Queries · q129 · VP Platform Engineering · week 1, night 2
“In the window where we have both job_runs and system_health_checks data, did weeks with more unhealthy health checks line up with weeks of more job failures?”
Fictional people at a fictional company, each played by a language-model agent. Who they are and what they are told
Week 1; shared with Samuel Whitcombe, Head of Merchant Success
Leadership wants a review of platform reliability in 2025: failed and slow background jobs, health check failures, rate limiting, webhook delivery problems, and which merchants felt them.
The manager, at the start of week 1
Subject: 2025 platform reliability review — what I need by Friday
Lucas,
Leadership wants a year-in-review on platform reliability for 2025, and I'm leading it jointly with Samuel Whitcombe (Merchant Success). The deliverable is a memo that tells leadership how reliable the platform actually was last year, where it hurt merchants most, and what that implies for our 2026 investment priorities (reliability engineering headcount, which integrations/carriers to fix or replace, whether we need stricter rate limiting or SLAs). Samuel's side will map our findings to specific merchant relationships and churn risk, so where possible, surface things in a way that can be tied back to individual merchants rather than only platform-wide averages.
Given you only get a handful of report slots a night, here's what matters most to me, roughly in priority order:
If you have to cut scope to fit your nightly slots, protect #1 and #2 — those are what the memo absolutely must answer. The rest can wait or be addressed at a higher level if time runs short.
Let me know if anything above is ambiguous before you spend a slot on it.
Diego
Subject: Platform reliability review — what I need from my side
Hi Lucas,
As you pull together the 2025 reliability picture, here's what matters from the merchant-health side:
Happy to flag specific merchant names if that helps scope the analysis.
Best, Samuel
The analyst · week 1, night 2, request 3 of 3
Big surprise from night 1: Report 1 only returned 3 rows (Oct/Nov/Dec 2025) even though I asked for the full year — job_runs apparently has no rows before 2025-10-01 (clean month boundary, growing row counts, no gaps), which looks like a rolling retention window rather than a data error. Report 3 similarly only returned Nov-Dec 2025 for system_health_checks. So "year-long trend" isn't deliverable from these raw tables as asked — I'll say so plainly in the memo and work within the window that actually exists (Oct-Dec for jobs, Nov-Dec for health checks), at finer (weekly) grain to still show a trend. Also, Report 2's tenant ranking was a dead end for Diego's #2: every one of the ~10 "large" tenants had exactly 2 failed runs and the rest had 0 — that's too uniform to be a real merchant-specific signal, and likely means failures are tied to a specific job type that happens to run for every large tenant, not to anything particular about the merchant. So tonight I pivot #2 from "which tenant" to "which job type," which better serves Diego's investment-priority question, and I still owe Diego the "late" half of #1 (my first report only measured run duration, not delay/lateness) and a first test of #3 (does health-check trouble line up with job failures, in the one window where both exist).
The engineer · 1 dry run on the empty schema, then the submission
WITH job_week AS ( SELECT cd.week_start, COUNT(*) AS total_job_runs, SUM(CASE WHEN jr.status = 'failed' THEN 1 ELSE 0 END) AS failed_job_runs FROM job_runs jr JOIN calendar_days cd ON cd.calendar_date = CAST(jr.queued_at AS date) WHERE jr.queued_at >= DATE '2025-11-01' AND jr.queued_at < DATE '2026-01-01' GROUP BY cd.week_start ), health_week AS ( SELECT cd.week_start, COUNT(*) AS total_health_checks, SUM(CASE WHEN shc.is_healthy = false THEN 1 ELSE 0 END) AS unhealthy_health_checks FROM system_health_checks shc JOIN calendar_days cd ON cd.calendar_date = CAST(shc.checked_at AS date) WHERE shc.checked_at >= DATE '2025-11-01' AND shc.checked_at < DATE '2026-01-01' GROUP BY cd.week_start ) SELECT jw.week_start, jw.total_job_runs, jw.failed_job_runs, ROUND(100.0 * jw.failed_job_runs / NULLIF(jw.total_job_runs, 0), 2) AS job_failure_rate_pct, hw.total_health_checks, hw.unhealthy_health_checks, ROUND(100.0 * hw.unhealthy_health_checks / NULLIF(hw.total_health_checks, 0), 2) AS health_check_unhealthy_rate_pct FROM job_week jw JOIN health_week hw ON hw.week_start = jw.week_start ORDER BY jw.week_start ASC
“Query compiles and returns the expected columns. Submitting.”
“Submitted. The report aggregates job_runs and system_health_checks independently to ISO week (via calendar_days.week_start), filtered to Nov 1 – Dec 31 2025, then inner-joins the two weekly series on week_start so Lucas can visually compare job failure rate against health-check unhealthy rate week by week.”
