A New Era of Unified Lakehouse: Who Will Reign? A Deep Dive into Apache Doris vs. ClickHouse

With the explosive growth of data, the demand for real-time analytics across industries is more urgent than ever. High-performance data warehouses are the backbone of real-time analysis, enabling enterprises to quickly gain insights and drive decision-making. Among many open-source solutions, Apache Doris and ClickHouse stand out as two of the most noteworthy contenders. This article provides an in-depth comparison between Apache Doris and ClickHouse, aiming to offer valuable insights for technical professionals and decision-makers when selecting real-time analytics solutions.

Apache Doris

Apache Doris is a modern open-source data warehouse based on a massively parallel processing (MPP) architecture, renowned for its exceptionally high query performance. Designed to provide sub-second query responses, Doris can effortlessly handle both high-concurrency point queries and complex, high-throughput analytical workloads. Its architecture consists of two main components: the Frontend (FE) and the Backend (BE). The FE handles user requests, query parsing, metadata management, and node management, while the BE is responsible for data storage and query execution—with data partitioned and stored in multiple replicas across different nodes. This design supports horizontal scaling, allowing a single Doris cluster to manage hundreds of machines and petabytes of data, all while supporting compute-storage separation for elastic scaling and efficient resource utilization.

Key features of Doris include:

• Real-time Data Ingestion: Supports data ingestion with only seconds of delay, via both push and stream-based methods, ensuring data remains up to date.
• Lightning-fast Query Performance: Leverages columnar storage, MPP architecture, a cost-based optimizer, and a vectorized execution engine to achieve high concurrency and throughput.
• Federated Query: Enables querying across various data lakes and databases, simplifying the data integration process.
• Elastic Architecture and Unified Lakehouse: Supports compute-storage decoupling and tiered storage, which not only reduces costs but also establishes a unified lakehouse architecture, allowing direct querying of data lake data.
• Open Ecosystem Compatibility: Highly compatible with the MySQL protocol and ANSI SQL, making it easy to integrate with various BI tools; it also provides open data APIs to support external engines like Spark and Flink.
• Semi-structured Data Support: Offers support for composite data types such as arrays, maps, and JSON, along with efficient text search indexing.

This simple yet flexible architecture significantly reduces both learning and maintenance costs, making Doris outstanding in scenarios such as reporting analysis, customer profiling, accelerated data lake queries, and building unified data warehouses.

ClickHouse

ClickHouse is an open-source columnar database management system developed by Yandex, specifically designed for online analytical processing (OLAP). It is celebrated for its excellent query speed and highly efficient data compression, making it particularly well-suited for processing large-scale datasets. By employing columnar storage, ClickHouse minimizes data scanning, improves query efficiency, and supports horizontal scaling to tackle petabyte-scale data analysis tasks.

Key features of ClickHouse include:

• Lightning-fast Query Performance: Optimized for read-intensive workloads, with particularly strong single-table query performance.
• Efficient Columnar Storage: Achieves significant storage savings and faster query execution through high compression ratios.
• Vectorized Execution: Utilizes SIMD instructions to process data in batches, enhancing overall performance.
• Flexible Table Engines: Offers various table engines to meet diverse storage and query requirements.
• SQL Compatibility: While it supports SQL close to ANSI standards, ClickHouse uses its own SQL dialect—some advanced features may require additional learning.

Although ClickHouse excels in read performance, it faces limitations when handling complex multi-table JOINs and frequent data updates. Its update operations are typically asynchronous, which can sometimes lead to data consistency issues. Moreover, managing clusters and tuning performance can be more complex, increasing maintenance challenges.

In-depth Feature Comparison

To clearly compare the strengths of each system, we review several key aspects:

Data Model

Apache Doris:

• Offers three data models: detailed, aggregate, and unique.
• The detailed model is used for storing raw data; the aggregate model improves query efficiency via pre-aggregation; the unique model supports row-level updates and deletions, making it ideal for frequently changing data.

ClickHouse:

• Primarily based on the MergeTree series of table engines, focusing on append-only writes and background merge optimizations.
• Although it supports some update operations, it is less flexible than Doris for high-frequency updates.

SQL Compatibility

Apache Doris:

• Highly compatible with the MySQL protocol and standard SQL, which simplifies querying and system migration while reducing the learning curve.

ClickHouse:

• Uses its own SQL dialect which, while largely compatible with ANSI SQL, has some differences in advanced query features.

