Benchmarking DuckDB Performance with Real-World Data

 

In today's world, data analytics has become an integral part of business decision-making. Whether you're dealing with massive datasets or working with small, highly specific queries, the ability to process data quickly and efficiently is crucial. This is where DuckDB, an open-source database designed for high-performance analytics, comes into play.

DuckDB is a columnar database management system (DBMS) designed for high-speed querying of data. It supports in-memory processing, and its columnar format allows for fast data scans, making it ideal for analytics use cases. In this blog post, we'll dive deep into benchmarking DuckDB's performance with real-world data, exploring how it fares in terms of speed, scalability, and ease of use.

What is DuckDB?

DuckDB is an in-process, analytical database system optimized for fast query execution and designed for local use cases. Its key features include:

1. In-memory processing: DuckDB is designed to run entirely in memory for high-speed access and processing.
2. Columnar storage: It stores data in a columnar format, which significantly enhances the performance of analytical queries by reducing I/O operations.
3. SQL Support: DuckDB supports a comprehensive subset of SQL, making it familiar to those who are used to working with relational databases like PostgreSQL or SQLite.
4. Multi-platform support: DuckDB works across multiple operating systems, including Linux, macOS, and Windows. It also supports integration with Python, R, and other programming environments.

Key Use Cases for DuckDB

• Data Exploration: DuckDB excels in environments where analysts or data scientists need to quickly query large datasets to uncover insights.
• Data Science and Machine Learning: It integrates seamlessly with Python and R, making it a great tool for data scientists who rely on SQL queries to prepare their datasets for machine learning models.
• Embedded Analytics: Its small footprint and ability to work without a server make DuckDB an attractive choice for applications that require embedded analytics.
• Business Intelligence (BI): With the ability to handle complex queries, DuckDB is an excellent choice for BI solutions that need to perform analytics on large datasets in real-time.

Why Benchmarking Matters

Benchmarking performance is essential to understanding how well a database handles different types of workloads. Performance benchmarking helps users gauge a DBMS's capabilities in areas such as:

• Query execution speed: How quickly the database can execute a variety of queries, from simple aggregations to complex joins.
• Scalability: How well the database can scale when handling growing datasets.
• Resource utilization: How much CPU, memory, and disk I/O the database consumes while executing queries.
• Concurrency: How the system handles multiple simultaneous queries or users.

By performing benchmark tests with real-world data, we can assess how DuckDB holds up against other popular databases and understand its strengths and weaknesses in practice.

Real-World Benchmarking Approach

In order to truly gauge DuckDB’s performance, it’s essential to use real-world data and simulate actual data analysis workloads. A typical benchmarking setup would include the following steps:

1. Choosing the dataset: Select a dataset that closely mirrors the type of data you’d work with in production. Public datasets like the TPC-H or TPC-DS benchmarks are commonly used for this purpose. Additionally, using actual business data from industries such as finance, healthcare, or e-commerce can yield valuable insights.

2. Defining benchmark queries: Real-world queries often involve a combination of complex joins, aggregations, filtering, and sorting. Simulating these queries will test the database’s ability to handle different workloads.

3. Testing environment: The environment in which the tests are conducted plays a significant role in performance. Ensure that the hardware, OS, and database configuration are consistent across tests for fair comparison.

4. Monitoring resources: Track key performance metrics, including query execution time, CPU usage, memory consumption, and disk I/O during the tests.

5. Comparing results: Once the tests are complete, compare DuckDB’s performance against other databases to see how it fares in real-world scenarios.

Setup for Benchmarking DuckDB

Before diving into the performance benchmarks, let’s walk through the setup process to get DuckDB running in your environment.

Installation

You can install DuckDB using different methods based on your operating system. To install DuckDB in Python, use the following pip command:

bash
pip install duckdb

To install DuckDB for use in R, you can run:

R
install.packages("duckdb")

Once installed, you can begin connecting to DuckDB from your programming environment and load data for testing.

Preparing the Dataset

Let’s assume we’re working with a large sales dataset that consists of customer orders, product details, and transaction records. This type of dataset is commonly found in e-commerce or retail scenarios and will allow us to simulate a variety of real-world queries.

