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daily product activity","question":"Which milestones are users completing on each day?","description":"Start with one event table, group durable product milestones into UTC days, and keep successful volume separate from all attempts.","track":"Foundations","difficulty":"Beginner","sql":"SELECT\n  CAST(date_trunc('day', timestamp_utc) AS DATE) AS activity_day,\n  COUNT(*) AS events,\n  SUM(CASE WHEN status = 'success' THEN 1 ELSE 0 END) AS successful_events,\n  COUNT(DISTINCT account_id) AS active_accounts\nFROM product_events\nGROUP BY CAST(date_trunc('day', timestamp_utc) AS DATE)\nORDER BY activity_day;","result":{"columns":["activity_day","events","successful_events","active_accounts"],"rows":[{"activity_day":"2026-07-01","events":2,"successful_events":2,"active_accounts":1},{"activity_day":"2026-07-02","events":4,"successful_events":4,"active_accounts":2},{"activity_day":"2026-07-03","events":3,"successful_events":2,"active_accounts":2},{"activity_day":"2026-07-04","events":4,"successful_events":4,"active_accounts":1},{"activity_day":"2026-07-05","events":3,"successful_events":3,"active_accounts":2},{"activity_day":"2026-07-06","events":2,"successful_events":2,"active_accounts":1},{"activity_day":"2026-07-08","events":1,"successful_events":1,"active_accounts":1},{"activity_day":"2026-07-09","events":1,"successful_events":1,"active_accounts":1},{"activity_day":"2026-07-12","events":1,"successful_events":1,"active_accounts":1}]},"visualization":{"title":"Daily successful product milestones","labelKey":"activity_day","valueKey":"successful_events","type":"trend"},"explanation":["The UTC day is derived from the event timestamp instead of a client-supplied date label.","Event volume and distinct active accounts answer different questions, so both remain visible.","The status condition prevents a failed integration attempt from being counted as a completed milestone."],"decisions":["Decide whether retries and repeated milestones should remain visible.","Exclude internal, test, or bot accounts before treating this as adoption.","Use complete time buckets when comparing recent days."],"relatedPaths":[{"label":"SQL time bucketing","path":"/docs/discussion-topics/sql-time-bucketing"},{"label":"Conditional aggregation","path":"/docs/discussion-topics/sql-conditional-aggregation"}]},{"slug":"account-activation-funnel","title":"Build an account activation funnel","question":"How many signed-up accounts connect data, run a first query, and create a dashboard?","description":"Join the account cohort to ordered product milestones so the denominator includes every eligible account, including those that never progress.","track":"Product analytics","difficulty":"Intermediate","sql":"WITH signups AS (\n  SELECT a.account_id, MIN(p.timestamp_utc) AS signed_up_at\n  FROM accounts AS a\n  LEFT JOIN product_events AS p\n    ON p.account_id = a.account_id\n    AND p.event_name = 'account_signed_up' AND p.status = 'success'\n  GROUP BY a.account_id\n),\nconnections AS (\n  SELECT s.account_id, s.signed_up_at, MIN(p.timestamp_utc) AS connected_at\n  FROM signups AS s\n  LEFT JOIN product_events AS p\n    ON p.account_id = s.account_id\n    AND p.event_name = 'integration_connected' AND p.status = 'success'\n    AND p.timestamp_utc >= s.signed_up_at\n  GROUP BY s.account_id, s.signed_up_at\n),\nfirst_queries AS (\n  SELECT c.account_id, c.signed_up_at, c.connected_at,\n    MIN(p.timestamp_utc) AS first_query_at\n  FROM connections AS c\n  LEFT JOIN product_events AS p\n    ON p.account_id = c.account_id\n    AND p.event_name = 'first_query_completed' AND p.status = 'success'\n    AND p.timestamp_utc >= c.connected_at\n  GROUP BY c.account_id, c.signed_up_at, c.connected_at\n),\nmilestones AS (\n  SELECT q.account_id, q.signed_up_at, q.connected_at, q.first_query_at,\n    MIN(p.timestamp_utc) AS dashboard_at\n  FROM first_queries AS q\n  LEFT JOIN product_events AS p\n    ON p.account_id = q.account_id\n    AND p.event_name = 'dashboard_created' AND p.status = 'success'\n    AND p.timestamp_utc >= q.first_query_at\n  GROUP BY q.account_id, q.signed_up_at, q.connected_at, q.first_query_at\n),\nstages AS (\n  SELECT 1 AS step, 'Signed up' AS stage, COUNT(signed_up_at) AS accounts\n  FROM milestones\n  UNION ALL\n  SELECT 2, 'Connected data', COUNT(connected_at) FROM milestones\n  UNION ALL\n  SELECT 3, 'Ran first query', COUNT(first_query_at) FROM milestones\n  UNION ALL\n  SELECT 4, 'Created dashboard', COUNT(dashboard_at) FROM milestones\n)\nSELECT\n  stage,\n  accounts,\n  ROUND(100.0 * accounts\n    / NULLIF(MAX(CASE WHEN step = 1 THEN accounts END) OVER (), 0), 1)\n    AS conversion_pct\nFROM stages\nORDER BY step;","result":{"columns":["stage","accounts","conversion_pct"],"rows":[{"stage":"Signed up","accounts":6,"conversion_pct":100},{"stage":"Connected data","accounts":5,"conversion_pct":83.3},{"stage":"Ran first query","accounts":4,"conversion_pct":66.7},{"stage":"Created dashboard","accounts":3,"conversion_pct":50}]},"visualization":{"title":"Signup-to-dashboard activation funnel","labelKey":"stage","valueKey":"conversion_pct","suffix":"%","type":"funnel"},"explanation":["The account table establishes the cohort independently from product-event volume.","Each stage selects the first successful milestone at or after the previous eligible stage; repeated events count once.","A missing or out-of-order milestone blocks every later stage. A later successful retry can restore valid progression."],"decisions":["Choose the account, user, or workspace as the funnel unit before writing SQL.","Set a cohort window and allow every account the same activation opportunity.","This funnel allows later successful retries and treats equal timestamps