Files
AutoGPT/autogpt_platform/analytics/queries/retention_login_weekly.sql
Zamil Majdy 6e1605994d feat(analytics): add documented SQL views with generation script
Introduces an analytics/ layer that wraps production Postgres data in
safe, read-only views exposed under the analytics schema.

- 14 documented query files in queries/ (one per Looker data source)
  covering auth activities, user activity, execution metrics, onboarding
  funnel, and cohort retention (login + execution, weekly + daily)
- setup.sql — one-time schema creation and role/grant setup for the
  analytics_readonly role (auth, platform, analytics schemas)
- generate_views.py — reads queries/*.sql and applies
  CREATE OR REPLACE VIEW analytics.<name> to the database;
  supports --dry-run, --only, and --db-url flags
- views.sql — pre-generated combined reference output
- README.md — full setup, deployment, and integration guide

Looker, PostHog Data Warehouse, and Supabase MCP (for Otto) all
connect to the same analytics.* views instead of raw tables.
2026-03-11 15:36:27 +07:00

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-- =============================================================
-- View: analytics.retention_login_weekly
-- Looker source alias: ds83 | Charts: 2
-- =============================================================
-- DESCRIPTION
-- Weekly cohort retention based on login sessions.
-- Users are grouped by the ISO week of their first ever login.
-- For each cohort × lifetime-week combination, outputs both:
-- - bounded rate: % active in exactly that week
-- - unbounded rate: % who were ever active on or after that week
-- Weeks are capped to the cohort's actual age (no future data points).
--
-- SOURCE TABLES
-- auth.sessions — Login session records
--
-- HOW TO READ THE OUTPUT
-- cohort_week_start The Monday of the week users first logged in
-- user_lifetime_week 0 = signup week, 1 = one week later, etc.
-- retention_rate_bounded = active_users_bounded / cohort_users
-- retention_rate_unbounded = retained_users_unbounded / cohort_users
--
-- OUTPUT COLUMNS
-- cohort_week_start DATE First day of the cohort's signup week
-- cohort_label TEXT ISO week label (e.g. '2025-W01')
-- cohort_label_n TEXT ISO week label with cohort size (e.g. '2025-W01 (n=42)')
-- user_lifetime_week INT Weeks since first login (0 = signup week)
-- cohort_users BIGINT Total users in this cohort (denominator)
-- active_users_bounded BIGINT Users active in exactly week k
-- retained_users_unbounded BIGINT Users active any time on/after week k
-- retention_rate_bounded FLOAT bounded active / cohort_users
-- retention_rate_unbounded FLOAT unbounded retained / cohort_users
-- cohort_users_w0 BIGINT cohort_users only at week 0, else 0 (safe to SUM in pivot tables)
--
-- EXAMPLE QUERIES
-- -- Week-1 retention rate per cohort
-- SELECT cohort_label, retention_rate_bounded AS w1_retention
-- FROM analytics.retention_login_weekly
-- WHERE user_lifetime_week = 1
-- ORDER BY cohort_week_start;
--
-- -- Overall average retention curve (all cohorts combined)
-- SELECT user_lifetime_week,
-- SUM(active_users_bounded)::float / NULLIF(SUM(cohort_users_w0), 0) AS avg_retention
-- FROM analytics.retention_login_weekly
-- GROUP BY 1 ORDER BY 1;
-- =============================================================
WITH params AS (SELECT 12::int AS max_weeks),
events AS (
SELECT s.user_id::text AS user_id, s.created_at::timestamptz AS created_at,
DATE_TRUNC('week', s.created_at)::date AS week_start
FROM auth.sessions s WHERE s.user_id IS NOT NULL
),
first_login AS (
SELECT user_id, MIN(created_at) AS first_login_time,
DATE_TRUNC('week', MIN(created_at))::date AS cohort_week_start
FROM events GROUP BY 1
),
activity_weeks AS (SELECT DISTINCT user_id, week_start FROM events),
user_week_age AS (
SELECT aw.user_id, fl.cohort_week_start,
((aw.week_start - DATE_TRUNC('week', fl.first_login_time)::date) / 7)::int AS user_lifetime_week
FROM activity_weeks aw JOIN first_login fl USING (user_id)
WHERE aw.week_start >= DATE_TRUNC('week', fl.first_login_time)::date
),
bounded_counts AS (
SELECT cohort_week_start, user_lifetime_week, COUNT(DISTINCT user_id) AS active_users_bounded
FROM user_week_age WHERE user_lifetime_week >= 0 GROUP BY 1,2
),
last_active AS (
SELECT cohort_week_start, user_id, MAX(user_lifetime_week) AS last_active_week FROM user_week_age GROUP BY 1,2
),
unbounded_counts AS (
SELECT la.cohort_week_start, gs AS user_lifetime_week, COUNT(*) AS retained_users_unbounded
FROM last_active la
CROSS JOIN LATERAL generate_series(0, LEAST(la.last_active_week,(SELECT max_weeks FROM params))) gs
GROUP BY 1,2
),
cohort_sizes AS (SELECT cohort_week_start, COUNT(DISTINCT user_id) AS cohort_users FROM first_login GROUP BY 1),
cohort_caps AS (
SELECT cs.cohort_week_start, cs.cohort_users,
LEAST((SELECT max_weeks FROM params),
GREATEST(0,((DATE_TRUNC('week',CURRENT_DATE)::date - cs.cohort_week_start)/7)::int)) AS cap_weeks
FROM cohort_sizes cs
),
grid AS (
SELECT cc.cohort_week_start, gs AS user_lifetime_week, cc.cohort_users
FROM cohort_caps cc CROSS JOIN LATERAL generate_series(0, cc.cap_weeks) gs
)
SELECT
g.cohort_week_start,
TO_CHAR(g.cohort_week_start,'IYYY-"W"IW') AS cohort_label,
TO_CHAR(g.cohort_week_start,'IYYY-"W"IW')||' (n='||g.cohort_users||')' AS cohort_label_n,
g.user_lifetime_week, g.cohort_users,
COALESCE(b.active_users_bounded,0) AS active_users_bounded,
COALESCE(u.retained_users_unbounded,0) AS retained_users_unbounded,
CASE WHEN g.cohort_users>0 THEN COALESCE(b.active_users_bounded,0)::float/g.cohort_users END AS retention_rate_bounded,
CASE WHEN g.cohort_users>0 THEN COALESCE(u.retained_users_unbounded,0)::float/g.cohort_users END AS retention_rate_unbounded,
CASE WHEN g.user_lifetime_week=0 THEN g.cohort_users ELSE 0 END AS cohort_users_w0
FROM grid g
LEFT JOIN bounded_counts b ON b.cohort_week_start=g.cohort_week_start AND b.user_lifetime_week=g.user_lifetime_week
LEFT JOIN unbounded_counts u ON u.cohort_week_start=g.cohort_week_start AND u.user_lifetime_week=g.user_lifetime_week
ORDER BY g.cohort_week_start, g.user_lifetime_week