distributed postgres

Amazon Aurora DSQL Skill

Aurora DSQL is a serverless, PostgreSQL-compatible distributed SQL database. This skill provides direct database interaction via MCP tools, schema management, migration support, and multi-tenant patterns.

Key capabilities:

• Direct query execution via MCP tools
• Schema management with DSQL constraints
• Migration support and safe schema evolution
• Multi-tenant isolation patterns
• IAM-based authentication

Reference Files

Load these files as needed for detailed guidance:

development-guide.md

When: ALWAYS load before implementing schema changes or database operations Contains: DDL rules, connection patterns, transaction limits, security best practices

MCP:

mcp-setup.md

When: Always load for guidance using or updating the DSQL MCP server Contains: Instructions for setting up the DSQL MCP server with 2 configuration options as sampled in .mcp.json

1. Documentation-Tools Only
2. Database Operations (requires a cluster endpoint)

mcp-tools.md

When: Load when you need detailed MCP tool syntax and examples Contains: Tool parameters, detailed examples, usage patterns

language.md

When: MUST load when making language-specific implementation choices Contains: Driver selection, framework patterns, connection code for Python/JS/Go/Java/Rust

dsql-examples.md

When: Load when looking for specific implementation examples Contains: Code examples, repository patterns, multi-tenant implementations

troubleshooting.md

When: Load when debugging errors or unexpected behavior Contains: Common pitfalls, error messages, solutions

onboarding.md

When: User explicitly requests to "Get started with DSQL" or similar phrase Contains: Interactive step-by-step guide for new users

access-control.md

When: MUST load when creating database roles, granting permissions, setting up schemas for applications, or handling sensitive data Contains: Scoped role setup, IAM-to-database role mapping, schema separation for sensitive data, role design patterns

ddl-migrations.md

When: MUST load when trying to perform DROP COLUMN, RENAME COLUMN, ALTER COLUMN TYPE, or DROP CONSTRAINT functionality Contains: Table recreation patterns, batched migration for large tables, data validation

mysql-to-dsql-migrations.md

When: MUST load when migrating from MySQL to DSQL or translating MySQL DDL to DSQL-compatible equivalents Contains: MySQL data type mappings, DDL operation translations, AUTO_INCREMENT/ENUM/SET/FOREIGN KEY migration patterns, ALTER TABLE ALTER COLUMN and DROP COLUMN via table recreation

MCP Tools Available

The aurora-dsql MCP server provides these tools:

Database Operations:

1. readonly_query - Execute SELECT queries (returns list of dicts)
2. transact - Execute DDL/DML statements in transaction (takes list of SQL statements)
3. get_schema - Get table structure for a specific table

Documentation & Knowledge: 4. dsql_search_documentation - Search Aurora DSQL documentation 5. dsql_read_documentation - Read specific documentation pages 6. dsql_recommend - Get DSQL best practice recommendations

Note: There is no list_tables tool. Use readonly_query with information_schema.

See mcp-setup.md for detailed setup instructions. See mcp-tools.md for detailed usage and examples.

CLI Scripts Available

Bash scripts for cluster management and direct psql connections. All scripts are located in scripts/.

Cluster Management:

• create-cluster.sh - Create new DSQL cluster with optional tags
• delete-cluster.sh - Delete cluster with confirmation prompt
• list-clusters.sh - List all clusters in a region
• cluster-info.sh - Get detailed cluster information

Database Connection:

• psql-connect.sh - Connect to DSQL using psql with automatic IAM auth token generation

Quick example:

./scripts/create-cluster.sh --region us-east-1
export CLUSTER=abc123def456
./scripts/psql-connect.sh

See scripts/README.md for detailed usage.

Quick Start

1. List tables and explore schema

Use readonly_query with information_schema to list tables
Use get_schema to understand table structure

2. Query data

Use readonly_query for SELECT queries
Always include tenant_id in WHERE clause for multi-tenant apps
Validate inputs carefully (no parameterized queries available)

3. Execute schema changes

Use transact tool with list of SQL statements
Follow one-DDL-per-transaction rule
Always use CREATE INDEX ASYNC in separate transaction

Common Workflows

Workflow 1: Create Multi-Tenant Schema

Goal: Create a new table with proper tenant isolation

Steps:

1. Create main table with tenant_id column using transact
2. Create async index on tenant_id in separate transact call
3. Create composite indexes for common query patterns (separate transact calls)
4. Verify schema with get_schema

Critical rules:

• Include tenant_id in all tables
• Use CREATE INDEX ASYNC (never synchronous)
• Each DDL in its own transact call: transact(["CREATE TABLE ..."])
• Store arrays/JSON as TEXT

Workflow 2: Safe Data Migration

Goal: Add a new column with defaults safely

Steps:

1. Add column using transact: transact(["ALTER TABLE ... ADD COLUMN ..."])
2. Populate existing rows with UPDATE in separate transact calls (batched under 3,000 rows)
3. Verify migration with readonly_query using COUNT
4. Create async index for new column using transact if needed

Critical rules:

• Add column first, populate later
• Never add DEFAULT in ALTER TABLE
• Batch updates under 3,000 rows in separate transact calls
• Each ALTER TABLE in its own transaction

Workflow 3: Application-Layer Referential Integrity

Goal: Safely insert/delete records with parent-child relationships

Steps for INSERT:

1. Validate parent exists with readonly_query
2. Throw error if parent not found
3. Insert child record using transact with parent reference

Steps for DELETE:

1. Check for dependent records with readonly_query (COUNT)
2. Return error if dependents exist
3. Delete record using transact if safe

Workflow 4: Query with Tenant Isolation

Goal: Retrieve data scoped to a specific tenant

Steps:

1. Always include tenant_id in WHERE clause
2. Validate and sanitize tenant_id input (no parameterized queries available!)
3. Use readonly_query with validated tenant_id
4. Never allow cross-tenant data access

Critical rules:

• Validate ALL inputs before building SQL (SQL injection risk!)
• ALL queries include WHERE tenant_id = 'validated-value'
• Reject cross-tenant access at application layer
• Use allowlists or regex validation for tenant IDs

Workflow 5: Set Up Scoped Database Roles

Goal: Create application-specific database roles instead of using the admin role

MUST load access-control.md for detailed guidance.

