ClickHouse is a distributed column-store database optimized for massive analytical workloads across clusters, while DuckDB is an embedded analytical database designed for single-machine OLAP queries with minimal setup. ClickHouse scales to petabytes; DuckDB prioritizes simplicity and runs within application processes.
ClickHouse vs DuckDB — Side-by-side comparison on A Versus B
Choose ClickHouse if you need to analyze terabytes across multiple servers, require high availability, or operate real-time analytics infrastructure at enterprise scale. Choose DuckDB if you need fast local analytics, are building data science workflows, integrating analytical queries into applications, or want zero operational overhead.
Distributed column-oriented OLAP database for petabyte-scale analytics
Embedded analytical SQL database optimized for in-process OLAP queries
Structured comparison data for ClickHouse vs DuckDB with 180 attributes.
P99 Query Latency (1B rows, aggregation)
Ingestion Latency (Kafka to query-ready)
Query Latency (100M rows, simple aggregation)
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)
Query Latency (1B rows, COUNT 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)
ClickHouse vs DuckDB — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for ClickHouse vs DuckDB.
Can DuckDB replace ClickHouse for my analytics?
Only if your dataset is under 1TB and you need single-machine analytics. DuckDB excels at this scale with faster query times and zero setup. ClickHouse is required for multi-TB datasets, distributed systems, or 24/7 real-time ingestion. DuckDB cannot cluster across machines.
DuckDB is free and open-source with no infrastructure costs. ClickHouse is free open-source but requires significant DevOps/infrastructure investment (servers, networking, monitoring). A 3-node ClickHouse cluster costs $500-2,000/month in cloud hosting; DuckDB embedded in an app costs nothing extra.
DuckDB wins for SQL compatibility—it follows PostgreSQL standards, making migration easier and supporting more third-party tools. ClickHouse uses a custom SQL dialect that's ~95% standard but requires learning proprietary syntax (e.g., different JOIN mechanics, SETTINGS).
Can I migrate from DuckDB to ClickHouse later?
Yes, but with caveats. Data exports are straightforward (CSV, Parquet), but you'll need to rewrite queries due to SQL dialect differences. The architecture shift (embedded → distributed) requires application redesign. Migration from ClickHouse to DuckDB is practical only for downsizing datasets.
DuckDB is faster on single machines (0.1-2s for 1TB) due to lower overhead. ClickHouse matches this on distributed datasets but adds network latency. For local analysis under 100GB, DuckDB is 2-5x faster. For 100TB+, ClickHouse's parallelization wins.