E\n","\u003Ch3\u003EGenerate fake traffic with Cortex Code\u003C/h3\u003E\n","\u003Cp\u003EIf you don't have a workload handy, Cortex Code (CoCo) can build sample data and a load generator for you. Install either:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-cli\"\u003ECoCo CLI\u003C/a\u003E for macOS, Linux, and Windows\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-desktop\"\u003ECoCo Desktop\u003C/a\u003E for macOS and Windows\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThen give it a prompt like this one:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EConnect to my Snowflake Postgres instance using psql. Create a sample e-commerce\nschema (customers, products, orders, order_items) and load it with about 100,000\nrows of realistic fake data. Then write and run a script that generates a mixed\nworkload for 10 minutes: frequent point SELECTs, a few slow JOIN/aggregate\nqueries, steady INSERTs and UPDATEs on orders, and occasional long-running\ntransactions that hold row locks so I can see blocking and wait events.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAdjust the row counts and duration to fit your instance size.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EExplore the Snowsight Insights Pages\u003C/h2\u003E\n","\u003Cp\u003EWhile the workload runs, open your instance in Snowsight. Go to \u003Cstrong\u003EData > Postgres\u003C/strong\u003E and click your instance name.\u003C/p\u003E\n","\u003Ch3\u003EOverview tab\u003C/h3\u003E\n","\u003Cp\u003EThe Overview tab is your starting point. It has three health gauges and four key metric charts.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowflake-postgres-insights/insights_overview.png?v=f1ff1de3\" alt=\"Snowsight Insights overview showing health gauges and key metrics\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EHealth gauges:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EConnection usage\u003C/strong\u003E — what percentage of \u003Ccode\u003Emax_connections\u003C/code\u003E is in use. Green up to 70%, amber 70-90%, red 90-100%.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EActive connections\u003C/strong\u003E — how many connections are actively executing queries. High active with low total means your workload is compute-heavy. High total with low active means you have idle connection buildup.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EXID wraparound risk\u003C/strong\u003E — how close the database is to transaction-ID wraparound. A rising value means vacuum is falling behind.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey metric charts:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase Load\u003C/strong\u003E — estimated active sessions over time. A rising line means more work or slower queries.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETransaction throughput\u003C/strong\u003E — commits and rollbacks per second.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESession counts\u003C/strong\u003E — total, active, and blocking sessions together.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase size\u003C/strong\u003E — on-disk growth over time.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe gauges tell you what is happening now. The charts tell you whether it is getting better or worse.\u003C/p\u003E\n","\u003Ch3\u003EInsights tabs\u003C/h3\u003E\n","\u003Cp\u003EBelow the Overview, there are seven Insights tabs. Use the \u003Cstrong\u003ETime range\u003C/strong\u003E, \u003Cstrong\u003EGroup by\u003C/strong\u003E, and \u003Cstrong\u003EDatabase filter\u003C/strong\u003E controls at the top to adjust the view.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowflake-postgres-insights/insights_tabs.png?v=f1ff1de3\" alt=\"Insights tabs showing System health, Database activity, Top queries, Wait events, Concurrency, Sessions, and Table hygiene\"\u003E\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETab\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat it shows\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhen to use it\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESystem health\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECPU, memory, IOPS, load average, network, database size\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Is the instance saturated?"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EDatabase activity\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESessions, transactions, I/O, WAL, health warnings\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"What is the workload doing?"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ETop queries\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERankings by call frequency, execution time, variability, WAL, I/O\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Which query should I tune?"