What is DuckDB?

Posted on by Ian

This article takes a look at DuckDB, a modern database management system (DBMS) designed specifically for data analysis and processing.

Often described as the “SQLite for analytics,” DuckDB is lightweight, embeddable, and highly efficient, making it a popular choice for those working with structured data.

Whether you’re considering DuckDB for your projects or simply curious about its capabilities, this article will give you a basic overview of what it is and how it fits into the data analytics landscape.

Introduction to DuckDB

DuckDB is a modern, high-performance analytical database management system designed for data analysis and processing. It provides a lightweight, embeddable, and easy-to-use database solution for individuals and organizations working with large datasets. Its unique architecture, seamless integration with programming languages, and impressive performance is making it an increasingly popular choice among data scientists, analysts, and developers.

DuckDB Features

Here’s a handful of the main features of DuckDB.

Columnar Storage Format

Unlike traditional row-based databases, DuckDB uses a columnar storage format, making it particularly well-suited for analytical workloads. This design enables faster querying and data processing as operations can target specific columns without scanning entire rows.

Embeddable and Lightweight

DuckDB can be embedded directly into applications without requiring a separate server or complex setup. This makes it an excellent choice for scenarios where portability and simplicity are critical.

In-Memory and Persistent Operations

DuckDB supports both in-memory and on-disk data processing. Users can perform quick, ad-hoc analyses in memory or save their data for persistent storage, providing flexibility for different use cases.

SQL Compliance

DuckDB supports standard SQL syntax, making it accessible to users already familiar with SQL. Its extensive support for analytical functions, such as window functions and aggregations, allows users to perform complex data manipulations with ease.

Seamless Integration with Programming Languages

DuckDB is designed to work seamlessly with popular programming environments, including Python, R, and Julia. This integration allows data scientists and analysts to query data directly within their preferred development environments without the need for additional tools.

Parallel Query Execution

DuckDB leverages multi-threading and parallel query execution to optimize performance. This ensures efficient utilization of modern hardware capabilities, enabling faster processing of large datasets.

Why Choose DuckDB?

Scalability for Analytical Workloads

DuckDB is optimized for analytical queries on datasets ranging from small to large-scale. Its columnar architecture and vectorized execution make it a robust choice for workloads that involve heavy aggregations and filtering.

Portability and Ease of Use

The ability to run DuckDB as an embedded database simplifies deployment and reduces dependencies. This portability makes it suitable for applications that need to operate in constrained or distributed environments.

Cost-Effectiveness

As an open-source project, DuckDB is free to use, providing a cost-effective solution for data analysis. Its lightweight nature also reduces resource consumption compared to traditional database management systems.

Versatility Across Use Cases

DuckDB caters to various use cases, including:

  • Exploratory Data Analysis: Quickly analyze datasets without the need for a dedicated database server.
  • Data Integration: Combine and manipulate data from different sources.
  • Machine Learning Pipelines: Preprocess and prepare data for machine learning models.
  • Reporting and Dashboards: Power real-time analytics and visualizations in embedded applications.

How Does DuckDB Compare to Other Databases?

DuckDB vs. SQLite

While SQLite is a general-purpose database, DuckDB is specifically optimized for analytical workloads. DuckDB’s columnar storage and support for parallel query execution give it a significant edge in data analysis tasks.

DuckDB vs. Pandas

For Python users, DuckDB can be an alternative to Pandas for large datasets. While Pandas operates in memory and can become slow with massive datasets, DuckDB efficiently processes large data, often outperforming Pandas in speed and scalability.

DuckDB vs. Data Warehouses

Unlike cloud-based data warehouses, DuckDB is an on-premise solution that doesn’t rely on external servers. While it lacks the distributed processing capabilities of tools like Snowflake or BigQuery, it excels in scenarios requiring local and embedded analytics.

Getting Started with DuckDB

It’s very easy to get up and running with DuckDB. Here’s a quick overview.

Installation

DuckDB is easy to install and can be integrated into your workflow in just a few steps. It is available as a standalone application, a library for Python, R, and other languages, and as a command-line tool.

See the DuckDB installation page for installation instructions.

