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 Prometheus and OpenTSDB so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how Prometheus and OpenTSDB 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.

Prometheus vs OpenTSDB Breakdown


 
Database Model

Time series database

Time series database

Architecture

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.

OpenTSDB can be deployed on-premises or in the cloud, with HBase running on a distributed cluster of nodes.

License

Apache 2.0

GNU LGPLv2.1

Use Cases

Monitoring, alerting, observability, system metrics, application metrics

Monitoring, observability, IoT, log data storage

Scalability

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)

Horizontally scalable across multiple nodes using HBase as its storage backend

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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.

OpenTSDB Overview

OpenTSDB (Open Time Series Database) is an open-source, distributed, and scalable time series database built on top of Apache HBase, a NoSQL database. OpenTSDB was designed to address the growing need for storing and processing large volumes of time series data generated by various sources, such as IoT devices, sensors, and monitoring systems. It was initially developed by StumbleUpon in 2010 and later became an independent project with an active community of contributors.


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.

OpenTSDB for Time Series Data

OpenTSDB is designed for time series data storage and analysis, making it an ideal choice for managing large scale time series datasets. Its architecture enables high write and query performance, and it can handle millions of data points per second with minimal resource consumption. OpenTSDB’s flexible querying capabilities allow users to perform complex analysis on time series data efficiently.


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.

OpenTSDB Key Concepts

  • Data Point: A single measurement in time consisting of a timestamp, metric, value, and associated tags.
  • Metric: A named value that represents a specific aspect of a system, such as CPU usage or temperature.
  • Tags: Key-value pairs associated with data points that provide metadata and help categorize and query the data.


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.

OpenTSDB Architecture

OpenTSDB is built on top of Apache HBase, a distributed and scalable NoSQL database, and relies on its architecture for data storage and management. OpenTSDB stores time series data in HBase tables, with data points organized by metric, timestamp, and tags. The database uses a schema-less data model, which allows for flexibility when adding new metrics and tags. The OpenTSDB architecture also supports horizontal scaling by distributing data across multiple HBase nodes.

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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.

OpenTSDB Features

Scalability

OpenTSDB’s distributed architecture allows for horizontal scaling, ensuring that the database can handle growing volumes of time series data.

Data Compression

OpenTSDB uses various compression techniques to reduce the storage footprint of time series data.

Query Language with time series support

OpenTSDB features a flexible query language that supports aggregation, downsampling, filtering, and other operations for analyzing time series 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.

OpenTSDB Use Cases

Monitoring and Alerting

OpenTSDB is well-suited for large-scale monitoring and alerting systems that generate vast amounts of time series data from various sources.

IoT Data Storage

OpenTSDB can store and analyze time series data generated by IoT devices, such as sensors and smart appliances, enabling real-time insights and analytics.

Performance Analysis

OpenTSDB’s flexible querying capabilities make it an ideal choice for analyzing system and application performance metrics over time.


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.

OpenTSDB Pricing Model

OpenTSDB is open-source software, which means it is free to use without any licensing fees. However, the cost of running OpenTSDB depends on the infrastructure required to support the underlying HBase database, such as cloud services or on-premises hardware.