sql-database-assistant

name	sql-database-assistant
description	Use when the user asks to write SQL queries, optimize database performance, generate migrations, explore database schemas, or work with ORMs like Prisma, Drizzle, TypeORM, or SQLAlchemy.

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.

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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

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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.

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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 transition period
• Validation queries — verify row counts and data integrity after each batch

Rollback Strategies

Every migration must have a reversible down script. For irreversible changes:

1. Backup before execution — pg_dump the affected tables
2. Feature flags — application can switch between old/new schema reads
3. Shadow tables — keep a copy of the original table during migration window

Migration Generator Tool

python scripts/migration_generator.py --change "add email_verified boolean to users" --dialect postgres --format sql
python scripts/migration_generator.py --change "rename column name to full_name in customers" --dialect mysql --format alembic --json

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Multi-Database Support

Dialect Differences

Feature	PostgreSQL	MySQL	SQLite	SQL Server
UPSERT	ON CONFLICT DO UPDATE	ON DUPLICATE KEY UPDATE	ON CONFLICT DO UPDATE	MERGE
Boolean	Native BOOLEAN	TINYINT(1)	INTEGER	BIT
Auto-increment	SERIAL / GENERATED	AUTO_INCREMENT	INTEGER PRIMARY KEY	IDENTITY
JSON	JSONB (indexed)	JSON	Text (ext)	NVARCHAR(MAX)
Array	Native ARRAY	Not supported	Not supported	Not supported
CTE (recursive)	Full support	8.0+	3.8.3+	Full support
Window functions	Full support	8.0+	3.25.0+	Full support
Full-text search	tsvector + GIN	FULLTEXT index	FTS5 extension	Full-text catalog
LIMIT/OFFSET	LIMIT n OFFSET m	LIMIT n OFFSET m	LIMIT n OFFSET m	OFFSET m ROWS FETCH NEXT n ROWS ONLY

Compatibility Tips

• Always use parameterized queries — prevents SQL injection across all dialects
• Avoid dialect-specific functions in shared code — wrap in adapter layer
• Test migrations on target engine — information_schema varies between engines
• Use ISO date format — 'YYYY-MM-DD' works everywhere
• Quote identifiers — use double quotes (SQL standard) or backticks (MySQL)

---

ORM Patterns

Prisma

Schema definition

model User {
  id        Int      @id @default(autoincrement())
  email     String   @unique
  name      String?
  posts     Post[]
  createdAt DateTime @default(now())
}

model Post {
  id       Int    @id @default(autoincrement())
  title    String
  author   User   @relation(fields: [authorId], references: [id])
  authorId Int
}

Migrations: npx prisma migrate dev --name add_user_email Query API: prisma.user.findMany({ where: { email: { contains: '@' } }, include: { posts: true } }) Raw SQL escape hatch: prisma.$queryRaw\SELECT * FROM users WHERE id = ${userId}``

Drizzle

Schema-first definition

export const users = pgTable('users', {
  id: serial('id').primaryKey(),
  email: varchar('email', { length: 255 }).notNull().unique(),
  name: text('name'),
  createdAt: timestamp('created_at').defaultNow(),
});

Query builder: db.select().from(users).where(eq(users.email, email)) Migrations: npx drizzle-kit generate:pg then npx drizzle-kit push:pg

TypeORM

Entity decorators

@Entity()
export class User {
  @PrimaryGeneratedColumn()
  id: number;

  @Column({ unique: true })
  email: string;

  @OneToMany(() => Post, post => post.author)
  posts: Post[];
}

Repository pattern: userRepo.find({ where: { email }, relations: ['posts'] }) Migrations: npx typeorm migration:generate -n AddUserEmail

SQLAlchemy

Declarative models

class User(Base):
    __tablename__ = 'users'
    id = Column(Integer, primary_key=True)
    email = Column(String(255), unique=True, nullable=False)
    name = Column(String(255))
    posts = relationship('Post', back_populates='author')

Session management: Always use with Session() as session: context manager Alembic migrations: alembic revision --autogenerate -m "add user email"

See references/orm_patterns.md for side-by-side comparisons and migration workflows per ORM.

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Data Integrity

Constraint Strategy

• Primary keys — every table must have one; prefer surrogate keys (serial/UUID)
• Foreign keys — enforce referential integrity; define ON DELETE behavior explicitly
• UNIQUE constraints — for business-level uniqueness (email, slug, API key)
• CHECK constraints — validate ranges, enums, and business rules at the DB level
• NOT NULL — default to NOT NULL; make nullable only when genuinely optional

Transaction Isolation Levels

Level	Dirty Read	Non-Repeatable Read	Phantom Read	Use Case
READ UNCOMMITTED	Yes	Yes	Yes	Never recommended
READ COMMITTED	No	Yes	Yes	Default for PostgreSQL, general OLTP
REPEATABLE READ	No	No	Yes (InnoDB: No)	Financial calculations
SERIALIZABLE	No	No	No	Critical consistency (billing, inventory)

Deadlock Prevention

1. Consistent lock ordering — always acquire locks in the same table/row order
2. Short transactions — minimize time between first lock and commit
3. Advisory locks — use pg_advisory_lock() for application-level coordination
4. Retry logic — catch deadlock errors and retry with exponential backoff

---

Backup & Restore

PostgreSQL

# Full backup
pg_dump -Fc --no-owner dbname > backup.dump
# Restore
pg_restore -d dbname --clean --no-owner backup.dump
# Point-in-time recovery: configure WAL archiving + restore_command

MySQL

# Full backup
mysqldump --single-transaction --routines --triggers dbname > backup.sql
# Restore
mysql dbname < backup.sql
# Binary log for PITR: mysqlbinlog --start-datetime="2025-01-01 00:00:00" binlog.000001

SQLite

# Backup (safe with concurrent reads)
sqlite3 dbname ".backup backup.db"

Backup Best Practices

• Automate — cron or systemd timer, never manual-only
• Test restores — untested backups are not backups
• Offsite copies — S3, GCS, or separate region
• Retention policy — daily for 7 days, weekly for 4 weeks, monthly for 12 months
• Monitor backup size and duration — sudden changes signal issues

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Anti-Patterns

Anti-Pattern	Problem	Fix
SELECT *	Transfers unnecessary data, breaks on schema changes	Explicit column list
Missing indexes on FK columns	Slow JOINs and cascading deletes	Add indexes on all foreign keys
N+1 queries	1 + N round trips to database	Eager loading or batch queries
Implicit type coercion	WHERE id = '123' prevents index use	Match types in predicates
No connection pooling	Exhausts connections under load	PgBouncer, ProxySQL, or ORM pool
Unbounded queries	No LIMIT risks returning millions of rows	Always paginate
Storing money as FLOAT	Rounding errors	Use DECIMAL(19,4) or integer cents
God tables	One table with 50+ columns	Normalize or use vertical partitioning
Soft deletes everywhere	Complicates every query with WHERE deleted_at IS NULL	Archive tables or event sourcing
Raw string concatenation	SQL injection	Parameterized queries always

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Cross-References

Skill	Relationship
database-designer	Schema architecture, normalization analysis, ERD generation
database-schema-designer	Visual ERD modeling, relationship mapping
migration-architect	Complex multi-step migration orchestration
api-design-reviewer	Ensuring API endpoints align with query patterns
observability-platform	Query performance monitoring, slow query alerts