- Installation
- Documentation
- Getting Started
- Connect
- Data Import
- Overview
- Data Sources
- CSV Files
- JSON Files
- Overview
- Creating JSON
- Loading JSON
- Writing JSON
- JSON Type
- JSON Functions
- Format Settings
- Installing and Loading
- SQL to / from JSON
- Caveats
- Multiple Files
- Parquet Files
- Partitioning
- Appender
- INSERT Statements
- Client APIs
- Overview
- ADBC
- C
- Overview
- Startup
- Configuration
- Query
- Data Chunks
- Vectors
- Values
- Types
- Prepared Statements
- Appender
- Table Functions
- Replacement Scans
- API Reference
- C++
- CLI
- Dart
- Go
- Java (JDBC)
- Julia
- Node.js (Deprecated)
- Node.js (Neo)
- ODBC
- Python
- Overview
- Data Ingestion
- Conversion between DuckDB and Python
- DB API
- Relational API
- Function API
- Types API
- Expression API
- Spark API
- API Reference
- Known Python Issues
- R
- Rust
- Swift
- Wasm
- SQL
- Introduction
- Statements
- Overview
- ANALYZE
- ALTER TABLE
- ALTER VIEW
- ATTACH and DETACH
- CALL
- CHECKPOINT
- COMMENT ON
- COPY
- CREATE INDEX
- CREATE MACRO
- CREATE SCHEMA
- CREATE SECRET
- CREATE SEQUENCE
- CREATE TABLE
- CREATE VIEW
- CREATE TYPE
- DELETE
- DESCRIBE
- DROP
- EXPORT and IMPORT DATABASE
- INSERT
- LOAD / INSTALL
- PIVOT
- Profiling
- SELECT
- SET / RESET
- SET VARIABLE
- SUMMARIZE
- Transaction Management
- UNPIVOT
- UPDATE
- USE
- VACUUM
- Query Syntax
- SELECT
- FROM and JOIN
- WHERE
- GROUP BY
- GROUPING SETS
- HAVING
- ORDER BY
- LIMIT and OFFSET
- SAMPLE
- Unnesting
- WITH
- WINDOW
- QUALIFY
- VALUES
- FILTER
- Set Operations
- Prepared Statements
- Data Types
- Overview
- Array
- Bitstring
- Blob
- Boolean
- Date
- Enum
- Interval
- List
- Literal Types
- Map
- NULL Values
- Numeric
- Struct
- Text
- Time
- Timestamp
- Time Zones
- Union
- Typecasting
- Expressions
- Overview
- CASE Expression
- Casting
- Collations
- Comparisons
- IN Operator
- Logical Operators
- Star Expression
- Subqueries
- TRY
- Functions
- Overview
- Aggregate Functions
- Array Functions
- Bitstring Functions
- Blob Functions
- Date Format Functions
- Date Functions
- Date Part Functions
- Enum Functions
- Interval Functions
- Lambda Functions
- List Functions
- Map Functions
- Nested Functions
- Numeric Functions
- Pattern Matching
- Regular Expressions
- Struct Functions
- Text Functions
- Time Functions
- Timestamp Functions
- Timestamp with Time Zone Functions
- Union Functions
- Utility Functions
- Window Functions
- Constraints
- Indexes
- Meta Queries
- DuckDB's SQL Dialect
- Overview
- Indexing
- Friendly SQL
- Keywords and Identifiers
- Order Preservation
- PostgreSQL Compatibility
- SQL Quirks
- Samples
- Configuration
- Extensions
- Overview
- Installing Extensions
- Advanced Installation Methods
- Distributing Extensions
- Versioning of Extensions
- Troubleshooting of Extensions
- Core Extensions
- Overview
- AutoComplete
- Avro
- AWS
- Azure
- Delta
- DuckLake
- Encodings
- Excel
- Full Text Search
- httpfs (HTTP and S3)
- Iceberg
- Overview
- Iceberg REST Catalogs
- Amazon S3 Tables
- Amazon SageMaker Lakehouse (AWS Glue)
- Troubleshooting
- ICU
- inet
- jemalloc
- MySQL
- PostgreSQL
- Spatial
- SQLite
- TPC-DS
- TPC-H
- UI
- VSS
- Guides
- Overview
- Data Viewers
- Database Integration
- File Formats
- Overview
- CSV Import
- CSV Export
- Directly Reading Files
- Excel Import
- Excel Export
- JSON Import
- JSON Export
- Parquet Import
- Parquet Export
- Querying Parquet Files
- File Access with the file: Protocol
- Network and Cloud Storage
- Overview
- HTTP Parquet Import
- S3 Parquet Import
- S3 Parquet Export
- S3 Iceberg Import
- S3 Express One
- GCS Import
- Cloudflare R2 Import
- DuckDB over HTTPS / S3
- Fastly Object Storage Import
- Meta Queries
- Describe Table
- EXPLAIN: Inspect Query Plans
- EXPLAIN ANALYZE: Profile Queries
- List Tables
- Summarize
- DuckDB Environment
- ODBC
- Performance
- Overview
- Environment
- Import
- Schema
- Indexing
- Join Operations
- File Formats
- How to Tune Workloads
- My Workload Is Slow
- Benchmarks
- Working with Huge Databases
- Python
- Installation
- Executing SQL
- Jupyter Notebooks
- marimo Notebooks
- SQL on Pandas
- Import from Pandas
- Export to Pandas
- Import from Numpy
- Export to Numpy
- SQL on Arrow
- Import from Arrow
- Export to Arrow
- Relational API on Pandas
- Multiple Python Threads
- Integration with Ibis
- Integration with Polars
- Using fsspec Filesystems
- SQL Editors
- SQL Features
- Snippets
- Creating Synthetic Data
- Dutch Railway Datasets
- Sharing Macros
- Analyzing a Git Repository
- Importing Duckbox Tables
- Copying an In-Memory Database to a File
- Troubleshooting
- Glossary of Terms
- Browsing Offline
- Operations Manual
- Overview
- DuckDB's Footprint
- Logging
- Securing DuckDB
- Non-Deterministic Behavior
- Limits
- Development
- DuckDB Repositories
- Profiling
- Building DuckDB
- Overview
- Build Configuration
- Building Extensions
- Android
- Linux
- macOS
- Raspberry Pi
- Windows
- Python
- R
- Troubleshooting
- Unofficial and Unsupported Platforms
- Benchmark Suite
- Testing
- Internals
- Why DuckDB
- FAQ
- Code of Conduct
- Release Calendar
- Roadmap
- Sitemap
- Live Demo
When Should You Build DuckDB?