as ordered. Use stricter timestamps if your product requires them."],"relatedPaths":[{"label":"Activation SQL recipe","path":"/sql/signup-activation-funnel"},{"label":"SQL funnel analysis","path":"/docs/discussion-topics/sql-funnel-analysis"},{"label":"Retention use case","path":"/for/product-analytics-retention"}]},{"slug":"llm-unit-economics","title":"Calculate LLM cost per accepted output","question":"Which plans consume the most model spend, and how much does each accepted output cost?","description":"Join normalized provider usage to account context, then compare raw request cost with a reviewed product outcome.","track":"AI economics","difficulty":"Intermediate","sql":"SELECT\n  a.plan,\n  COUNT(*) AS requests,\n  SUM(r.retry_count) AS automatic_retries,\n  ROUND(SUM(r.estimated_cost_usd), 4) AS estimated_cost_usd,\n  SUM(CASE WHEN r.accepted THEN 1 ELSE 0 END) AS accepted_outputs,\n  ROUND(\n    SUM(r.estimated_cost_usd)\n      / NULLIF(SUM(CASE WHEN r.accepted THEN 1 ELSE 0 END), 0),\n    4\n  ) AS cost_per_accepted_output_usd\nFROM llm_requests AS r\nJOIN accounts AS a ON a.account_id = r.account_id\nWHERE r.status = 'success'\nGROUP BY a.plan\nORDER BY estimated_cost_usd DESC;","result":{"columns":["plan","requests","automatic_retries","estimated_cost_usd","accepted_outputs","cost_per_accepted_output_usd"],"rows":[{"plan":"growth","requests":6,"automatic_retries":1,"estimated_cost_usd":0.129,"accepted_outputs":5,"cost_per_accepted_output_usd":0.0258},{"plan":"business","requests":2,"automatic_retries":0,"estimated_cost_usd":0.021,"accepted_outputs":1,"cost_per_accepted_output_usd":0.021},{"plan":"starter","requests":2,"automatic_retries":0,"estimated_cost_usd":0.011,"accepted_outputs":0,"cost_per_accepted_output_usd":null},{"plan":"free","requests":1,"automatic_retries":0,"estimated_cost_usd":0.004,"accepted_outputs":1,"cost_per_accepted_output_usd":0.004}]},"visualization":{"title":"Estimated LLM cost by plan","labelKey":"plan","valueKey":"estimated_cost_usd","suffix":" USD","type":"bar"},"explanation":["Provider-specific usage is normalized before the query, preserving a stable analysis contract.","Acceptance is a product outcome, not a property of the provider response itself.","NULLIF leaves cost per accepted output undefined when a segment has no accepted outputs."],"decisions":["Keep current model pricing in versioned configuration and retain the pricing version on each event.","Separate automatic retries from deliberate user regenerations.","Use invoices as billing truth; the event field is an analytical estimate."],"relatedPaths":[{"label":"OpenAI cost attribution","path":"/for/openai-cost-tracking"},{"label":"LLM cost recipe","path":"/sql/llm-cost-by-feature"},{"label":"Accepted output per dollar","path":"/sql/accepted-ai-output-per-dollar"}]},{"slug":"incident-customer-impact","title":"Connect API failures to customer impact","question":"Which plans and recurring revenue were exposed to API failures during the incident?","description":"Reduce request failures to one row per account before joining commercial context, preventing high-volume customers from being counted repeatedly.","track":"Reliability","difficulty":"Advanced","sql":"WITH account_errors AS (\n  SELECT\n    account_id,\n    SUM(CASE WHEN status_code >= 500 THEN 1 ELSE 0 END) AS errors\n  FROM api_requests\n  GROUP BY account_id\n)\nSELECT\n  a.plan,\n  COUNT(*) AS accounts,\n  SUM(CASE WHEN COALESCE(e.errors, 0) > 0 THEN 1 ELSE 0 END) AS impacted_accounts,\n  ROUND(\n    100.0 * SUM(CASE WHEN COALESCE(e.errors, 0) > 0 THEN 1 ELSE 0 END)\n      / NULLIF(COUNT(*), 0),\n    1\n  ) AS impacted_pct,\n  SUM(CASE\n    WHEN COALESCE(e.errors, 0) > 0 THEN a.monthly_revenue_usd\n    ELSE 0\n  END) AS mrr_exposed_usd\nFROM accounts AS a\nLEFT JOIN account_errors AS e ON e.account_id = a.account_id\nGROUP BY a.plan\nORDER BY mrr_exposed_usd DESC;","result":{"columns":["plan","accounts","impacted_accounts","impacted_pct","mrr_exposed_usd"],"rows":[{"plan":"growth","accounts":2,"impacted_accounts":2,"impacted_pct":100,"mrr_exposed_usd":4200},{"plan":"business","accounts":1,"impacted_accounts":1,"impacted_pct":100,"mrr_exposed_usd":1200},{"plan":"starter","accounts":2,"impacted_accounts":1,"impacted_pct":50,"mrr_exposed_usd":199},{"plan":"free","accounts":1,"impacted_accounts":0,"impacted_pct":0,"mrr_exposed_usd":0}]},"visualization":{"title":"Recurring revenue exposed to API failures","labelKey":"plan","valueKey":"mrr_exposed_usd","suffix":" USD","type":"bar"},"explanation":["Request rows are reduced to one account-level impact result before the commercial join.","The left join preserves accounts with no errors so the impacted percentage has the correct denominator.","Exposed MRR describes incident scope; it does not claim that revenue was lost."],"decisions":["Define the incident window and affected routes before calculating account impact.","Use privacy-approved identifiers and restrict access to commercial dimensions.","Keep exposed, at-risk, refunded, and actually lost revenue as different measures."],"relatedPaths":[{"label":"Customer impact recipe","path":"/sql/incident-customer-impact-by-plan"},{"label":"Incident response guide","path":"/docs/guides/incident-response-with-sql"},{"label":"API reliability use case","path":"/for/api-reliability-monitoring"}]},{"slug":"job-recovery-by-plan","title":"Measure job recovery by customer plan","question":"Which failed jobs recovered on retry, and which accounts still need intervention?","description":"Collapse attempt-level worker events into one logical job before comparing recovery and unresolved failures by plan.","track":"Reliability","difficulty":"Advanced","sql":"WITH logical_jobs AS (\n  SELECT\n    job_id,\n    account_id,\n    MAX(attempt) AS