ClickHouse vs AWS Redshift: A Comprehensive Analysis

ClickHouse Overview. What is ClickHouse?

ClickHouse is a powerful and high-performance open-source columnar database management system designed for real-time analytics. It was developed by Yandex, the Russian multinational IT company, and was first released in 2016. ClickHouse is specifically engineered to handle large volumes of data with exceptional speed, making it an ideal choice for analytical workloads that require fast query performance.

The architecture of ClickHouse is optimized for analytical processing, leveraging a columnar storage format that allows for efficient data compression and retrieval. It excels in scenarios where quick insights are essential, such as data analytics, business intelligence, and monitoring applications. ClickHouse is well-suited for handling diverse data types and is capable of supporting complex analytical queries across massive datasets.

One of the key strengths of ClickHouse is its ability to scale horizontally, enabling users to effortlessly expand their analytical capabilities as data volumes grow. This scalability, combined with its open-source nature, has contributed to ClickHouse gaining popularity across a wide range of industries and use cases. The main distinguishing feature of Clickhouse is the built-in compression feature, this saves on instance size, which results in lower cost of cloud services.

AWS Redshift Overview. What is Amazon Redshift?

Amazon Redshift is a fully managed, petabyte-scale data warehouse service provided by Amazon Web Services (AWS). Launched in 2012, AWS Redshift has since become a prominent solution for organizations aiming to efficiently analyze vast amounts of data in a scalable and cost-effective manner. This cloud-based data warehouse was developed by Amazon, leveraging the company’s extensive experience in handling massive datasets and delivering robust cloud computing services.

AWS Redshift is designed to address the challenges associated with processing and analyzing large volumes of data for business intelligence and data warehousing purposes. It utilizes a columnar storage format and parallel processing capabilities to deliver high-performance query results, making it well-suited for complex analytical workloads. With its seamless integration with other AWS services, Redshift allows users to easily manage and analyze data while taking advantage of the scalability and flexibility of cloud computing.

One of the standout features of AWS Redshift is its ability to scale effortlessly as data volumes increase, accommodating the evolving needs of businesses and ensuring consistent performance. It supports various data types and provides advanced optimization features to enhance query performance.

As organizations increasingly rely on data-driven insights to make informed decisions, AWS Redshift continues to play a crucial role in facilitating efficient and reliable data warehousing and analytics in the cloud. Its ongoing development and enhancements underline its commitment to staying at the forefront of cloud-based data solutions.

ClickHouse Architecture:

Data Storage and Format:

• ClickHouse utilizes a columnar storage format, storing data in columns rather than rows. This design facilitates efficient compression and speeds up analytical queries, making it well-suited for scenarios where rapid data insights are critical.

Distribution and Sharding:

• ClickHouse employs a distributed architecture that emphasizes horizontal scaling, distributing data across multiple nodes. It includes sharding functionality, allowing users to strategically store related data together on specific nodes. This helps minimize the necessity for data movement during query execution, enhancing overall system efficiency.

Query Execution:

• ClickHouse’s query execution engine is highly optimized for parallel processing. It leverages the distributed nature of its architecture to execute complex analytical queries efficiently across the cluster of nodes.

Materialized Views and MergeTree Tables:

• ClickHouse supports materialized views, which store precomputed results of queries to enhance performance. Additionally, it employs MergeTree tables for write-intensive operations, managing periodic inserts and merges effectively.

Scalability:

• ClickHouse achieves scalability through horizontal scaling. Users can add more nodes to the cluster to accommodate increased workloads and growing data volumes. This scalability is particularly advantageous for applications with fast growing data processing requirements.

AWS Redshift Architecture:

Massively Parallel Processing (MPP):

• AWS Redshift follows a Massively Parallel Processing (MPP) architecture. It uses a leader-node model for query coordination and optimization, while compute nodes handle data storage and execution. This architecture enables the parallel execution of queries across multiple nodes.

Columnar Storage:

• Similar to ClickHouse, AWS Redshift employs a columnar storage format for data. This enhances compression and query performance, making it suitable for data warehousing and analytical processing.

Data Distribution and Sorting:

• Redshift distributes data across nodes based on a key distribution method. It also provides features such as automatic sort keys and zone maps to optimize query performance by reducing the amount of data that needs to be scanned.

Scalability and Elastic Resize:

• AWS Redshift offers both vertical and horizontal scalability. Users can resize clusters, adding or removing nodes based on compute and storage requirements. Elastic Resize allows for on-the-fly cluster scaling without downtime.

