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 SQL Server and TDengine so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how SQL Server and TDengine 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.

SQL Server vs TDengine Breakdown


 
Database Model

Relational database

Time series database

Architecture

SQL Server can be deployed on-premises, in virtual machines, or as a managed cloud service (Azure SQL Database) on Microsoft Azure. It is available in multiple editions tailored to different use cases, such as Express, Standard, and Enterprise.

TDengine can be deployed on-premises, in the cloud, or as a hybrid solution, allowing flexibility in deployment and management.

License

Closed source

AGPL 3.0

Use Cases

Transaction processing, business intelligence, data warehousing, analytics, web applications, enterprise applications

IoT data storage, industrial monitoring, smart energy, smart home, monitoring and observability

Scalability

Supports vertical and horizontal scaling, with features like partitioning, sharding, and replication for distributed environments

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

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.

SQL Server Overview

Microsoft SQL Server is a powerful and widely used relational database management system developed by Microsoft. Initially released in 1989, it has evolved over the years to become one of the most popular database systems for businesses of all sizes. SQL Server is known for its robust performance, security, and ease of use. It supports a variety of platforms, including Windows, Linux, and containers, providing flexibility for different deployment scenarios.

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.


SQL Server for Time Series Data

While Microsoft SQL Server is primarily a relational database, it does offer support for time series data through various features and optimizations. Temporal tables allow for tracking changes in data over time, providing an efficient way to store and query historical data. Indexing and partitioning can be leveraged to optimize time series data storage and retrieval. However, SQL Server may not be the best choice for applications requiring high write or query throughput specifically for time series data, as specialized time series databases offer more optimized solutions as well as a variety of developer productivity features that speed up development time for applications that heavily use time series data.

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.


SQL Server Key Concepts

  • T-SQL: Transact-SQL, an extension of SQL that adds procedural programming elements, such as loops, conditional statements, and error handling, to the standard SQL language.
  • SSMS: SQL Server Management Studio, an integrated environment for managing SQL Server instances, databases, and objects.
  • Always On: A suite of high availability and disaster recovery features in SQL Server, including Always On Availability Groups and Always On Failover Cluster Instances.

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.


SQL Server Architecture

Microsoft SQL Server is a relational database that uses SQL for querying and manipulating data. It follows a client-server architecture, with the database server hosting the data and processing requests from clients. SQL Server supports both on-premises and cloud-based deployment through Azure SQL Database, a managed service offering in the Microsoft Azure cloud. SQL Server’s architecture includes components such as the Database Engine, which processes data storage and retrieval, and various services for reporting, integration, and analysis.

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).

Free Time-Series Database Guide

Get a comprehensive review of alternatives and critical requirements for selecting yours.

SQL Server Features

Security

SQL Server offers advanced security features, such as Transparent Data Encryption, Always Encrypted, and row-level security, to protect sensitive data.

Scalability

SQL Server supports scaling out through features like replication, distributed partitioned views, and Always On Availability Groups.

Integration Services

SQL Server Integration Services (SSIS) is a powerful platform for building high-performance data integration and transformation solutions.

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.


SQL Server Use Cases

Enterprise Applications

SQL Server is commonly used as the backend database for enterprise applications, providing a reliable and secure data storage solution.

Data Warehousing and Business Intelligence

SQL Server’s built-in analytical features, such as Analysis Services and Reporting Services, make it suitable for data warehousing and business intelligence applications.

E-commerce Platforms

SQL Server’s performance and scalability features enable it to support the demanding workloads of e-commerce platforms, handling high volumes of transactions and user 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.


SQL Server Pricing Model

Microsoft SQL Server offers a variety of licensing options, including per-core, server + CAL (Client Access License), and subscription-based models for cloud deployments. Costs depend on factors such as the edition (Standard, Enterprise, or Developer), the number of cores, and the required features. For cloud-based deployments, Azure SQL Database offers a pay-as-you-go model with various service tiers to accommodate different performance and resource requirements.

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.