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EWait events\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EActive sessions by wait-event type, blocking PIDs\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"What are sessions stuck on?"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EConcurrency\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELock tree, blocking hierarchy, lock types\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Who is blocking whom?"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESessions\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESession breakdown by state and database, individual session details\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Who is connected and what are they doing?"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ETable hygiene\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECache hit ratio, index miss ratio, bloat, dead rows, vacuum status\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"Which tables need maintenance?"\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EEvery chart has a \u003Cstrong\u003EDetails\u003C/strong\u003E link that opens a larger drill-down view.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowflake-postgres-insights/insights_top_queries.png?v=f1ff1de3\" alt=\"Top queries tab showing call frequency and execution time rankings\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EThe metric, interpretation, action workflow\u003C/h3\u003E\n","\u003Cp\u003ERather than memorizing every chart, learn this three-step pattern:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EMetric\u003C/strong\u003E — the Overview gauges or a chart tells you something is off, connection usage is red, database load is spiking, a wait-event category is dominating.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EInterpretation\u003C/strong\u003E — drill into the relevant Insights tab to identify the cause, which query is slow, which session is blocking, which table has dead rows.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAction\u003C/strong\u003E — fix the root cause, tune the query, kill the blocker, add an index, run vacuum, adjust connection pooling.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EFor example: Connection usage gauge is red → Sessions tab shows many idle connections from the same application → action is to enable connection pooling.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EQuery pgInsights with SQL\u003C/h2\u003E\n","\u003Cp\u003EEverything you see in the Snowsight Insights pages is backed by the tables in the \u003Ccode\u003Einsights\u003C/code\u003E schema. You can query them directly for ad-hoc troubleshooting, create Grafana dashboards, or automated alerts.\u003C/p\u003E\n","\u003Ch3\u003ECurrent connection state\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT metric_name, metric_value::numeric AS value\nFROM   insights.pgi_metric\nWHERE  col_name = 'pg_session'\nORDER  BY metric_name;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ETop 10 slowest queries\u003C/h3\u003E\n","\u003Cp\u003EThis is often the most useful query. It shows exactly where your database spends its time:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT s.query,\n       m.calls,\n       round(m.total_exec_time::numeric, 2) AS total_ms,\n       round(m.mean_exec_time::numeric, 2)  AS mean_ms\nFROM   insights.pgi_sql_metric_history m\nJOIN   insights.pgi_sql s USING (queryid, datname, rolname)\nWHERE  m.snapshot_dt = (SELECT max(snapshot_dt) FROM insights.pgi_sql_metric_history)\nORDER  BY m.total_exec_time DESC\nLIMIT  10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EActive wait events (last 5 minutes)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT wait_event_type, wait_event, count(*) AS sessions\nFROM   insights.pgi_wait_event\nWHERE  snapshot_dt > now() - interval '5 minutes'\n  AND  wait_event_type IS NOT NULL\nGROUP  BY wait_event_type, wait_event\nORDER  BY sessions DESC;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EAnomaly detection\u003C/h3\u003E\n","\u003Cp\u003EpgInsights builds an expected range for each metric at each hour of the week. This query surfaces anything outside the normal range right now:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT m.col_name, m.metric_name,\n       m.metric_value::numeric AS current_val,\n       t.floor_val, t.ceiling_val,\n       CASE WHEN m.metric_value::numeric > t.ceiling_val THEN 'HIGH' ELSE 'LOW' END AS direction\nFROM   insights.pgi_metric m\nJOIN   insights.pgi_metric_trend t\n  ON   m.col_name    = t.col_name\n AND   m.metric_name = t.metric_name\n AND   COALESCE(m.metric_key::text, '') = COALESCE(t.metric_key::text, '')\n AND   t.dow = EXTRACT(dow  FROM now())::int\n AND   t.hr  = EXTRACT(hour FROM now())::int\nWHERE  m.data_type = 'number'\n  AND  (m.metric_value::numeric < t.floor_val OR m.metric_value::numeric > t.ceiling_val)\nORDER  BY m.col_name, m.metric_name;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E This query only returns results after pgInsights has collected enough history to build baselines — at least a few days of data.