Introduction
Since its inception as a research project at CWI Amsterdam in 2019, DuckDB has risen to prominence among analytical databases. Whether it's in notebooks, ETL pipelines, or even embedded in SaaS products, DuckDB has won over users with its simplicity and performance. Why is this database so fast? This is what we'll explore in this first part of our series on DuckDB internals.
An In-Process Database
DuckDB stands out because it is "in-process." Unlike giants like Snowflake or BigQuery that require a server connection, DuckDB integrates directly into your program as a library, just as you would with NumPy. This means no cluster, no heavy configuration, just a simple and fast setup with pip install duckdb.
Concrete Example
Imagine pointing DuckDB at a 6 GB Parquet file on your laptop. Results appear in under a second. It's not magic; it's simply the efficiency of in-process execution.
Columnar Compressed Storage
One of the secrets to its speed lies in its columnar compressed storage. By organizing data by column rather than by row, DuckDB optimizes read and write operations. "Zonemaps" also help reduce the volume of data to scan, thus speeding up queries.
Supported by Numbers
According to a recent study, DuckDB can process analytical queries 10 times faster than some traditional databases, while using fewer system resources.
Vectorized Execution
DuckDB uses vectorized execution, a concept that allows processing multiple data "batches" at once, rather than row by row. This significantly improves the throughput of analytical queries.
Morsel-Driven Parallelism
Parallelism is crucial for leveraging modern processors. DuckDB divides tasks into small chunks called "morsels," which can be executed in parallel. This ensures optimal utilization of CPU resources.
Snapshot Isolation with Optimistic MVCC
To manage concurrent transactions, DuckDB uses snapshot isolation with optimistic multiversion concurrency control (MVCC). This allows transactions to proceed without locking, enhancing fluidity and speed.
Conclusion
DuckDB is not only fast, but ingeniously designed. Its in-process architecture, columnar compressed storage, and advanced execution and parallelism techniques make it an indispensable tool for modern data analytics.
Let's discuss your project in 15 minutes.