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
Embedded analytical SQL database optimized for in-process OLAP queries
Distributed column-oriented OLAP database for petabyte-scale analytics
Deployment Model. Setup Complexity. Query Performance at 10GB. Maximum Practical Dataset Size. SQL Dialect Compatibility. License. Operational Overhead
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.
DuckDB: 1 (single machine) | ClickHouse: 100+
DuckDB: 10-50ms | ClickHouse: 500-2000ms
DuckDB: 4:1 to 8:1 | ClickHouse: 10:1 to 40:1
DuckDB: 10-50MB | ClickHouse: 500-2000MB
DuckDB: 100K-500K single machine | ClickHouse: 1M+ with batching
DuckDB: 95% standard SQL | ClickHouse: 70% standard SQL
DuckDB: Partial (batch insert-optimized) | ClickHouse: N/A
DuckDB: Single-threaded writes only | ClickHouse: N/A
DuckDB: 5 years (since 2019) | ClickHouse: N/A
DuckDB: Parquet, CSV, JSON, Iceberg, Hugging Face | ClickHouse: N/A
DuckDB: Growing (100K+) | ClickHouse: N/A
DuckDB: ~100 GB | ClickHouse: ~1,000,000+ GB (1+ PB)
Query Latency (100M rows, simple aggregation)
DuckDB: 50-200ms | ClickHouse: 500-1500ms
DuckDB: 50-100 MB | ClickHouse: 500-2000 MB
DuckDB: 12+ formats | ClickHouse: 12+ formats (TSV, Native, Avro, Protobuf, etc.)
DuckDB: Python, SQL, C++, R, Julia, Node.js | ClickHouse: N/A
DuckDB: 2-5 (local install) | ClickHouse: N/A
DuckDB: No (single machine) | ClickHouse: N/A
DuckDB: Fully supported | ClickHouse: N/A
DuckDB: C++ (Rust bindings available) | ClickHouse: N/A
DuckDB: 5 (Python, R, Java, Node.js, Go) | ClickHouse: N/A
DuckDB: v0.10.0 (2024) | ClickHouse: N/A
Total Cost of Ownership (Annual, 100TB dataset)
DuckDB: < 1 minute | ClickHouse: 30-120 minutes
Query Latency (10GB dataset, simple aggregate)
Query Latency (1TB dataset, complex join)
DuckDB: 1-5 simultaneous | ClickHouse: N/A
DuckDB: No (requires external integration) | ClickHouse: N/A
DuckDB: Batch-focused only | ClickHouse: N/A
DuckDB: Limited by single machine | ClickHouse: N/A
DuckDB: Python, R, Java, C++, Node.js, Go | ClickHouse: N/A
Annual Infrastructure Cost (1TB dataset)
Query Performance on 10GB Parquet File (GROUP BY aggregation)
DuckDB: 2.1 GB (with compression) | ClickHouse: N/A
DuckDB: 45ms (lightweight binary) | ClickHouse: N/A
Number of Built-in Data Transformation Methods
DuckDB: 65 SQL functions + standard | ClickHouse: N/A
DuckDB: 8.2K questions | ClickHouse: N/A
Maximum Dataset Size (without disk streaming)
DuckDB: 1000+ GB (out-of-core) | ClickHouse: N/A
DuckDB: Yes (ROW_NUMBER, LAG, LEAD, RANK, etc.) | ClickHouse: N/A
DuckDB: Unlimited (disk-dependent) | ClickHouse: N/A
DuckDB: None (local only) | ClickHouse: N/A
DuckDB: 7 (Python, Node.js, Go, Rust, R, Java, C++) | ClickHouse: N/A
DuckDB: No (embedded library) | ClickHouse: N/A
Setup Time (fresh install to first query)
DuckDB: 280+ contributors | ClickHouse: N/A
DuckDB: No (single-node only) | ClickHouse: N/A
DuckDB: No (batch only) | ClickHouse: N/A
DuckDB: 200 MB (compressed) | ClickHouse: N/A
DuckDB: Full ANSI SQL + extensions | ClickHouse: N/A
DuckDB: 7 (requires SQL) | ClickHouse: 7/10 (moderate-hard)
DuckDB: 2019 | ClickHouse: 2016 (Yandex)
DuckDB: 512MB standalone | ClickHouse: N/A
DuckDB: 0.1-0.5 seconds | ClickHouse: N/A
DuckDB: 2-5 (download and run) | ClickHouse: N/A
DuckDB: 5 (SQL, Python, R, C++, Go) | ClickHouse: N/A
DuckDB: 10-100 (vectorized) | ClickHouse: N/A
DuckDB: < 1 minute | ClickHouse: 30-60 minutes
DuckDB: ~1TB (limited by single machine) | ClickHouse: Petabytes (1000+ TB)
Query Latency (1B rows, COUNT aggregation)
DuckDB: 8-15x for analytics | ClickHouse: 10-40x for analytics
DuckDB: 20,000+ stars | ClickHouse: 35,000+ stars