attempts,\n    MAX(CASE WHEN status = 'failed' THEN 1 ELSE 0 END) AS had_failure,\n    MAX(CASE WHEN status = 'completed' THEN 1 ELSE 0 END) AS completed\n  FROM job_runs\n  GROUP BY job_id, account_id\n)\nSELECT\n  a.plan,\n  COUNT(*) AS logical_jobs,\n  SUM(had_failure) AS jobs_with_failure,\n  SUM(CASE WHEN had_failure = 1 AND completed = 1 THEN 1 ELSE 0 END)\n    AS recovered_jobs,\n  SUM(CASE WHEN had_failure = 1 AND completed = 0 THEN 1 ELSE 0 END)\n    AS unresolved_jobs\nFROM logical_jobs AS j\nJOIN accounts AS a ON a.account_id = j.account_id\nGROUP BY a.plan\nORDER BY unresolved_jobs DESC, jobs_with_failure DESC, a.plan;","result":{"columns":["plan","logical_jobs","jobs_with_failure","recovered_jobs","unresolved_jobs"],"rows":[{"plan":"starter","logical_jobs":2,"jobs_with_failure":2,"recovered_jobs":0,"unresolved_jobs":2},{"plan":"growth","logical_jobs":2,"jobs_with_failure":1,"recovered_jobs":1,"unresolved_jobs":0},{"plan":"business","logical_jobs":1,"jobs_with_failure":0,"recovered_jobs":0,"unresolved_jobs":0},{"plan":"free","logical_jobs":1,"jobs_with_failure":0,"recovered_jobs":0,"unresolved_jobs":0}]},"visualization":{"title":"Unresolved logical jobs by plan","labelKey":"plan","valueKey":"unresolved_jobs","type":"bar"},"explanation":["Attempts are grouped by logical job identifier before failure and recovery are counted.","A job may have both a failed attempt and a completed retry, which is recovery rather than permanent failure.","Joining after the reduction prevents a retried job from multiplying account-level counts."],"decisions":["Define terminal job statuses and the maximum expected retry delay.","Distinguish automatic retry recovery from manual replay.","Alert on unresolved work and age, not every transient failed attempt."],"relatedPaths":[{"label":"Job retry-rate recipe","path":"/sql/background-job-retry-rate"},{"label":"Stalled jobs recipe","path":"/sql/find-stalled-background-jobs"},{"label":"Background-job use case","path":"/for/background-job-monitoring"}]},{"slug":"retained-activity-and-revenue","title":"Connect retained product activity to revenue context","question":"Which plans activate, return after seven days, and contribute recurring revenue?","description":"Reduce product events to one account-level lifecycle before joining recurring revenue and collected billing outcomes.","track":"Revenue","difficulty":"Advanced","sql":"WITH account_activity AS (\n  SELECT\n    a.account_id,\n    a.plan,\n    a.monthly_revenue_usd,\n    MAX(CASE\n      WHEN p.event_name = 'dashboard_created' AND p.status = 'success'\n      THEN 1 ELSE 0\n    END) AS activated,\n    MAX(CASE\n      WHEN p.timestamp_utc >= a.signup_at + INTERVAL '7 days'\n        AND p.status = 'success'\n      THEN 1 ELSE 0\n    END) AS returned_after_7d\n  FROM accounts AS a\n  LEFT JOIN product_events AS p ON p.account_id = a.account_id\n  GROUP BY a.account_id, a.plan, a.monthly_revenue_usd\n),\ncollections AS (\n  SELECT\n    account_id,\n    SUM(CASE\n      WHEN event_name = 'invoice_paid' AND status = 'success'\n      THEN amount_usd ELSE 0\n    END) AS collected_usd\n  FROM billing_events\n  GROUP BY account_id\n)\nSELECT\n  activity.plan,\n  COUNT(*) AS accounts,\n  SUM(activity.activated) AS activated_accounts,\n  SUM(activity.returned_after_7d) AS returned_accounts,\n  SUM(activity.monthly_revenue_usd) AS recurring_revenue_usd,\n  SUM(COALESCE(collections.collected_usd, 0)) AS collected_usd\nFROM account_activity AS activity\nLEFT JOIN collections ON collections.account_id = activity.account_id\nGROUP BY activity.plan\nORDER BY recurring_revenue_usd DESC, activity.plan;","result":{"columns":["plan","accounts","activated_accounts","returned_accounts","recurring_revenue_usd","collected_usd"],"rows":[{"plan":"growth","accounts":2,"activated_accounts":2,"returned_accounts":2,"recurring_revenue_usd":4200,"collected_usd":4200},{"plan":"business","accounts":1,"activated_accounts":1,"returned_accounts":0,"recurring_revenue_usd":1200,"collected_usd":1200},{"plan":"starter","accounts":2,"activated_accounts":0,"returned_accounts":0,"recurring_revenue_usd":498,"collected_usd":0},{"plan":"free","accounts":1,"activated_accounts":0,"returned_accounts":0,"recurring_revenue_usd":0,"collected_usd":0}]},"visualization":{"title":"Recurring revenue with retained product activity","labelKey":"plan","valueKey":"recurring_revenue_usd","suffix":" USD","type":"bar"},"explanation":["Product rows are collapsed to activation and return flags before revenue is introduced.","The seven-day return test is anchored to each account's signup time rather than a global calendar week.","Recurring revenue and collected invoice value stay separate because a contract value is not proof of payment."],"decisions":["Choose a durable activation milestone that predicts value for this product.","Give every cohort the same return window before comparing retention.","Do not present correlation between activity and revenue as causal lift."],"relatedPaths":[{"label":"Cohort retention recipe","path":"/sql/weekly-cohort-retention"},{"label":"Net and gross retention recipe","path":"/sql/net-gross-revenue-retention"},{"label":"Product journey guide","path":"/docs/guides/product-journey-analysis-with-sql"}]},{"slug":"ai-gross-margin-by-account","title":"Estimate AI gross-margin inputs by account","question":"How does accepted-output cost compare with successfully collected revenue for each account?","description":"Join account-level model cost and accepted outcomes to billing results without treating analytical estimates as invoice truth.","track":"AI economics","difficulty":"Advanced","sql":"WITH ai_usage AS (\n  SELECT\n    account_id,\n    ROUND(SUM(estimated_cost_usd), 4) AS estimated_ai_cost_usd,\n    SUM(CASE WHEN accepted