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ECapacity forecast\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT forecast_dt, avg_val AS projected_connections\nFROM   insights.pgi_metric_forecast\nWHERE  col_name = 'pg_session'\n  AND  metric_name = 'active'\nORDER  BY forecast_dt;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EForecasts require at least a week of historical data to compute a linear regression.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EPractical Tips\u003C/h2\u003E\n","\u003Cp\u003EA few things you might run into while testing:\u003C/p\u003E\n","\u003Ch3\u003EStorage Utilization chart appears blank\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Ccode\u003Epg_db_size\u003C/code\u003E collection (exact filesystem walk) is \u003Cstrong\u003Edisabled by default\u003C/strong\u003E because it can be expensive on large databases. The hourly \u003Ccode\u003Epg_db_size_est\u003C/code\u003E estimate only fires once per hour. On a fresh instance, the Storage Utilization chart may show nothing.\u003C/p\u003E\n","\u003Cp\u003ETo populate it immediately:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Enable the exact size collector\nUPDATE insights.pgi_collection SET enabled = true WHERE col_name = 'pg_db_size';\n\n-- Or force the estimate to run now\nUPDATE insights.pgi_collection SET next_collect = current_timestamp WHERE col_name = 'pg_db_size_est';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDatabase Load chart shows zero\u003C/h3\u003E\n","\u003Cp\u003EThe Database Load chart (Average Active Sessions) samples every 30 seconds. If all your queries complete in milliseconds, they won't be active when the sampler fires. This is expected for fast workloads — it means your database is under light load.\u003C/p\u003E\n","\u003Ch3\u003ETrend and forecast tables are empty\u003C/h3\u003E\n","\u003Cp\u003E\u003Ccode\u003Epgi_metric_trend\u003C/code\u003E (anomaly baselines) needs several days of data to build hour-of-week patterns. \u003Ccode\u003Epgi_metric_forecast\u003C/code\u003E (capacity projections) needs at least a week. These tables populate automatically over time — no action required.\u003C/p\u003E\n","\u003Ch3\u003EExtension not available on older instances\u003C/h3\u003E\n","\u003Cp\u003EpgInsights requires Postgres 18.6 or later. If the \u003Ccode\u003Einsights\u003C/code\u003E extension doesn't appear in \u003Ccode\u003Epg_available_extensions\u003C/code\u003E, your instance may be on an older Postgres image. Refresh your Postgres instance to get the latest extensions.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EClean Up\u003C/h2\u003E\n","\u003Cp\u003EIf you are done testing and no longer want Insights metrics collection, you can remove the extension.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Remove the extension and all insights tables\nDROP EXTENSION insights CASCADE;\n\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EResources\u003C/h2\u003E\n\u003Cul\u003E\u003Cli\u003ERead the \u003Ca href=\"https://docs.snowflake.com/user-guide/snowflake-postgres/insights\"\u003EInsights extension documentation\u003C/a\u003E for the complete table and metric reference\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Monitor Snowflake Postgres with pgInsights",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","value":"\u003C!-- ------------------------ --\u003E\n## Overview\n\npgInsights is a built-in Postgres extension for Snowflake Postgres that continuously collects performance and health metrics from your Snowflake Postgres instance. It samples about 34 different Postgres statistics views every 30 seconds and stores the results in queryable tables inside your database. No external agents, collectors, or time-series databases are required.\n\nIn this guide you will enable pgInsights, explore the Snowsight Insights pages, and query the underlying data with plain SQL. By the end you will know how to use the \"metric, interpretation, action\" workflow to spot health issues, identify the queries causing them, and take action before they become application incidents.\n\n### What You'll Need\n- A Snowflake Postgres instance running **Postgres 18.6 or later**. If you don't have one, follow the [Getting Started with Snowflake Postgres](https://quickstarts.snowflake.com/guide/getting-started-with-snowflake-postgres/) quickstart first.