DuckDB vs TDengine

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

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

TDengine Overview

TDengine is a high-performance, open source time series database designed to handle massive amounts of time series data efficiently. It was created by TAOS Data in 2017 and is specifically designed for Internet of Things (IoT), Industrial IoT, and IT infrastructure monitoring use cases. TDengine has a unique hybrid architecture that combines the advantages of both relational and NoSQL databases, providing high performance, easy-to-use SQL for querying, and flexible data modeling capabilities.

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.

TDengine for Time Series Data

TDengine is designed from the ground up as a time series database, so it will be a good fit for most use cases that heavily involve storing and analyzing time series data.

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

TDengine Key Concepts

• Super Table: A template for creating multiple tables with the same schema. It’s similar to the concept of table inheritance in some other databases.
• Sub Table: A table created based on a Super Table, inheriting its schema. Sub Tables can have additional tags for categorization and querying purposes.
• Tag: A metadata attribute used to categorize and filter Sub Tables in a Super Table. Tags are indexed and optimized for efficient querying.

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.

TDengine Architecture

TDengine uses a cloud native architecture that combines the advantages of relational databases (support for SQL querying) and NoSQL databases (scalability and flexibility).