Benchmarking DuckDB: Query Types and Performance Metrics

1. Simple Aggregation Queries

Aggregation queries are often used in business intelligence applications to summarize large datasets. Let’s start by testing DuckDB’s performance with simple aggregation queries, such as calculating the total revenue by product category.

sql
SELECT category, SUM(price * quantity) AS total_revenue
FROM sales
GROUP BY category
ORDER BY total_revenue DESC;

Expected outcome: DuckDB should perform well for this type of query due to its columnar storage format, which enables it to efficiently scan large datasets for aggregations.

2. Join Operations

Joins are one of the most complex operations in SQL, especially when working with large datasets. Let’s test DuckDB’s ability to join multiple tables, such as joining customer information with their order history.

sql
SELECT c.customer_id, c.name, SUM(o.total_amount) AS total_spent
FROM customers c
JOIN orders o ON c.customer_id = o.customer_id
GROUP BY c.customer_id, c.name
ORDER BY total_spent DESC;

Expected outcome: DuckDB’s ability to handle joins efficiently will be put to the test here. Columnar databases are often optimized for analytical queries involving large join operations, so we expect DuckDB to handle this well.

3. Complex Filtering and Sorting

For complex filtering and sorting, consider a query that identifies the top 10 customers by total spend in a particular region.

sql
SELECT c.customer_id, c.name, SUM(o.total_amount) AS total_spent
FROM customers c
JOIN orders o ON c.customer_id = o.customer_id
WHERE o.order_date BETWEEN '2023-01-01' AND '2023-12-31'
  AND c.region = 'North America'
GROUP BY c.customer_id, c.name
ORDER BY total_spent DESC
LIMIT 10;

Expected outcome: This query involves filtering, grouping, sorting, and limiting the results, which will test DuckDB’s ability to handle more complex analytical queries.

4. Handling Large Datasets

DuckDB’s real strength lies in its ability to handle large datasets with ease. A benchmark that involves loading and processing data with millions of rows will test its scalability. For example, let’s load a large dataset (e.g., 10 million rows of transaction records) and perform an aggregation query.

sql
SELECT category, COUNT(*) AS count, AVG(price) AS avg_price
FROM sales
GROUP BY category;

Expected outcome: DuckDB should be able to perform this aggregation quickly, even on large datasets. The columnar storage model allows it to efficiently scan and aggregate data without loading everything into memory.

Performance Metrics to Track

When benchmarking DuckDB, it’s important to track several key performance metrics:

1. Query Execution Time: The time it takes for DuckDB to execute a query, from start to finish. This is the primary metric for performance.
2. CPU Usage: The amount of CPU power used during query execution. High CPU usage may indicate inefficient query execution plans.
3. Memory Usage: The amount of RAM used during query execution. Ideally, DuckDB should use memory efficiently without causing excessive paging to disk.
4. Disk I/O: The amount of disk read/write activity. A high amount of disk I/O may indicate that the database is unable to keep data in memory or is inefficient in its data retrieval.

Comparative Benchmarking

To truly assess DuckDB’s performance, we must compare it with other popular database systems such as SQLite, PostgreSQL, and Apache Spark. Here’s how DuckDB fares in comparison:

• SQLite: DuckDB is often faster than SQLite for analytical queries due to its columnar storage format and in-memory processing.
• PostgreSQL: While PostgreSQL is a powerful relational database, it is not optimized for analytical workloads. DuckDB, with its columnar architecture, is likely to outperform PostgreSQL in query speed for complex analytical queries.
• Apache Spark: Apache Spark is a distributed processing framework, and while it excels in big data scenarios, DuckDB may outperform it in local, single-node environments for smaller to mid-sized datasets.

Conclusion

Benchmarking DuckDB with real-world data highlights its ability to handle complex queries, large datasets, and high-performance workloads with impressive speed and efficiency. Its columnar storage format, in-memory processing capabilities, and ease of use make it an ideal choice for data scientists, analysts, and developers working with local analytics or embedded systems.

By testing DuckDB on a variety of real-world scenarios, we've seen that it competes favorably with other database systems in terms of query execution speed, scalability, and resource efficiency. Whether you're working with transactional data, performing complex aggregations, or analyzing large datasets, DuckDB’s performance is certainly worth considering in your database toolkit.