DuckDB: MIT - No restrictions | ClickHouse: AGPL - Source disclosure required
DuckDB: 0.1-2 seconds | ClickHouse: 1-5 seconds
DuckDB: 15-25 MB | ClickHouse: 300-500 MB
DuckDB: 99% (PostgreSQL compatible) | ClickHouse: 95% (custom dialect)
DuckDB: Not supported | ClickHouse: Native (Zookeeper/Keeper)
DuckDB: 0.5-1 (embed library) | ClickHouse: 40-100 (cluster setup)
P99 Query Latency (1B rows, aggregation)
Ingestion Latency (Kafka to query-ready)
DuckDB: N/A | ClickHouse: Proprietary ANSI-SQL variant
DuckDB: N/A | ClickHouse: 2-10TB per node
DuckDB: N/A | ClickHouse: Kafka (basic), S3, File
DuckDB: N/A | ClickHouse: ClickHouse Inc (limited SLAs)
DuckDB: N/A | ClickHouse: 1,000,000 events/sec
DuckDB: N/A | ClickHouse: Open-source (free) + optional support
DuckDB: N/A | ClickHouse: Standard SQL with extensions
DuckDB: N/A | ClickHouse: Limited/requires custom logic
DuckDB: N/A | ClickHouse: 1000+ nodes tested
DuckDB: N/A | ClickHouse: Limited (Kafka via TableEngine)
DuckDB: N/A | ClickHouse: No ACID (eventual consistency)
DuckDB: N/A | ClickHouse: 100TB+ efficiently
DuckDB: N/A | ClickHouse: 95% (PostgreSQL-compatible)
DuckDB: N/A | ClickHouse: Standard SQL; requires manual time bucketing
DuckDB: N/A | ClickHouse: 100,000-500,000 events/sec
DuckDB: N/A | ClickHouse: 1000TB+ (unlimited with tiering)
Query Latency (1B row scan, 10 column aggregate)
DuckDB: N/A | ClickHouse: 1 (can run standalone)
DuckDB: N/A | ClickHouse: Unlimited (resource-based)
DuckDB: N/A | ClickHouse: Microseconds to milliseconds
DuckDB: N/A | ClickHouse: 5-10 (via third-party)
Query Latency (1 billion rows, simple SELECT)
DuckDB: N/A | ClickHouse: $500 (ClickHouse Cloud starter)
DuckDB: N/A | ClickHouse: 4+ (AWS, Azure, GCP, on-premise)
DuckDB: N/A | ClickHouse: 60-300 (manual cluster resize required)
DuckDB: N/A | ClickHouse: MySQL-compatible with ClickHouse extensions
Average Query Latency (1 Billion Row Scan)
DuckDB: N/A | ClickHouse: 1440 (self-managed) / 60 (managed)
DuckDB: N/A | ClickHouse: 99.0% (self-managed) / 99.95% (managed)
DuckDB: N/A | ClickHouse: 3 (S3, Kinesis via 3rd party, basic)
DuckDB: N/A | ClickHouse: No (third-party integration required)
DuckDB: N/A | ClickHouse: Native optimization, ideal for billions of events
DuckDB: N/A | ClickHouse: 99.5% (self-hosted dependent)
DuckDB: N/A | ClickHouse: 1-2 components
Average Query Latency (Standard Aggregation)
DuckDB: N/A | ClickHouse: Replica, Shard (simplified)
DuckDB: N/A | ClickHouse: 3-5 seconds (batched)
How to choose between DuckDB and ClickHouse
DuckDB vs ClickHouse — Structured Comparison Data
Machine-readable comparison dataset for DuckDB vs ClickHouse. Contains 180 attribute comparisons, 5 FAQ pairs.
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.
How to Choose Between DuckDB and ClickHouse
Deployment Model: DuckDB has Embedded/In-process, while ClickHouse has Distributed cluster-based.
Setup Complexity: DuckDB has Minimal (single file), while ClickHouse has Complex (requires cluster management).
Query Performance at 10GB: DuckDB has < 100ms typical, while ClickHouse has < 50ms typical.
Consider: Maximum Practical Dataset Size
Maximum Practical Dataset Size: DuckDB has Up to 1TB (single machine), while ClickHouse has Petabytes (distributed).
SQL Dialect Compatibility: DuckDB has Standard SQL + PostgreSQL extensions, while ClickHouse has ClickHouse SQL + some PostgreSQL compatibility.
License: DuckDB has Open Source (MIT), while ClickHouse has Open Source (AGPL) + Commercial.
Operational Overhead: DuckDB has Minimal (no cluster management), while ClickHouse has High (replication, sharding, monitoring).