OSI PI Data Historian vs Redis
A detailed comparison
Compare OSI PI Data Historian and Redis 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 Redis 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 Redis 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 Redis Breakdown
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 |
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| Database Model | Time series database/data historian |
In-memory 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. |
Redis can be deployed on-premises, in the cloud, or as a managed service |
| License | Closed source |
BSD 3 |
| Use Cases | Industrial data management, real-time monitoring, asset health tracking, predictive maintenance, energy management |
Caching, message brokering, real-time analytics, session storage, geospatial data processing |
| Scalability | Supports horizontal scaling through distributed architecture, data replication, and data federation for large-scale deployments |
Horizontally scalable via partitioning and clustering, supports data replication |
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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.
Redis Overview
Redis, which stands for Remote Dictionary Server, is an open-source, in-memory data structure store that can be used as a database, cache, and message broker. It was created by Salvatore Sanfilippo in 2009 and has since gained significant popularity due to its high performance and flexibility. Redis supports various data structures, such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, and geospatial indexes with radius queries.
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.
Redis for Time Series Data
Redis has a dedicated module for working with time series data called RedisTimeSeries. RedisTimeSeries offers functionality like downsampling, data retention policies, and specialized queries for time series data in Redis. Being an in-memory database, Redis will be very fast for reading and writing time series data, but due to the cost of RAM compared to disk using Redis could become expensive depending on the size of your dataset. If your use case doesn’t require extremely fast response times, you could save money by going with a more traditional time series database.
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.
Redis Key Concepts
- In-memory store: Redis stores data in memory, which allows for faster data access and manipulation compared to disk-based databases .
- Data structures: Redis supports a wide range of data structures, including strings, hashes, lists, sets, and more, which provide flexibility in how data is modeled and stored.
- Persistence: Redis offers optional data persistence, allowing data to be periodically saved to disk or written to a log for durability.
- Pub/Sub: Redis provides a publish/subscribe messaging system, enabling real-time communication between clients without the need for a centralized message broker.
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.
Redis Architecture
Redis is a NoSQL database that uses a key-value data model, where each key is associated with a value stored as one of Redis’ supported data structures. The database is single-threaded, which simplifies its internal architecture and reduces contention. Redis can be deployed as a standalone server, a cluster, or a master-replica setup for scalability and high availability. The Redis Cluster mode automatically shards data across multiple nodes, providing data partitioning and fault tolerance.
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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.
Redis Features
Atomicity
Redis supports atomic operations on complex data types, allowing developers to perform powerful operations without worrying about race conditions or other concurrent processing issues.
Broad data structure support
Redis supports a range of data structures such as lists, sets, sorted sets, hashes, bitmaps, hyperloglog, and geospatial indexes. This flexibility allows developers to use Redis for a wide variety of tasks by using data structures that are optimized for their data in terms of performance characteristics.
Pub/Sub messaging
Redis provides a publish/subscribe messaging system for real-time communication between clients.
Lua Scripting
Developers can run Lua scripts in the Redis server, enabling complex operations to be executed atomically in the server itself, reducing network round trips.
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.
Redis Use Cases
Caching
Redis is often used as a cache to store frequently accessed data and reduce the load on other databases or services, improving application performance and reducing latency.
Task queues
Redis can be used to implement task queues, which are useful for managing tasks that take longer to process and should be executed asynchronously. This is particularly common in web applications, where background tasks can be processed independently of the request/response cycle
Real-time analysis and machine learning
Redis’ high performance and low-latency data access make it suitable for real-time analysis and machine learning applications, such as processing streaming data, media streaming, and handling time-series data. This can be achieved using Redis’ data structures and capabilities like sorted sets, timestamps, and pub/sub messaging.
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.
Redis Pricing Model
Redis is open-source software, which means it can be deployed and used freely on your own infrastructure. However, there are also managed Redis services available, such as Redis Enterprise which offer additional features, support, and ease of deployment. Pricing for these services typically depends on factors like the size of the instance, data storage, and data transfer.
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