TDengine vs OpenTSDB
A detailed comparison
Compare TDengine and OpenTSDB 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 TDengine and OpenTSDB so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how TDengine and OpenTSDB 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.
TDengine vs OpenTSDB Breakdown
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| Database Model | Time series database |
Time series database |
| Architecture | TDengine can be deployed on-premises, in the cloud, or as a hybrid solution, allowing flexibility in deployment and management. |
OpenTSDB can be deployed on-premises or in the cloud, with HBase running on a distributed cluster of nodes. |
| License | AGPL 3.0 |
GNU LGPLv2.1 |
| Use Cases | IoT data storage, industrial monitoring, smart energy, smart home, monitoring and observability |
Monitoring, observability, IoT, log data storage |
| Scalability | Horizontally scalable with clustering and built-in load balancing. TDengine also provides decoupled compute and storage as well as object storage support for data tiering in some versions |
Horizontally scalable across multiple nodes using HBase as its storage backend |
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TDengine Overview
TDengine is a high-performance, open source time series database designed to handle massive amounts of time series data efficiently. It was created by TAOS Data in 2017 and is specifically designed for Internet of Things (IoT), Industrial IoT, and IT infrastructure monitoring use cases. TDengine has a unique hybrid architecture that combines the advantages of both relational and NoSQL databases, providing high performance, easy-to-use SQL for querying, and flexible data modeling capabilities.
OpenTSDB Overview
OpenTSDB (Open Time Series Database) is an open-source, distributed, and scalable time series database built on top of Apache HBase, a NoSQL database. OpenTSDB was designed to address the growing need for storing and processing large volumes of time series data generated by various sources, such as IoT devices, sensors, and monitoring systems. It was initially developed by StumbleUpon in 2010 and later became an independent project with an active community of contributors.
TDengine for Time Series Data
TDengine is designed from the ground up as a time series database, so it will be a good fit for most use cases that heavily involve storing and analyzing time series data.
OpenTSDB for Time Series Data
OpenTSDB is designed for time series data storage and analysis, making it an ideal choice for managing large scale time series datasets. Its architecture enables high write and query performance, and it can handle millions of data points per second with minimal resource consumption. OpenTSDB’s flexible querying capabilities allow users to perform complex analysis on time series data efficiently.
TDengine Key Concepts
- Super Table: A template for creating multiple tables with the same schema. It’s similar to the concept of table inheritance in some other databases.
- Sub Table: A table created based on a Super Table, inheriting its schema. Sub Tables can have additional tags for categorization and querying purposes.
- Tag: A metadata attribute used to categorize and filter Sub Tables in a Super Table. Tags are indexed and optimized for efficient querying.
OpenTSDB Key Concepts
- Data Point: A single measurement in time consisting of a timestamp, metric, value, and associated tags.
- Metric: A named value that represents a specific aspect of a system, such as CPU usage or temperature.
- Tags: Key-value pairs associated with data points that provide metadata and help categorize and query the data.
TDengine Architecture
TDengine uses a cloud native architecture that combines the advantages of relational databases (support for SQL querying) and NoSQL databases (scalability and flexibility).
OpenTSDB Architecture
OpenTSDB is built on top of Apache HBase, a distributed and scalable NoSQL database, and relies on its architecture for data storage and management. OpenTSDB stores time series data in HBase tables, with data points organized by metric, timestamp, and tags. The database uses a schema-less data model, which allows for flexibility when adding new metrics and tags. The OpenTSDB architecture also supports horizontal scaling by distributing data across multiple HBase nodes.
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TDengine Features
Data ingestion
TDengine supports high-speed data ingestion, with the ability to handle millions of data points per second. It supports batch and individual data insertion.
Data querying
TDengine provides ANSI SQL support with additional that allows users to easily query time series data using familiar SQL syntax. It supports various aggregation functions, filtering, and joins.
Data retention and compression
TDengine automatically compresses data to save storage space and provides data retention policies to automatically delete old data.
OpenTSDB Features
Scalability
OpenTSDB’s distributed architecture allows for horizontal scaling, ensuring that the database can handle growing volumes of time series data.
Data Compression
OpenTSDB uses various compression techniques to reduce the storage footprint of time series data.
Query Language with time series support
OpenTSDB features a flexible query language that supports aggregation, downsampling, filtering, and other operations for analyzing time series data.
TDengine Use Cases
IoT data storage and analysis
TDengine is designed to handle massive amounts of time series data generated by IoT devices. Its high-performance ingestion, querying, and storage capabilities make it a suitable choice for IoT data storage and analysis.
Industrial IoT monitoring
TDengine can be used to store and analyze data from industrial IoT sensors and devices, helping organizations monitor equipment performance, detect anomalies, and optimize operations.
Infrastructure Monitoring
TDengine can be used to collect and analyze time series data from IT infrastructure components, such as servers, networks, and applications, facilitating real-time monitoring, alerting, and performance optimization.
OpenTSDB Use Cases
Monitoring and Alerting
OpenTSDB is well-suited for large-scale monitoring and alerting systems that generate vast amounts of time series data from various sources.
IoT Data Storage
OpenTSDB can store and analyze time series data generated by IoT devices, such as sensors and smart appliances, enabling real-time insights and analytics.
Performance Analysis
OpenTSDB’s flexible querying capabilities make it an ideal choice for analyzing system and application performance metrics over time.
TDengine Pricing Model
TDengine is open source and free to use under the AGPLv3 license. TDengine also offers commercial licenses and enterprise support options for organizations that require additional features, support, or compliance with specific licensing requirements.
OpenTSDB Pricing Model
OpenTSDB is open-source software, which means it is free to use without any licensing fees. However, the cost of running OpenTSDB depends on the infrastructure required to support the underlying HBase database, such as cloud services or on-premises hardware.
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