DuckDB vs Prometheus
A detailed comparison
Compare DuckDB and Prometheus for time series and OLAP workloads
Learn About Time Series DatabasesChoosing 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 Prometheus so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how DuckDB and Prometheus 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 Prometheus Breakdown
 |
 |
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| Database Model | Columnar database |
Time series database |
| Architecture | DuckDB is intended for use as an embedded database and is primariliy focused on single node performance. |
Prometheus uses a pull-based model where it scrapes metrics from configured targets at given intervals. It stores time series data in a custom, efficient, local storage format, and supports multi-dimensional data collection, querying, and alerting. It can be deployed as a single binary on a server or on a container platform like Kubernetes. |
| License | MIT |
Apache 2.0 |
| Use Cases | Embedded analytics, Data Science, Data processing, ETL pipelines |
Monitoring, alerting, observability, system metrics, application metrics |
| Scalability | Embedded and single-node focused, with limited support for parallelism |
Prometheus is designed for reliability and can scale vertically (single node with increased resources) or through federation (hierarchical setup where Prometheus servers scrape metrics from other Prometheus servers) |
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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.
Prometheus Overview
Prometheus is an open-source monitoring and alerting toolkit initially developed at SoundCloud in 2012. It has since become a widely adopted monitoring solution and a part of the Cloud Native Computing Foundation (CNCF) project. Prometheus focuses on providing real-time insights and alerts for containerized and microservices-based environments. Its primary use case is monitoring infrastructure and applications, with an emphasis on reliability and scalability.
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.
Prometheus for Time Series Data
Prometheus is specifically designed for time series data, as its primary focus is on monitoring and alerting based on the state of infrastructure and applications. It uses a pull-based model, where the Prometheus server scrapes metrics from the target systems at regular intervals. This model is suitable for monitoring dynamic environments, as it allows for automatic discovery and monitoring of new instances. However, Prometheus is not intended as a general-purpose time series database and might not be the best choice for high cardinality or long-term data storage.
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
Prometheus Key Concepts
- Metric: A numeric representation of a particular aspect of a system, such as CPU usage or memory consumption.
- Time Series: A collection of data points for a metric, indexed by timestamp.
- Label: A key-value pair that provides metadata and context for a metric, enabling more granular querying and aggregation.
- PromQL: Prometheus uses its own query language called PromQL (Prometheus Query Language) for querying time series data and generating alerts.
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.
Prometheus Architecture
Prometheus is a single-server, standalone monitoring system that uses a pull-based approach to collect metrics from target systems. It stores time series data in a custom, highly compressed, on-disk format, optimized for fast querying and low resource usage. The architecture of Prometheus is modular and extensible, with components like exporters, service discovery mechanisms, and integrations with other monitoring systems. As a non-distributed system, it lacks built-in clustering or horizontal scalability, but it supports federation, allowing multiple Prometheus servers to share and aggregate data.
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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.
Prometheus Features
Pull-based Model
Prometheus collects metrics by actively scraping targets, enabling automatic discovery and monitoring of dynamic environments.
PromQL
The powerful Prometheus Query Language allows for expressive and flexible querying of time series data.
Alerting
Prometheus supports alerting based on user-defined rules and integrates with various alert management and notification systems.
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.
Prometheus Use Cases
Infrastructure Monitoring
Prometheus is widely used for monitoring the health and performance of containerized and microservices-based infrastructure, including Kubernetes and Docker environments.
Application Performance Monitoring (APM)
Prometheus can collect custom application metrics using client libraries and monitor application performance in real-time.
Alerting and Anomaly Detection
Prometheus enables organizations to set up alerts based on specific thresholds or conditions, helping them identify and respond to potential issues or anomalies quickly.
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.
Prometheus Pricing Model
Prometheus is an open-source project, and there are no licensing fees associated with its use. However, costs can arise from hardware, hosting, and operational expenses when deploying a self-managed Prometheus server. Additionally, several cloud-based managed Prometheus services, such as Grafana Cloud and Weave Cloud, offer different pricing models based on factors like data retention, query rate, and support.
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