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 StarRocks and TDengine so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how StarRocks 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.
StarRocks vs TDengine Breakdown
Time series database
StarRocks can be deployed on-premises, in the cloud, or in a hybrid environment, depending on your infrastructure preferences and requirements.
TDengine can be deployed on-premises, in the cloud, or as a hybrid solution, allowing flexibility in deployment and management.
Business intelligence, analytics, real-time data processing, large-scale data storage
IoT data storage, industrial monitoring, smart energy, smart home, monitoring and observability
Horizontally scalable, with support for distributed storage and query processing
Linearly scalable with clustering and built-in load balancing
StarRocks is an open source high-performance analytical data warehouse that enables real-time, multi-dimensional, and highly concurrent data analysis. It features an MPP (Massively Parallel Processing) architecture and is equipped with a fully vectorized execution engine and a columnar storage engine that supports real-time updates.
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-like querying, and flexible data modeling capabilities.
StarRocks for Time Series Data
StarRocks is primarily focused on data warehousing workloads but can be used for time series data. StarRocks can be used for real time analytics and historical data analysis.
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.
StarRocks Key Concepts
- MPP Architecture: StarRocks utilizes an MPP architecture, which enables parallel processing and distributed execution of queries, allowing for high-performance and scalability.
- Vectorized Execution Engine: StarRocks employs a fully vectorized execution engine that leverages SIMD (Single Instruction, Multiple Data) instructions to process data in batches, resulting in optimized query performance.
- Columnar Storage Engine: The columnar storage engine in StarRocks organizes data by column, which improves query performance by only accessing the necessary columns during query execution.
- Cost-Based Optimizer (CBO): StarRocks includes a fully-customized cost-based optimizer that evaluates different query execution plans and selects the most efficient plan based on estimated costs.
- Materialized View: StarRocks supports intelligent materialized views, which are precomputed summaries of data that accelerate query performance by providing faster access to aggregated data.
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.
- Stable: A synonym for Super Table.
- TSQL: TDengine’s SQL-like query language, designed specifically for time series data manipulation and retrieval.
StarRock’s architecture includes a fully vectorized execution engine and a columnar storage engine for efficient data processing and storage. It also incorporates features like a cost-based optimizer and materialized views for optimized query performance. StarRocks supports real-time and batch data ingestion from a variety of sources and enables direct analysis of data stored in data lakes without data migration
TDengine uses a hybrid architecture that combines the advantages of relational databases (support for SQL-like querying) and NoSQL databases (scalability and flexibility). It is based on a distributed, columnar storage model and uses a time series data model. TDengine uses data nodes to store data and handle queries. Management nodes coordinate the data nodes and store metadata like schema and cluster information.
Free Time-Series Database Guide
Get a comprehensive review of alternatives and critical requirements for selecting yours.
StarRocks supports multi-dimensional analysis, enabling users to explore data from different dimensions and perspectives.
StarRocks is designed to handle high levels of concurrency, allowing multiple users to execute queries simultaneously.
StarRocks supports materialized views, which provide precomputed summaries of data for faster query performance.
TDengine supports high-speed data ingestion, with the ability to handle millions of data points per second. It supports batch and individual data insertion using TSQL.
TDengine provides a SQL-like query language (TSQL) 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.
StarRocks Use Cases
StarRocks is well-suited for real-time analytics scenarios, where users need to analyze data as it arrives, enabling them to make timely and data-driven decisions.
With its high-performance and highly concurrent data analysis capabilities, StarRocks is ideal for ad-hoc querying, allowing users to explore and analyze data interactively.
Data Lake Analytics
StarRocks supports analyzing data directly from data lakes without the need for data migration. This makes it a valuable tool for organizations leveraging data lakes for storage and analysis.
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.
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.
StarRocks Pricing Model
StarRocks can be deployed on your own hardware using the open source project. There are also a number of commercial vendors offering managed services to run StarRocks in the cloud.
TDengine Pricing Model
TDengine is open source and free to use under the AGPLv3 license. TAOS Data also offers commercial licenses and enterprise support options for organizations that require additional features, support, or compliance with specific licensing requirements.
Get started with InfluxDB for free
InfluxDB Cloud is the fastest way to start storing and analyzing your time series data.