Extending DuckDB with User-Defined Functions (UDFs): A Comprehensive Guide

 

DuckDB, an increasingly popular embedded database, is widely known for its high performance and flexibility. It is designed to be lightweight, enabling users to run complex queries on massive datasets with minimal setup. One of the key features of DuckDB is its extensibility, particularly through User-Defined Functions (UDFs).

User-Defined Functions (UDFs) allow users to extend DuckDB's capabilities by writing custom functions tailored to specific needs. These functions can significantly enhance the database's performance, simplify complex queries, and support a wide range of specialized operations. Whether you're working on data analytics, machine learning, or handling unique data processing tasks, UDFs offer a powerful way to integrate custom logic directly into DuckDB.

In this blog post, we will explore the concept of UDFs in DuckDB, how to write them, and how to use them to extend DuckDB's functionality. By the end of this guide, you'll have the knowledge to leverage UDFs in your own DuckDB workflows.

What is DuckDB?

Before diving into UDFs, it's essential to understand DuckDB and why it’s gaining traction. DuckDB is an in-process SQL OLAP (Online Analytical Processing) database management system. It is designed to run analytical queries over large datasets efficiently and is often compared to databases like SQLite and PostgreSQL.

DuckDB offers features such as:

• In-memory analytics: DuckDB is designed to efficiently perform queries over in-memory data, making it a great choice for interactive data analysis.
• Support for complex queries: DuckDB is optimized for analytical workloads and can handle complex joins, aggregations, and other advanced operations.
• Ease of integration: It can be embedded into applications with minimal setup, making it easy to use for developers working with various programming languages.

Despite its strengths, DuckDB is not a monolithic system like PostgreSQL or MySQL. It focuses on providing a minimalist set of features for performing analytical queries. However, one of the best things about DuckDB is its extensibility, especially through User-Defined Functions.

What Are User-Defined Functions (UDFs)?

User-Defined Functions (UDFs) are custom functions that users can define and register within DuckDB. UDFs extend the built-in functionality of the database and enable users to implement custom logic that is not supported natively by the database engine.

In the context of DuckDB, UDFs allow you to:

• Add new operations: Perform custom calculations, transformations, or aggregations that are not available in the default DuckDB functions.
• Optimize queries: By writing efficient custom logic, you can make your queries faster and more tailored to your specific needs.
• Integrate with external libraries: You can use UDFs to integrate DuckDB with other programming libraries, making it easier to apply machine learning models or perform advanced data processing within the database.

DuckDB supports the creation of UDFs in C++ and Python, and each of these languages offers different advantages depending on your use case.

Types of UDFs in DuckDB

There are two primary types of UDFs you can create in DuckDB:

1. Scalar UDFs: These functions operate on individual rows of data and return a single result per row. Scalar UDFs can be used for computations such as data transformations, string manipulations, or mathematical operations.

2. Aggregate UDFs: Aggregate UDFs perform calculations on multiple rows and return a single result. These are ideal for custom aggregation logic, such as computing averages, sums, or more advanced statistics.

Both types of UDFs are incredibly powerful, but the implementation details differ slightly. In this blog, we’ll explore how to create both types of UDFs using Python and C++.

How to Create User-Defined Functions in DuckDB

1. Scalar UDFs in Python

Python is a popular language for data science and analytics, and DuckDB’s Python API allows you to register custom Python functions as UDFs. Below is a basic example of how to create a scalar UDF in Python for DuckDB.

Step-by-Step Guide

1. Install DuckDB: If you haven’t installed DuckDB yet, you can do so via pip:

   bash
   pip install duckdb
   

2. Define the Python Function: Let’s say you want to create a custom function to compute the square of a number. The Python function would look like this:

   python
   def square(x):
       return x * x
   

3. Register the UDF in DuckDB: You can now register this Python function as a UDF in DuckDB using the duckdb.query API. Here’s how you can do it:

   python
   import duckdb
   
   # Connect to DuckDB
   con = duckdb.connect()
   
   # Register the Python function as a UDF
   con.create_function('square', square)
   
   # Query using the UDF
   result = con.execute('SELECT square(4)').fetchall()
   
   # Output the result
   print(result)  # [(16,)]
   

In this example, the Python function square() is registered as a UDF, and DuckDB can now use it in SQL queries.

