Queries · q130 · VP Platform Engineering · week 1, night 3
“Were the 10 tenants hit by the November carts.remind job failures large/top-tier accounts, or did exposure look unrelated to merchant size?”
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 3, request 1 of 3
Night 2 reframed the whole job-reliability story: queue delay and run duration are flat and tiny every single week (no "lateness" problem exists at all), and ALL 20 failures in the entire Oct-Dec window happened in one week (2025-11-24) in one handler (carts.remind), hitting exactly 10 tenants. So this isn't a chronic reliability problem - it's one contained one-week incident tied to the abandoned-cart-reminder job. Report 3 showed something genuinely useful: the health-check unhealthy rate spiked (2.44% vs a ~1% baseline) in that exact same week, suggesting monitoring did catch it. Given this, tonight I pivot to what Samuel needs about those 10 tenants specifically (are they top-tier/large accounts, did it track with order volume, did it coincide with ticket pain) since that's now answerable precisely rather than generically, and I use my third slot to open up webhook delivery reliability (Diego's #5), which I haven't touched yet and which is a more directly merchant-facing failure mode than internal jobs turned out to be.
The engineer · 1 dry run on the empty schema, then the submission
WITH order_rev AS ( SELECT o.tenant_id, COUNT(*) AS total_orders_2025, SUM(o.grand_total / er.rate) AS total_revenue_eur_2025 FROM orders o JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = o.currency_code AND er.as_of = DATE(o.placed_at) WHERE o.placed_at >= TIMESTAMP '2025-01-01' AND o.placed_at < TIMESTAMP '2026-01-01' GROUP BY o.tenant_id ), affected AS ( SELECT DISTINCT jr.tenant_id FROM job_runs jr JOIN jobs j ON j.id = jr.job_id WHERE j.handler = 'carts.remind' AND jr.status = 'failed' AND jr.queued_at >= TIMESTAMP '2025-11-01' AND jr.queued_at < TIMESTAMP '2025-12-01' ), ranked AS ( SELECT t.id AS tenant_id, t.name AS tenant_name, COALESCE(orv.total_orders_2025, 0) AS total_orders_2025, COALESCE(orv.total_revenue_eur_2025, 0) AS total_revenue_eur_2025, DENSE_RANK() OVER (ORDER BY COALESCE(orv.total_revenue_eur_2025, 0) DESC) AS revenue_rank, CASE WHEN a.tenant_id IS NOT NULL THEN 'Yes' ELSE 'No' END AS was_affected_by_nov_carts_remind_failure FROM tenants t LEFT JOIN order_rev orv ON orv.tenant_id = t.id LEFT JOIN affected a ON a.tenant_id = t.id ) SELECT tenant_name, total_orders_2025, ROUND(total_revenue_eur_2025, 2) AS total_revenue_eur_2025, revenue_rank, was_affected_by_nov_carts_remind_failure FROM ranked ORDER BY was_affected_by_nov_carts_remind_failure DESC, revenue_rank ASC, tenant_name ASC LIMIT 30
“Query compiles cleanly with the expected columns. Submitting.”
“Submitted. The report ranks all tenants by 2025 EUR-converted order revenue, flags the ones hit by November carts.remind job failures, and returns the 10 affected tenants plus the next ~20 largest unaffected ones (sorted Yes-first, then by revenue rank) so Lucas can see at a glance whether the outage concentrated on top-tier accounts or spread across the tail.”
