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### Changes 🏗️ Adds `autogpt_platform/analytics/` — 14 SQL view definitions that expose production data safely through a locked-down `analytics` schema. **Security model:** - Views use `security_invoker = false` (PostgreSQL 15+), so they execute as their owner (`postgres`), not the caller - `analytics_readonly` role only has access to `analytics.*` — cannot touch `platform` or `auth` tables directly **Files:** - `backend/generate_views.py` — does everything; auto-reads credentials from `backend/.env` - `analytics/queries/*.sql` — 14 documented view definitions (auth, user activity, executions, onboarding funnel, cohort retention) --- ### Running locally (dev) ```bash cd autogpt_platform/backend # First time only — creates analytics schema, role, grants poetry run analytics-setup # Create / refresh views (auto-reads backend/.env) poetry run analytics-views ``` ### Running in production (Supabase) ```bash cd autogpt_platform/backend # Step 1 — first time only (run in Supabase SQL Editor as postgres superuser) poetry run analytics-setup --dry-run # Paste the output into Supabase SQL Editor and run # Step 2 — apply views (use direct connection host, not pooler) poetry run analytics-views --db-url "postgresql://postgres:PASSWORD@db.<ref>.supabase.co:5432/postgres" # Step 3 — set password for analytics_readonly so external tools can connect # Run in Supabase SQL Editor: # ALTER ROLE analytics_readonly WITH PASSWORD 'your-password'; ``` --- ### Checklist 📋 #### For code changes: - [x] I have clearly listed my changes in the PR description - [x] I have made a test plan - [x] I have tested my changes according to the test plan: - [x] Setup + views applied cleanly on local Postgres 15 - [x] `analytics_readonly` can `SELECT` from all 14 `analytics.*` views - [x] `analytics_readonly` gets `permission denied` on `platform.*` and `auth.*` directly --------- Co-authored-by: Otto (AGPT) <otto@agpt.co>
82 lines
3.9 KiB
SQL
82 lines
3.9 KiB
SQL
-- =============================================================
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-- View: analytics.retention_execution_weekly
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-- Looker source alias: ds92 | Charts: 2
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-- =============================================================
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-- DESCRIPTION
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-- Weekly cohort retention based on agent executions.
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-- Cohort anchor = week of user's FIRST ever agent execution
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-- (not first login). Only includes cohorts from the last 180 days.
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-- Useful when you care about product engagement, not just visits.
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--
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-- SOURCE TABLES
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-- platform.AgentGraphExecution — Execution records
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--
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-- OUTPUT COLUMNS
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-- Same pattern as retention_login_weekly.
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-- cohort_week_start = week of first execution (not first login)
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--
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-- EXAMPLE QUERIES
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-- -- Week-2 execution retention
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-- SELECT cohort_label, retention_rate_bounded
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-- FROM analytics.retention_execution_weekly
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-- WHERE user_lifetime_week = 2 ORDER BY cohort_week_start;
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-- =============================================================
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WITH params AS (SELECT 12::int AS max_weeks, (CURRENT_DATE - INTERVAL '180 days') AS cohort_start),
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events AS (
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SELECT e."userId"::text AS user_id, e."createdAt"::timestamptz AS created_at,
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DATE_TRUNC('week', e."createdAt")::date AS week_start
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FROM platform."AgentGraphExecution" e WHERE e."userId" IS NOT NULL
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),
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first_exec AS (
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SELECT user_id, MIN(created_at) AS first_exec_at,
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DATE_TRUNC('week', MIN(created_at))::date AS cohort_week_start
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FROM events GROUP BY 1
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HAVING MIN(created_at) >= (SELECT cohort_start FROM params)
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),
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activity_weeks AS (SELECT DISTINCT user_id, week_start FROM events),
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user_week_age AS (
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SELECT aw.user_id, fe.cohort_week_start,
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((aw.week_start - DATE_TRUNC('week',fe.first_exec_at)::date)/7)::int AS user_lifetime_week
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FROM activity_weeks aw JOIN first_exec fe USING (user_id)
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WHERE aw.week_start >= DATE_TRUNC('week',fe.first_exec_at)::date
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),
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bounded_counts AS (
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SELECT cohort_week_start, user_lifetime_week, COUNT(DISTINCT user_id) AS active_users_bounded
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FROM user_week_age WHERE user_lifetime_week >= 0 GROUP BY 1,2
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),
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last_active AS (
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SELECT cohort_week_start, user_id, MAX(user_lifetime_week) AS last_active_week FROM user_week_age GROUP BY 1,2
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),
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unbounded_counts AS (
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SELECT la.cohort_week_start, gs AS user_lifetime_week, COUNT(*) AS retained_users_unbounded
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FROM last_active la
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CROSS JOIN LATERAL generate_series(0, LEAST(la.last_active_week,(SELECT max_weeks FROM params))) gs
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GROUP BY 1,2
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),
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cohort_sizes AS (SELECT cohort_week_start, COUNT(DISTINCT user_id) AS cohort_users FROM first_exec GROUP BY 1),
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cohort_caps AS (
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SELECT cs.cohort_week_start, cs.cohort_users,
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LEAST((SELECT max_weeks FROM params),
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GREATEST(0,((DATE_TRUNC('week',CURRENT_DATE)::date-cs.cohort_week_start)/7)::int)) AS cap_weeks
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FROM cohort_sizes cs
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),
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grid AS (
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SELECT cc.cohort_week_start, gs AS user_lifetime_week, cc.cohort_users
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FROM cohort_caps cc CROSS JOIN LATERAL generate_series(0, cc.cap_weeks) gs
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)
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SELECT
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g.cohort_week_start,
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TO_CHAR(g.cohort_week_start,'IYYY-"W"IW') AS cohort_label,
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TO_CHAR(g.cohort_week_start,'IYYY-"W"IW')||' (n='||g.cohort_users||')' AS cohort_label_n,
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g.user_lifetime_week, g.cohort_users,
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COALESCE(b.active_users_bounded,0) AS active_users_bounded,
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COALESCE(u.retained_users_unbounded,0) AS retained_users_unbounded,
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CASE WHEN g.cohort_users>0 THEN COALESCE(b.active_users_bounded,0)::float/g.cohort_users END AS retention_rate_bounded,
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CASE WHEN g.cohort_users>0 THEN COALESCE(u.retained_users_unbounded,0)::float/g.cohort_users END AS retention_rate_unbounded,
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CASE WHEN g.user_lifetime_week=0 THEN g.cohort_users ELSE 0 END AS cohort_users_w0
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FROM grid g
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LEFT JOIN bounded_counts b ON b.cohort_week_start=g.cohort_week_start AND b.user_lifetime_week=g.user_lifetime_week
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LEFT JOIN unbounded_counts u ON u.cohort_week_start=g.cohort_week_start AND u.user_lifetime_week=g.user_lifetime_week
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ORDER BY g.cohort_week_start, g.user_lifetime_week;
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