As Lucas Moreau saw it, from the small dataset the agents worked on
week_start | total_job_runs | failed_job_runs | job_failure_rate_pct | total_health_checks | unhealthy_health_checks | health_check_unhealthy_rate_pct -----------+----------------+-----------------+----------------------+---------------------+-------------------------+-------------------------------- 2025-11-24 | 3861 | 20 | 0.52 | 14832 | 362 | 2.44 2025-12-01 | 3934 | 0 | 0.0 | 25956 | 246 | 0.95 2025-12-08 | 3829 | 0 | 0.0 | 25956 | 250 | 0.96 2025-12-15 | 3828 | 0 | 0.0 | 25956 | 271 | 1.04 2025-12-22 | 3828 | 0 | 0.0 | 25956 | 249 | 0.96 2025-12-29 | 1653 | 0 | 0.0 | 11124 | 115 | 1.03 (6 rows)
The memo and the queries that followed; links found by title, so inferred
Lucas Moreau's memo to Diego Morales at the end of the week:
4. Monitoring caught it — barely, and maybe too late. Health-check unhealthy rate jumped to 2.44% the week of the incident vs. ~0.95–1.04% baseline in the surrounding weeks. Source: "2025 Q4 Weekly Job Failures vs Health Check Alerts." But ticket volume for the 10 affected tenants was already climbing 3–4 weeks before the job failures appeared in logs (1.0/tenant/week on Oct 27 → 5.8 by Nov 24). I'm not confident this is a clean "we caught it early" story — merchants may have felt it before our systems flagged it.
Later queries that build on this report:
It builds on:
Changed only where the benchmark needs the same result on every system
WITH job_week AS ( SELECT cd.week_start, cd.week_start AS week_start, COUNT(*) AS total_job_runs, SUM(CASE WHEN jr.status = 'failed' THEN 1 ELSE 0 END) AS failed_job_runs FROM job_runs jr JOIN calendar_days cd ON cd.calendar_date = CAST(jr.queued_at AS date) WHERE jr.queued_at >= DATE '2025-11-01' AND jr.queued_at < DATE '2026-01-01' GROUP BY cd.week_start),health_week AS ( SELECT cd.week_start, cd.week_start AS week_start, COUNT(*) AS total_health_checks, SUM(CASE WHEN shc.is_healthy = false THEN 1 ELSE 0 END) AS unhealthy_health_checks FROM system_health_checks shc JOIN calendar_days cd ON cd.calendar_date = CAST(shc.checked_at AS date) WHERE shc.checked_at >= DATE '2025-11-01' AND shc.checked_at < DATE '2026-01-01' GROUP BY cd.week_start)SELECT jw.week_start, jw.total_job_runs, jw.failed_job_runs, ROUND(100.0 * jw.failed_job_runs / NULLIF(jw.total_job_runs, 0), 2) AS job_failure_rate_pct, hw.total_health_checks, hw.unhealthy_health_checks, ROUND(100.0 * hw.unhealthy_health_checks / NULLIF(hw.total_health_checks, 0), 2) AS health_check_unhealthy_rate_pct jw.week_start AS week_start, jw.total_job_runs AS total_job_runs, jw.failed_job_runs AS failed_job_runs, 100.0 * jw.failed_job_runs / NULLIF(jw.total_job_runs, 0) AS job_failure_rate_pct, hw.total_health_checks AS total_health_checks, hw.unhealthy_health_checks AS unhealthy_health_checks, 100.0 * hw.unhealthy_health_checks / NULLIF(hw.total_health_checks, 0) AS health_check_unhealthy_rate_pctFROM job_week jwJOIN health_week hw ON hw.week_start = jw.week_startORDER BY jw.week_start ASCORDER BY jw.week_start ASC NULLS LAST
WITH job_week AS ( SELECT cd.week_start AS week_start, COUNT(*) AS total_job_runs, SUM(CASE WHEN jr.status = 'failed' THEN 1 ELSE 0 END) AS failed_job_runs FROM job_runs jr JOIN calendar_days cd ON cd.calendar_date = CAST(jr.queued_at AS date) WHERE jr.queued_at >= DATE '2025-11-01' AND jr.queued_at < DATE '2026-01-01' GROUP BY cd.week_start ), health_week AS ( SELECT cd.week_start AS week_start, COUNT(*) AS total_health_checks, SUM(CASE WHEN shc.is_healthy = false THEN 1 ELSE 0 END) AS unhealthy_health_checks FROM system_health_checks shc JOIN calendar_days cd ON cd.calendar_date = CAST(shc.checked_at AS date) WHERE shc.checked_at >= DATE '2025-11-01' AND shc.checked_at < DATE '2026-01-01' GROUP BY cd.week_start ) SELECT jw.week_start AS week_start, jw.total_job_runs AS total_job_runs, jw.failed_job_runs AS failed_job_runs, 100.0 * jw.failed_job_runs / NULLIF(jw.total_job_runs, 0) AS job_failure_rate_pct, hw.total_health_checks AS total_health_checks, hw.unhealthy_health_checks AS unhealthy_health_checks, 100.0 * hw.unhealthy_health_checks / NULLIF(hw.total_health_checks, 0) AS health_check_unhealthy_rate_pct FROM job_week jw JOIN health_week hw ON hw.week_start = jw.week_start ORDER BY jw.week_start ASC NULLS LAST
