DuckDB vs QuestDB

Choosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of DuckDB and QuestDB so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how DuckDB and QuestDB perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn’t intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.

DuckDB Overview

DuckDB is an in-process SQL OLAP (Online Analytical Processing) database management system. It is designed to be simple, fast, and feature-rich. DuckDB can be used for processing and analyzing tabular datasets, such as CSV or Parquet files. It provides a rich SQL dialect with support for transactions, persistence, extensive SQL queries, and direct querying of Parquet and CSV files. DuckDB is built with a vectorized engine that is optimized for analytics and supports parallel query processing. It is designed to be easy to install and use, with no external dependencies and support for multiple programming languages.

QuestDB Overview

QuestDB is an open-source relational column-oriented database designed specifically for time series and event data. It combines high-performance ingestion capabilities with SQL analytics, making it a powerful tool for managing and analyzing large volumes of time-based data. QuestDB addresses the challenges of handling high throughput and provides a simple way to analyze ingested data through SQL queries. It is well-suited for use cases such as financial market data and application metrics.

DuckDB for Time Series Data

DuckDB can be used effectively with time series data. It supports processing and analyzing tabular datasets, which can include time series data stored in CSV or Parquet files. With its optimized analytics engine and support for complex SQL queries, DuckDB can perform aggregations, joins, and other time series analysis operations efficiently. However, it’s important to note that DuckDB is not specifically designed for time series data management and may not have specialized features tailored for time series analysis like some dedicated time series databases.

QuestDB for Time Series Data

QuestDB excels in managing and analyzing time series data. With its high-performance ingestion capabilities, it can handle high data throughput, making it suitable for real-time data ingestion scenarios. QuestDB’s SQL extensions for time series enable users to perform real-time analytics and gain valuable insights from their time-stamped data. Whether it’s financial market data or application metrics, QuestDB simplifies the process of ingesting and analyzing time series data through its fast SQL queries and operational simplicity.

DuckDB Key Concepts

• In-process: DuckDB operates in-process, meaning it runs within the same process as the application using it, without the need for a separate server.
• OLAP: DuckDB is an OLAP database, which means it is optimized for analytical query processing.
• Vectorized engine: DuckDB utilizes a vectorized engine that operates on batches of data, improving query performance.
• Transactions: DuckDB supports transactional operations, ensuring the atomicity, consistency, isolation, and durability (ACID) properties of data operations.
• SQL dialect: DuckDB provides a rich SQL dialect with advanced features such as arbitrary and nested correlated subqueries, window functions, collations, and support for complex types like arrays and structs

QuestDB Key Concepts

• Time Series: QuestDB focuses on time series data, which represents data points indexed by time. It is optimized for storing and processing time-stamped data efficiently.
• Column-Oriented: QuestDB employs a column-oriented storage format, where data is organized and stored column by column rather than row by row. This format enables efficient compression and faster query performance.
• SQL Extensions: QuestDB extends the SQL language with functionalities specifically tailored for time series data. These extensions facilitate real-time analytics and allow users to leverage familiar SQL constructs for querying time-based data.

DuckDB Architecture

DuckDB follows an in-process architecture, running within the same process as the application. It is a relational table-oriented database management system that supports SQL queries for producing analytical results. DuckDB is built using C++11 and is designed to have no external dependencies. It can be compiled as a single file, making it easy to install and integrate into applications.

QuestDB Architecture

QuestDB follows a hybrid architecture that combines features of columnar and row-based databases. It leverages a column-oriented storage format for efficient compression and query performance while retaining the ability to handle relational data with SQL capabilities. QuestDB supports both SQL and NoSQL-like functionalities, providing users with flexibility in their data modeling and querying approaches. The database consists of multiple components, including the ingestion engine, storage engine, and query engine, working together to ensure high-performance data ingestion and retrieval.