Build the safeguards
Eight explained steps, a source example and a checkpoint at each decision.
Read the tutorialPostgreSQL / Analytics / Data correctness
Run it. Change it. Verify it.
Start in your browser, then take the complete SQL Reporting Lab into an independent local environment.
Try it without installing a toolchain
- Open the playground and keep its default scenario.
- Run the comparison and read the results before opening the code.
- Change the scenario, run again, and identify which decision changed.
- Open View the code, choose a source file and read the explanation above it.
- Reset the environment, including a run that is still pending.
Run your downloaded copy
Download and extract the ZIP into a new folder. With Python 3 installed, open a terminal in that folder and run the command below. It serves only this package on loopback; opening index.html directly is insufficient for workers and WebAssembly.
python preview.pyOpen http://127.0.0.1:4210/ in your browser. Stop the server with Ctrl+C. The prebuilt preview needs no dependency installation and makes no automatic requests to another site. The source code and notices are included alongside it.
Run the native exercise
The commands below start in the extracted package root. Node-based examples require Node 24 or later; the Go adapter requires Go 1.27 or later; the Python adapter requires Python 3.11 or later. Install only the toolchain used by this lab. Exact dependency versions and the Node lockfile are in the download.
npm ci
npm test
npm run rebuild
python preview.pyRevenue is 42000 cents with the multiplying join and 30000 at order grain. The local-day example returns 26000 cents; the UTC-day query returns 24000. The safeguarded cohort has one A user and two B users.
Read GETTING_STARTED.md for the native request examples and shutdown steps. Native servers bind to 127.0.0.1. The optional monitoring containers expose only loopback ports. They are a separate exercise from the browser preview.
Make one meaningful change
Add two distinct orders with the same amount and demonstrate why SUM(DISTINCT amount_cents) is an invalid fanout repair. Then define a late-arrival cutoff and keep that policy fixed while comparing implementations.
Add the expected result first, reproduce the failure, repair it, then rerun the native and browser checks. Use npm run rebuild for the Node/browser examples. The Go package includes rebuild_wasm.py; the Python package includes refresh_source.py.
Record what you learned
Capture the scenario, edition, input records, observed outcome and the test that checks it. Explain one alternative you rejected and why. Avoid converting a small local result into a throughput, security or accuracy claim about a production system.
Read the primary documentation
Rebuy’s staff role calls for accurate experimentation reports, data models and analytical queries.
Choose your next step
Compare the alternatives
Understand where each pattern helps and which tradeoffs remain.
Compare approachesRun and extend it locally
Download the independent environment, run the native checks and try a specific extension.
Open the how-to guideLab discussion / SQL Reporting Lab
What would you try next?
Ask about the code, compare an approach, or describe an edge case. Reader comments are reviewed before publication.
Code walkthrough
Follow the implementation in code.
These short excerpts connect the lab’s behavior to its implementation. Each example links to the full tutorial step for its context, tradeoffs and checkpoint. Names and dependencies belong to that source; the excerpts are not standalone programs.
Write the reporting contract
The fixture stores revenue in integer cents on orders. Lines represent items, not additional revenue. State the expected population, grain, currency, time zone and cutoff before writing the query. A report definition is a product decision that a query merely implements.
SELECT id, amount_cents FROM orders ORDER BY id;Restore the intended grain
Aggregate the many side to one row per order before joining. Another valid approach is an EXISTS filter when you only need to know whether a matching line exists. SUM(DISTINCT amount) is not a substitute: two legitimate orders can share an amount.
WITH item_counts AS (
SELECT order_id,COUNT(*) FROM lines GROUP BY order_id
)
SELECT SUM(o.amount_cents) FROM orders o
JOIN item_counts i ON i.order_id=o.id;Choose one exposure per person
Repeated exposures should not automatically create repeated people in an experiment denominator. ROW_NUMBER partitions events by person and uses event time plus event ID to choose a stable first exposure. The chosen variant becomes that person’s attribution rule in this exercise.
ROW_NUMBER() OVER (
PARTITION BY user_id ORDER BY happened_at,event_id
)