THEN 1 ELSE 0 END) AS accepted_outputs\n  FROM llm_requests\n  WHERE status = 'success'\n  GROUP BY account_id\n),\ncollections AS (\n  SELECT\n    account_id,\n    SUM(CASE\n      WHEN event_name = 'invoice_paid' AND status = 'success'\n      THEN amount_usd ELSE 0\n    END) AS collected_usd\n  FROM billing_events\n  GROUP BY account_id\n)\nSELECT\n  a.account_id,\n  a.plan,\n  COALESCE(collections.collected_usd, 0) AS collected_usd,\n  usage.estimated_ai_cost_usd,\n  usage.accepted_outputs,\n  ROUND(\n    usage.estimated_ai_cost_usd / NULLIF(usage.accepted_outputs, 0),\n    4\n  ) AS cost_per_accepted_output_usd,\n  ROUND(\n    100.0 * usage.estimated_ai_cost_usd\n      / NULLIF(collections.collected_usd, 0),\n    4\n  ) AS ai_cost_pct_of_collected\nFROM ai_usage AS usage\nJOIN accounts AS a ON a.account_id = usage.account_id\nLEFT JOIN collections ON collections.account_id = usage.account_id\nORDER BY usage.estimated_ai_cost_usd DESC, a.account_id;","result":{"columns":["account_id","plan","collected_usd","estimated_ai_cost_usd","accepted_outputs","cost_per_accepted_output_usd","ai_cost_pct_of_collected"],"rows":[{"account_id":"acct_delta","plan":"growth","collected_usd":1800,"estimated_ai_cost_usd":0.079,"accepted_outputs":3,"cost_per_accepted_output_usd":0.0263,"ai_cost_pct_of_collected":0.0044},{"account_id":"acct_acme","plan":"growth","collected_usd":2400,"estimated_ai_cost_usd":0.05,"accepted_outputs":2,"cost_per_accepted_output_usd":0.025,"ai_cost_pct_of_collected":0.0021},{"account_id":"acct_beacon","plan":"business","collected_usd":1200,"estimated_ai_cost_usd":0.021,"accepted_outputs":1,"cost_per_accepted_output_usd":0.021,"ai_cost_pct_of_collected":0.0018},{"account_id":"acct_ember","plan":"starter","collected_usd":0,"estimated_ai_cost_usd":0.006,"accepted_outputs":0,"cost_per_accepted_output_usd":null,"ai_cost_pct_of_collected":null},{"account_id":"acct_cinder","plan":"starter","collected_usd":0,"estimated_ai_cost_usd":0.005,"accepted_outputs":0,"cost_per_accepted_output_usd":null,"ai_cost_pct_of_collected":null},{"account_id":"acct_fjord","plan":"free","collected_usd":0,"estimated_ai_cost_usd":0.004,"accepted_outputs":1,"cost_per_accepted_output_usd":0.004,"ai_cost_pct_of_collected":null}]},"visualization":{"title":"Estimated AI cost by account","labelKey":"account_id","valueKey":"estimated_ai_cost_usd","suffix":" USD","type":"bar"},"explanation":["Model usage is reduced to one account row before the billing join.","Cost per accepted output uses a reviewed product outcome rather than provider success alone.","Accounts without collected revenue remain visible with a null cost percentage instead of an invented infinite margin."],"decisions":["Use versioned provider prices and reconcile estimates with provider invoices.","Define accepted outcomes per feature and guard against missing feedback.","Include non-model infrastructure and labor costs before claiming gross margin."],"relatedPaths":[{"label":"LLM cost tracker template","path":"/templates/llm-cost-tracker"},{"label":"Accepted output per dollar","path":"/sql/accepted-ai-output-per-dollar"},{"label":"Usage-based billing analytics","path":"/for/usage-based-billing-analytics"}]},{"slug":"billing-failure-risk-triage","title":"Triage billing failures with operational context","question":"Which failed payments coincide with API errors, unresolved jobs, or incomplete activation?","description":"Start from failed billing outcomes, then add reduced operational signals so a review queue explains why each account needs attention.","track":"Revenue","difficulty":"Advanced","sql":"WITH api_health AS (\n  SELECT\n    account_id,\n    SUM(CASE WHEN status_code >= 500 THEN 1 ELSE 0 END) AS api_errors\n  FROM api_requests\n  GROUP BY account_id\n),\nlogical_jobs AS (\n  SELECT\n    job_id,\n    account_id,\n    MAX(CASE WHEN status = 'failed' THEN 1 ELSE 0 END) AS had_failure,\n    MAX(CASE WHEN status = 'completed' THEN 1 ELSE 0 END) AS completed\n  FROM job_runs\n  GROUP BY job_id, account_id\n),\njob_health AS (\n  SELECT\n    account_id,\n    SUM(CASE\n      WHEN had_failure = 1 AND completed = 0 THEN 1 ELSE 0\n    END) AS unresolved_jobs\n  FROM logical_jobs\n  GROUP BY account_id\n),\nactivation AS (\n  SELECT\n    account_id,\n    MAX(CASE\n      WHEN event_name = 'dashboard_created' AND status = 'success'\n      THEN 1 ELSE 0\n    END) AS activated\n  FROM product_events\n  GROUP BY account_id\n)\nSELECT\n  a.account_id,\n  a.plan,\n  b.amount_usd AS failed_payment_usd,\n  COALESCE(api.api_errors, 0) AS api_errors,\n  COALESCE(jobs.unresolved_jobs, 0) AS unresolved_jobs,\n  COALESCE(activation.activated, 0) AS activated,\n  1\n    + CASE WHEN COALESCE(api.api_errors, 0) > 0 THEN 1 ELSE 0 END\n    + CASE WHEN COALESCE(jobs.unresolved_jobs, 0) > 0 THEN 1 ELSE 0 END\n    + CASE WHEN COALESCE(activation.activated, 0) = 0 THEN 1 ELSE 0 END\n    AS review_signals\nFROM billing_events AS b\nJOIN accounts AS a ON a.account_id = b.account_id\nLEFT JOIN api_health AS api ON api.account_id = b.account_id\nLEFT JOIN job_health AS jobs ON jobs.account_id = b.account_id\nLEFT JOIN activation ON activation.account_id = b.account_id\nWHERE b.event_name = 'payment_failed' AND b.status = 'error'\nORDER BY review_signals DESC, failed_payment_usd DESC;","result":{"columns":["account_id","plan","failed_payment_usd","api_errors","unresolved_jobs","activated","review_signals"],"rows":[{"account_id":"acct_ember","plan":"starter","failed_payment_usd":199,"api_errors":1,"unresolved_jobs":1,"activated":0,"review_signals":4},{"account_id":"acct_cinder","plan":"starter","failed_payment_usd":299,"api_errors":0,"unresolved_jobs":1,"activated":0,"review_signals":3}]},"visualization":{"title":"Operational review signals for failed