Steps:

1. Connect as admin (the only time admin should be used)
2. Create database roles with CREATE ROLE <name> WITH LOGIN
3. Create an IAM role with dsql:DbConnect for each database role
4. Map database roles to IAM roles with AWS IAM GRANT
5. Create dedicated schemas for sensitive data (e.g., users_schema)
6. Grant schema and table permissions per role
7. Applications connect using generate-db-connect-auth-token (not the admin variant)

Critical rules:

• ALWAYS use scoped database roles for application connections
• MUST place user PII and sensitive data in dedicated schemas, not public
• ALWAYS use dsql:DbConnect for application IAM roles
• SHOULD create separate roles per service component (read-only, read-write, user service, etc.)

Workflow 6: Table Recreation DDL Migration

Goal: Perform DROP COLUMN, RENAME COLUMN, ALTER COLUMN TYPE, or DROP CONSTRAINT using the table recreation pattern.

MUST load ddl-migrations.md for detailed guidance.

Steps:

1. MUST validate table exists and get row count with readonly_query
2. MUST get current schema with get_schema
3. MUST create new table with desired structure using transact
4. MUST migrate data (batched in 500-1,000 row chunks for tables > 3,000 rows)
5. MUST verify row counts match before proceeding
6. MUST swap tables: drop original, rename new
7. MUST recreate indexes using CREATE INDEX ASYNC

Rules:

• MUST use batching for tables exceeding 3,000 rows
• PREFER batches of 500-1,000 rows for optimal throughput
• MUST validate data compatibility before type changes (abort if incompatible)
• MUST NOT drop original table until new table is verified
• MUST recreate all indexes after table swap using ASYNC

Workflow 6: MySQL to DSQL Schema Migration

Goal: Migrate MySQL table schemas and DDL operations to DSQL-compatible equivalents, including data type mapping, ALTER TABLE ALTER COLUMN, and DROP COLUMN operations.

MUST load mysql-to-dsql-migrations.md for detailed guidance.

Steps:

1. MUST map all MySQL data types to DSQL equivalents (e.g., AUTO_INCREMENT → UUID/IDENTITY/SEQUENCE, ENUM → VARCHAR with CHECK, JSON → TEXT)
2. MUST remove MySQL-specific features (ENGINE, FOREIGN KEY, ON UPDATE CURRENT_TIMESTAMP, FULLTEXT INDEX)
3. MUST implement application-layer replacements for removed features (referential integrity, timestamp updates)
4. For ALTER TABLE ... ALTER COLUMN col datatype or MODIFY COLUMN: MUST use table recreation pattern
5. For ALTER TABLE ... DROP COLUMN col: MUST use table recreation pattern
6. MUST convert all index creation to CREATE INDEX ASYNC in separate transactions
7. MUST validate data compatibility before type changes (abort if incompatible)

Rules:

• MUST use table recreation pattern for ALTER COLUMN and DROP COLUMN (not directly supported)
• MUST replace FOREIGN KEY with application-layer referential integrity
• MUST replace ENUM with VARCHAR and CHECK constraint
• MUST replace SET with TEXT (comma-separated)
• MUST replace JSON columns with TEXT
• MUST convert AUTO_INCREMENT to UUID, IDENTITY column, or SEQUENCE (SERIAL not supported)
• MUST replace UNSIGNED integers with CHECK (col >= 0)
• MUST use batching for tables exceeding 3,000 rows
• MUST NOT drop original table until new table is verified

Best Practices

• SHOULD read guidelines first - Check development_guide.md before making schema changes
• SHOULD use preferred language patterns - Check language.md
• SHOULD Execute queries directly - PREFER MCP tools for ad-hoc queries
• REQUIRED: Follow DDL Guidelines - Refer to DDL Rules
• SHALL repeatedly generate fresh tokens - Refer to Connection Limits
• ALWAYS use ASYNC indexes - CREATE INDEX ASYNC is mandatory
• MUST Serialize arrays/JSON as TEXT - Store arrays/JSON as TEXT (comma separated, JSON.stringify)
• ALWAYS Batch under 3,000 rows - maintain transaction limits
• REQUIRED: Sanitize SQL inputs with allowlists, regex, and quote escaping - See Input Validation
• MUST follow correct Application Layer Patterns - when multi-tenant isolation or application referential itegrity are required; refer to Application Layer Patterns
• REQUIRED use DELETE for truncation - DELETE is the only supported operation for truncation
• SHOULD test any migrations - Verify DDL on dev clusters before production
• Plan for Horizontal Scale - DSQL is designed to optimize for massive scales without latency drops; refer to Horizontal Scaling
• SHOULD use connection pooling in production applications - Refer to Connection Pooling
• SHOULD debug with the troubleshooting guide: - Always refer to the resources and guidelines in troubleshooting.md
• ALWAYS use scoped roles for applications - Create database roles with dsql:DbConnect; refer to Access Control

Additional Resources

• Aurora DSQL Documentation
• Code Samples Repository
• PostgreSQL Compatibility
• IAM Authentication Guide
• CloudFormation Resource