ClickHouse vs SQL Server
A detailed comparison
Compare ClickHouse and SQL Server 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 ClickHouse and SQL Server so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how ClickHouse and SQL Server 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.
ClickHouse vs SQL Server Breakdown
 |
 |
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| Database Model | Columnar database |
Relational database |
| Architecture | ClickHouse can be deployed on-premises, in the cloud, or as a managed service. |
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. |
| License | Apache 2.0 |
Closed source |
| Use Cases | Real-time analytics, big data processing, event logging, monitoring, IoT, data warehousing |
Transaction processing, business intelligence, data warehousing, analytics, web applications, enterprise applications |
| Scalability | Horizontally scalable, supports distributed query processing and parallel execution |
Supports vertical and horizontal scaling, with features like partitioning, sharding, and replication for distributed environments |
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ClickHouse Overview
ClickHouse is an open source columnar database management system designed for high-performance online analytical processing (OLAP) tasks. It was developed by Yandex, a leading Russian technology company. ClickHouse is known for its ability to process large volumes of data in real-time, providing fast query performance and real-time analytics. Its columnar storage architecture enables efficient data compression and faster query execution, making it suitable for large-scale data analytics and business intelligence applications.
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.
ClickHouse for Time Series Data
ClickHouse can be used for storing and analyzing time series data effectively, although it is not explicitly optimized for working with time series data. While ClickHouse can query time series data very quickly once ingested, it tends to struggle with very high write scenarios where data needs to be ingested in smaller batches so it can be analyzed in real time.
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.
ClickHouse Key Concepts
- Columnar storage: ClickHouse stores data in a columnar format, which means that data for each column is stored separately. This enables efficient compression and faster query execution, as only the required columns are read during query execution.
- Distributed processing: ClickHouse supports distributed processing, allowing queries to be executed across multiple nodes in a cluster, improving query performance and scalability.
- Data replication: ClickHouse provides data replication, ensuring data availability and fault tolerance in case of hardware failures or node outages.
- Materialized Views: ClickHouse supports materialized views, which are precomputed query results stored as tables. Materialized views can significantly improve query performance, as they allow for faster data retrieval by avoiding the need to recompute the results for each query.
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.
ClickHouse Architecture
ClickHouse’s architecture is designed to support high-performance analytics on large datasets. ClickHouse stores data in a columnar format. This enables efficient data compression and faster query execution, as only the required columns are read during query execution. ClickHouse also supports distributed processing, which allows for queries to be executed across multiple nodes in a cluster. ClickHouse uses the MergeTree storage engine as its primary table engine. MergeTree is designed for high-performance OLAP tasks and supports data replication, data partitioning, and indexing.
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.
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ClickHouse Features
Real-time analytics
ClickHouse is designed for real-time analytics and can process large volumes of data with low latency, providing fast query performance and real-time insights.
Data compression
ClickHouse’s columnar storage format enables efficient data compression, reducing storage requirements and improving query performance.
Materialized views
ClickHouse supports materialized views, which can significantly improve query performance by precomputing and storing query results as tables.
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.
ClickHouse Use Cases
Large-scale data analytics
ClickHouse’s high-performance query engine and columnar storage format make it suitable for large-scale data analytics and business intelligence applications.
Real-time reporting
ClickHouse’s real-time analytics capabilities enable organizations to generate real-time reports and dashboards, providing up-to-date insights for decision-making.
Log and event data analysis
ClickHouse’s ability to process large volumes of data in real-time makes it a suitable choice for log and event data analysis, such as analyzing web server logs or application events.
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.
ClickHouse Pricing Model
ClickHouse is an open source database and can be deployed on your own hardware. The developers of ClickHouse have also recently created ClickHouse Cloud which is a managed service for deploying ClickHouse.
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.
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