payments","labelKey":"account_id","valueKey":"review_signals","type":"bar"},"explanation":["The failed payment is the review-queue grain; API and job tables are reduced before joining.","Activation is a visible context field, not an assertion that onboarding caused payment failure.","The signal count prioritizes investigation but deliberately avoids an opaque health score."],"decisions":["Define the support workflow and access boundary for billing context.","Choose incident windows so unrelated historical failures do not inflate current risk.","Keep payment recovery, product recovery, and account retention as separate outcomes."],"relatedPaths":[{"label":"Payment recovery recipe","path":"/sql/payment-failure-recovery"},{"label":"Customer impact guide","path":"/docs/guides/customer-impact-analysis-with-sql"},{"label":"Billing analytics use case","path":"/for/usage-based-billing-analytics"}]},{"slug":"release-observability-coverage","title":"Audit release coverage before comparing regressions","question":"Do product and API events cover the same releases well enough for a regression comparison?","description":"Build a shared release spine before calculating rates so missing instrumentation remains null instead of looking like perfect reliability.","track":"Foundations","difficulty":"Intermediate","sql":"WITH releases AS (\n  SELECT release FROM product_events\n  UNION\n  SELECT release FROM api_requests\n),\nproduct_coverage AS (\n  SELECT\n    release,\n    COUNT(*) AS product_events,\n    SUM(CASE WHEN status = 'success' THEN 1 ELSE 0 END)\n      AS successful_product_events\n  FROM product_events\n  GROUP BY release\n),\napi_coverage AS (\n  SELECT\n    release,\n    COUNT(*) AS api_requests,\n    SUM(CASE WHEN status_code >= 500 THEN 1 ELSE 0 END) AS api_errors\n  FROM api_requests\n  GROUP BY release\n)\nSELECT\n  releases.release,\n  COALESCE(product.product_events, 0) AS product_events,\n  COALESCE(product.successful_product_events, 0)\n    AS successful_product_events,\n  COALESCE(api.api_requests, 0) AS api_requests,\n  COALESCE(api.api_errors, 0) AS api_errors,\n  ROUND(\n    100.0 * api.api_errors / NULLIF(api.api_requests, 0),\n    1\n  ) AS api_error_pct\nFROM releases\nLEFT JOIN product_coverage AS product\n  ON product.release = releases.release\nLEFT JOIN api_coverage AS api\n  ON api.release = releases.release\nORDER BY releases.release;","result":{"columns":["release","product_events","successful_product_events","api_requests","api_errors","api_error_pct"],"rows":[{"release":"2026.07.1","product_events":13,"successful_product_events":12,"api_requests":0,"api_errors":0,"api_error_pct":null},{"release":"2026.07.2","product_events":8,"successful_product_events":8,"api_requests":10,"api_errors":6,"api_error_pct":60}]},"visualization":{"title":"API request coverage by release","labelKey":"release","valueKey":"api_requests","type":"table"},"explanation":["The release spine includes versions present in either source instead of starting from whichever table happens to have rows.","A release with no API requests keeps a null error rate, preventing absent coverage from appearing as zero-percent errors.","Product event counts and API request counts expose whether the populations are comparable before a regression claim is made."],"decisions":["Define which services and clients share a meaningful release identifier.","Require minimum coverage and complete observation windows before comparing rates.","Investigate instrumentation changes separately from product regressions."],"relatedPaths":[{"label":"Feature rollout analysis","path":"/docs/guides/feature-rollout-analysis-with-sql"},{"label":"Schema adoption recipe","path":"/sql/schema-version-adoption"},{"label":"API reliability by release","path":"/sql/api-reliability-by-release"}]},{"slug":"daily-service-reliability","title":"Trend daily service reliability","question":"Did the newest complete days contain a broad reliability regression?","description":"Use the larger service-event fact table to calculate daily volume, errors, and error rate while retaining the denominator beside the rate.","track":"Reliability","difficulty":"Beginner","sql":"SELECT\n  CAST(date_trunc('day', timestamp_utc) AS DATE) AS service_day,\n  COUNT(*) AS operations,\n  SUM(CASE WHEN status = 'error' THEN 1 ELSE 0 END) AS errors,\n  ROUND(\n    100.0 * SUM(CASE WHEN status = 'error' THEN 1 ELSE 0 END)\n    / NULLIF(COUNT(*), 0),\n    2\n  ) AS error_rate_pct\nFROM service_events\nWHERE timestamp_utc >= TIMESTAMP '2026-07-22 00:00:00'\nGROUP BY CAST(date_trunc('day', timestamp_utc) AS DATE)\nORDER BY service_day;","result":{"columns":["service_day","operations","errors","error_rate_pct"],"rows":[{"service_day":"2026-07-22","operations":360,"errors":53,"error_rate_pct":14.72},{"service_day":"2026-07-23","operations":360,"errors":9,"error_rate_pct":2.5},{"service_day":"2026-07-24","operations":360,"errors":9,"error_rate_pct":2.5},{"service_day":"2026-07-25","operations":360,"errors":8,"error_rate_pct":2.22},{"service_day":"2026-07-26","operations":360,"errors":9,"error_rate_pct":2.5},{"service_day":"2026-07-27","operations":360,"errors":8,"error_rate_pct":2.22},{"service_day":"2026-07-28","operations":360,"errors":10,"error_rate_pct":2.78}]},"visualization":{"title":"Daily service error rate","labelKey":"service_day","valueKey":"error_rate_pct","suffix":"%","type":"trend"},"explanation":["The UTC day is a visible reporting grain and the window ends with complete synthetic days.","Volume and error count stay beside the rate so a change in denominator remains visible.","The July 22 spike is a discovery signal; the next query must segment by service, release, region, or incident."],"decisions":["Choose a complete-bucket delay that matches ingestion latency.","Set an objective and minimum volume before turning the rate into an alert.","Separate scheduled maintenance and known test traffic with reviewed dimensions."],"relatedPaths":[{"label":"Error-budget burn recipe","path":"/sql/api-error-budget-burn-rate"},{"label":"Reliability dashboard","path":"/examples/api-reliability"},{"label":"Time-bucketing guide","path":"/docs/discussion-topics/sql-time-bucketing"}]},{"slug":"release-regression-comparison","title":"Compare release reliability","question":"Which checkout release coincides with higher errors and average latency?","description":"Compare equal 10-day release windows from the service-event table while keeping request volume, failures, rate, and latency together.","track":"Reliability","difficulty":"Intermediate","sql":"SELECT\n  release,\n  COUNT(*) AS checkout_requests,\n  SUM(CASE WHEN status = 'error' THEN 1 ELSE 0 END) AS errors,\n  ROUND(\n    100.0 * SUM(CASE WHEN status = 'error' THEN 1 ELSE 0 END)\n    / NULLIF(COUNT(*), 0),\n    2\n  ) AS error_rate_pct,\n  ROUND(AVG(latency_ms), 1) AS average_latency_ms\nFROM service_events\nWHERE service = 'checkout'\nGROUP BY release\nORDER BY release;","result":{"columns":["release","checkout_requests","errors","error_rate_pct","average_latency_ms"],"rows":[{"release":"2026.06.4","checkout_requests":900,"errors":22,"error_rate_pct":2.44,"average_latency_ms":310.5},{"release":"2026.07.1","checkout_requests":900,"errors":84,"error_rate_pct":9.33,"average_latency_ms":453.1},{"release":"2026.07.2","checkout_requests":900,"errors":24,"error_rate_pct":2.67,"average_latency_ms":309.5}]},"visualization":{"title":"Checkout error rate by release","labelKey":"release","valueKey":"error_rate_pct","suffix":"%","type":"bar"},"explanation":["Filtering to one service avoids mixing workflows with different latency and error baselines.","Each synthetic release has equal volume and a complete 10-day window, making the comparison shape inspectable.","The query identifies correlation with a release; it does not establish that the deployment caused the regression."],"decisions":["Compare equivalent rollout populations, routes, regions, and observation windows.","Use a tail percentile instead of an average when the engine-specific function has been validated.","Confirm a suspected regression with deploy metadata, error categories, and traces."],"relatedPaths":[{"label":"Release reliability recipe","path":"/sql/api-reliability-by-release"},{"label":"Release dashboard pattern","path":"/examples/api-reliability"},{"label":"Feature rollout guide","path":"/docs/guides/feature-rollout-analysis-with-sql"}]},{"slug":"declared-incident-impact","title":"Quantify declared incident impact","question":"How many accounts and operations were affected by each declared incident?","description":"Reduce service events to an account-and-incident grain before adding incident severity and account revenue context.","track":"Reliability","difficulty":"Advanced","sql":"WITH account_impact AS (\n  SELECT\n    incident_id,\n    account_id,\n    COUNT(*) AS impacted_operations,\n    SUM(CASE WHEN status = 'error' THEN 1 ELSE 0 END)\n      AS failed_operations\n  FROM service_events\n  WHERE incident_id IS NOT NULL\n  GROUP BY incident_id, account_id\n)\nSELECT\n  i.incident_id,\n  i.severity,\n  COUNT(*) AS affected_accounts,\n  SUM(impact.impacted_operations) AS impacted_operations,\n  SUM(impact.failed_operations) AS failed_operations,\n  SUM(a.monthly_revenue_usd) AS represented_mrr_usd\nFROM account_impact AS impact\nJOIN incidents AS i ON i.incident_id = impact.incident_id\nJOIN accounts AS a ON a.account_id = impact.account_id\nGROUP BY i.incident_id, i.severity\nORDER BY failed_operations DESC;","result":{"columns":["incident_id","severity","affected_accounts","impacted_operations","failed_operations","represented_mrr_usd"],"rows":[{"incident_id":"inc_checkout_latency","severity":"sev_2","affected_accounts":6,"impacted_operations":180,"failed_operations":64,"represented_mrr_usd":5898},{"incident_id":"inc_worker_retries","severity":"sev_2","affected_accounts":5,"impacted_operations":75,"failed_operations":45,"represented_mrr_usd":5898}]},"visualization":{"title":"Failed operations by declared incident","labelKey":"incident_id","valueKey":"failed_operations","type":"bar"},"explanation":["The CTE prevents many service events from multiplying account revenue when the dimensions are joined.","Affected account count and failed operations distinguish breadth from repeated impact.","Represented revenue is communication context, not a severity formula or a contractual damages calculation."],"decisions":["Define the impact rule that associates an operation with a declared incident.","Keep free, low-revenue, and internal accounts visible even when prioritizing outreach.","Reconcile the event-derived list with support cases and status-page communications."],"relatedPaths":[{"label":"Incident impact dashboard","path":"/examples/incident-customer-impact"},{"label":"Customer-impact error recipe","path":"/sql/top-error-fingerprints"},{"label":"Incident response guide","path":"/docs/guides/incident-response-with-sql"}]},{"slug":"database-query-regressions-by-release","title":"Find database query regressions by release","description":"Compare normalized query families across releases to find slower or newly failing operations.","question":"Which database operations became slower or less reliable after a release?","difficulty":"Intermediate","track":"Database reliability","sql":"SELECT\n  query_fingerprint,\n  release,\n  COUNT(*) AS operations,\n  ROUND(AVG(duration_ms), 1) AS average_duration_ms,\n  SUM(CASE WHEN status <> 'success' THEN 1 ELSE 0 END) AS failed_operations,\n  ROUND(\n    100.0 * SUM(CASE WHEN status <> 