DuckDB is an in-process OLAP database optimized for analytical queries on local datasets with minimal setup, while ClickHouse is a distributed columnar database designed for large-scale, high-throughput analytics across multiple servers. DuckDB excels in simplicity and embedded use cases; ClickHouse dominates in handling massive data volumes and complex distributed queries.
DuckDB vs ClickHouse — Side-by-side comparison on A Versus B
Choose DuckDB if you need a lightweight, easy-to-deploy analytical database for datasets under 1TB, embedded analytics in applications, or rapid prototyping without infrastructure overhead. Choose ClickHouse if you're processing petabyte-scale data, require distributed query execution across dozens of nodes, need sub-second response times on massive aggregations, and have the operational expertise to manage a cluster.
Embedded analytical SQL database optimized for in-process OLAP queries
Distributed column-oriented OLAP database for petabyte-scale analytics
Structured comparison data for DuckDB vs ClickHouse with 180 attributes.
Query Latency (100M rows, simple aggregation)
Total Cost of Ownership (Annual, 100TB dataset)
Query Latency (10GB dataset, simple aggregate)
Query Latency (1TB dataset, complex join)
Annual Infrastructure Cost (1TB dataset)
Query Performance on 10GB Parquet File (GROUP BY aggregation)
Number of Built-in Data Transformation Methods
Maximum Dataset Size (without disk streaming)
Setup Time (fresh install to first query)
Query Latency (1B rows, COUNT aggregation)
P99 Query Latency (1B rows, aggregation)
Ingestion Latency (Kafka to query-ready)
Query Latency (1B row scan, 10 column aggregate)
Query Latency (1 billion rows, simple SELECT)
Average Query Latency (1 Billion Row Scan)
Average Query Latency (Standard Aggregation)
DuckDB vs ClickHouse — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for DuckDB vs ClickHouse.
When should I choose DuckDB over ClickHouse?
Choose DuckDB if your dataset is under 1TB, you need zero operational overhead, you want a database that embeds directly in your application with no separate server infrastructure, or you're building data analysis tools for Python/R/JavaScript environments. DuckDB is ideal for data scientists, analysts working with local datasets, and teams prioritizing simplicity over distributed scale.
Can DuckDB handle real-time data ingestion like ClickHouse?
DuckDB supports data ingestion but is optimized for analytical queries on static or batch-loaded datasets. ClickHouse excels at real-time event streaming with insert rates of 1M+ rows per second, native Kafka integration, and built-in data replication. For real-time analytics at scale (>100K events/second), ClickHouse is the better choice.
What are the licensing implications of using ClickHouse commercially?
ClickHouse is licensed under AGPL v3, which requires that any modifications or derivative works using ClickHouse must publish their source code. Organizations can: (1) use ClickHouse unmodified under AGPL, (2) purchase a commercial license from ClickHouse Inc. to use proprietary modifications, or (3) use DuckDB's MIT license if source code privacy is critical. Many enterprises choose the commercial license to avoid AGPL obligations.
How do query languages differ between DuckDB and ClickHouse?
DuckDB uses standard SQL with PostgreSQL extensions, making it nearly 100% compatible with PostgreSQL syntax and allowing easy migration. ClickHouse uses ClickHouse SQL, a custom dialect with extensions like specialized aggregation functions (quantiles, groupArrayInsertAt) and lacks some standard features (common table expressions with limited support). Teams familiar with PostgreSQL will find DuckDB's SQL more intuitive, while ClickHouse's SQL is more powerful for specific analytics use cases.
Which database is better for machine learning preprocessing?
DuckDB integrates seamlessly with Python/R/Arrow ecosystems, making it superior for ML preprocessing pipelines. It has native support for DuckDB-Arrow format, Parquet files, and Python UDFs. ClickHouse requires additional ETL steps to export data for ML. For ML workflows, DuckDB is the clear choice due to its tight integration with data science tools and zero operational friction.