\n- `psql` installed locally and connected to your instance\n- An instance that has been refreshed recently. If your instance has been running for a while, run a **Refresh** so it picks up the latest minor version, OS patches, and Snowflake Postgres features (including pgInsights). Check your current version with `SELECT version();`.\n- An existing database with some table activity (queries, inserts, updates) so pgInsights has metrics to collect\n\n### Refresh your instance\n\nA Refresh replaces your instance without changing its configured resources. Your connection string stays the same, but there is a brief service interruption during the failover, so make sure your applications can reconnect.\n\nTo refresh from Snowsight, go to **Postgres**, select your instance, then choose **Manage** \u003E **Refresh**. Select **Bypass maintenance window and apply immediately** if you don't want to wait for your next maintenance window.\n\nTo refresh with SQL, run `ALTER POSTGRES INSTANCE` with the instance's **current** `COMPUTE_FAMILY` value:\n\n```sql\nALTER POSTGRES INSTANCE my_instance\n  SET COMPUTE_FAMILY = STANDARD_M\n  APPLY IMMEDIATELY;\n```\n\nUse `DESCRIBE POSTGRES INSTANCE my_instance;` to track progress. The refresh is complete when the instance returns to the `Ready` state. For more detail, see [Snowflake Postgres instance management](https://docs.snowflake.com/en/user-guide/snowflake-postgres/managing-instances).\n\n### What You'll Learn\n- How to enable and verify pgInsights with two SQL commands\n- How to read the Snowsight Insights pages: health gauges, query rankings, wait events, and more\n- How to query pgInsights tables directly with SQL for ad-hoc troubleshooting and automation\n\n\n### What You'll Build\n- A Snowflake Postgres instance with pgInsights enabled and actively collecting metrics\n- Hands-on experience with the 8 Insights tabs in Snowsight\n\n\u003C!-- ------------------------ --\u003E\n## Enable pgInsights\n\nConnect to your Snowflake Postgres instance with `psql` and enable the two extensions:\n\n```sql\nCREATE EXTENSION IF NOT EXISTS pg_stat_statements;\nCREATE EXTENSION insights CASCADE;\n```\n\n`pg_stat_statements` is a standard Postgres contrib extension that tracks per-query execution statistics. The `insights` extension builds on top of it, adding continuous collection, history, rollups, baselines, and forecasts.\n\n### Verify the installation\n\nConfirm the extension is installed and the background workers are running:\n\n```sql\n-- Check the extension version\nSELECT extname, extversion FROM pg_extension WHERE extname = 'insights';\n\n-- Confirm the background workers are active\nSELECT pid, application_name\nFROM   pg_stat_activity\nWHERE  application_name LIKE 'insights%';\n```\n\nYou should see two workers: `insights orchestrator` and `insights scheduler: postgres`. These begin collecting metrics immediately, no restart or additional configuration is required.\n\n### What gets created\n\nAll pgInsights objects live in the `insights` schema. Run `\\dt insights.*` in psql to see the new tables. The most important ones:\n\n| Table | Purpose |\n|---|---|\n| `pgi_metric` | Current value of every metric (latest snapshot) |\n| `pgi_metric_history` | Raw time-series at ~30-second granularity (30-day retention) |\n| `pgi_sql` | Normalized query text keyed by query ID |\n| `pgi_sql_metric_history` | Per-statement execution stats per 1-minute snapshot |\n| `pgi_wait_event` | Per-session wait-event samples (1-day retention) |\n| `pgi_metric_trend` | Expected value range per hour-of-week (anomaly baselines) |\n| `pgi_metric_forecast` | Linear-regression projections, up to 180 days out |\n| `pgi_collection` | What is being collected, how often, and whether it is enabled |\n\nFor the complete table and metric reference, see the [pgInsights documentation](https://docs.snowflake.com/LIMITEDACCESS/user-guide/snowflake-postgres/postgres-insights).\n\n\u003C!-- ------------------------ --\u003E\n## Generate Traffic\n\npgInsights needs real database activity to have something to show. Run your application's normal workload, replay production queries against a staging copy, or simply run a mix of queries by hand — SELECTs, INSERTs, UPDATEs, and JOINs all contribute useful metrics.\n\nLet the workload run for at least 5 minutes so pgInsights has time to capture multiple snapshots.