Why Is DuckDB Fast? (Part 1)

DuckDB Internals

Based on Hacker News Discussion

149 Points

02

Understanding DuckDB

The in-memory analytical database gaining rapid adoption

DuckDB at a Glance

storage

Embedded OLAP Database

In-process analytical database engine designed for speed

analytics

Columnar Storage

Optimized for analytical workloads with efficient compression

integration_instructions

Easy Integration

Works seamlessly with Python, R, and data science tools

speed

Zero Dependencies

Single binary, no external requirements

security

ACID Compliant

Full transaction support with data integrity

bolt

Blazing Fast

Designed for fast analytical queries from the ground up

Key Architectural Decisions

01

In-Memory Processing Primary storage in RAM enables maximum speed for analytical queries
02

Vectorized Execution Processes batches of data at once, not row-by-row
03

Columnar Layout Stores data by columns for better compression and query performance
04

Parallel Query Execution Utilizes multiple CPU cores for query processing

05

Performance Optimizations

The engineering decisions that make DuckDB exceptionally fast

Query Processing Pipeline

1

SQL Parser

Converts SQL to logical query plan

→

2

Optimizer

Rewrites queries for optimal execution

→

3

Physical Planner

Creates parallel execution strategy

→

4

Vectorized Engine

Executes operations on data batches

Performance Comparison: DuckDB vs Traditional Systems

95Queries/sec (normalized)

DuckDB

35Queries/sec (normalized)

SQLite

42Queries/sec (normalized)

PostgreSQL

28Queries/sec (normalized)

MySQL

Source: DuckDB benchmarks on analytical workloads

Key Takeaways

DuckDB's speed comes from intentional architectural choices: in-memory processing, vectorized execution, columnar storage, and parallel query processing

https://www.greybeam.ai/blog/duckdb-internals-part-1