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 vs VictoriaMetrics Breakdown

Database Model

Columnar database

Time series database


DuckDB is intended for use as an embedded database and is primariliy focused on single node performance.

VictoriaMetrics can be deployed as a single-node instance for small-scale applications or as a clustered setup for large-scale applications, offering horizontal scalability and replication.



Apache 2.0

Use Cases

Embedded analytics, Data Science, Data processing, ETL pipelines

Monitoring, observability, IoT, real-time analytics, DevOps, application performance monitoring


Embedded and single-node focused, with limited support for parallelism

Horizontally scalable, supports clustering and replication for high availability and performance

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.

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DuckDB Features

Transactions and Persistence

DuckDB supports transactional operations, ensuring data integrity and durability. It allows for persistent storage of data between sessions.

Extensive SQL Support

DuckDB provides a rich SQL dialect with support for advanced query features, including correlated subqueries, window functions, and complex data types.

Direct Parquet & CSV Querying

DuckDB allows direct querying of Parquet and CSV files, enabling efficient analysis of data stored in these formats.

Fast Analytical Queries

DuckDB is designed to run analytical queries efficiently, thanks to its vectorized engine and optimization for analytics workloads.

Parallel Query Processing

DuckDB can process queries in parallel, taking advantage of multi-core processors to improve query performance.

VictoriaMetrics Features

High performance

VictoriaMetrics is optimized for high-performance storage and retrieval of time series data. It can efficiently handle millions of metrics per second and offers fast query execution for real-time analysis.


The architecture of VictoriaMetrics allows for both vertical and horizontal scalability, enabling users to scale their monitoring and time series database infrastructure as their data volume and demand grow.


VictoriaMetrics provides a cost-effective solution for managing time series data. Its efficient storage and query capabilities contribute to minimizing operational costs while maintaining high performance.

DuckDB Use Cases

Processing and Storing Tabular Datasets

DuckDB is well-suited for scenarios where you need to process and store tabular datasets, such as data imported from CSV or Parquet files. It provides efficient storage and retrieval mechanisms for working with structured data.

Interactive Data Analysis

DuckDB is ideal for interactive data analysis tasks, particularly when dealing with large tables. It enables you to perform complex operations like joining and aggregating multiple large tables efficiently, allowing for rapid exploration and extraction of insights from your data.

Large Result Set Transfer to Client

When you need to transfer large result sets from the database to the client application, DuckDB can be a suitable choice. Its optimized query processing and efficient data transfer mechanisms enable fast and seamless retrieval of large amounts of data.

VictoriaMetrics Use Cases

Monitoring and Observability

VictoriaMetrics is widely used for monitoring and observability purposes, allowing organizations to collect, store, and analyze metrics and performance data from various systems and applications. It provides the necessary tools and capabilities to track and visualize key performance indicators, troubleshoot issues, and gain insights into system behavior.

IoT Data Management

VictoriaMetrics is suitable for handling large volumes of time series data generated by IoT devices. It can efficiently store and process sensor data, enabling real-time monitoring and analysis of IoT ecosystems. VictoriaMetrics allows for tracking and analyzing data from factories, manufacturing plants, satellites, and other IoT devices.

Capacity Planning

VictoriaMetrics enables retrospective analysis and forecasting of metrics for capacity planning purposes. It allows organizations to analyze historical data, identify patterns and trends, and make informed decisions about resource allocation and future capacity requirements.

DuckDB Pricing Model

DuckDB is a free and open-source database management system released under the permissive MIT License. It can be freely used, modified, and distributed without any licensing costs.

VictoriaMetrics Pricing Model

VictoriaMetrics is an open source project, which means it is available for free usage and doesn’t require any licensing fees. Users can download the binary releases, Docker images, or source code to set up and deploy VictoriaMetrics without incurring any direct costs. VictoriaMetrics also has paid offerings for on-prem Enterprise products and managed VictoriaMetrics instances.

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