sql-database-assistant

SQL Database Assistant - POWERFUL Tier Skill

Overview

The operational companion to database design. While database-designer focuses on schema architecture and database-schema-designer handles ERD modeling, this skill covers the day-to-day: writing queries, optimizing performance, generating migrations, and bridging the gap between application code and database engines.

Core Capabilities

• Natural Language to SQL — translate requirements into correct, performant queries
• Schema Exploration — introspect live databases across PostgreSQL, MySQL, SQLite, SQL Server
• Query Optimization — EXPLAIN analysis, index recommendations, N+1 detection, rewrite patterns
• Migration Generation — up/down scripts, zero-downtime strategies, rollback plans
• ORM Integration — Prisma, Drizzle, TypeORM, SQLAlchemy patterns and escape hatches
• Multi-Database Support — dialect-aware SQL with compatibility guidance

Tools

| Script | Purpose | |--------|---------| | scripts/query_optimizer.py | Static analysis of SQL queries for performance issues | | scripts/migration_generator.py | Generate migration file templates from change descriptions | | scripts/schema_explorer.py | Generate schema documentation from introspection queries |

Natural Language to SQL

Translation Patterns

When converting requirements to SQL, follow this sequence:

1. Identify entities — map nouns to tables
2. Identify relationships — map verbs to JOINs or subqueries
3. Identify filters — map adjectives/conditions to WHERE clauses
4. Identify aggregations — map "total", "average", "count" to GROUP BY
5. Identify ordering — map "top", "latest", "highest" to ORDER BY + LIMIT

Common Query Templates

Top-N per group (window function)

SELECT * FROM (
  SELECT *, ROW_NUMBER() OVER (PARTITION BY department_id ORDER BY salary DESC) AS rn
  FROM employees
) ranked WHERE rn <= 3;

Running totals

SELECT date, amount,
  SUM(amount) OVER (ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS running_total
FROM transactions;

Gap detection

SELECT curr.id, curr.seq_num, prev.seq_num AS prev_seq
FROM records curr
LEFT JOIN records prev ON prev.seq_num = curr.seq_num - 1
WHERE prev.id IS NULL AND curr.seq_num > 1;

UPSERT (PostgreSQL)

INSERT INTO settings (key, value, updated_at)
VALUES ('theme', 'dark', NOW())
ON CONFLICT (key) DO UPDATE SET value = EXCLUDED.value, updated_at = EXCLUDED.updated_at;

UPSERT (MySQL)

INSERT INTO settings (key_name, value, updated_at)
VALUES ('theme', 'dark', NOW())
ON DUPLICATE KEY UPDATE value = VALUES(value), updated_at = VALUES(updated_at);

See references/query_patterns.md for JOINs, CTEs, window functions, JSON operations, and more.

Schema Exploration

Introspection Queries

PostgreSQL — list tables and columns

SELECT table_name, column_name, data_type, is_nullable, column_default
FROM information_schema.columns
WHERE table_schema = 'public'
ORDER BY table_name, ordinal_position;

PostgreSQL — foreign keys

SELECT tc.table_name, kcu.column_name,
  ccu.table_name AS foreign_table, ccu.column_name AS foreign_column
FROM information_schema.table_constraints tc
JOIN information_schema.key_column_usage kcu ON tc.constraint_name = kcu.constraint_name
JOIN information_schema.constraint_column_usage ccu ON tc.constraint_name = ccu.constraint_name
WHERE tc.constraint_type = 'FOREIGN KEY';

MySQL — table sizes

SELECT table_name, table_rows,
  ROUND(data_length / 1024 / 1024, 2) AS data_mb,
  ROUND(index_length / 1024 / 1024, 2) AS index_mb
FROM information_schema.tables
WHERE table_schema = DATABASE()
ORDER BY data_length DESC;

SQLite — schema dump

SELECT name, sql FROM sqlite_master WHERE type = 'table' ORDER BY name;

SQL Server — columns with types

SELECT t.name AS table_name, c.name AS column_name,
  ty.name AS data_type, c.max_length, c.is_nullable
FROM sys.columns c
JOIN sys.tables t ON c.object_id = t.object_id
JOIN sys.types ty ON c.user_type_id = ty.user_type_id
ORDER BY t.name, c.column_id;