As Lucas Moreau saw it, from the small dataset the agents worked on
tenant_name | total_orders_2025 | total_revenue_eur_2025 | revenue_rank | was_affected_by_nov_carts_remind_failure -----------------------+-------------------+------------------------+--------------+----------------------------------------- Cragsley Outfitters | 3635 | 724835.93 | 1 | Yes Marrowby & Vane | 1758 | 288909.93 | 4 | Yes Cinder Peak Sports Co. | 338 | 124756.84 | 6 | Yes Fennel & Flint | 389 | 65764.13 | 7 | Yes Petalcourt Beauty | 706 | 63508.22 | 8 | Yes Kibblewick | 672 | 23801.41 | 14 | Yes Ampfeld Elektronik | 0 | 0.0 | 58 | Yes Brennquell Kaffee | 0 | 0.0 | 58 | Yes Gratlinie Outdoor | 0 | 0.0 | 58 | Yes Studio Vrij | 0 | 0.0 | 58 | Yes OfficeHub Nordic | 170 | 428472.93 | 2 | No Castell Fasteners | 159 | 387619.54 | 3 | No Deskline Supplies | 75 | 285178.35 | 5 | No Kayak Corner | 109 | 40600.69 | 9 | No Fell & Fjell | 85 | 36717.18 | 10 | No Grind Collective | 1111 | 25610.52 | 11 | No Oakhurst Tailoring | 165 | 25311.11 | 12 | No Velvet Ash | 134 | 24443.06 | 13 | No Circuit Harbor | 19 | 22953.82 | 15 | No Coastline Interiors | 155 | 21468.31 | 16 | No Willow & Wren | 97 | 19084.7 | 17 | No Nordic Bloom Cosmetics | 151 | 19074.7 | 18 | No Juniper Apothecary | 157 | 18018.14 | 19 | No Honeycomb Larder | 620 | 17674.84 | 20 | No Bytefield | 23 | 13014.33 | 21 | No The Balm Room | 104 | 12921.67 | 22 | No Thistlecombe | 92 | 12570.49 | 23 | No Meadow Row | 24 | 9825.26 | 24 | No Fjordline Wear | 57 | 8615.11 | 25 | No Courtside Collective | 20 | 8514.48 | 26 | No (30 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:
3. It did reach top-tier accounts, not just the long tail. Of the 10 affected tenants, 6 had meaningful 2025 volume, including Cragsley Outfitters — our #1 tenant by 2025 EUR revenue — plus tenants ranked #4, #6, #7, #8. The other 4 affected tenants had zero 2025 orders (likely inactive/test accounts). Source: "Tenant Size vs Carts.remind Job Failure Exposure." I'm confident this wasn't volume-driven — small and large affected tenants had identical failure counts (2 each); it looks tied to a feature flag/config, not load.
Later queries that build on this report:
It builds on:
Changed only where the benchmark needs the same result on every system
WITH order_rev AS ( SELECT o.tenant_id, o.tenant_id AS tenant_id, COUNT(*) AS total_orders_2025, SUM(o.grand_total / er.rate) AS total_revenue_eur_2025 SUM(CAST(o.grand_total / er.rate AS NUMERIC(18,6))) AS total_revenue_eur_2025 FROM orders o JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = o.currency_code AND er.as_of = DATE(o.placed_at) AND er.as_of = CAST(o.placed_at AS DATE) WHERE o.placed_at >= TIMESTAMP '2025-01-01' AND o.placed_at < TIMESTAMP '2026-01-01' GROUP BY o.tenant_id),affected AS ( SELECT DISTINCT jr.tenant_id SELECT DISTINCT jr.tenant_id AS tenant_id FROM job_runs jr JOIN jobs j ON j.id = jr.job_id WHERE j.handler = 'carts.remind' AND jr.status = 'failed' AND jr.queued_at >= TIMESTAMP '2025-11-01' AND jr.queued_at < TIMESTAMP '2025-12-01'),ranked AS ( SELECT t.id AS tenant_id, t.name AS tenant_name, COALESCE(orv.total_orders_2025, 0) AS total_orders_2025, COALESCE(orv.total_revenue_eur_2025, 0) AS total_revenue_eur_2025, DENSE_RANK() OVER (ORDER BY COALESCE(orv.total_revenue_eur_2025, 0) DESC) AS revenue_rank, DENSE_RANK() OVER (ORDER BY COALESCE(orv.total_revenue_eur_2025, 0) DESC NULLS FIRST) AS revenue_rank, CASE WHEN a.tenant_id IS NOT NULL THEN 'Yes' ELSE 'No' END AS was_affected_by_nov_carts_remind_failure FROM tenants t LEFT JOIN order_rev orv ON orv.tenant_id = t.id LEFT JOIN affected a ON a.tenant_id = t.id)SELECT tenant_name, total_orders_2025, ROUND(total_revenue_eur_2025, 2) AS total_revenue_eur_2025, total_revenue_eur_2025 AS total_revenue_eur_2025, revenue_rank, was_affected_by_nov_carts_remind_failureFROM rankedORDER BY was_affected_by_nov_carts_remind_failure DESC, revenue_rank ASC, tenant_name ASCLIMIT 30ORDER BY was_affected_by_nov_carts_remind_failure DESC NULLS FIRST, revenue_rank ASC NULLS LAST, tenant_name ASC NULLS LAST, tenant_id NULLS LAST{{fetch_first(30)}}
