DuckDB vs VictoriaMetrics

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

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

VictoriaMetrics Overview

VictoriaMetrics is an open source time series database developed by the company VictoriaMetrics. The database aims to assist individuals and organizations in addressing their big data challenges by providing state-of-the-art monitoring and observability solutions. VictoriaMetrics is designed to be a fast, cost-effective, and scalable monitoring solution and time series database.

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.

VictoriaMetrics for Time Series Data

VictoriaMetrics is designed for time series data, making it a solid choice for applications that involve the storage and analysis of time-stamped data. It provides high-performance storage and retrieval capabilities, enabling efficient handling of large volumes of 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

VictoriaMetrics Key Concepts

• Time Series: VictoriaMetrics stores data in the form of time series, which are sequences of data points indexed by time.
• Metric: A metric represents a specific measurement or observation that is tracked over time.
• Tag: Tags are key-value pairs associated with a time series and are used for filtering and grouping data.
• Field: Fields contain the actual data values associated with a time series.
• Query Language: VictoriaMetrics supports its own query language, which allows users to retrieve and analyze time series data based on specific criteria.

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.

VictoriaMetrics Architecture

VictoriaMetrics is available in two forms: Single-server-VictoriaMetrics and VictoriaMetrics Cluster. The Single-server-VictoriaMetrics is an all-in-one binary that is easy to use and maintain. It vertically scales well and can handle millions of metrics per second. On the other hand, VictoriaMetrics Cluster consists of components that allow for building horizontally scalable clusters, enabling high availability and scalability in demanding environments. The architecture of VictoriaMetrics enables users to choose the deployment option that best suits their needs and scale their database infrastructure as required.