mysql-advanced-sql

MySQL Advanced SQL

Use When

• Advanced MySQL 8 SQL techniques: window functions, CTEs, recursive queries, pivoting, JSON operations, stored procedures, triggers, and complex aggregations. Use when writing analytical queries, transforming data, implementing reporting SQL...
• The task needs reusable judgment, domain constraints, or a proven workflow rather than ad hoc advice.

Do Not Use When

• The task is unrelated to mysql-advanced-sql or would be better handled by a more specific companion skill.
• The request only needs a trivial answer and none of this skill's constraints or references materially help.

Required Inputs

• Gather relevant project context, constraints, and the concrete problem to solve; load references only as needed.
• Confirm the desired deliverable: design, code, review, migration plan, audit, or documentation.

Workflow

• Read this SKILL.md first, then load only the referenced deep-dive files that are necessary for the task.
• Apply the ordered guidance, checklists, and decision rules in this skill instead of cherry-picking isolated snippets.
• Produce the deliverable with assumptions, risks, and follow-up work made explicit when they matter.

Quality Standards

• Keep outputs execution-oriented, concise, and aligned with the repository's baseline engineering standards.
• Preserve compatibility with existing project conventions unless the skill explicitly requires a stronger standard.
• Prefer deterministic, reviewable steps over vague advice or tool-specific magic.

Anti-Patterns

• Treating examples as copy-paste truth without checking fit, constraints, or failure modes.
• Loading every reference file by default instead of using progressive disclosure.

Outputs

• A concrete result that fits the task: implementation guidance, review findings, architecture decisions, templates, or generated artifacts.
• Clear assumptions, tradeoffs, or unresolved gaps when the task cannot be completed from available context alone.
• References used, companion skills, or follow-up actions when they materially improve execution.

Evidence Produced

Category	Artifact	Format	Example
Correctness	Advanced query test plan	Markdown doc covering window function, recursive CTE, and JSON-path test cases	docs/data/mysql-advanced-tests.md

References

• Use the references/ directory for deep detail after reading the core workflow below.

Expert patterns for MySQL 8+ beyond basic CRUD. Drawn from Leveling Up with SQL (Mark Simon, Apress 2023) plus production-hardened techniques.

Sections: Window Functions | Recursive CTEs | Pivoting | JSON | Gaps & Islands | Deduplication | Triggers | Stored Procedures | Conditional Aggregation | GROUP BY | Subqueries | Views | Anti-Patterns

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1. Window Functions

Window functions compute a value per row over a related set of rows without collapsing them into a GROUP BY summary. Introduced in MySQL 8.0.

Syntax skeleton:

fn() OVER (
  [PARTITION BY col, ...]   -- subgroup (like GROUP BY)
  [ORDER BY col [ASC|DESC]] -- enables cumulative / running calcs
  [ROWS|RANGE frame_clause] -- optional sliding window
)

ROW_NUMBER -- latest record per group (replaces correlated subquery)

SELECT * FROM (
  SELECT *,
    ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date DESC) AS rn
  FROM orders
) t WHERE rn = 1;

RANK vs DENSE_RANK vs ROW_NUMBER

Function	Ties	Next rank after tie
ROW_NUMBER()	Arbitrary order	n+1 always
RANK()	Same rank	Skips (1,1,3)
DENSE_RANK()	Same rank	No skip (1,1,2)

Running total with explicit frame

SELECT date, amount,
  SUM(amount) OVER (
    PARTITION BY customer_id
    ORDER BY date
    ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
  ) AS running_total,
  AVG(amount) OVER (
    ORDER BY date
    ROWS 6 PRECEDING          -- 7-day rolling average
  ) AS week_avg
FROM orders;

ROWS vs RANGE: ROWS counts physical rows; RANGE groups rows with identical ORDER BY values. Use ROWS for running totals to avoid double-counting ties.

Period-over-period comparison

SELECT month, revenue,
  LAG(revenue, 1)  OVER (ORDER BY month) AS prev_month,
  revenue - LAG(revenue, 1) OVER (ORDER BY month) AS change,
  LEAD(revenue, 1) OVER (ORDER BY month) AS next_month
FROM monthly_revenue;

Quartile segmentation

SELECT customer_id, total_spend,
  NTILE(4) OVER (ORDER BY total_spend) AS quartile
FROM customer_totals;

Caution: NTILE splits by row count, so tied values can land in different tiles. For fair grouping use FLOOR((RANK() OVER (ORDER BY val) - 1) / bin_size) + 1.

Named window -- reuse across multiple functions

SELECT *,
  ROW_NUMBER() OVER w AS rn,
  SUM(amount)  OVER w AS running_total,
  AVG(amount)  OVER w AS running_avg
FROM orders
WINDOW w AS (PARTITION BY customer_id ORDER BY order_date
             ROWS UNBOUNDED PRECEDING);

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Additional Guidance

Extended guidance for mysql-advanced-sql was moved to references/skill-deep-dive.md to keep this entrypoint compact and fast to load.

Use that deep dive for:

• 2. Recursive CTEs
• 3. Pivoting Data
• 4. JSON Operations
• 5. Gaps and Islands
• 6. Deduplication
• 7. Triggers
• 8. Stored Procedures and Error Handling
• 9. Conditional Aggregation
• 10. Advanced GROUP BY
• 11. Subqueries and CTEs as Variables
• 12. Views and Sargable Date Patterns
• 13. Anti-Patterns Reference
• Additional deep-dive sections continue in the reference file.