\n\n### Generate fake traffic with Cortex Code\n\nIf you don't have a workload handy, Cortex Code (CoCo) can build sample data and a load generator for you. Install either:\n\n- [CoCo CLI](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-cli) for macOS, Linux, and Windows\n- [CoCo Desktop](https://docs.snowflake.com/en/user-guide/cortex-code/cortex-code-desktop) for macOS and Windows\n\nThen give it a prompt like this one:\n\n```\nConnect to my Snowflake Postgres instance using psql. Create a sample e-commerce\nschema (customers, products, orders, order_items) and load it with about 100,000\nrows of realistic fake data. Then write and run a script that generates a mixed\nworkload for 10 minutes: frequent point SELECTs, a few slow JOIN/aggregate\nqueries, steady INSERTs and UPDATEs on orders, and occasional long-running\ntransactions that hold row locks so I can see blocking and wait events.\n```\n\nAdjust the row counts and duration to fit your instance size.\n\n\u003C!-- ------------------------ --\u003E\n## Explore the Snowsight Insights Pages\n\nWhile the workload runs, open your instance in Snowsight. Go to **Data \u003E Postgres** and click your instance name.\n\n### Overview tab\n\nThe Overview tab is your starting point. It has three health gauges and four key metric charts.\n\n![Snowsight Insights overview showing health gauges and key metrics](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowflake-postgres-insights/insights_overview.png?v=f1ff1de3)\n\n**Health gauges:**\n- **Connection usage** — what percentage of `max_connections` is in use. Green up to 70%, amber 70-90%, red 90-100%.\n- **Active connections** — how many connections are actively executing queries. High active with low total means your workload is compute-heavy. High total with low active means you have idle connection buildup.\n- **XID wraparound risk** — how close the database is to transaction-ID wraparound. A rising value means vacuum is falling behind.\n\n**Key metric charts:**\n- **Database Load** — estimated active sessions over time. A rising line means more work or slower queries.\n- **Transaction throughput** — commits and rollbacks per second.\n- **Session counts** — total, active, and blocking sessions together.\n- **Database size** — on-disk growth over time.\n\nThe gauges tell you what is happening now. The charts tell you whether it is getting better or worse.\n\n### Insights tabs\n\nBelow the Overview, there are seven Insights tabs. Use the **Time range**, **Group by**, and **Database filter** controls at the top to adjust the view.\n\n![Insights tabs showing System health, Database activity, Top queries, Wait events, Concurrency, Sessions, and Table hygiene](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowflake-postgres-insights/insights_tabs.png?v=f1ff1de3)\n\n| Tab | What it shows | When to use it |\n|---|---|---|\n| **System health** | CPU, memory, IOPS, load average, network, database size | \"Is the instance saturated?\" |\n| **Database activity** | Sessions, transactions, I/O, WAL, health warnings | \"What is the workload doing?\" |\n| **Top queries** | Rankings by call frequency, execution time, variability, WAL, I/O | \"Which query should I tune?\" |\n| **Wait events** | Active sessions by wait-event type, blocking PIDs | \"What are sessions stuck on?\" |\n| **Concurrency** | Lock tree, blocking hierarchy, lock types | \"Who is blocking whom?\" |\n| **Sessions** | Session breakdown by state and database, individual session details | \"Who is connected and what are they doing?\" |\n| **Table hygiene** | Cache hit ratio, index miss ratio, bloat, dead rows, vacuum status | \"Which tables need maintenance?\" |\n\nEvery chart has a **Details** link that opens a larger drill-down view.\n\n![Top queries tab showing call frequency and execution time rankings](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/snowflake-postgres-insights/insights_top_queries.png?v=f1ff1de3)\n\n### The metric, interpretation, action workflow\n\nRather than memorizing every chart, learn this three-step pattern:\n\n1. **Metric** — the Overview gauges or a chart tells you something is off, connection usage is red, database load is spiking, a wait-event category is dominating.\n2. **Interpretation** — drill into the relevant Insights tab to identify the cause, which query is slow, which session is blocking, which table has dead rows.\n3. **Action** — fix the root cause, tune the query, kill the blocker, add an index, run vacuum, adjust connection pooling.\n\nFor example: Connection usage gauge is red → Sessions tab shows many idle connections from the same application → action is to enable connection pooling.\n\n\u003C!-- ------------------------ --\u003E\n## Query pgInsights with SQL\n\nEverything you see in the Snowsight Insights pages is backed by the tables in the `insights` schema. You can query them directly for ad-hoc troubleshooting, create Grafana dashboards, or automated alerts.