For large schemas and teams, managing your database schema as a single file becomes unwieldy. The dump --multi-file option allows you to split your schema into modular files organized by database objects, enabling better collaboration, clearer ownership, and easier maintenance. This approach is ideal for:
  • Large schemas with hundreds of tables, views, and functions
  • Teams where different developers/teams own different parts of the schema
  • Schemas that need granular code review processes

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Workflow

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Step 1: Initialize Multi-File Structure

Set up your schema directory structure and dump your existing schema into multiple files: 
# Create schema directory
mkdir -p schema

# Dump existing database schema into multiple files
pgschema dump --host localhost --db myapp --user postgres \
  --multi-file --file schema/main.sql

# Examine the generated structure
tree schema/
# schema/
# ├── main.sql              # Main entry file with \i directives
# ├── tables/
# │   ├── users.sql
# │   ├── orders.sql
# │   └── products.sql
# ├── views/
# │   ├── user_orders.sql
# │   └── product_stats.sql
# ├── functions/
# │   ├── calculate_discount.sql
# │   └── update_inventory.sql
# └── indexes/
#     ├── idx_users_email.sql
#     └── idx_orders_date.sql

# View the main.sql file
cat schema/main.sql
# \i tables/users.sql
# \i tables/orders.sql
# \i tables/products.sql
# \i views/user_orders.sql
# \i views/product_stats.sql
# \i functions/calculate_discount.sql
# \i functions/update_inventory.sql
# \i indexes/idx_users_email.sql
# \i indexes/idx_orders_date.sql

# Create CODEOWNERS file for GitHub to define team ownership
cat > .github/CODEOWNERS << 'EOF'
schema/tables/users* @user-team
schema/tables/orders* @orders-team  
schema/tables/products* @inventory-team
schema/views/user_* @user-team
schema/views/order_* @orders-team
schema/functions/*inventory* @inventory-team
EOF
Each database object is now in its own file, organized by object type for easy navigation and maintenance.

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Step 2: Development Workflow

Use the multi-file structure to define the desired state of your schema components: 
# Edit schema files to define the desired state
# Example: Update users table to include new column
cat > schema/tables/users.sql << 'EOF'
CREATE TABLE users (
    id SERIAL PRIMARY KEY,
    email VARCHAR(255) UNIQUE NOT NULL,
    name VARCHAR(100) NOT NULL,
    created_at TIMESTAMP DEFAULT NOW(),
    last_login TIMESTAMP  -- New desired column
);
EOF

# Generate plan to see what changes are needed
pgschema plan --host localhost --db myapp --user postgres \
  --file schema/main.sql \
  --output-json plan.json \
  --output-human plan.txt

# Review the plan to see migration steps
cat plan.txt

# Apply changes after review
pgschema apply --host localhost --db myapp --user postgres --plan plan.json
Each team defines the desired state of their schema components, and pgschema generates the necessary migration steps.

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Step 3: Team Collaboration

Teams work on separate branches and test independently before combining changes for production deployment: 
# Team A develops and tests their changes in isolation
git checkout -b feature/user-profile-enhancements
# ... modify schema/tables/users.sql and related files ...

# Test Team A changes independently
pgschema plan --host test-a.db.com --db myapp --user postgres \
  --file schema/main.sql --output-json team-a-plan.json
pgschema apply --host test-a.db.com --db myapp --user postgres \
  --plan team-a-plan.json

# Team B develops and tests their changes separately  
git checkout -b feature/inventory-tracking
# ... modify schema/tables/products.sql and related files ...

# Test Team B changes independently
pgschema plan --host test-b.db.com --db myapp --user postgres \
  --file schema/main.sql --output-json team-b-plan.json
pgschema apply --host test-b.db.com --db myapp --user postgres \
  --plan team-b-plan.json

# Combine changes for production deployment
git checkout main
git merge feature/user-profile-enhancements
git merge feature/inventory-tracking

# Generate single combined plan for production
pgschema plan --host prod.db.com --db myapp --user postgres \
  --file schema/main.sql \
  --output-json prod_plan.json \
  --output-human prod_plan.txt

# Review combined plan before production deployment
cat prod_plan.txt
Each team validates their changes independently in test environments, then combines into a coordinated production deployment.

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Step 4: Production Deployment

Deploy schema changes safely to production using the established multi-file workflow: 
# Generate production deployment plan
pgschema plan --host prod.db.com --db myapp --user postgres \
  --file schema/main.sql \
  --output-json prod_plan.json \
  --output-human prod_plan.txt

# Review production plan thoroughly
less prod_plan.txt

# Apply to production (consider maintenance windows)
pgschema apply --host prod.db.com --db myapp --user postgres \
  --plan prod_plan.json \
  --lock-timeout 30s

# Verify deployment success
mkdir -p verification/
pgschema dump --host prod.db.com --db myapp --user postgres \
  --multi-file --file verification/main.sql

# Compare entire schema structure recursively
diff -r schema/ verification/

# Clean up verification files
rm -rf verification/

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File Organization Strategies

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By Object Type (Default)

pgschema generates files organized by database object type: 
schema/
├── main.sql               # Generated with \i directives
├── tables/
├── views/
├── functions/
├── procedures/
├── indexes/
├── triggers/
└── types/

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Custom Organization

You can reorganize files into any structure you prefer and update the \i directives in main.sql:

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By Business Domain

schema/
├── main.sql               # Updated with custom \i paths
├── user_management/
│   ├── users_table.sql
│   ├── user_sessions_table.sql
│   └── hash_password_function.sql
├── inventory/
│   ├── products_table.sql
│   ├── inventory_table.sql
│   └── update_stock_procedure.sql
└── orders/
    ├── orders_table.sql
    ├── order_items_table.sql
    └── calculate_total_function.sql
Update main.sql to match your structure: 
-- main.sql with custom organization
\i user_management/users_table.sql
\i user_management/user_sessions_table.sql  
\i inventory/products_table.sql
\i inventory/inventory_table.sql
\i orders/orders_table.sql
\i orders/order_items_table.sql
\i user_management/hash_password_function.sql
\i inventory/update_stock_procedure.sql
\i orders/calculate_total_function.sql

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Hybrid Approach

schema/
├── main.sql
├── core/
│   ├── tables/
│   └── functions/
├── user_management/
│   ├── tables/
│   └── views/
└── reporting/
    ├── views/
    └── materialized_views/
The key is ensuring your main.sql file contains the correct \i directives that match your chosen file organization.

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Nested Includes

The \i directive can be nested, allowing for hierarchical file organization: 
schema/
├── main.sql
├── core/
│   ├── init.sql           # Includes all core components
│   ├── tables/
│   │   ├── users.sql
│   │   └── products.sql
│   └── functions/
│       └── utilities.sql
└── modules/
    ├── auth/
    │   ├── auth.sql       # Includes all auth components
    │   ├── tables.sql
    │   └── functions.sql
    └── reporting/
        └── reporting.sql  # Includes all reporting components
With nested includes: 
-- main.sql
\i core/init.sql
\i modules/auth/auth.sql
\i modules/reporting/reporting.sql

-- core/init.sql
\i tables/users.sql
\i tables/products.sql
\i functions/utilities.sql

-- modules/auth/auth.sql
\i tables.sql
\i functions.sql
This approach allows for modular organization where each subsystem manages its own includes.