DuckDB binaries are available for stable and preview builds on the installation page.
In most cases, it's recommended to use these binaries.
When you are running on an experimental platform (e.g., Raspberry Pi) or you would like to build the project for an unmerged pull request,
you can build DuckDB from source based on the duckdb/duckdb
repository hosted on GitHub.
This page explains the steps for building DuckDB.
Prerequisites
DuckDB needs CMake and a C++11-compliant compiler (e.g., GCC, Apple-Clang, MSVC). Additionally, we recommend using the Ninja build system, which automatically parallelizes the build process.
Getting Started
A Makefile
wraps the build process.
See Build Configuration for targets and configuration flags.
make
make release # same as plain make
make debug
GEN=ninja make # for use with Ninja
BUILD_BENCHMARK=1 make # Build with benchmarks
Platforms
Platforms with Full Support
DuckDB fully supports Linux, macOS and Windows. Both x86_64 (amd64) and AArch64 (arm64) builds are available for these platforms, and almost all extensions are distributed for these platforms.
Platform name | Description |
---|---|
linux_amd64 |
Linux x86_64 (amd64) with glibc |
linux_arm64 |
Linux AArch64 (arm64) with glibc |
osx_amd64 |
macOS 12+ amd64 (Intel CPUs) |
osx_arm64 |
macOS 12+ arm64 (Apple Silicon CPUs) |
windows_amd64 |
Windows 10+ x86_64 (amd64) |
windows_arm64 |
Windows 10+ AArch64 (arm64) |
For these platforms, builds are available for both the latest stable version and the preview version (nightly build). In some circumstances, you may still want to build DuckDB from source, e.g., to test an unmerged pull request. For build instructions on these platforms, see:
Platforms with Partial Support
There are several partially supported platforms. For some platforms, DuckDB binaries and extensions (or a subset of extensions) are distributed. For others, building from source is possible.
Platform name | Description |
---|---|
linux_amd64_musl |
Linux x86_64 (amd64) with musl libc, e.g., Alpine Linux |
linux_arm64_musl |
Linux AArch64 (arm64) with musl libc, e.g., Alpine Linux |
linux_arm64_android |
Android AArch64 (arm64) |
wasm_eh |
WebAssembly Exception Handling |
Below, we provide detailed build instructions for some platforms:
Platforms with Best Effort Support
Platform name | Description |
---|---|
freebsd_amd64 |
FreeBSD x86_64 (amd64) |
freebsd_arm64 |
FreeBSD AArch64 (arm64) |
wasm_mvp |
WebAssembly Minimum Viable Product |
windows_amd64_mingw |
Windows 10+ x86_64 (amd64) with MinGW |
windows_arm64_mingw |
Windows 10+ AArch64 (arm64) with MinGW |
These platforms are not covered by DuckDB's community support. For details on commercial support, see the support policy page.
See also the “Unofficial and Unsupported Platforms” page for details.
Outdated Platforms
Some platforms were supported in older DuckDB versions but are no longer supported.
Platform name | Description |
---|---|
linux_amd64_gcc4 |
Linux AMDM64 (x86_64) with GCC 4, e.g., CentOS 7 |
linux_arm64_gcc4 |
Linux AArch64 (arm64) with GCC 4, e.g., CentOS 7 |
windows_amd64_rtools |
Windows 10+ x86_64 (amd64) for RTools |
DuckDB can also be built for end-of-life platforms such as macOS 11 and CentOS 7/8 using the instructions provided for macOS and Linux.
Amalgamation Build
DuckDB can be build as a single pair of C++ header and source code files (duckdb.hpp
and duckdb.cpp
) with approximately 0.5M lines of code.
To generate this file, run:
python scripts/amalgamation.py
Note that amalgamation build is provided on a best-effort basis and is not officially supported.
Limitations
Currently, DuckDB has the following limitations:
- The DuckDB codebase is not compatible with C++23. Therefore, trying to compile DuckDB with
-std=c++23
will fail. - The
-march=native
build flag, i.e., compiling DuckDB with the local machine's native instructions set, is not supported.
Troubleshooting Guides
We provide troubleshooting guides for building DuckDB: