DuckDB vs. ClickHouse Local: A Comparative Analysis for Analytical Workloads

Disclaimer: this is a report generated with my tool: https://github.com/DTeam-Top/tsw-cli. See it as an experiment not a formal research, 😄。

This time, I added "grounding = True" when generating the final report. looks like Gemini removed that not-fully correct JSON statement in the first version, lool!

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Summary

This report compares DuckDB and ClickHouse Local, two analytical database systems, highlighting their strengths, weaknesses, and ideal use cases. DuckDB excels in local analytics on small to medium-sized datasets, offering ease of use and integration with data science tools. ClickHouse Local, a single-node version of the distributed ClickHouse, is better suited for larger datasets and benefits from ClickHouse's optimized columnar storage and query execution. The analysis considers factors like data size, query complexity, ease of setup, and performance, and also addresses the debate around DuckDB's unique value proposition compared to other in-process analytical tools.

Introduction

The landscape of data analytics tools is diverse, with solutions ranging from lightweight, in-process databases to distributed, large-scale systems. DuckDB and ClickHouse Local represent two points on this spectrum, each designed to address specific analytical needs. DuckDB is an in-process OLAP database, emphasizing ease of use and integration with languages like Python. ClickHouse Local provides a single-node deployment option for ClickHouse, a columnar database known for its performance in handling large datasets. This report aims to provide a detailed comparison of these two systems, helping analysts and data engineers choose the right tool for their specific workloads.

Background research involved analyzing blog posts, performance benchmarks, and community discussions to synthesize a comprehensive comparison.

DuckDB: The Embedded Analytical Powerhouse

Key Findings

DuckDB is designed for local analytical workloads, prioritizing ease of setup and integration with data science tools. It operates within the same process as the application, eliminating the overhead of client-server communication.

• Strengths:
  • Ease of Use: Zero configuration and seamless integration with Python/Pandas make DuckDB accessible for data scientists and analysts.
  • In-Memory Performance: DuckDB's in-memory capabilities and optimized query execution enable fast analytical queries on small to medium-sized datasets.
  • Data Science Focus: Designed for local use and integration with Python/Pandas, suiting medium-sized data projects.
• Weaknesses:
  • Limited Scalability: DuckDB is not designed for distributed computing and struggles with very large datasets that exceed available memory.
  • Performance Compared to Alternatives: Some sources suggest that faster DataFrame tools like Polars might offer better performance for certain in-process analytics tasks.
  • Value Proposition Debate: Some question DuckDB's unique value, viewing it as a more complex SQLite, especially with faster in-process analytics alternatives available.

Suggested Actions

• Ideal Use Cases: Local data analysis, prototyping data pipelines, and integrating analytical capabilities into applications.
• Integration with Apache Airflow: DuckDB can be effectively used with Apache Airflow for data transformation and analysis tasks within workflows.
• E2E Data Engineering Projects: DuckDB is suitable for end-to-end data engineering projects.

Risks and Challenges

• Memory Constraints: Large datasets that don't fit in memory can significantly degrade DuckDB's performance.
• Competition: Consider alternatives like Polars for in-process analytics, especially if performance is critical.

ClickHouse Local: Single-Node Analytics at Scale

Key Findings

ClickHouse Local is the single-node version of ClickHouse, a high-performance columnar database management system (DBMS) designed for online analytical processing (OLAP).

• Strengths:
  • Scalability: Designed for large-scale analytics, making it suitable for big data applications.
  • Optimized Columnar Storage: ClickHouse's columnar storage format and vectorized query execution enable efficient processing of large datasets.
• Weaknesses:
  • Complexity: Setting up and managing ClickHouse Local can be more complex than DuckDB, requiring more configuration and system administration expertise.
  • Resource Intensive: ClickHouse Local can consume significant system resources, especially memory and disk I/O, when processing large datasets.
  • Single point of failure: Since it is a single node solution, if the node goes down all of the data is lost.

Suggested Actions

• Ideal Use Cases: Analyzing large datasets on a single server, prototyping ClickHouse deployments, and running analytical workloads that benefit from columnar storage and vectorized execution.
• Benchmarking: Conduct thorough performance testing to ensure ClickHouse Local meets the required performance SLAs.
• When to use ClickHouse over DuckDB: When dealing with a large amount of data.

Risks and Challenges

• Resource Limitations: Ensure the server has sufficient resources to handle the dataset size and query complexity.
• Operational Overhead: Be prepared for the operational overhead of managing a ClickHouse instance, including configuration, monitoring, and maintenance.

Insights

• Data Size Matters: DuckDB is ideal for small to medium-sized datasets, while ClickHouse Local excels with larger datasets that benefit from columnar storage and vectorized execution.
• Ease of Use vs. Performance: DuckDB prioritizes ease of use and integration, while ClickHouse Local emphasizes performance and scalability.
• Consider Alternatives: Evaluate other in-process analytics tools like Polars if performance is a primary concern for smaller datasets.
• Workload-Specific Choice: The choice between DuckDB and ClickHouse Local depends heavily on the specific analytical workload, data size, and performance requirements.

Conclusion

DuckDB and ClickHouse Local are both valuable tools for analytical workloads, each with its strengths and weaknesses. DuckDB offers a user-friendly, in-process solution for analyzing small to medium-sized datasets, while ClickHouse Local provides a single-node deployment option for the high-performance ClickHouse database, suitable for larger datasets and more demanding analytical tasks. The selection of which tool to use depends on the specific requirements of the project.

References

• https://www.cloudraft.io/blog/clickhouse-vs-duckdb
• https://airbyte.com/data-engineering-resources/clickhouse-vs-duckdb
• https://www.influxdata.com/comparison/clickhouse-vs-duckdb/
• https://risingwave.com/blog/clickhouse-vs-duckdb-performance-analysis/
• https://www.vantage.sh/blog/clickhouse-local-vs-duckdb
• https://www.reddit.com/r/dataengineering/comments/zp6ai6/what_are_the_actual_use_cases_for_duckdb_when_you/
• https://medium.com/apache-airflow/how-to-best-use-duckdb-with-apache-airflow-63a079160d5d
• https://motherduck.com/blog/duckdb-python-e2e-data-engineering-project-part-1/
• https://medium.com/towards-data-science/my-first-billion-of-rows-in-duckdb-11873e5edbb5

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Report generated by TSW-X
Advanced Research Systems Division
Date: 2025-03-04