- Installation
- Documentation
- Getting Started
- Connect
- Data Import
- Overview
- CSV Files
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- Multiple Files
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- Partitioning
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- INSERT Statements
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- Overview
- C
- Overview
- Startup
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- API Reference
- C++
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- Overview
- Data Ingestion
- Conversion between DuckDB and Python
- DB API
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- API Reference
- Known Python Issues
- R
- Rust
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- Introduction
- Statements
- Overview
- ANALYZE
- ALTER TABLE
- ALTER VIEW
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- CALL
- CHECKPOINT
- COMMENT ON
- COPY
- CREATE INDEX
- CREATE MACRO
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- CREATE SECRET
- CREATE SEQUENCE
- CREATE TABLE
- CREATE VIEW
- CREATE TYPE
- DELETE
- DESCRIBE
- DROP
- EXPORT/IMPORT DATABASE
- INSERT
- PIVOT
- Profiling
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- UNPIVOT
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- Query Syntax
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- SAMPLE
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- Overview
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- Nested Functions
- Numeric Functions
- Pattern Matching
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- Struct Functions
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- Timestamp with Time Zone Functions
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- Overview
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- Arrow
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- Guides
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- File Formats
- Overview
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- Excel Export
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- JSON Export
- Parquet Import
- Parquet Export
- Querying Parquet Files
- Network & 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
- Meta Queries
- Describe Table
- EXPLAIN: Inspect Query Plans
- EXPLAIN ANALYZE: Profile Queries
- List Tables
- Summarize
- DuckDB Environment
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- Overview
- Import
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- Environment
- File Formats
- How to Tune Workloads
- My Workload Is Slow
- Benchmarks
- Python
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- Jupyter Notebooks
- SQL on Pandas
- Import from Pandas
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- Import from Numpy
- Export to Numpy
- SQL on Arrow
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- Relational API on Pandas
- Multiple Python Threads
- Integration with Ibis
- Integration with Polars
- Using fsspec Filesystems
- SQL Editors
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- Snippets
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- Browse Offline
- Operations Manual
- Overview
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- DuckDB's Footprint
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- Overview
- sqllogictest Introduction
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- Building
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- Supported Platforms
- Troubleshooting
- Benchmark Suite
- Internals
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- Why DuckDB
- Media
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- Code of Conduct
- Live Demo
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.
Clone the DuckDB repository.
git clone https://github.com/duckdb/duckdb
We recommend creating a full clone of the repository. Note that the directory uses approximately 1.2 GB of disk space.
Linux Packages
Install the required packages with the package manager of your distribution.
Ubuntu and Debian:
sudo apt-get update && sudo apt-get install -y git g++ cmake ninja-build libssl-dev
Fedora, CentOS, and Red Hat:
sudo yum install -y git g++ cmake ninja-build openssl-devel
Alpine Linux:
apk add g++ git make cmake ninja
macOS
Install Xcode and Homebrew. Then, install the required packages with:
brew install cmake ninja
Windows
Consult the Windows CI workflow for a list of packages used to build DuckDB on Windows.
On Windows, the DuckDB Python package requires the Microsoft Visual C++ Redistributable package to be built and to run.
Building DuckDB
To build DuckDB we use a Makefile which in turn calls into CMake. We also advise using Ninja as the generator for CMake.
GEN=ninja make
Bestpractice It is not advised to directly call CMake, as the Makefile sets certain variables that are crucial to properly building the package.
For testing, it can be useful to build DuckDB with statically linked core extensions. To do so, run:
CORE_EXTENSIONS='autocomplete;icu;parquet;json' GEN=ninja make
This option also accepts out-of-tree extensions:
CORE_EXTENSIONS='autocomplete;icu;parquet;json;delta' GEN=ninja make
For more details, see the “Building Extensions” page.
Troubleshooting
The Build Runs Out of Memory
Ninja parallelizes the build, which can cause out-of-memory issues on systems with limited resources. They also occur on Alpine Linux. In these cases, avoid using Ninja:
make