'success' THEN 1 ELSE 0 END) / COUNT(*),\n    2\n  ) AS error_rate_pct\nFROM database_operations\nGROUP BY query_fingerprint, release\nORDER BY query_fingerprint, release;","explanation":["Query fingerprints group equivalent statements without exposing literal values.","Release-level aggregates separate application changes from one-off slow queries.","Latency and error rate belong together: either can identify a harmful regression."],"decisions":["Investigate fingerprints whose latency or error rate rises in the newest release.","Correlate a regression with deployment and incident timelines before rollback.","Create an alert only after choosing a volume floor that controls low-sample noise."],"relatedPaths":[{"label":"Slow queries by fingerprint","path":"/sql/slow-database-queries-by-fingerprint"},{"label":"Designing an event schema","path":"/docs/discussion-topics/designing-event-schema"}],"result":{"columns":["query_fingerprint","release","operations","average_duration_ms","failed_operations","error_rate_pct"],"rows":[{"query_fingerprint":"billing_lookup","release":"2026.07.1","operations":2,"average_duration_ms":155,"failed_operations":0,"error_rate_pct":0},{"query_fingerprint":"billing_lookup","release":"2026.07.2","operations":3,"average_duration_ms":300,"failed_operations":1,"error_rate_pct":33.33},{"query_fingerprint":"select_order","release":"2026.07.1","operations":3,"average_duration_ms":133.3,"failed_operations":0,"error_rate_pct":0},{"query_fingerprint":"select_order","release":"2026.07.2","operations":3,"average_duration_ms":633.3,"failed_operations":1,"error_rate_pct":33.33},{"query_fingerprint":"update_account","release":"2026.07.1","operations":2,"average_duration_ms":87.5,"failed_operations":0,"error_rate_pct":0},{"query_fingerprint":"update_account","release":"2026.07.2","operations":3,"average_duration_ms":140,"failed_operations":1,"error_rate_pct":33.33}]},"visualization":{"type":"bar","title":"Average duration by query and release","labelKey":"query_fingerprint","valueKey":"average_duration_ms","suffix":" ms"}},{"slug":"database-connection-pool-saturation","title":"Measure database connection-pool saturation","description":"Rank services by maximum pool use, acquisition wait, and timeout samples.","question":"Which service is exhausting its database connection pool?","difficulty":"Beginner","track":"Database reliability","sql":"SELECT\n  service,\n  ROUND(MAX(100.0 * active_connections / max_connections), 1) AS max_saturation_pct,\n  ROUND(AVG(acquire_wait_ms), 1) AS average_acquire_wait_ms,\n  SUM(CASE WHEN timed_out THEN 1 ELSE 0 END) AS timeout_samples\nFROM database_pool_samples\nGROUP BY service\nORDER BY max_saturation_pct DESC, service;","explanation":["Pool saturation normalizes active connections against each service's configured limit.","Acquisition wait catches pressure before requests begin timing out.","Timeout samples distinguish sustained contention from harmless utilization spikes."],"decisions":["Profile slow or long-held transactions before increasing the pool limit.","Check the database connection ceiling before changing application pool sizes.","Alert on sustained wait and timeout behavior, not utilization alone."],"relatedPaths":[{"label":"Connection-pool SQL recipe","path":"/sql/database-connection-pool-saturation"},{"label":"Database reliability use case","path":"/for/database-reliability-monitoring"}],"result":{"columns":["service","max_saturation_pct","average_acquire_wait_ms","timeout_samples"],"rows":[{"service":"checkout","max_saturation_pct":100,"average_acquire_wait_ms":124.3,"timeout_samples":1},{"service":"worker","max_saturation_pct":100,"average_acquire_wait_ms":157.5,"timeout_samples":1},{"service":"api","max_saturation_pct":41.7,"average_acquire_wait_ms":8.5,"timeout_samples":0}]},"visualization":{"type":"bar","title":"Maximum connection-pool saturation","labelKey":"service","valueKey":"max_saturation_pct","suffix":"%"}},{"slug":"database-lock-waits-and-deadlocks","title":"Rank database lock waits and deadlocks","description":"Aggregate lock events by blocked operation to prioritize the most disruptive contention.","question":"Which transaction families cause the longest waits and deadlocks?","difficulty":"Intermediate","track":"Database reliability","sql":"SELECT\n  blocked_operation,\n  COUNT(*) AS wait_events,\n  MAX(wait_ms) AS max_wait_ms,\n  SUM(CASE WHEN deadlock THEN 1 ELSE 0 END) AS deadlocks\nFROM database_lock_events\nGROUP BY blocked_operation\nORDER BY max_wait_ms DESC, blocked_operation;","explanation":["The blocked operation identifies the user-facing work delayed by contention.","Maximum wait exposes severe tail behavior that an average can hide.","Deadlock counts identify transaction pairs that need ordering or scope changes."],"decisions":["Inspect transaction order for operation families with deadlocks.","Reduce transaction scope or batch size where waits are long but eventually resolve.","Preserve the blocking-operation field in alerts so responders know where to start."],"relatedPaths":[{"label":"Lock-wait SQL recipe","path":"/sql/database-lock-waits-and-deadlocks"},{"label":"Database reliability template","path":"/templates/database-reliability"}],"result":{"columns":["blocked_operation","wait_events","max_wait_ms","deadlocks"],"rows":[{"blocked_operation":"ledger_rollup","wait_events":2,"max_wait_ms":6200,"deadlocks":1},{"blocked_operation":"payment_update","wait_events":3,"max_wait_ms":4200,"deadlocks":1},{"blocked_operation":"account_update","wait_events":1,"max_wait_ms":400,"deadlocks":0}]},"visualization":{"type":"bar","title":"Longest lock wait by operation","labelKey":"blocked_operation","valueKey":"max_wait_ms","suffix":" ms"}},{"slug":"database-replication-lag","title":"Monitor database