WITH order_rev AS ( SELECT o.tenant_id AS tenant_id, COUNT(*) AS total_orders_2025, SUM(CAST(o.grand_total / er.rate AS NUMERIC(18,6))) AS total_revenue_eur_2025 FROM orders o JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = o.currency_code AND er.as_of = CAST(o.placed_at AS DATE) WHERE o.placed_at >= TIMESTAMP '2025-01-01' AND o.placed_at < TIMESTAMP '2026-01-01' GROUP BY o.tenant_id ), affected AS ( SELECT DISTINCT jr.tenant_id AS tenant_id FROM job_runs jr JOIN jobs j ON j.id = jr.job_id WHERE j.handler = 'carts.remind' AND jr.status = 'failed' AND jr.queued_at >= TIMESTAMP '2025-11-01' AND jr.queued_at < TIMESTAMP '2025-12-01' ), ranked AS ( SELECT t.id AS tenant_id, t.name AS tenant_name, COALESCE(orv.total_orders_2025, 0) AS total_orders_2025, COALESCE(orv.total_revenue_eur_2025, 0) AS total_revenue_eur_2025, DENSE_RANK() OVER (ORDER BY COALESCE(orv.total_revenue_eur_2025, 0) DESC NULLS FIRST) AS revenue_rank, CASE WHEN a.tenant_id IS NOT NULL THEN 'Yes' ELSE 'No' END AS was_affected_by_nov_carts_remind_failure FROM tenants t LEFT JOIN order_rev orv ON orv.tenant_id = t.id LEFT JOIN affected a ON a.tenant_id = t.id ) SELECT tenant_name, total_orders_2025, total_revenue_eur_2025 AS total_revenue_eur_2025, revenue_rank, was_affected_by_nov_carts_remind_failure FROM ranked ORDER BY was_affected_by_nov_carts_remind_failure DESC NULLS FIRST, revenue_rank ASC NULLS LAST, tenant_name ASC NULLS LAST, tenant_id NULLS LAST FETCH FIRST 30 ROWS ONLY
WITH order_rev AS ( SELECT o.tenant_id AS tenant_id, COUNT(*) AS total_orders_2025, SUM(CAST(o.grand_total / er.rate AS NUMERIC(18,6))) AS total_revenue_eur_2025 FROM orders o JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = o.currency_code AND er.as_of = CAST(o.placed_at AS DATE) WHERE o.placed_at >= TIMESTAMP '2025-01-01' AND o.placed_at < TIMESTAMP '2026-01-01' GROUP BY o.tenant_id ), affected AS ( SELECT DISTINCT jr.tenant_id AS tenant_id FROM job_runs jr JOIN jobs j ON j.id = jr.job_id WHERE j.handler = 'carts.remind' AND jr.status = 'failed' AND jr.queued_at >= TIMESTAMP '2025-11-01' AND jr.queued_at < TIMESTAMP '2025-12-01' ), ranked AS ( SELECT t.id AS tenant_id, t.name AS tenant_name, COALESCE(orv.total_orders_2025, 0) AS total_orders_2025, COALESCE(orv.total_revenue_eur_2025, 0) AS total_revenue_eur_2025, DENSE_RANK() OVER (ORDER BY COALESCE(orv.total_revenue_eur_2025, 0) DESC NULLS FIRST) AS revenue_rank, CASE WHEN a.tenant_id IS NOT NULL THEN 'Yes' ELSE 'No' END AS was_affected_by_nov_carts_remind_failure FROM tenants t LEFT JOIN order_rev orv ON orv.tenant_id = t.id LEFT JOIN affected a ON a.tenant_id = t.id ) SELECT tenant_name, total_orders_2025, total_revenue_eur_2025 AS total_revenue_eur_2025, revenue_rank, was_affected_by_nov_carts_remind_failure FROM ranked ORDER BY was_affected_by_nov_carts_remind_failure DESC NULLS FIRST, revenue_rank ASC NULLS LAST, tenant_name ASC NULLS LAST, tenant_id NULLS LAST LIMIT 30
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: Tenant Size vs Carts.remind Job Failure Exposure, 2025 Question: Were the 10 tenants hit by the November carts.remind job failures large/top-tier accounts, or did exposure look unrelated to merchant size? Grain: one row per tenant Columns: - tenant_name - total_orders_2025 - total_revenue_eur_2025 - revenue_rank - was_affected_by_nov_carts_remind_failure Details: Tables: orders (placed_at in 2025, any status) for total_orders_2025 and total_revenue_eur_2025 (sum of grand_total converted to EUR using exchange_rates: base_code='EUR', quote_code=orders.currency_code, as_of matching date(placed_at), amount_eur = grand_total / rate). revenue_rank = dense rank of tenants by total_revenue_eur_2025 descending (1 = largest). was_affected_by_nov_carts_remind_failure = 'Yes' if the tenant has at least one row in job_runs joined to jobs (job_runs.job_id = jobs.id) where jobs.handler = 'carts.remind', job_runs.status = 'failed', and job_runs.queued_at in November 2025, else 'No'. Order by was_affected_by_nov_carts_remind_failure descending (Yes first), then revenue_rank ascending. Limit to 30 rows so all 10 affected tenants appear plus the next ~20 largest unaffected tenants for comparison. Check your query with the warehouse's dry_run tool, then hand it in with submit.
