Most organizations dealing with high-volume, high-velocity data depend on a modern tech stack for streamlined data collection, processing, storage, and analysis. A robust database management system (DBMS) is a crucial aspect of this modern tech stack. It majorly influences the performance and efficacy of your organization’s workflows.
Selecting the right DBMS requires a thorough understanding of the specific use cases for which this technology is needed. The following article compares two popular DBMS options, ClickHouse vs MySQL, and will help you decide which platform best aligns with your organization’s objectives.
However, before exploring their features and capabilities, you should familiarize yourself with the fundamentals of OLTP and OLAP. These terms describe different types of database operations and can significantly impact your final choice of the DBMS.
What Is OLTP and OLAP?
OLTP and OLAP are two core data processing systems that serve different purposes. OLAP represents Online Analytical Processing, and OLTP is short for Online Transaction Processing. You can employ these processes to enhance your organization’s operational efficiency and decision-making.
Online Transaction Processing (OLTP)
With OLTP, you can manage a large number of frequent and concurrent transactional workloads in real-time. It is optimized to support high-speed, repetitive tasks such as order processing, payments, and customer interactions.
The typical applications of OLTP include quick and reliable responses in online banking, inventory management at retail stores, and airline reservations. It also ensures data consistency and accuracy by allowing you to implement data integrity rules and constraints. This protects the quality of your data and streamlines the downstream tasks.
Online Analytical Processing (OLAP)
OLAP enables you to perform complex, large-scale data analysis and reporting. It supports multidimensional analyses and advanced operations like slicing, dicing, drill-downs, and roll-ups. This helps you explore information from different perspectives (dimensions), including time, location, and product type, and gain insights to identify business opportunities and risks.
You can also leverage OLAP to run complex queries for data mining, forecasting, and trend analysis. By gaining insights into customer behavior, revenue generated, and market trends, you can strategically plan your marketing, budgeting, and product development efforts.
Overviewing ClickHouse
ClickHouse is an open-source columnar database management system (DBMS) that helps you to implement OLAP workloads and generate real-time data reports using SQL queries. It is best suited for applications that require low-latency query execution, such as business intelligence, reporting, and data warehousing.
Additionally, the DBMS provides data replication and sharding to allow you to scale and distribute your data across multiple servers for fault tolerance. You can easily deploy ClickHouse on-premises, on private cloud infrastructures with the Bring Your Own Cloud (BYOC) feature, and in a public cloud (AWS, GCP, and Azure).
Key Features of ClickHouse
Some of the key features of ClickHouse include:
- Auto-Scaling: ClickHouse’s Production tier of services offers both horizontal and vertical auto-scaling. It adjusts resources based on CPU and memory usage to achieve vertical scaling and increases the number of replicas in the existing shards to facilitate horizontal scalability.
- Machine Learning and AI: You can leverage ClickHouse’s ML and GenAI capabilities to simplify data exploration, preparation, feature generation, and storage for online and offline use cases. It supports real-time data analytics for efficient ML model training, deployment, and inference.
- Vector Search: This feature is best suited for when you want to combine vector matching with metadata filtering, aggregation, or joins. It also supports linear distance matching across petabyte-sized vector databases by efficiently distributing this work across multiple CPU cores with no additional configuration.
- Materialized Views: With materialized views, you can trigger queries and store the pre-calculated results in separate tables. The query results are updated automatically as new data arrives, providing real-time insights. This significantly improves query performance by shifting computational costs from query time to insert time.
- Advanced Security: Clickhouse offers robust security features, such as multi-factor authentication, role-based access control (RBAC), and data encryption in transit and at rest to protect your data. It also provides a dedicated security team to monitor for misconfigurations, vulnerabilities, and threats.
When to Use ClickHouse?
ClickHouse is a versatile platform with high performance, scalability, and flexibility. Below are some use cases where you can use ClickHouse:
- Log and Event Data Analysis: ClickHouse allows you to perform real-time monitoring and analysis of your logs, traces, events, and other time series data. It helps you detect anomalies, frauds, network activity, and infrastructure issues.
- Data Warehousing: With ClickHouse, you can execute high-speed concurrent workflows that run user-facing applications. Its distributed architecture and massive storage capacity make it suitable for data warehousing applications.
- Business Intelligence: You can easily integrate ClickHouse with visualization tools like Tableau and Looker and interactively slice and dice your data for further analysis and reporting. It helps you better understand user behavior, market dynamics, ad performances, and more.
Overviewing MySQL
MySQL is an open-source, relational database management system (RDBMS) widely used in OLTP workflows, where frequent updates and quick responses are crucial. While SQL is its primary query language, it is also compatible with PHP, Python, and Java. Due to its structured data storage and retrieval capabilities, MySQL is ideal for applications such as web development, e-commerce platforms, and content management systems.
With MySQL, you can achieve horizontal scalability and accommodate your increasing data volumes by distributing them across multiple servers. The platform is also known for its reliability and advanced security features, such as user authentication, transparent encryption, data masking, and auditing. Based on your organizational needs, you can deploy MySQL on-premises, in private clouds, or through managed cloud services.
Key Features of MySQL
MySQL is the second-most popular database tool among developers in 2024. This popularity is mostly because of its features that help simplify the developers’ work. Below are some key features of MySQL for you to explore:
- Enhanced Connectivity: MySQL supports multiple connection methods, including TCP/IP sockets, Unix domain socket files, and Windows named pipes. It offers client libraries written in C and APIs for C++, Python, PHP, Perl, and Java to enable database interactions across diverse platforms.
- Portability: The platform leverages CMake for configuration and is compatible with various compilers, making it easily adaptable across environments. You can run it on macOS, Windows, Solaris, and Linux operating systems.
- Geospatial Data Support: MySQL provides extensive support for OpenGIS spatial data types. It allows you to store and perform geographic and spatial data queries directly within the database.
- Event Scheduler: You can use the event scheduler to trigger the automatic execution of tasks at specific intervals or times. This provides a flexible and efficient way to automate various database maintenance and administrative tasks, reducing the chances of manual errors.
- Full-text Searches: MySQL supports full-text indexing and searches for InnoDB and MyISAM tables. It offers three types of searches: natural language searches, boolean searches, and searches with query expansion, enhancing data retrieval in large datasets.
When to Use MySQL?
MySQL is the most stable and secure SQL-based database management system. Some of the use cases of the platform include:
- E-Commerce Applications: Online shopping platforms like WooCommerce, Shopify, and Magento depend on MySQL to store and manage product catalogs, customer details, orders, and financial transactions.
- LAMP-Based Applications: LAMP stack is a combination of open-source software, Linux, Apache, MySQL, and Python/Perl/PHP, used to build dynamic websites and web applications. MySQL is a crucial component of this LAMP stack due to its reliability and performance.
- Embedded Systems: You can use MySQL in many electronic devices, IoT applications, and routers. It offers a compact and dependable way to store and manage data in these embedded systems.
Key Distinctions Between Clickhouse Vs MySQL
ClickHouse and MySQL are powerful DBMS platforms but have distinct strengths and characteristics. MySQL is best suited for transactional workloads that require compliance with ACID (atomicity, consistency, isolation, and durability) properties. Conversely, ClickHouse is a great choice for advanced real-time analytics and reporting. Let’s further explore the key differences between these database solutions.
The main difference between ClickHouse and MySQL is that ClickHouse is a columnar database optimized for high-performance analytics and large-scale data processing, while MySQL is a row-based relational database designed for transactional processing and smaller-scale applications.
MySQL vs ClickHouse: Data Storage Architecture
ClickHouse follows a columnar storage model. This enables faster query executions by allowing you to read only necessary columns from disk and reducing the number of input/output operations. Columnar storage also facilitates efficient data compression and encoding implementations for improved performance of OLAP workloads.
On the other hand, MySQL utilizes row-oriented storage, which ensures faster inserts and updates and is optimized for OLTP workflows. Its architecture follows a client-server model and includes a storage engine framework where you can choose from different storage engines, including MyISAM, InnoDB, and Memory.
ClickHouse vs MySQL: Query Performance
ClickHouse and MySQL differ significantly in query performance. You can leverage ClickHouse’s vectorized query processing and SIMD (Single Instruction, Multiple Data) instructions to operate on several data points within a single CPU cycle. This, along with its parallel processing capabilities across multiple nodes, allows you to perform real-time, high-concurrency queries efficiently.