Real-time Performance and Update Capabilities

Apache Doris:

• Achieves low-latency real-time data ingestion and sub-second query responses.
• Employs a strongly consistent primary key storage model to support synchronous updates and deletions, ensuring data consistency.

ClickHouse:

• Exhibits strong write performance; however, its update operations are asynchronous, potentially leading to temporary data inconsistencies.

Usability and Maintenance

Apache Doris:

• Boasts a simple architecture, easy deployment, and a MySQL-friendly SQL interface, which accelerates adoption and minimizes maintenance costs.
• Features such as automatic scaling, compute-storage separation, and a unified lakehouse architecture further simplify system management and data pipeline operations.

ClickHouse:

• While feature-rich, its cluster management, load balancing, and complex query optimizations demand more specialized expertise, making maintenance and scaling more challenging.

Scalability and Deployment

Apache Doris:

• Supports flexible horizontal and vertical scaling, with automatic elastic scaling achieved through compute-storage separation—ideal for building a unified data lakehouse.

ClickHouse:

• Performs well in horizontal scaling; however, tasks like manual tuning for sharding, replication, and load balancing can be labor-intensive.

Suitable Use Cases

Apache Doris:

• Ideal for high-concurrency point queries, complex multi-table JOINs, real-time updates, and unified data warehouse construction.
• Its unified lakehouse architecture allows direct querying of data lakes, simplifying data pipelines, reducing redundant storage, and lowering costs—making it a strong candidate for enterprise-level analytics.

ClickHouse:

• Better suited for read-intensive single-table queries, log analysis, and time series data analysis, but may struggle with complex JOINs and real-time update demands.

Community Activity and Maintenance Cost

Apache Doris:

• Supported by an increasingly active community and robust commercial backing, its simple architecture and elastic expansion mechanisms lead to lower deployment and maintenance costs.

ClickHouse:

• Although it has a mature ecosystem, its complexity can result in higher operational and scaling costs.

Why Choose Apache Doris?

In summary, Apache Doris has clear advantages in several areas:

• High Concurrency and Efficient JOIN Performance: Doris handles complex multi-table JOINs and high-concurrency scenarios more effectively.
• Flexible Data Updates and Consistency Assurance: Its unique model and synchronous update mechanism ensure real-time data consistency.
• Simple and User-friendly Architecture: With MySQL-compatible SQL interfaces and a straightforward FE/BE architecture, deployment, maintenance, and data migration are significantly simplified.
• Unified Lakehouse Architecture: Support for compute-storage decoupling and direct data lake querying reduces data pipeline complexity, storage redundancy, and overall costs, paving the way for modern unified data warehouses.
• Enterprise-level Support and Elastic Scalability: Features such as auto-scaling, load balancing, and mature commercial support offer a cost-effective, high-performance analytics solution for enterprises.

Real-World Case Studies: Apache Doris in Action

Drawing on the migration cases featured on the Doris vs. ClickHouse page, several compelling examples illustrate the benefits of migrating to Apache Doris:

• Tencent Music: Their data platform migrated from ClickHouse to Apache Doris, leading to improved data timeliness and reduced maintenance costs. Doris’ flexible ingestion methods and robust consistency protocol ensured high availability and reliability.
• Enhanced Multi-table JOIN Performance: Companies have reported massive boosts in multi-table JOIN performance after switching to Doris, with complex join queries running 2–10 times faster than before.
• Simplified Data Pipelines: For instance, Kwai upgraded to a lakehouse architecture by replacing ClickHouse with Doris, which streamlined the data pipeline and eliminated the need for data import, as Doris can directly query data from data lakes.
• Improved Query Performance: Across various scenarios, core business queries have been executed 2–3 times faster, while complex join queries have seen performance improvements of up to 10 times.
• Flexible Data Governance: With materialized views, Doris offers enhanced data governance, providing enterprises with a more agile approach to managing their analytics workloads.

Feature Comparison Table

Conclusion

In conclusion, Apache Doris stands out with its high concurrency processing, flexible data update mechanisms, user-friendly architecture, and innovative unified lakehouse approach. For enterprises aiming for efficient queries, real-time data updates, and streamlined data pipelines, Doris is an ideal choice. With a growing community and strengthened commercial support, Apache Doris is poised to play an increasingly critical role in the future of data analytics and help drive the digital transformation of businesses.