DuckDB vs MySQL
- ✦ In-process execution
- ✦ Parquet/CSV/JSON direct query
- ✦ S3 and GCS file access
- ✦ ACID Transactions (InnoDB)
- ✦ Replication
- ✦ Full-Text Search
Real-World Scenarios: When to Choose Which
The question that matters: “In what situation will I regret choosing A over B after 3 months?”
Query 50GB Parquet files on S3 directly from Python without ETL, returning results in seconds
Replace pandas aggregations with SQL-based DuckDB queries for 10-50x faster group-by operations
Run dbt models locally against DuckDB instead of cloud warehouses to cut development cycle time
Use DuckDB as a compute engine over Delta Lake or Iceberg tables without a dedicated cluster
MySQL replication routes heavy analytical queries to a read replica, removing contention with write-heavy OLTP traffic and keeping application response times below 100ms during peak loads.
MySQL's InnoDB full-text index supports natural language and boolean search queries on text columns without a separate Elasticsearch deployment for basic in-app search needs.
ProxySQL in front of MySQL pools thousands of application connections into tens of database connections, preventing connection exhaustion on deployments that scale web processes horizontally.
Pricing Intelligence
DuckDB Plans
Free tier available
- • MIT license
- • Embedded in-process
- • No server needed
MySQL Plans
Free tier available
- • GPL license
- • Full SQL
- • Community support
- • Commercial license
- • Oracle support
- • Advanced security
Feature Matrix
9 differences found across 18 standardized features
- •In-process execution
- •Parquet/CSV/JSON direct query
- •S3 and GCS file access
- •SQL support
- •Python/R/Node.js integration
- •Vectorized execution
- •Parallel query processing
- •Apache Arrow integration
- •Zero-copy pandas exchange
- •Window functions
- •ACID transactions
- •Column-oriented storage
- •Schema inference
- •HTTPFS extension
- •ACID Transactions (InnoDB)
- •Replication
- •Full-Text Search
- •JSON Support
- •InnoDB Cluster
- •MySQL Shell
- •Partitioning
- •Stored Procedures
- •Views & Triggers
- •Performance Schema
- •Point-in-Time Recovery
- •SSL/TLS
- •Row-Level Locking
- •HeatWave (MySQL Analytics)
- •Cloud Services
- •PHP Integration
Pros & Cons Face-Off
Evaluative strengths and weaknesses: not feature lists
- +Runs in-process with zero infrastructure setup
- +Directly queries Parquet and CSV on S3 without ETL
- +Outperforms many server-based DBs on single-machine workloads
- −Single-node only - no horizontal scaling or clustering
- −Not suitable for multi-user concurrent write workloads
- +Most widely deployed database: abundant expertise and tooling
- +Battle-tested for 30 years on the web
- +HeatWave adds analytics and ML without ETL
- +Available managed on AWS RDS, Azure, GCP
- −PostgreSQL has surpassed it in features for new projects
- −Oracle ownership creates open-source licensing concerns
At a Glance
Frequently Asked Questions
Related Comparisons
Sources
- 1.DuckDB Official Pricing–Vendor pricing page
- 2.MySQL Official Pricing–Vendor pricing page
- 3.DuckDB Official Website–Official product website
- 4.MySQL Official Website–Official product website