\n\n### Current connection state\n\n```sql\nSELECT metric_name, metric_value::numeric AS value\nFROM   insights.pgi_metric\nWHERE  col_name = 'pg_session'\nORDER  BY metric_name;\n```\n\n### Top 10 slowest queries\n\nThis is often the most useful query. It shows exactly where your database spends its time:\n\n```sql\nSELECT s.query,\n       m.calls,\n       round(m.total_exec_time::numeric, 2) AS total_ms,\n       round(m.mean_exec_time::numeric, 2)  AS mean_ms\nFROM   insights.pgi_sql_metric_history m\nJOIN   insights.pgi_sql s USING (queryid, datname, rolname)\nWHERE  m.snapshot_dt = (SELECT max(snapshot_dt) FROM insights.pgi_sql_metric_history)\nORDER  BY m.total_exec_time DESC\nLIMIT  10;\n```\n\n### Active wait events (last 5 minutes)\n\n```sql\nSELECT wait_event_type, wait_event, count(*) AS sessions\nFROM   insights.pgi_wait_event\nWHERE  snapshot_dt \u003E now() - interval '5 minutes'\n  AND  wait_event_type IS NOT NULL\nGROUP  BY wait_event_type, wait_event\nORDER  BY sessions DESC;\n```\n\n### Anomaly detection\n\npgInsights builds an expected range for each metric at each hour of the week. This query surfaces anything outside the normal range right now:\n\n```sql\nSELECT m.col_name, m.metric_name,\n       m.metric_value::numeric AS current_val,\n       t.floor_val, t.ceiling_val,\n       CASE WHEN m.metric_value::numeric \u003E t.ceiling_val THEN 'HIGH' ELSE 'LOW' END AS direction\nFROM   insights.pgi_metric m\nJOIN   insights.pgi_metric_trend t\n  ON   m.col_name    = t.col_name\n AND   m.metric_name = t.metric_name\n AND   COALESCE(m.metric_key::text, '') = COALESCE(t.metric_key::text, '')\n AND   t.dow = EXTRACT(dow  FROM now())::int\n AND   t.hr  = EXTRACT(hour FROM now())::int\nWHERE  m.data_type = 'number'\n  AND  (m.metric_value::numeric \u003C t.floor_val OR m.metric_value::numeric \u003E t.ceiling_val)\nORDER  BY m.col_name, m.metric_name;\n```\n\n\u003E **Note:** This query only returns results after pgInsights has collected enough history to build baselines — at least a few days of data.\n\n### Capacity forecast\n\n```sql\nSELECT forecast_dt, avg_val AS projected_connections\nFROM   insights.pgi_metric_forecast\nWHERE  col_name = 'pg_session'\n  AND  metric_name = 'active'\nORDER  BY forecast_dt;\n```\n\nForecasts require at least a week of historical data to compute a linear regression.\n\n\u003C!-- ------------------------ --\u003E\n## Practical Tips\n\nA few things you might run into while testing:\n\n### Storage Utilization chart appears blank\n\nThe `pg_db_size` collection (exact filesystem walk) is **disabled by default** because it can be expensive on large databases. The hourly `pg_db_size_est` estimate only fires once per hour. On a fresh instance, the Storage Utilization chart may show nothing.\n\nTo populate it immediately:\n\n```sql\n-- Enable the exact size collector\nUPDATE insights.pgi_collection SET enabled = true WHERE col_name = 'pg_db_size';\n\n-- Or force the estimate to run now\nUPDATE insights.pgi_collection SET next_collect = current_timestamp WHERE col_name = 'pg_db_size_est';\n```\n\n### Database Load chart shows zero\n\nThe Database Load chart (Average Active Sessions) samples every 30 seconds. If all your queries complete in milliseconds, they won't be active when the sampler fires. This is expected for fast workloads — it means your database is under light load.\n\n### Trend and forecast tables are empty\n\n`pgi_metric_trend` (anomaly baselines) needs several days of data to build hour-of-week patterns. `pgi_metric_forecast` (capacity projections) needs at least a week. These tables populate automatically over time — no action required.\n\n### Extension not available on older instances\n\npgInsights requires Postgres 18.6 or later. If the `insights` extension doesn't appear in `pg_available_extensions`, your instance may be on an older Postgres image. Refresh your Postgres instance to get the latest extensions.\n\n\u003C!-- ------------------------ --\u003E\n## Clean Up\n\nIf you are done testing and no longer want Insights metrics collection, you can remove the extension.\n\n```sql\n-- Remove the extension and all insights tables\nDROP EXTENSION insights CASCADE;\n\n```\n\n\u003C!-- ------------------------ --\u003E\n## Resources\n\n- Read the [Insights extension documentation](https://docs.snowflake.com/user-guide/snowflake-postgres/insights) for the complete table and metric reference\n\n\n","title":"Quickstart Article Body","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-25e643d81e","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-d7fcd3e42b",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-887f95ff4f","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-10-27",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-47e2128557","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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