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Common Table Expressions and Query Pipelines / write query
M23-A06 - Transfer checkpoint: Common Table Expressions and Query Pipelines
M23-A06 - Transfer checkpoint: Common Table Expressions and Query Pipelines. Build readable multi-stage queries with named intermediate grains.
- Result grain
- one row per sold product
- Exact columns
- category_id; product_id; product_name; revenue; revenue_rank
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Cursor at line 1, column 1.
Scenario
Use the visible seed to understand the task, then pass hidden deterministic variants.
Common Table Expressions and Query Pipelines / write query
One-sentence task
M23-A06 - Transfer checkpoint: Common Table Expressions and Query Pipelines. Build readable multi-stage queries with named intermediate grains.
Learn mode disclosure
Theory, concept names, full schema help, and progressive hints are available.
Structured output contract
- Result grain
- one row per sold product
- Exact columns
- category_id; product_id; product_name; revenue; revenue_rank
- Source population
- Use the prompt setup plus FROM, JOIN, WHERE, and subquery predicates as the source population. Visible rows are only examples.
- Grouping
- Group only at the requested output grain: one row per sold product.
- Ordering
- use deterministic order for ties
- Validation
- select-only; hidden deterministic variants.
Relevant tables
Time and difficulty
- Estimated time
- 11 minutes
- Difficulty
- 5/5
Objective and concepts
Debug the requested SQL output contract for common table expressions and query pipelines using source grain, columns, ordering, and edge-case evidence.
Glossary links
Concept material
SQL Trail treats every query as an evidence trail: identify source grain, transform rows deliberately, then compare output to a shared contract.
A passing query must handle hidden nulls, ties, boundaries, and no-match rows when the contract makes them relevant.
Syntax card
SELECT <requested_columns>
FROM <source_table>
WHERE <source_population_filter>
GROUP BY <result_grain_columns>
ORDER BY <deterministic_tie_breakers>;- <requested_columns> means the exact output columns, aliases, and order from the visible contract.
- <source_population_filter> means the row population definition, not a copied visible-row value.
- <deterministic_tie_breakers> means all ordering and tie rules needed for repeatable output.
Why this works
The CTE establishes product grain before the window function ranks products within each category.
Edge cases
Hidden variants preserve nulls, ties, duplicates, boundaries, no-match rows, and alternate row order when those risks apply.
PostgreSQL note
The local engine uses PostgreSQL-compatible syntax, including explicit NULL predicates, deterministic ORDER BY clauses, and transactional grading.
Worked example
WITH product_revenue AS (SELECT p.category_id, p.product_id, p.product_name, SUM(oi.quantity * oi.unit_price)::numeric(10,2) AS revenue FROM products p JOIN order_items oi ON oi.product_id = p.product_id GROUP BY p.category_id, p.product_id, p.product_name) SELECT category_id, product_id, product_name, revenue, DENSE_RANK() OVER (PARTITION BY category_id ORDER BY revenue DESC, product_id) AS revenue_rank FROM product_revenue ORDER BY category_id NULLS LAST, revenue_rank, product_id;Assumptions, dialect notes, and common traps
- Duplicate policy
- Preserve duplicate facts unless the prompt explicitly asks for distinct tuples or set semantics.
- Null policy
- Preserve NULL, empty string, zero, and false as distinct values unless the contract says to display a fallback.
- Tie-breakers
- Use every ordering rule in the contract and end tied business metrics with deterministic secondary keys when needed.
- Zero-related entities
- Do not invent zero rows unless the contract asks for preserved parents, missing entities, or complete periods.
- Numeric tolerance
- Round only at the requested final stage; hidden checks use the contract precision rather than visible formatting luck.
PostgreSQL-compatible local checks
Queries run in a local PGlite worker with PostgreSQL-style syntax and transactional grading.
- Wrong grain: The row count looks plausible but duplicates or missing zero rows appear. Repair: Name the intended grain, then inspect joins and GROUP BY clauses against that grain.
- Unstable order: The same rows appear in a different order during checks. Repair: Add a deterministic secondary sort key when ties are possible.
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