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 Mimir and OSI PI Data Historian so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Mimir and OSI PI Data Historian 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.
Mimir vs OSI PI Data Historian Breakdown
Time series database
Time series database/data historian
Grafana Mimir is a time series database designed for high-performance, real-time monitoring, and analytics. It features a distributed architecture, allowing for horizontal scaling across multiple nodes to handle large volumes of data and queries. It can be deployed on-prem due to being open source or as a managed solution hosted by Grafana
OSIsoft PI System is a suite of software products designed for real-time data collection, storage, and analysis of time series data in industrial environments. The PI System is built around the PI Server, which stores, processes, and serves data to clients, and it can be deployed on-premises or in the cloud.
Monitoring, observability, IoT
Industrial data management, real-time monitoring, asset health tracking, predictive maintenance, energy management
Supports horizontal scaling through distributed architecture, data replication, and data federation for large-scale deployments
Grafana Mimir is an open-source software project that provides a scalable long-term storage solution for Prometheus. Started at Grafana Labs and announced in 2022, Grafana Mimir aims to become the most scalable and performant open-source time series database for metrics. The project incorporates the knowledge and experience gained by Grafana Labs engineers from running Grafana Enterprise Metrics and Grafana Cloud Metrics at massive scale.
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.
Mimir for Time Series Data
Grafana Mimir is well-suited for handling time series data, making it a suitable choice for scenarios involving metric storage and analysis. It provides long-term storage capabilities for Prometheus, a popular open-source monitoring and alerting system. With Grafana Mimir, users can store and query time series metrics over extended periods, allowing for historical analysis and trend detection. It is especially useful for applications that require scalable and performant storage of time series data for metrics monitoring and observability purposes.
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.
Mimir Key Concepts
- Metrics: In Grafana Mimir, metrics represent the measurements or observations tracked over time. They can include various types of data, such as system metrics, application performance metrics, or sensor data.
- Long-term Storage: Grafana Mimir provides a storage solution specifically tailored for long-term retention of time series data, allowing users to store and query historical metrics over extended periods.
- Microservices: Grafana Mimir adopts a microservices-based architecture, where the system consists of multiple horizontally scalable microservices that can operate independently and in parallel.
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.
Grafana Mimir adopts a microservices-based architecture, where the system comprises multiple horizontally scalable microservices. These microservices can operate independently and in parallel, allowing for efficient distribution of workload and scalability. Grafana Mimir’s components are compiled into a single binary, providing a unified and cohesive system. The architecture is designed to be highly available and multi-tenant, enabling multiple users and applications to utilize the database concurrently. This distributed architecture ensures scalability and resilience in handling large-scale metric storage and retrieval scenarios.
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.
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Grafana Mimir is designed to scale horizontally, enabling the system to handle growing data volumes and increasing workloads. Its horizontally scalable microservices architecture allows for seamless expansion and improved performance.
Grafana Mimir provides high availability by ensuring redundancy and fault tolerance. It allows for replication and distribution of data across multiple nodes, ensuring data durability and continuous availability of stored metrics.
Grafana Mimir offers a dedicated solution for long-term storage of time series metrics. It provides efficient storage and retrieval mechanisms, allowing users to retain and analyze historical metric data over extended periods.
OSI PI Data Historian Features
Data collection and storage
OSI PI’s PI Interfaces and PI Connectors enable seamless data collection from a wide variety of sources, while the PI Server efficiently stores and manages the data.
The PI System is highly scalable, allowing organizations to handle large volumes of data and a growing number of data sources without compromising performance.
The PI Asset Framework (AF) provides a powerful way to model assets and their associated data, making it easier to understand and analyze complex industrial processes.
Tools like PI DataLink and PI ProcessBook enable users to analyze and visualize data stored in the PI System, facilitating better decision-making and process optimization.
Mimir Use Cases
Metrics Monitoring and Observability
Grafana Mimir is well-suited for monitoring and observability use cases. It enables the storage and analysis of time series metrics, allowing users to monitor the performance, health, and behavior of their systems and applications in real-time.
Long Term Metric Storage
With its focus on providing scalable long-term storage, Grafana Mimir is ideal for applications that require retaining and analyzing historical metric data over extended periods. It allows users to store and query large volumes of time series data generated by Prometheus.
Trend and anomaly detection
By using Mimir for storing long term historical data it can be useful for detecting trends in your metrics and also for comparing current metrics to historical data to detect outliers and anomalies
OSI PI Data Historian Use Cases
OSI PI can help organizations identify inefficiencies, monitor performance, and optimize their industrial processes by providing real-time insights into time series data from sensors and other sources.
By analyzing historical data and detecting patterns or anomalies, OSI PI enables organizations to implement predictive maintenance strategies, reducing equipment downtime and maintenance costs.
OSI PI can be used to track energy consumption across various assets and processes, allowing organizations to identify areas for improvement and implement energy-saving measures.
Mimir Pricing Model
Grafana Mimir is an open-source project, which means it is freely available for usage and does not require any licensing fees. Users can download the source code and deploy Grafana Mimir on their own infrastructure without incurring direct costs. However, it’s important to consider the operational costs associated with hosting and maintaining the database infrastructure.
OSI PI Data Historian Pricing Model
Pricing for OSI PI is typically based on a combination of factors such as the number of data sources, the number of users, and the level of support required. Pricing details are not publicly available, as they are provided on a quote basis depending on the specific needs of the organization.
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