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kemal.earth
7ed8ab83a8
## I have read the [CONTRIBUTING.md](https://github.com/supabase/supabase/blob/master/CONTRIBUTING.md) file. YES ## What kind of change does this PR introduce? This introduces Query Insights. It's the first edition of possible future updates. This takes our old prototype and builds upon it for a more action driven insights view. --------- Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com> Co-authored-by: Ali Waseem <waseema393@gmail.com>
67 lines
3.0 KiB
TypeScript
67 lines
3.0 KiB
TypeScript
export const SUPAMONITOR_EXCLUDED_ROLES = [
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'supabase_admin',
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'supabase_auth_admin',
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'supabase_storage_admin',
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'supabase_realtime_admin',
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'pgbouncer',
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'dashboard_user',
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] as const
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export const SUPAMONITOR_EXCLUDED_APP_NAMES = ['supabase-dashboard', 'mgmt-api'] as const
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export const TRANSACTION_CONTROL_REGEX =
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/^\s*(BEGIN|COMMIT|ROLLBACK|SET\s|RESET\s|DISCARD|DEALLOCATE|SHOW\s)/i
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export const SCHEMA_INTROSPECTION_REGEX =
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/\bFROM\s+(?:pg_catalog\.|information_schema\.|pg_class\b|pg_attribute\b|pg_type\b|pg_namespace\b)/i
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export const getSupamonitorLogsQuery = (startTime: string, endTime: string) => {
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// Validate and canonicalize to ISO 8601 UTC before embedding in SQL.
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// new Date().toISOString() throws RangeError on invalid input and always
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// produces "YYYY-MM-DDTHH:mm:ss.mmmZ" which contains no SQL special characters.
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const safeStart = new Date(startTime).toISOString()
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const safeEnd = new Date(endTime).toISOString()
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return `
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-- This query is run by Supabase Query Insights to aggregate pg_stat_statements
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-- data collected by the supamonitor extension. It reads from Logflare and groups
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-- execution metrics (timing, call counts, percentiles) by query and minute so
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-- the dashboard can surface slow queries, high-call patterns, and planning overhead.
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-- If you see this query in your logs, it is a read-only analytics query and safe to ignore.
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select
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TIMESTAMP_TRUNC(sml.timestamp, MINUTE) as timestamp,
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CAST(sml_parsed.application_name AS STRING) as application_name,
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SUM(sml_parsed.calls) as calls,
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CAST(sml_parsed.database_name AS STRING) as database_name,
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CAST(sml_parsed.query AS STRING) as query,
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sml_parsed.query_id as query_id,
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SUM(sml_parsed.total_exec_time) as total_exec_time,
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SUM(sml_parsed.total_plan_time) as total_plan_time,
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CAST(sml_parsed.user_name AS STRING) as user_name,
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CASE WHEN SUM(sml_parsed.calls) > 0
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THEN SUM(sml_parsed.total_exec_time) / SUM(sml_parsed.calls)
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ELSE 0
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END as mean_exec_time,
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MIN(NULLIF(sml_parsed.total_exec_time, 0)) as min_exec_time,
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MAX(sml_parsed.total_exec_time) as max_exec_time,
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CASE WHEN SUM(sml_parsed.calls) > 0
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THEN SUM(sml_parsed.total_plan_time) / SUM(sml_parsed.calls)
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ELSE 0
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END as mean_plan_time,
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MIN(NULLIF(sml_parsed.total_plan_time, 0)) as min_plan_time,
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MAX(sml_parsed.total_plan_time) as max_plan_time,
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APPROX_QUANTILES(sml_parsed.total_exec_time, 100)[OFFSET(50)] as p50_exec_time,
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APPROX_QUANTILES(sml_parsed.total_exec_time, 100)[OFFSET(95)] as p95_exec_time,
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APPROX_QUANTILES(sml_parsed.total_plan_time, 100)[OFFSET(50)] as p50_plan_time,
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APPROX_QUANTILES(sml_parsed.total_plan_time, 100)[OFFSET(95)] as p95_plan_time
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from supamonitor_logs as sml
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cross join unnest(sml.metadata) as sml_metadata
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cross join unnest(sml_metadata.supamonitor) as sml_parsed
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WHERE sml.event_message = 'log'
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AND sml.timestamp >= CAST('${safeStart}' AS TIMESTAMP)
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AND sml.timestamp <= CAST('${safeEnd}' AS TIMESTAMP)
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GROUP BY timestamp, user_name, database_name, application_name, query_id, query
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ORDER BY timestamp DESC
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`.trim()
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}
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