MongoDB vs OSI PI Data Historian
A detailed comparison
Compare MongoDB and OSI PI Data Historian 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 MongoDB 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 MongoDB 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.
MongoDB vs OSI PI Data Historian Breakdown
 |
 |
|
| Database Model | Document database |
Time series database/data historian |
| Architecture | MongoDB uses a flexible, JSON-like document model for storing data, which allows for dynamic schema changes without downtime. It supports ad hoc queries, indexing, and real-time aggregation. MongoDB can be deployed as a standalone server, in a replica set configuration for high availability, or as a sharded cluster for horizontal scaling. It is also available as a managed cloud service called MongoDB Atlas, which provides additional features like automated backups, monitoring, and global distribution. |
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. |
| License | SSPL for community edition, commercial licenses for other versions |
Closed source |
| Use Cases | Content management systems, mobile applications, real-time analytics, IoT data management, e-commerce platforms |
Industrial data management, real-time monitoring, asset health tracking, predictive maintenance, energy management |
| Scalability | Horizontally scalable with support for data sharding, replication, and automatic load balancing |
Supports horizontal scaling through distributed architecture, data replication, and data federation for large-scale deployments |
Looking for the most efficient way to get started?
Whether you are looking for cost savings, lower management overhead, or open source, InfluxDB can help.
MongoDB Overview
MongoDB is a popular, open-source NoSQL database launched in 2009. Designed to handle large volumes of unstructured and semi-structured data, MongoDB offers a flexible, schema-less data model, horizontal scalability, and high performance. Its ease of use, JSON-based document storage, and support for a wide range of programming languages have contributed to its widespread adoption across various industries and applications.
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.
MongoDB for Time Series Data
Although MongoDB is a general-purpose NoSQL database, it can be used for storing and processing time series data. The flexible data model of MongoDB allows for easy adaptation to the evolving structure of time series data, such as the addition of new metrics or the modification of existing ones. MongoDB provides built-in support for time-to-live (TTL) indexes, which automatically expire old data after a specified time period, making it suitable for managing large volumes of time series data with a limited storage capacity. MongoDB has also recently added a custom columnar storage engine and time series collection for time series use cases, meant to improve performance over the default MongoDB storage engine in terms of data compression and query performance.
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.
MongoDB Key Concepts
Some key terminology and concepts specific to MongoDB include:
- Database: A MongoDB database is a container for collections, which are groups of related documents.
- Collection: A collection in MongoDB is analogous to a table in relational databases, holding a set of documents.
- Document: A document in MongoDB is a single record, stored in a JSON-like format called BSON (Binary JSON). Documents within a collection can have different structures.
- Field: A field is a key-value pair within a document, similar to an attribute or column in a relational database.
- Index: An index in MongoDB is a data structure that improves the query performance on specific fields within a collection.
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.
MongoDB Architecture
MongoDB’s architecture is centered around its flexible, document-based data model. As a NoSQL database, MongoDB supports a schema-less structure, which allows for the storage and querying of diverse data types, such as nested arrays and documents. MongoDB can be deployed as a standalone server, a replica set, or a sharded cluster. Replica sets provide high availability through automatic failover and data redundancy, while sharded clusters enable horizontal scaling and load balancing by distributing data across multiple servers based on a shard key.
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.
Free Time-Series Database Guide
Get a comprehensive review of alternatives and critical requirements for selecting yours.
MongoDB Features
Flexible Data Model
MongoDB’s schema-less data model allows for the storage and querying of diverse data types, making it well-suited for handling complex and evolving data structures.
High Availability
MongoDB’s replica set feature ensures high availability through automatic failover and data redundancy.
Horizontal Scalability
MongoDB’s sharded cluster architecture enables horizontal scaling and load balancing, allowing it to handle large-scale data processing and querying.
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.
MongoDB Use Cases
Content Management Systems
MongoDB’s flexible data model makes it an ideal choice for content management systems, which often require the ability to store and manage diverse content types, such as articles, images, and videos. The schema-less nature of MongoDB allows for easy adaptation to changing content structures and requirements.
IoT Data Storage and Analytics
MongoDB’s support for high data volumes and horizontal scalability makes it suitable for storing and processing data generated by IoT devices, such as sensor readings and device logs. Its ability to index and query data efficiently allows for real-time analytics and monitoring of IoT devices.
E-commerce Platforms
MongoDB’s flexibility and performance features make it an excellent choice for e-commerce platforms, where diverse product information, customer data, and transaction records need to be stored and queried efficiently. The flexible data model enables easy adaptation to changes in product attributes and customer preferences, while the high availability and scalability features ensure a smooth and responsive user experience.
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.
MongoDB Pricing Model
MongoDB offers various pricing options, including a free, open-source Community Edition and a commercial Enterprise Edition, which includes advanced features, management tools, and support. MongoDB Inc. also offers a fully managed cloud-based database-as-a-service, MongoDB Atlas, with a pay-as-you-go pricing model based on storage, data transfer, and compute resources. MongoDB Atlas offers a free tier with limited resources for users who want to try the service without incurring costs.
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.
Get started with InfluxDB for free
InfluxDB Cloud is the fastest way to start storing and analyzing your time series data.