dry_runWITH order_rev AS ( SELECT o.tenant_id, COUNT(*) AS total_orders_2025, SUM(o.grand_total / er.rate) AS total_revenue_eur_2025 FROM orders o JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = o.currency_code AND er.as_of = DATE(o.placed_at) WHERE o.placed_at >= TIMESTAMP '2025-01-01' AND o.placed_at < TIMESTAMP '2026-01-01' GROUP BY o.tenant_id ), affected AS ( SELECT DISTINCT jr.tenant_id FROM job_runs jr JOIN jobs j ON j.id = jr.job_id WHERE j.handler = 'carts.remind' AND jr.status = 'failed' AND jr.queued_at >= TIMESTAMP '2025-11-01' AND jr.queued_at < TIMESTAMP '2025-12-01' ), ranked AS ( SELECT t.id AS tenant_id, t.name AS tenant_name, COALESCE(orv.total_orders_2025, 0) AS total_orders_2025, COALESCE(orv.total_revenue_eur_2025, 0) AS total_revenue_eur_2025, DENSE_RANK() OVER (ORDER BY COALESCE(orv.total_revenue_eur_2025, 0) DESC) AS revenue_rank, CASE WHEN a.tenant_id IS NOT NULL THEN 'Yes' ELSE 'No' END AS was_affected_by_nov_carts_remind_failure FROM tenants t LEFT JOIN order_rev orv ON orv.tenant_id = t.id LEFT JOIN affected a ON a.tenant_id = t.id ) SELECT tenant_name, total_orders_2025, ROUND(total_revenue_eur_2025, 2) AS total_revenue_eur_2025, revenue_rank, was_affected_by_nov_carts_remind_failure FROM ranked ORDER BY was_affected_by_nov_carts_remind_failure DESC, revenue_rank ASC, tenant_name ASC LIMIT 30
STATUS: COMPILES (dry run on the empty schema, no data read) COLUMNS: tenant_name, total_orders_2025, total_revenue_eur_2025, revenue_rank, was_affected_by_nov_carts_remind_failure
submitWITH order_rev AS ( SELECT o.tenant_id, COUNT(*) AS total_orders_2025, SUM(o.grand_total / er.rate) AS total_revenue_eur_2025 FROM orders o JOIN exchange_rates er ON er.base_code = 'EUR' AND er.quote_code = o.currency_code AND er.as_of = DATE(o.placed_at) WHERE o.placed_at >= TIMESTAMP '2025-01-01' AND o.placed_at < TIMESTAMP '2026-01-01' GROUP BY o.tenant_id ), affected AS ( SELECT DISTINCT jr.tenant_id FROM job_runs jr JOIN jobs j ON j.id = jr.job_id WHERE j.handler = 'carts.remind' AND jr.status = 'failed' AND jr.queued_at >= TIMESTAMP '2025-11-01' AND jr.queued_at < TIMESTAMP '2025-12-01' ), ranked AS ( SELECT t.id AS tenant_id, t.name AS tenant_name, COALESCE(orv.total_orders_2025, 0) AS total_orders_2025, COALESCE(orv.total_revenue_eur_2025, 0) AS total_revenue_eur_2025, DENSE_RANK() OVER (ORDER BY COALESCE(orv.total_revenue_eur_2025, 0) DESC) AS revenue_rank, CASE WHEN a.tenant_id IS NOT NULL THEN 'Yes' ELSE 'No' END AS was_affected_by_nov_carts_remind_failure FROM tenants t LEFT JOIN order_rev orv ON orv.tenant_id = t.id LEFT JOIN affected a ON a.tenant_id = t.id ) SELECT tenant_name, total_orders_2025, ROUND(total_revenue_eur_2025, 2) AS total_revenue_eur_2025, revenue_rank, was_affected_by_nov_carts_remind_failure FROM ranked ORDER BY was_affected_by_nov_carts_remind_failure DESC, revenue_rank ASC, tenant_name ASC LIMIT 30
Submitted to tonight's batch. Your work on this request is done.