Generating Documentation from Schema

Use scripts/schema_explorer.py to produce markdown or JSON documentation:

python scripts/schema_explorer.py --dialect postgres --tables all --format md
python scripts/schema_explorer.py --dialect mysql --tables users,orders --format json --json

Query Optimization

EXPLAIN Analysis Workflow

1. Run EXPLAIN ANALYZE (PostgreSQL) or EXPLAIN FORMAT=JSON (MySQL)
2. Identify the costliest node — Seq Scan on large tables, Nested Loop with high row estimates
3. Check for missing indexes — sequential scans on filtered columns
4. Look for estimation errors — planned vs actual rows divergence signals stale statistics
5. Evaluate JOIN order — ensure the smallest result set drives the join

Index Recommendation Checklist

• Columns in WHERE clauses with high selectivity
• Columns in JOIN conditions (foreign keys)
• Columns in ORDER BY when combined with LIMIT
• Composite indexes matching multi-column WHERE predicates (most selective column first)
• Partial indexes for queries with constant filters (e.g., WHERE status = 'active')
• Covering indexes to avoid table lookups for read-heavy queries

Query Rewriting Patterns

| Anti-Pattern | Rewrite | |-------------|---------| | SELECT * FROM orders | SELECT id, status, total FROM orders (explicit columns) | | WHERE YEAR(created_at) = 2025 | WHERE created_at >= '2025-01-01' AND created_at < '2026-01-01' (sargable) | | Correlated subquery in SELECT | LEFT JOIN with aggregation | | NOT IN (SELECT ...) with NULLs | NOT EXISTS (SELECT 1 ...) | | UNION (dedup) when not needed | UNION ALL | | LIKE '%search%' | Full-text search index (GIN/FULLTEXT) | | ORDER BY RAND() | Application-side random sampling or TABLESAMPLE |

N+1 Detection

Symptoms:

• Application loop that executes one query per parent row
• ORM lazy-loading related entities inside a loop
• Query log shows hundreds of identical SELECT patterns with different IDs

Fixes:

• Use eager loading (include in Prisma, joinedload in SQLAlchemy)
• Batch queries with WHERE id IN (...)
• Use DataLoader pattern for GraphQL resolvers

Static Analysis Tool

python scripts/query_optimizer.py --query "SELECT * FROM orders WHERE status = 'pending'" --dialect postgres
python scripts/query_optimizer.py --query queries.sql --dialect mysql --json

See references/optimization_guide.md for EXPLAIN plan reading, index types, and connection pooling.

Migration Generation

Zero-Downtime Migration Patterns

Adding a column (safe)

-- Up
ALTER TABLE users ADD COLUMN phone VARCHAR(20);

-- Down
ALTER TABLE users DROP COLUMN phone;

Renaming a column (expand-contract)

-- Step 1: Add new column
ALTER TABLE users ADD COLUMN full_name VARCHAR(255);
-- Step 2: Backfill
UPDATE users SET full_name = name;
-- Step 3: Deploy app reading both columns
-- Step 4: Deploy app writing only new column
-- Step 5: Drop old column
ALTER TABLE users DROP COLUMN name;

Adding a NOT NULL column (safe sequence)

-- Step 1: Add nullable
ALTER TABLE orders ADD COLUMN region VARCHAR(50);
-- Step 2: Backfill with default
UPDATE orders SET region = 'unknown' WHERE region IS NULL;
-- Step 3: Add constraint
ALTER TABLE orders ALTER COLUMN region SET NOT NULL;
ALTER TABLE orders ALTER COLUMN region SET DEFAULT 'unknown';

Index creation (non-blocking, PostgreSQL)

CREATE INDEX CONCURRENTLY idx_orders_status ON orders (status);

Data Backfill Strategies

• Batch updates — process in chunks of 1000-10000 rows to avoid lock contention
• Background jobs — run backfills asynchronously with progress tracking
• Dual-write — write to old and new columns during transiti