OSI PI Data Historian vs Prometheus

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 OSI PI Data Historian and Prometheus so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how OSI PI Data Historian 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.

OSI PI Data Historian Overview

OSI PI, also known as OSIsoft PI System, is an enterprise-level data management and analytics platform specifically designed for handling time series data from industrial processes, sensors, and other sources. Developed by OSIsoft (acquired by AVEVA in 2021), the PI System has been widely used in various industries such as energy, manufacturing, utilities, and pharmaceuticals since its introduction in the 1980s. It provides the ability to collect, store, analyze, and visualize large volumes of time series data in real-time, allowing organizations to gain insights, optimize processes, and improve decision-making.

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.

OSI PI Data Historian for Time Series Data

OSI PI was created for storing time series data, making it an ideal choice for organizations that need to manage large volumes of sensor and process data. Its architecture and components are optimized for collecting, storing, and analyzing time series data with high efficiency and minimal latency. The PI System’s scalability and performance make it a suitable solution for organizations dealing with vast amounts of data generated by industrial processes, IoT devices, or other sources.

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.

OSI PI Data Historian Key Concepts

• PI Server: The core component of the PI System, responsible for data collection, storage, and management.
• PI Interfaces and PI Connectors: Software components that collect data from various sources and send it to the PI Server.
• PI Asset Framework: A modeling framework that allows users to create a hierarchical structure of assets and their associated metadata, making it easier to understand and analyze data.
• PI DataLink: An add-in for Microsoft Excel that enables users to access and analyze PI System data directly from Excel.
• PI ProcessBook: A visualization tool for creating interactive, graphical displays of PI System data.

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.

OSI PI Data Historian Architecture

OSI PI is a data management platform built around the PI Server, which is responsible for data collection, storage, and management. The PI System uses a highly efficient, proprietary time series database to store data. PI Interfaces and PI Connectors collect data from various sources and send it to the PI Server. The PI Asset Framework (AF) allows users to model their assets and their associated data in a hierarchical structure, making it easier to understand and analyze the data. Various client tools, such as PI DataLink and PI ProcessBook, enable users to access and visualize data stored in the PI System.

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.