Using DuckDB from the Command-Line Interface (CLI)

DuckDB comes with a simple and powerful command-line interface (CLI) that allows users to interact with the database directly. The CLI is ideal for quick exploration, testing, or running SQL queries without needing a separate programming environment.

Installing the DuckDB CLI

To use the CLI, the DuckDB CLI will need to be installed. The DuckDB installation package provides an option that includes the CLI, so choose that option in order to install the DuckDB CLI.

As the DuckDB documentation states:

Each DuckDB client is installed without relying on any other DuckDB clients. For example, the Python library can use a different version than the CLI client. Therefore, they need to be updated separately.

So to use DuckDB CLI, download that option. To use DuckDB with Python, use the Python option. Same for R, ODBC, and any other option that’s available.

But for now, we’re only interested in the CLI option.

Launching the CLI

Once installed, you can start the CLI by running the duckdb command in your terminal or command prompt:

duckdb

By default, DuckDB will open in interactive mode. You’ll see a prompt where you can enter SQL commands.

Basic CLI Commands

Here are some common commands you can use in the DuckDB CLI:

  • Create a Database:
    To create a new database file, specify the file name when starting DuckDB: duckdb my_database.db
  • Execute SQL Queries:
    Enter any valid SQL statement directly in the CLI, such as: SELECT 1 + 1;
  • Run SQL from a File:
    To execute a SQL script saved in a file, use the .read command: .read my_script.sql
  • Import Data:
    You can import data from files like CSVs directly using SQL commands: COPY my_table FROM 'data.csv' (AUTO_DETECT TRUE);
  • Export Query Results:
    Save the results of a query to a file: COPY (SELECT * FROM my_table) TO 'output.csv' (HEADER TRUE);

Exiting the CLI

To exit the DuckDB CLI, use the .exit command, or simply press Ctrl+D or  Ctrl+C (depending on your system).

If using a persistent database, DuckDB will automatically save the latest edits to disk and close.

Importing and Querying Data

While you can certainly create your own tables and insert data using an INSERT statement, DuckDB also makes it easy to import data. DuckDB supports a range of data sources, including:

  • AWS S3 buckets and storage with S3-compatible API
  • Azure Blob Storage
  • Cloudflare
  • R2
  • CSV
  • Delta Lake Excel (via the spatial extension)
  • httpfs
  • Iceberg
  • JSON
  • MySQL
  • Parquet
  • PostgreSQL
  • SQLite

DuckDB is designed to make data querying intuitive and efficient. You can query data using SQL, just like any other SQL based system.

Running Queries on CSV Files

DuckDB allows you to query CSV files without importing them into a database. For example:

SELECT * FROM 'data.csv';

DuckDB will auto-detect the schema and query the file as if it were a table.

Loading Data into Tables

You can also load external data into DuckDB tables for more complex workflows:

CREATE TABLE my_table AS SELECT * FROM 'data.csv';

That creates a table called my_table and loads the data from data.csv into it.

Python Example

Here’s a quick example of using DuckDB with Python:

import duckdb

# Create a connection
con = duckdb.connect()

# Run a SQL query
data = con.execute("SELECT * FROM 'data.csv'").fetchdf()
print(data)

See DuckDB’s Python API page for more.

R Example

Here’s a quick example of using DuckDB with R:

library(duckdb)

# Create a connection
con <- dbConnect(duckdb::duckdb())

# Run a SQL query
data <- dbGetQuery(con, "SELECT * FROM 'data.csv'")
print(data)

See DuckDB’s R API page for more.

Query Performance

DuckDB optimizes query execution using its columnar storage and vectorized processing. This allows it to handle large datasets efficiently, even on a single machine.

The combination of SQL functionality, support for various file formats, and efficient performance makes DuckDB an excellent tool for data exploration and analysis.

Community and Ecosystem

DuckDB has a growing community of developers and users. Its active development and open-source nature encourage contributions, making it a continually evolving tool for modern data analysis. The project provides extensive documentation, tutorials, and examples to help users get started quickly.

Conclusion

DuckDB appears to be revolutionizing the way data analysis is performed, offering a powerful yet lightweight solution for processing large datasets. With its columnar storage, parallel processing capabilities, and seamless integration with popular programming languages, DuckDB is a versatile tool for a wide range of analytical use cases.

See the DuckDB official website for more.