Integration with Other AWS Services:

• Redshift seamlessly integrates with other AWS services, providing a comprehensive ecosystem for data management and analytics. This integration includes data ingestion, storage, and analytics services within the AWS cloud.

Leader Node and Compute Nodes:

• Redshift’s architecture includes a leader node responsible for query coordination and optimization. Compute nodes handle data storage and query execution. This separation enhances the efficiency of query processing.

Fields of use

ClickHouse and Amazon Redshift are both powerful analytical databases, but they are often employed in different scenarios due to their distinct strengths.

ClickHouse, known for its exceptional performance with high-volume data and real-time analytics, is commonly used in industries like e-commerce, telecommunications, and IoT. Its columnar storage and efficient compression make it ideal for handling massive datasets and executing complex analytical queries quickly.

On the other hand, Amazon Redshift, a fully managed data warehouse service, is widely utilized in enterprise settings that leverage the broader AWS ecosystem. Its seamless integration with other AWS services makes it suitable for businesses relying heavily on Amazon’s cloud infrastructure. Redshift excels in handling complex queries and is often preferred by large enterprises for data warehousing and business intelligence applications.

Benchmarks

In our performance evaluation, we conducted benchmarks using a 2-node dc2.8xlarge cluster on AWS, equipped with a total of 64 cores and 488GB RAM. This cluster is recommended by AWS for compute-intensive workloads on datasets under 1TB when compressed. The benchmark results were compared to those obtained from a single ClickHouse Cloud node boasting 60 cores and 240GB RAM.

The benchmark methodology involved running 42 queries on a 100 million row web analytics dataset, and the detailed steps can be found in the associated repository. The obtained results, illustrated in the provided detailed-comparison.png, highlight that our 60-core ClickHouse Cloud node outperformed a comparable Redshift cluster by an average factor of 2.5x. This suggests a significant speed advantage for ClickHouse Cloud over Redshift, even when the Redshift cluster had substantially higher resources.

Source — https://clickhouse.com/blog/redshift-vs-clickhouse-comparison

For further insights, please feel free to explore additional comparisons, especially those involving Redshift clusters with considerably greater resources. The findings underscore the efficiency and performance benefits of ClickHouse Cloud, making it a compelling choice for data-intensive tasks.

Compression

Compression Algorithms:

• ClickHouse employs a variety of compression algorithms, including LZ4, LZ4 High Compression (HC), Zstandard, Deflate, Delta, DoubleDelta (an extension of Delta), GCD, Gorilla, FPC, and T64. Among these, LZ4 stands out for its swift compression and balanced trade-off between compression ratio and decompression speed. Additionally, Delta compression plays a role in efficiently compressing consecutive identical values within a column.
• Redshift: Amazon Redshift uses a variety of compression algorithms, including Run-Length Encoding (RLE), Zstandard, and mostly Zstandard for newer versions. Zstandard is a modern compression algorithm that aims for a good balance between compression efficiency and speed.

Compression Ratios:

• ClickHouse: ClickHouse generally achieves high compression ratios, especially when dealing with repetitive or highly structured data. The compression ratios can vary based on the data types and patterns within the dataset.
• Redshift: Redshift also provides good compression ratios, and the effectiveness depends on the nature of the data. Zstandard, being a more modern compression algorithm, can provide competitive compression ratios.

Compression Impact on Query Performance:

• ClickHouse: ClickHouse’s use of efficient compression algorithms contributes to its fast query performance. The reduced storage requirements also lead to improved I/O performance.
• Redshift: Redshift’s compression can significantly reduce the amount of data that needs to be read from disk during query execution, contributing to faster query performance.

Compression and Storage Formats:

• ClickHouse: ClickHouse supports various storage formats, including MergeTree, which is the default format and uses the described compression algorithms. Additionally, other formats like Log, TinyLog, and others may have different compression options.
• Redshift: Redshift uses a combination of columnar storage and compression to optimize query performance. The underlying storage format is not directly configurable by users.

Ease of Configuration:

• ClickHouse: ClickHouse provides users with a high degree of control over compression settings, allowing for fine-tuning based on specific use cases and data characteristics.
• Redshift: Redshift abstracts much of the compression configuration from users, automatically managing compression based on the chosen data types and workload patterns. This can simplify administration but may offer less granular control.

Integration and Ecosystem:

• ClickHouse: ClickHouse is an open-source system and can be deployed on-premises or in the cloud. It has a growing ecosystem and community support.
• Redshift: Redshift is a fully managed cloud service provided by AWS, and it seamlessly integrates with other AWS services. It benefits from the broader AWS ecosystem.