The agents' own records, as the role play wrote them; only the personal data of the person who ran it is redacted
Report request from Lucas Moreau (analyst for the VP Platform Engineering): Title: 2025 Q4 Weekly Job Failures vs Health Check Alerts Question: In the window where we have both job_runs and system_health_checks data, did weeks with more unhealthy health checks line up with weeks of more job failures? Grain: one row per ISO week (week_start date), Nov 1 - Dec 31 2025 (the overlap window both tables cover) Columns: - week_start - total_job_runs - failed_job_runs - job_failure_rate_pct - total_health_checks - unhealthy_health_checks - health_check_unhealthy_rate_pct Details: Two sources aggregated to the same week grain and joined: job_runs (queued_at) and system_health_checks (checked_at), both filtered to >= 2025-11-01 and < 2026-01-01, the window confirmed present in both tables from night-1 Reports 1 and 3. Group each by ISO week (calendar_days.week_start) then join on week_start; platform-wide (all tenants/components combined), no tenant filter. job_failure_rate_pct and health_check_unhealthy_rate_pct rounded to 2 decimals. Order by week_start ascending. Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runWITH job_week AS ( SELECT cd.week_start, COUNT(*) AS total_job_runs, SUM(CASE WHEN jr.status = 'failed' THEN 1 ELSE 0 END) AS failed_job_runs FROM job_runs jr JOIN calendar_days cd ON cd.calendar_date = CAST(jr.queued_at AS date) WHERE jr.queued_at >= DATE '2025-11-01' AND jr.queued_at < DATE '2026-01-01' GROUP BY cd.week_start ), health_week AS ( SELECT cd.week_start, COUNT(*) AS total_health_checks, SUM(CASE WHEN shc.is_healthy = false THEN 1 ELSE 0 END) AS unhealthy_health_checks FROM system_health_checks shc JOIN calendar_days cd ON cd.calendar_date = CAST(shc.checked_at AS date) WHERE shc.checked_at >= DATE '2025-11-01' AND shc.checked_at < DATE '2026-01-01' GROUP BY cd.week_start ) SELECT jw.week_start, jw.total_job_runs, jw.failed_job_runs, ROUND(100.0 * jw.failed_job_runs / NULLIF(jw.total_job_runs, 0), 2) AS job_failure_rate_pct, hw.total_health_checks, hw.unhealthy_health_checks, ROUND(100.0 * hw.unhealthy_health_checks / NULLIF(hw.total_health_checks, 0), 2) AS health_check_unhealthy_rate_pct FROM job_week jw JOIN health_week hw ON hw.week_start = jw.week_start ORDER BY jw.week_start ASC
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: week_start, total_job_runs, failed_job_runs, job_failure_rate_pct, total_health_checks, unhealthy_health_checks, health_check_unhealthy_rate_pct
submitWITH job_week AS ( SELECT cd.week_start, COUNT(*) AS total_job_runs, SUM(CASE WHEN jr.status = 'failed' THEN 1 ELSE 0 END) AS failed_job_runs FROM job_runs jr JOIN calendar_days cd ON cd.calendar_date = CAST(jr.queued_at AS date) WHERE jr.queued_at >= DATE '2025-11-01' AND jr.queued_at < DATE '2026-01-01' GROUP BY cd.week_start ), health_week AS ( SELECT cd.week_start, COUNT(*) AS total_health_checks, SUM(CASE WHEN shc.is_healthy = false THEN 1 ELSE 0 END) AS unhealthy_health_checks FROM system_health_checks shc JOIN calendar_days cd ON cd.calendar_date = CAST(shc.checked_at AS date) WHERE shc.checked_at >= DATE '2025-11-01' AND shc.checked_at < DATE '2026-01-01' GROUP BY cd.week_start ) SELECT jw.week_start, jw.total_job_runs, jw.failed_job_runs, ROUND(100.0 * jw.failed_job_runs / NULLIF(jw.total_job_runs, 0), 2) AS job_failure_rate_pct, hw.total_health_checks, hw.unhealthy_health_checks, ROUND(100.0 * hw.unhealthy_health_checks / NULLIF(hw.total_health_checks, 0), 2) AS health_check_unhealthy_rate_pct FROM job_week jw JOIN health_week hw ON hw.week_start = jw.week_start ORDER BY jw.week_start ASC
Submitted to tonight's batch. Your work on this request is done.