replication and consumer lag","description":"Compare replica, CDC, and search-consumer lag with explicit health classifications.","question":"Which downstream database consumer is falling behind?","difficulty":"Beginner","track":"Database reliability","sql":"SELECT\n  consumer_name,\n  source_type,\n  MAX(lag_seconds) AS max_lag_seconds,\n  ROUND(AVG(lag_seconds), 1) AS average_lag_seconds,\n  SUM(CASE WHEN status <> 'healthy' THEN 1 ELSE 0 END) AS unhealthy_samples\nFROM replication_samples\nGROUP BY consumer_name, source_type\nORDER BY max_lag_seconds DESC, consumer_name;","explanation":["A shared consumer model makes operational replicas and asynchronous pipelines comparable.","Maximum lag captures the worst user-visible staleness in the sample window.","Health classifications can encode consumer-specific thresholds without hiding raw lag."],"decisions":["Prioritize consumers with both high lag and repeated unhealthy samples.","Use backlog bytes to distinguish low-throughput staleness from sustained ingestion pressure.","Set different service-level objectives for read replicas, CDC, and search indexes."],"relatedPaths":[{"label":"Replication and CDC SQL recipe","path":"/sql/database-replication-and-cdc-lag"},{"label":"Alerts guide","path":"/docs/discussion-topics/alerts"}],"result":{"columns":["consumer_name","source_type","max_lag_seconds","average_lag_seconds","unhealthy_samples"],"rows":[{"consumer_name":"warehouse_cdc","source_type":"cdc_pipeline","max_lag_seconds":120,"average_lag_seconds":51.8,"unhealthy_samples":2},{"consumer_name":"read_replica_us","source_type":"postgres_replica","max_lag_seconds":18,"average_lag_seconds":6.8,"unhealthy_samples":1},{"consumer_name":"search_index","source_type":"search_replica","max_lag_seconds":10,"average_lag_seconds":6.3,"unhealthy_samples":0}]},"visualization":{"type":"bar","title":"Maximum replication lag","labelKey":"consumer_name","valueKey":"max_lag_seconds","suffix":" s"}},{"slug":"database-migration-reliability","title":"Audit database migration reliability","description":"Measure migration success, rollback behavior, and duration by application release.","question":"Did a release make database migrations less reliable?","difficulty":"Beginner","track":"Database reliability","sql":"SELECT\n  release,\n  COUNT(*) AS migration_runs,\n  SUM(CASE WHEN status = 'success' THEN 1 ELSE 0 END) AS successful_runs,\n  SUM(CASE WHEN status <> 'success' THEN 1 ELSE 0 END) AS failed_runs,\n  ROUND(\n    100.0 * SUM(CASE WHEN status = 'success' THEN 1 ELSE 0 END) / COUNT(*),\n    1\n  ) AS success_rate_pct,\n  ROUND(AVG(duration_ms), 1) AS average_duration_ms\nFROM migration_runs\nGROUP BY release\nORDER BY release;","explanation":["Treating rollbacks as unsuccessful makes the release-level reliability measure explicit.","Duration trends can reveal increasingly expensive schema changes before they fail.","The migration identifier remains available for drilling into individual executions."],"decisions":["Block promotion when a release contains an unexplained failed or rolled-back migration.","Review long-running migrations for locking and deploy-window risk.","Separate retry success from first-attempt success if your runner emits attempts."],"relatedPaths":[{"label":"Migration failures SQL recipe","path":"/sql/database-migration-failures-by-release"},{"label":"Database reliability use case","path":"/for/database-reliability-monitoring"}],"result":{"columns":["release","migration_runs","successful_runs","failed_runs","success_rate_pct","average_duration_ms"],"rows":[{"release":"2026.07.1","migration_runs":3,"successful_runs":3,"failed_runs":0,"success_rate_pct":100,"average_duration_ms":1300},{"release":"2026.07.2","migration_runs":4,"successful_runs":2,"failed_runs":2,"success_rate_pct":50,"average_duration_ms":1450},{"release":"2026.07.3","migration_runs":2,"successful_runs":2,"failed_runs":0,"success_rate_pct":100,"average_duration_ms":1125}]},"visualization":{"type":"trend","title":"Migration success rate by release","labelKey":"release","valueKey":"success_rate_pct","suffix":"%"}},{"slug":"database-failures-by-customer-plan","title":"Measure database impact by customer plan","description":"Join database operations to account dimensions to quantify the customer impact of failures.","question":"Which customer plans are affected by database errors?","difficulty":"Advanced","track":"Database reliability","sql":"SELECT\n  a.plan,\n  COUNT(*) AS operations,\n  SUM(CASE WHEN d.status <> 'success' THEN 1 ELSE 0 END) AS failed_operations,\n  COUNT(DISTINCT CASE WHEN d.status <> 'success' THEN d.account_id END) AS failed_accounts,\n  ROUND(AVG(d.duration_ms), 1) AS average_duration_ms\nFROM database_operations d\nJOIN accounts a ON d.account_id = a.account_id\nGROUP BY a.plan\nORDER BY a.plan;","explanation":["The account dimension turns an infrastructure symptom into customer-impact evidence.","Distinct failed accounts prevents a noisy customer from dominating the affected-account count.","Plan 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dashboard","path":"/examples/incident-customer-impact"}],"result":{"columns":["plan","operations","failed_operations","failed_accounts","average_duration_ms"],"rows":[{"plan":"business","operations":4,"failed_operations":0,"failed_accounts":0,"average_duration_ms":265},{"plan":"free","operations":1,"failed_operations":1,"failed_accounts":1,"average_duration_ms":210},{"plan":"growth","operations":6,"failed_operations":1,"failed_accounts":1,"average_duration_ms":333.3},{"plan":"starter","operations":5,"failed_operations":1,"failed_accounts":1,"average_duration_ms":167}]},"visualization":{"type":"bar","title":"Failed database operations by plan","labelKey":"plan","valueKey":"failed_operations"}}]}