In summary, if efficient compression and storage utilization are critical factors, ClickHouse may be a preferred choice, while Redshift might be favored for its overall performance and integration with the broader AWS ecosystem.

Integration with other tool

ClickHouse, an open-source columnar database management system, is known for its exceptional performance in handling analytical queries. It supports a variety of data formats and has a strong focus on real-time analytics. ClickHouse can be integrated with popular BI tools like Tableau and Grafana, as well as various ETL (Extract, Transform, Load) tools, making it versatile for different data workflows.

It should be noted that DoubleCloud’s Date Transfer allows you to upload data to Clickhouse from dozens of sources, such as Apache Kafka, MySQL, PostgreSQL, MongoDB, Redshift, BigQuery, and many others.

On the other hand, Amazon Redshift, a fully managed data warehouse service, seamlessly integrates with the broader AWS ecosystem. It offers native integration with tools like Amazon S3 for data storage, AWS Glue for ETL, and Amazon QuickSight for business intelligence. Redshift’s integration with AWS services simplifies data management and allows for easy scaling based on business needs.

Community and support

ClickHouse has a strong open-source community, making it a great choice for users who value flexibility and open collaboration. Redshift, as a managed service on AWS, benefits from the extensive AWS support network, providing a robust environment for users who prefer a fully managed solution with strong cloud-based support. The choice between them depends on specific use cases, preferences, and the overall infrastructure strategy.

Prices

Since ClickHouse is open source, the software itself is free to use. However, you may incur costs for infrastructure, support, and any additional tools or services you choose to use with ClickHouse.

Infrastructure costs: If you deploy ClickHouse on cloud services like AWS, GCP, or Azure, you will incur costs associated with the virtual machines, storage, and other resources you use. And this is its important difference from Redshift, which can only be hosted on Amazon servers.

In both cases, you pay for the number and type of nodes in your cluster, as well as the amount of storage you use. Вut due to the fact that Clickhouse has built-in compression, it saves on instance size and it leads to reducing cost of the cloud services.

Support and services: If you want a managed service that allows for convenient management for ClickHouse, the price depends on the pricing policy of the particular service. Here you can use the cost calculator.

Summary

Pros of ClickHouse

• Optimized for fast analytical queries and real-time analytics with high data volumes
• Open-source columnar database allowing flexibility and customization
• Horizontally scalable to handle increasing data volumes
• Can achieve high compression ratios, reducing storage needs
• Strong community support as open-source software

Cons of ClickHouse

• Lacks some advanced security features of enterprise-grade systems
• Requires expertise to properly configure for high performance
• Limited ecosystem compared to commercial alternatives

Pros of Redshift

• Fully-managed cloud data warehouse simplifying deployment
• Integrates seamlessly with other AWS services
• Massively parallel query processing architecture
• Automatic compression and data distribution optimization
• Robust security measures leveraging Amazon’s infrastructure

Cons of Redshift

• Higher cost than open-source options like ClickHouse
• Constraints ability to customize some aspects of the system
• Performance limits when scaling to extreme data sizes

FAQ

What is the main difference between ClickHouse and Redshift?

The main difference between ClickHouse and Redshift lies in their architecture and focus: ClickHouse, an open-source database, is optimized for exceptional analytical query performance with a columnar storage format, while Redshift, a managed service by AWS, emphasizes seamless integration into the AWS ecosystem, scalability, and parallel processing for large-scale data warehouses.

Is ClickHouse faster than Redshift?

The question of which database is faster, ClickHouse or AWS Redshift, doesn’t have a straightforward answer because it depends on various factors related to your specific use case, data characteristics, and workload patterns. Both ClickHouse and AWS Redshift are designed for high-performance analytics, but their architectures and features differ.

Which system is safer?

ClickHouse provides basic security measures such as authentication and authorization. However, being open source means that the responsibility for implementing additional security features often falls on the user or administrator. ClickHouse lacks some advanced security features found in enterprise-grade systems.

On the other hand, Amazon Redshift is part of Amazon Web Services (AWS) and benefits from AWS’s robust security infrastructure. Redshift offers features like Virtual Private Cloud (VPC) support, encryption at rest and in transit, and integration with AWS Identity and Access Management (IAM). AWS regularly updates and patches its services to address security vulnerabilities.

When should I use ClickHouse and when should I use Redshift?

If you prioritize performance, cost-effectiveness, and real-time analytics, ClickHouse might be a good fit. If you value seamless integration with AWS, scalability, and ease of use, Redshift could be the preferred choice. Always evaluate based on your specific use case and requirements.