OSI PI Data Historian vs PostgreSQL
A detailed comparison
Compare OSI PI Data Historian and PostgreSQL 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 OSI PI Data Historian and PostgreSQL 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 PostgreSQL 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 vs PostgreSQL Breakdown
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| Database Model | Time series database/data historian |
Relational database |
| Architecture | 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. |
PostgreSQL can be deployed on various platforms, such as on-premises, in virtual machines, or as a managed cloud service like Amazon RDS, Google Cloud SQL, or Azure Database for PostgreSQL. |
| License | Closed source |
PostgreSQL license (similar to MIT or BSD) |
| Use Cases | Industrial data management, real-time monitoring, asset health tracking, predictive maintenance, energy management |
Web applications, geospatial data, business intelligence, analytics, content management systems, financial applications, scientific applications |
| Scalability | Supports horizontal scaling through distributed architecture, data replication, and data federation for large-scale deployments |
Supports vertical scaling, horizontal scaling through partitioning, sharding, and replication using available tools |
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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.
PostgreSQL Overview
PostgreSQL, also known as Postgres, is an open-source relational database management system that was first released in 1996. It has a long history of being a robust, reliable, and feature-rich database system, widely used in various industries and applications. PostgreSQL is known for its adherence to the SQL standard and extensibility, which allows users to define their own data types, operators, and functions. It is developed and maintained by a dedicated community of contributors and is available on multiple platforms, including Windows, Linux, and macOS.
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.
PostgreSQL for Time Series Data
PostgreSQL can be used for time series data storage and analysis, although it was not specifically designed for this use case. With its rich set of data types, indexing options, and window function support, PostgreSQL can handle time series data. However, Postgres will not be as optimized for time series data as specialized time series databases when it comes to things like data compression, write throughput, and query speed. PostgreSQL also lacks a number of features that are useful for working with time series data like downsampling, retention policies, and custom SQL functions for time series data analysis.
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.
PostgreSQL Key Concepts
- MVCC: Multi-Version Concurrency Control is a technique used by PostgreSQL to allow multiple transactions to be executed concurrently without conflicts or locking.
- WAL: Write-Ahead Logging is a method used to ensure data durability by logging changes to a journal before they are written to the main data files.
- TOAST: The Oversized-Attribute Storage Technique is a mechanism for storing large data values in a separate table to reduce the main table’s disk space consumption.
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.
PostgreSQL Architecture
PostgreSQL is a client-server relational database system that uses the SQL language for querying and manipulation. It employs a process-based architecture, with each connection to the database being handled by a separate server process. This architecture provides isolation between different users and sessions. PostgreSQL supports ACID transactions and uses a combination of MVCC, WAL, and other techniques to ensure data consistency, durability, and performance. It also supports various extensions and external modules to enhance its functionality.
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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.
Scalability
The PI System is highly scalable, allowing organizations to handle large volumes of data and a growing number of data sources without compromising performance.
Asset modeling
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.
Data visualization
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.
PostgreSQL Features
Extensibility
PostgreSQL allows users to define custom data types, operators, and functions, making it highly adaptable to specific application requirements.
Full-text search
PostgreSQL has built-in support for full-text search, enabling users to perform complex text-based queries and analyses.
Geospatial support
With the PostGIS extension, PostgreSQL can store and manipulate geospatial data, making it suitable for GIS applications.
OSI PI Data Historian Use Cases
Process optimization
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.
Predictive maintenance
By analyzing historical data and detecting patterns or anomalies, OSI PI enables organizations to implement predictive maintenance strategies, reducing equipment downtime and maintenance costs.
Energy management
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.
PostgreSQL Use Cases
Enterprise applications
PostgreSQL is a popular choice for large-scale enterprise applications due to its reliability, performance, and feature set.
GIS applications
With the PostGIS extension, PostgreSQL can be used for storing and analyzing geospatial data in applications like mapping, routing, and geocoding.
OLTP workloads
As a relational database, PostgreSQL is a good fit for pretty much any application that involves transactional workloads.
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.
PostgreSQL Pricing Model
PostgreSQL is open source software, 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 PostgreSQL server. Several cloud-based managed PostgreSQL services, such as Amazon RDS, Google Cloud SQL, and Azure Database for PostgreSQL, offer different pricing models based on factors like storage, computing resources, and support.
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