Conversely, MySQL is a general-purpose database with limited parallel processing ability and can face locking and blocking issues when dealing with real-time data processing. The RDBMS software can handle high-volume datasets, but it may not provide the same level of performance as ClickHouse.
MySQL vs ClickHouse: Indexing
Both MySQL and ClickHouse have different indexing approaches. In MySQL, indexing strategies primarily involve B-tree indexes. However, there are several exceptions, such as R-tree indexes for spatial data, hash indexes for Memory-engine-based tables, and full-text indexes for InnoDB-based tables.
In contrast, ClickHouse uses sparse primary indexes and data skipping indexes. B-tree indexing in MySQL provides efficient multi-column comparisons and range searches, while sparse indexing in ClickHouse enables increased performance by minimizing disk and memory overhead.
ClickHouse vs MySQL: Data Compression
ClickHouse and MySQL offer data compression techniques to reduce the amount of data that needs to be read from the disk. ClickHouse supports various compression algorithms, such as LZ4, ZSTD, Delta, and T64. Based on your data types, you can use these codecs to achieve enhanced compression ratios.
On the other hand, MySQL's InnoDB storage engine supports page-level and table-level compression by leveraging the zlib library. The library utilizes the LZ77 compression algorithm, which provides a good balance between compression ratio and speed. However, MySQL offers less flexibility in compression options than ClickHouse.
MySQL vs ClickHouse: Cost Considerations
ClickHouse provides an open-source version that you can download for free. Apart from this, the platform has three paid plans: Development, Production, and Dedicated. Their pricing changes based on factors like storage volume, region, and cloud provider.
MySQL also provides a free and open-source version, MySQL Community Edition, and three chargeable commercial editions: Standard, Enterprise, and Cluster Carrier Grade Edition. The pricing here starts at $4280 and changes depending on the number of socket servers and the services each edition offers.
Comparison Table: ClickHouse vs MySQL
The following table offers a brief comparison between MySQL vs ClickHouse:
Streamlined Data Integration with Airbyte
As a large organization, it is natural for your data to reside in disparate sources such as databases, web analytics applications, CRMs, and ERPs. However, having a unified view helps you better understand your data and plan future steps for downstream analytics and reporting. Airbyte, an AI-powered data integration and replication tool, can streamline this process.
With Airbyte’s library of 400+ pre-built connectors, you can easily transfer your data from ClickHouse to MySQL or any other source-destination combination. It also allows you to build custom connectors in minutes using the low-code connector development kit (CDK).
Here are some key features of Airbyte that can ease your data movement efforts:
- PyAirbyte: Airbyte provides an open-source Python library, PyAirbyte. You can use this library to leverage Airbyte connectors to extract data and load it into SQL caches like Postgres, Snowflake, and DuckDB. This cached data is compatible with Pandas (Python library), SQL-based tools, and LLM frameworks.
- Change Data Capture (CDC): You can leverage the CDC feature and configure Airbyte to detect the incremental data changes occurring at the source and reflect them at the destination. This lets you keep track of the updated information while maintaining data consistency.
- Data Transformation: Airbyte allows integration with LLM frameworks like LlamdaIndex and LangChain to help you perform RAG transformations like chunking, and store transformed data in vector databases. Additionally, you can integrate Airbyte with dbt Cloud to run dbt transformations that help convert raw data into a format suitable for analysis and reporting.
To learn more about how you can incorporate Airbyte into your existing infrastructure, you can contact the experts at Airbyte or refer to the documentation.
Final Thoughts
Choosing between ClickHouse and MySQL largely depends on your specific data management needs. If you prioritize real-time analytics, high-performance queries, and the ability to handle large datasets, ClickHouse should be your go-to solution. Its columnar storage, vectorized query processing, and parallel processing capabilities make it ideal for OLAP workloads.
Conversely, you can depend on MySQL if you require a robust, reliable, ACID-compliant database for transactional workloads. Its row-based storage, indexing mechanisms, and support for various storage engines make it suitable for OLTP applications.
For a better understanding, you can contact the experts at MySQL and ClickHouse and learn how each platform impacts your specific use cases. This enables you to make informed decisions that meet your organization's requirements.