DuckDB vs AWS Redshift

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 AWS Redshift so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how DuckDB and AWS Redshift 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.

AWS Redshift Overview

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. It was launched in 2012 as part of the AWS suite of products. Redshift is designed for analytic workloads and integrates with various data loading and ETL tools, as well as business intelligence and reporting tools. It uses columnar storage to optimize storage costs and improve query performance.

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.

AWS Redshift for Time Series Data

AWS Redshift can be used for time series data workloads, although Redshift is optimized for more general data warehouse use cases. Users can utilize date and time-based functions to aggregate, filter, and transform time series data. Redshift also offers ‘time-series tables’ which allow data to be stored in tables based on a fixed retention period.

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

AWS Redshift Key Concepts

• Cluster: A Redshift cluster is a set of nodes, which consists of a leader node and one or more compute nodes. The leader node manages communication with client applications and coordinates query execution among compute nodes.
• Compute Node: These nodes store data and execute queries in parallel. The number of compute nodes in a cluster affects its storage capacity and query performance.
• Columnar Storage: Redshift uses a columnar storage format, which stores data in columns rather than rows. This format improves query performance and reduces storage space requirements.
• Node slices: Compute nodes are divided into slices. Each slice is allocated an equal portion of the node’s memory and disk space, where it processes a portion of the loaded 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.

AWS Redshift Architecture

Redshift’s architecture is based on a distributed and shared-nothing architecture. A cluster consists of a leader node and one or more compute nodes. The leader node is responsible for coordinating query execution, while compute nodes store data and execute queries in parallel. Data is stored in a columnar format, which improves query performance and reduces storage space requirements. Redshift uses Massively Parallel Processing (MPP) to distribute and execute queries across multiple nodes, allowing it to scale horizontally and provide high performance for large-scale data warehousing workloads.