2. Scalar UDFs in C++

Creating UDFs in C++ allows you to write high-performance, low-level functions that are faster than Python-based UDFs. DuckDB is built in C++, and creating UDFs in this language can be highly efficient, especially when dealing with large datasets.

Step-by-Step Guide

1. Set Up the Development Environment: To write C++ UDFs, you need to set up your development environment with the necessary C++ tools and DuckDB’s source code.

2. Write the UDF in C++: Here’s an example of how to create a UDF in C++ that calculates the factorial of a number:

   cpp
   #include "duckdb.hpp"
   
   using namespace duckdb;
   
   // Function to calculate factorial
   static void factorial(DataChunk &args, ExpressionState &state, Vector &result) {
       // Fetch the input number from the argument
       int64_t input = args.GetValue(0, 0).GetValue<int64_t>();
   
       // Calculate the factorial
       int64_t result_value = 1;
       for (int64_t i = 1; i <= input; ++i) {
           result_value *= i;
       }
   
       // Set the result
       result.SetValue(0, 0, Value(result_value));
   }
   
   // Register the function with DuckDB
   void register_factorial(DatabaseInstance &db) {
       db.GetFunctionRegistry().AddFunction(
           ScalarFunction("factorial", {LogicalType::BIGINT}, LogicalType::BIGINT, factorial));
   }
   

3. Compile and Integrate the UDF: After writing the C++ function, you need to compile it and link it with DuckDB’s source. This process is more involved compared to Python, as it requires knowledge of compiling C++ code and working with DuckDB’s internal APIs.

4. Using the UDF in Queries: Once the UDF is registered, you can use it just like any other built-in DuckDB function:

   sql
   SELECT factorial(5);
   

The result would be 120, as the factorial of 5 is 120.

3. Aggregate UDFs in DuckDB

Aggregate UDFs work similarly to scalar UDFs but are designed for operations that involve multiple rows. You can write an aggregate UDF in Python or C++, though implementing them in C++ tends to offer better performance.

Here’s a simplified example in Python of a UDF that calculates the custom average:

python
import duckdb

# Define the UDF for custom average
def custom_avg(state, value):
    if state is None:
        state = [0, 0]  # [sum, count]
    if value is not None:
        state[0] += value
        state[1] += 1
    return state

def custom_avg_final(state):
    if state is None or state[1] == 0:
        return None
    return state[0] / state[1]

# Register the aggregate UDF
con = duckdb.connect()
con.create_function('custom_avg', custom_avg, aggregate=True)
con.create_function('custom_avg_final', custom_avg_final, aggregate=True)

# Use the UDF in a query
result = con.execute("""
    SELECT custom_avg(value), custom_avg_final(custom_avg(value)) 
    FROM my_table
""").fetchall()

Best Practices for Writing UDFs in DuckDB

1. Optimize Performance: Writing UDFs in C++ typically yields better performance compared to Python, especially when processing large datasets.
2. Handle Nulls Gracefully: Ensure that your UDFs handle null values appropriately, as SQL databases often encounter null values in data.
3. Test Thoroughly: Since UDFs extend DuckDB’s behavior, it’s essential to test them thoroughly, particularly in terms of performance and correctness.
4. Document Your Functions: Just like with any code, proper documentation and comments will help you and others understand and maintain your UDFs.

Conclusion

User-Defined Functions (UDFs) are an excellent way to extend DuckDB’s functionality and tailor it to your specific needs. Whether you’re working with Python or C++, DuckDB makes it easy to register custom functions and use them within SQL queries. Scalar and aggregate UDFs provide a broad range of possibilities, from simple mathematical operations to complex data processing.

By following the steps outlined in this guide, you can start building your own UDFs in DuckDB and unlock new levels of performance and flexibility in your data workflows.

As DuckDB continues to grow in popularity, UDFs will undoubtedly play a crucial role in helping developers and data scientists create highly customized and efficient analytics solutions.