5-minute tutorial

Migrate DuckDB to MySQL in 60 Seconds

Learn how to copy your DuckDB data to MySQL with a single command using ingestr - no code required.

One command Zero code Production ready

What you'll learn

How to install and set up ingestr in seconds
Connect to DuckDB and MySQL with proper authentication
Copy entire tables or specific data with a single command
Set up incremental loading for continuous data synchronization

Prerequisites

  • Python 3.8 or higher installed
  • DuckDB installed locally or database file accessible
  • Write permissions for database file location
  • Sufficient memory for in-memory operations
  • Compatible file format version
  • MySQL server running and accessible
  • User with appropriate GRANT permissions
  • Database exists or permission to create
  • Network access to MySQL port

Step 1: Install ingestr

Install ingestr in seconds using pip. Choose the method that works best for you:

Recommended: Using uv (fastest)

# Install uv first if you haven't already
pip install uv

# Run ingestr using uvx
uvx ingestr

Alternative: Global installation

# Install globally using uv
uv pip install --system ingestr

# Or using standard pip
pip install ingestr

Verify installation: Run ingestr --version to confirm it's installed correctly.

Step 2: Your First Migration

Let's copy a table from DuckDB to MySQL. This example shows a complete, working command you can adapt to your needs.

Set up your connections

DuckDB connection format:

duckdb:///path/to/database.duckdb

Parameters:

  • • path: Path to database file (use :memory: for in-memory)
  • • read_only: Optional flag for read-only access
  • • threads: Number of threads to use

MySQL connection format:

mysql://username:password@host:port/database

Parameters:

  • • username: MySQL user
  • • password: User password
  • • host: Server hostname or IP
  • • port: Server port (default 3306)
  • • database: Database name
  • • charset: Optional character set

BigQuery Setup Required

Before running the command:

  1. Create a service account in Google Cloud Console
  2. Grant it BigQuery Data Editor and Job User roles
  3. Download the JSON key file
  4. Use the path to this file in your connection string

Run your first copy

Copy the entire users table from DuckDB to MySQL:

ingestr ingest \
    --source-uri 'duckdb:///home/user/analytics.duckdb' \
    --source-table 'main.analytics' \
    --dest-uri 'mysql://root:password@localhost:3306/myapp' \
    --dest-table 'raw.analytics'

What this does:

  • • Connects to your DuckDB database
  • • Reads all data from the specified table
  • • Creates the table in MySQL if needed
  • • Copies all rows to the destination

Command breakdown:

  • --source-uri Your source database
  • --source-table Table to copy from
  • --dest-uri Your destination
  • --dest-table Where to write data

Step 3: Verify your data

After the migration completes, verify your data was copied correctly:

Check row count in MySQL:

-- Run this in MySQL
SELECT COUNT(*) as row_count 
FROM raw.analytics;

-- Check a sample of the data
SELECT * 
FROM raw.analytics 
LIMIT 10;

Advanced Patterns

Once you've mastered the basics, use these patterns for production workloads.

Only copy new or updated records since the last sync. Perfect for daily updates. 

ingestr ingest \
    --source-uri 'duckdb:///home/user/analytics.duckdb' \
    --source-table 'public.orders' \
    --dest-uri 'mysql://root:password@localhost:3306/myapp' \
    --dest-table 'raw.orders' \
    --incremental-strategy merge \
    --incremental-key updated_at \
    --primary-key order_id

How it works: The merge strategy updates existing rows and inserts new ones based on the primary key. Only rows where updated_at has changed will be processed.

Common Use Cases

Ready-to-use commands for typical DuckDB to MySQL scenarios.

Daily Customer Data Sync

Keep your analytics warehouse updated with the latest customer information every night. 

# Add this to your cron job or scheduler
ingestr ingest \
    --source-uri 'duckdb:///home/user/analytics.duckdb' \
    --source-table 'public.customers' \
    --dest-uri 'mysql://root:password@localhost:3306/myapp' \
    --dest-table 'analytics.customers' \
    --incremental-strategy merge \
    --incremental-key updated_at \
    --primary-key customer_id

Historical Data Migration

One-time migration of all historical records to your data warehouse. 

# One-time full table copy
ingestr ingest \
    --source-uri 'duckdb:///home/user/analytics.duckdb' \
    --source-table 'public.transactions' \
    --dest-uri 'mysql://root:password@localhost:3306/myapp' \
    --dest-table 'warehouse.transactions_historical'

Development Environment Sync

Copy production data to your development MySQL instance (with sensitive data excluded). 

# Copy sample data to development
ingestr ingest \
    --source-uri 'duckdb:///home/user/analytics.duckdb' \
    --source-table 'public.products' \
    --dest-uri 'mysql://root:password@localhost:3306/myapp' \
    --dest-table 'dev.products' \
    --limit 1000  # Only copy 1000 rows for testing

Troubleshooting Guide

Solutions to common issues when migrating from DuckDB to MySQL.

Connection refused or timeout errors

Check your connection details:

  • Ensure database file path is accessible
  • Check file permissions for read/write access
  • Verify DuckDB version compatibility
  • Consider memory limits for large operations
  • Check bind-address in my.cnf (should not be 127.0.0.1 for remote)
  • Verify user has permission from connecting host
  • Ensure port 3306 is not blocked by firewall
  • Test with mysql client to isolate issues
Authentication failures

Common authentication issues:

  • Ensure database file path is accessible
  • Check file permissions for read/write access
  • Verify DuckDB version compatibility
  • Consider memory limits for large operations
  • Check bind-address in my.cnf (should not be 127.0.0.1 for remote)
  • Verify user has permission from connecting host
  • Ensure port 3306 is not blocked by firewall
  • Test with mysql client to isolate issues
Schema or data type mismatches

Handling data type differences:

  • ingestr automatically handles most type conversions
  • DuckDB: LIST and STRUCT types for complex data
  • DuckDB: Native support for nested data structures
  • DuckDB: Automatic type inference from files
  • DuckDB: Efficient NULL handling
  • MySQL: JSON type available in MySQL 5.7+
  • MySQL: DATETIME vs TIMESTAMP timezone handling
  • MySQL: Character set and collation settings
  • MySQL: Strict mode affects data validation
Performance issues with large tables

Optimize large data transfers:

  • Use incremental loading to process data in chunks
  • Run migrations during off-peak hours
  • Split very large tables by date ranges using interval parameters

Ready to scale your data pipeline?

You've learned how to migrate data from DuckDB to MySQL with ingestr. For production workloads with monitoring, scheduling, and data quality checks, explore Bruin Cloud.

Star ingestr on GitHub