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 MySQL and StarRocks so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how MySQL and StarRocks 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.
MySQL vs StarRocks Breakdown
MySQL uses a client-server model with a multi-layered server design. It supports the SQL query language and offers various storage engines, such as InnoDB and MyISAM, for different use cases. MySQL can be deployed on-premises, in the cloud, or as a managed service.
StarRocks can be deployed on-premises, in the cloud, or in a hybrid environment, depending on your infrastructure preferences and requirements.
GNU General Public License v2 (for the open-source Community Edition)
Web applications, e-commerce, data warehousing, content management systems, business applications
Business intelligence, analytics, real-time data processing, large-scale data storage
Supports vertical scaling by adding more resources to a single node; horizontal scaling can be achieved through replication, sharding, and third-party tools
Horizontally scalable, with support for distributed storage and query processing
MySQL is an open source relational database management system that was first released in 1995. It is one of the most popular databases worldwide due to its ease of use, reliability, and performance. MySQL is widely used for web applications, online transaction processing, and data warehousing. Oracle Corporation acquired MySQL in 2010, but it remains open source software with an active community of contributors.
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.
MySQL for Time Series Data
MySQL can be used for storing and analyzing time series data, but it will not be as efficient as a dedicated time series databases. MySQL’s flexibility and support for various indexing techniques can make it a suitable choice for small to medium sized time series datasets. For large-scale time series data workloads, with high write throughput or use cases where low latency queries are required, MySQL will tend to struggle unless highly customized.
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.
MySQL Key Concepts
- Table: A collection of related data organized in rows and columns, which is the primary structure for storing data in MySQL.
- Primary Key: A unique identifier for each row in a table, used to enforce data integrity and enable efficient querying.
- Foreign Key: A column or set of columns in a table that refers to the primary key in another table, used to establish relationships between tables.
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.
MySQL is a relational database management system that uses SQL for defining and manipulating data. It follows the client-server model, where a MySQL server accepts connections from multiple clients and processes their queries. MySQL’s architecture includes a storage engine framework that allows users to choose from different storage engines, such as InnoDB, MyISAM, or Memory, to optimize the database for specific use cases.
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
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MySQL supports transactions and adheres to the ACID (Atomicity, Consistency, Isolation, Durability) properties, ensuring data integrity and consistency.
MySQL can scale both vertically and horizontally, depending on the storage engine and configuration.
Replication and high availability
MySQL supports various replication techniques, including master-slave and master-master replication, to provide high availability and fault tolerance.
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.
MySQL Use Cases
MySQL is a popular choice for powering web applications, content management systems, and e-commerce platforms due to its flexibility, ease of use, and performance.
Online transaction processing (OLTP)
MySQL is suitable for OLTP systems that require high concurrency, fast response times, and support for transactions.
While not specifically designed for data warehousing, MySQL can be used for small to medium-sized data warehouses, leveraging its support for indexing, partitioning, and other optimization techniques.
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.
MySQL Pricing Model
MySQL is available in multiple editions with different feature sets and pricing models. The MySQL Community Edition is open source and free to use, while the MySQL Enterprise Edition includes additional features, such as advanced security, monitoring, and management tools, and requires a subscription. Pricing for the Enterprise Edition depends on the number of server instances and the level of support required.
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.
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