https://duckdb.org

DuckDB – An in-process SQL OLAP database management systemDuckDB is an in-process SQL OLAP database management system. Simple, feature-rich, fast & open source.DuckDB – An in-process SQL OLAP database management system Documentation Getting Started Installation Guides Data Import Client APIs SQL Introduction Why DuckDB FAQ Resources Blog Media Events Webshop GitHub 26.7k Support Support Search Shortcut cmd + k | ctrl + k DuckDB is a fast | database system Query and transform your data anywhere using DuckDB's feature-rich SQL dialect Installation Documentation SQL Python R Java Node.js -- Get the top-3 busiest train stations SELECT station_name, count(*) AS num_services FROM train_services GROUP BY ALL ORDER BY num_services DESC LIMIT 3; Aggregation query Join query Read remote CSV files Read remote Parquet files Spatial extension Live demo DuckDB at a glance Simple DuckDB is easy to install and deploy. It has zero external dependencies and runs in-process in its host application or as a single binary. Read more Portable DuckDB runs on Linux, macOS, Windows, and all popular hardware architectures. It has idiomatic client APIs for major programming languages. Read more Feature-rich DuckDB offers a rich SQL dialect. It can read and write file formats such as CSV, Parquet, and JSON, to and from the local file system and remote endpoints such as S3 buckets. Read more Fast DuckDB runs analytical queries at blazing speed thanks to its columnar engine, which supports parallel execution and can process larger-than-memory workloads. Read more Extensible DuckDB is extensible by third-party features such as new data types, functions, file formats and new SQL syntax. Read more Free DuckDB and its core extensions are open-source under the permissive MIT License. Read more Installation DuckDB is seamlessly integrated with major programming languages. It can be installed in less than 20 seconds on most platforms. More installation options Command line Python R Java Node.js ODBC Rust Go Command Line Python R Java Node.js ODBC Rust Go curl https://install.duckdb.org | sh Latest release: DuckDB 1.2.0 | System detected: pip install duckdb install.packages("duckdb") org.duckdb duckdb_jdbc 1.2.0 npm install @duckdb/node-api curl https://install.duckdb.org | sh https://github.com/duckdb/duckdb/releases/download/v1.2.0/duckdb_cli-linux-amd64.zip winget install DuckDB.cli duckdb_odbc-osx-universal.zip https://github.com/duckdb/duckdb/releases/download/v1.2.0/duckdb_odbc-linux-amd64.zip https://github.com/duckdb/duckdb/releases/download/v1.2.0/duckdb_odbc-windows-amd64.zip cargo add duckdb --features bundled go get github.com/marcboeker/go-duckdb Blog deep dive Planning AsOf Joins 2025-02-19 Richard Wesley deep dive Flying Through Windows 2025-02-14 Richard Wesley All blog posts Aggregation query Join query Read CSV files Read Parquet files from S3 Spatial extension SQL query Pandas integration Custom UDFs SQL query duckplyr dplyr integration SQL query Appender SQL query Web service integration -- Get the top-3 busiest train stations SELECT station_name, count(*) AS num_services FROM train_services GROUP BY ALL ORDER BY num_services DESC LIMIT 3; -- Load CSV file to a table. DuckDB auto-detects -- the CSV's format, column name and types CREATE TABLE stations AS FROM 's3://duckdb-blobs/stations.csv'; -- Directly query Parquet file in S3 SELECT station_name, count(*) AS num_services FROM 's3://duckdb-blobs/train_services.parquet' GROUP BY ALL ORDER BY num_services DESC LIMIT 10; -- Find the top-3 longest domestic train routes SELECT s1.name_short, s2.name_short, d.distance FROM distances d JOIN stations s1 ON d.station1 = s1.code JOIN stations s2 ON d.station2 = s2.code WHERE s1.country = s2.country AND s1.code < s2.code ORDER BY distance DESC LIMIT 3; -- List the closest IC stations (as the crow flies) SELECT s1.name_long AS station1, s2.name_long AS station2, ST_Distance( ST_Point(s1.geo_lng, s1.geo_lat), ST_Point(s2.geo_lng, s2.geo_lat) ) * 111_139 AS distance FROM stations s1, stations s2 WHERE s1.type LIKE '%Intercity%' AND s2.type LIKE '%Intercity%' AND s1.id < s2.id ORDER BY distance ASC LIMIT 3; # Get the top-3 busiest train stations import duckdb duckdb.sql(""" SELECT station, count(*) AS num_services FROM train_services GROUP BY ALL ORDER BY num_services DESC LIMIT 3; """) # Reading and writing Pandas dataframes import pandas as pd import duckdb df_in = pd.DataFrame({ 'station': ['Delft', 'Delft', 'Gouda', 'Gouda'], 'day': ['Mon', 'Tue', 'Mon', 'Tue'], 'num_services' : [22, 20, 27, 25]}) # Run query on a dataframe and return a dataframe df_out = duckdb.sql(""" SELECT station, sum(num_services) FROM df_in GROUP BY station """).to_df() # Create custom user-defined function import duckdb def plus_one(x): return x + 1 con = duckdb.connect() con.create_function('plus_one', plus_one, ['BIGINT'], 'BIGINT', type='native') con.sql(""" SELECT sum(plus_one(i)) FROM range(10) tbl(i); """) # Find the largest sepals/petals in the Iris data set library(duckdb) con <- dbConnect(duckdb()) duckdb_register(con, "iris", iris) query <- r'( SELECT count(*) AS num_observations, max("Sepal.Width") AS max_width, max("Petal.Length") AS max_petal_length FROM iris WHERE "Sepal.Length" > 5 GROUP BY ALL )' dbGetQuery(con, query) # Find the largest sepals/petals in the Iris data set # using duckplyr library("duckplyr") iris |> filter(Sepal.Length > 5) |> group_by(Species) |> summarize( num_observations = n(), max_width = max(Sepal.Width), max_petal_length = max(Petal.Length), na.rm = TRUE) |> collect() # Find the largest sepals/petals in the Iris data set # using dplyr library("duckdb") library("dplyr") con <- dbConnect(duckdb()) duckdb_register(con, "iris", iris) tbl(con, "iris") |> filter(Sepal.Length > 5) |> group_by(Species) |> summarize( num_observations = count(), max_width = max(Sepal.Width), max_petal_length = max(Petal.Length), na.rm = TRUE) |> collect() // Get a list of train stations by traffic Connection conn = DriverManager.getConnection("jdbc:duckdb:"); Statement st = conn.createStatement(); ResultSet rs = st.executeQuery( "SELECT station_name,\n" + " count(*) AS num_services\n" + "FROM train_services\n" + "GROUP BY ALL\n" + "ORDER BY num_services DESC;"); System.out.println(rs.next()); // Perform bulk inserts using the Appender API DuckDBConnection conn = (DuckDBConnection) DriverManager.getConnection("jdbc:duckdb:"); Statement st = conn.createStatement(); st.execute("CREATE TABLE person " + "(name VARCHAR, age INT)"); var appender = conn.createAppender( DuckDBConnection.DEFAULT_SCHEMA, "person"); appender.beginRow(); appender.append("MC Ducky"); appender.append(49); appender.endRow(); appender.close(); // Get the top-3 busiest train stations in May import { DuckDBInstance } from '@duckdb/node-api'; const instance = await DuckDBInstance.create(); const connection = await instance.connect(); const reader = await connection.runAndReadAll( `SELECT station_name, count(*) AS num_services FROM 'http://blobs.duckdb.org/train_services.parquet' WHERE monthname(date) = 'May' GROUP BY ALL ORDER BY num_services DESC LIMIT 3;` ); console.table(reader.getRows()); // Web Service Integration: // Create endpoint to generate numbers import express from "express"; import { DuckDBInstance } from '@duckdb/node-api'; const app = express(); const instance = await DuckDBInstance.create(); const connection = await instance.connect(); app.get("/getnumbers", async (req, res) => { const reader = await connection.runAndReadAll( "SELECT random() AS num FROM range(10)"); res.end(JSON.stringify(reader.getRows())); }); app.listen(8082, () => console.log( "Go to: http://localhost:8082/getnumbers")); Documentation Getting Started Installation Guides Data Import Client APIs SQL Introduction Why DuckDB FAQ Resources Blog Media Events Webshop Code of Conduct Trademark Use Release Calendar RSS Feed Status Page Organizations DuckDB Foundation DuckDB Labs Community Community Extensions Bluesky LinkedIn X (Twitter) Discord Stack Overflow © 2025 DuckDB Foundation, Amsterdam NLenen1740422881https://duckdb.org

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