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
Distributed column-oriented OLAP database for petabyte-scale analytics
Embedded analytical SQL database optimized for in-process OLAP queries
Deployment Model. Scalability Range. Setup Complexity. Query Speed (1TB dataset). Multi-node Support. SQL Compatibility. Use Case Focus
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.
P99 Query Latency (1B rows, aggregation)
Ingestion Latency (Kafka to query-ready)
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: 7/10 (moderate-hard) | DuckDB: 7 (requires SQL)
ClickHouse: 500-2000ms | DuckDB: 10-50ms
ClickHouse: 10:1 to 40:1 | DuckDB: 4:1 to 8:1
ClickHouse: 500-2000MB | DuckDB: 10-50MB
ClickHouse: 1M+ with batching | DuckDB: 100K-500K single machine
ClickHouse: 30-120 minutes | DuckDB: < 1 minute
ClickHouse: 70% standard SQL | DuckDB: 95% standard SQL
ClickHouse: 100+ | DuckDB: 1 (single machine)
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: ~1,000,000+ GB (1+ PB) | DuckDB: ~100 GB
ClickHouse: 500-2000 MB | DuckDB: 50-100 MB
ClickHouse: 12+ formats (TSV, Native, Avro, Protobuf, etc.) | DuckDB: 12+ formats
Query Latency (100M rows, simple aggregation)
ClickHouse: 500-1500ms | DuckDB: 50-200ms
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) | DuckDB: N/A
Query Latency (1B row scan, 10 column aggregate)
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) | DuckDB: N/A
Query Latency (1 billion rows, simple SELECT)
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 | DuckDB: N/A
Average Query Latency (1 Billion Row Scan)
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 | DuckDB: N/A
Average Query Latency (Standard Aggregation)
ClickHouse: Replica, Shard (simplified) | DuckDB: N/A
ClickHouse: 3-5 seconds (batched) | DuckDB: N/A
ClickHouse: 2016 (Yandex) | DuckDB: 2019
ClickHouse: 30-60 minutes | DuckDB: < 1 minute
Query Latency (1B rows, COUNT aggregation)
ClickHouse: 10-40x for analytics | DuckDB: 8-15x for analytics
ClickHouse: 40-100 (cluster setup) | DuckDB: 0.5-1 (embed library)
ClickHouse: 35,000+ stars | DuckDB: 20,000+ stars
ClickHouse: AGPL - Source disclosure required | DuckDB: MIT - No restrictions
ClickHouse: Petabytes (1000+ TB) | DuckDB: ~1TB (limited by single machine)
ClickHouse: 1-5 seconds | DuckDB: 0.1-2 seconds
ClickHouse: 300-500 MB | DuckDB: 15-25 MB
ClickHouse: 95% (custom dialect) | DuckDB: 99% (PostgreSQL compatible)
ClickHouse: Native (Zookeeper/Keeper) | DuckDB: Not supported
ClickHouse: N/A | 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: 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)
Total Cost of Ownership (Annual, 100TB dataset)
Query Latency (10GB dataset, simple aggregate)
Query Latency (1TB dataset, complex join)
ClickHouse: N/A | 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
Annual Infrastructure Cost (1TB dataset)
Query Performance on 10GB Parquet File (GROUP BY aggregation)
ClickHouse: N/A | DuckDB: 2.1 GB (with compression)
ClickHouse: N/A | DuckDB: 45ms (lightweight binary)
Number of Built-in Data Transformation Methods
ClickHouse: N/A | DuckDB: 65 SQL functions + standard
ClickHouse: N/A | DuckDB: 8.2K questions
Maximum Dataset Size (without disk streaming)
ClickHouse: N/A | 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)
Setup Time (fresh install to first query)
ClickHouse: N/A | 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: 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)
How to choose between ClickHouse and DuckDB
ClickHouse vs DuckDB — Structured Comparison Data
Machine-readable comparison dataset for ClickHouse vs DuckDB. Contains 180 attribute comparisons, 5 FAQ pairs.
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.
How to Choose Between ClickHouse and DuckDB
Deployment Model: ClickHouse has Distributed server architecture (client-server), while DuckDB has Embedded in-process database.
Scalability Range: ClickHouse has Petabyte-scale across clusters, while DuckDB has Single machine (up to available RAM/disk).
Setup Complexity: ClickHouse has Requires cluster management, replication config, while DuckDB has Single file, zero config installation.
Query Speed (1TB dataset): ClickHouse has 1-5 seconds (distributed), while DuckDB has 0.1-2 seconds (single machine).
Multi-node Support: ClickHouse has Native built-in replication and sharding, while DuckDB has Not supported; single-machine only.
SQL Compatibility: ClickHouse has ClickHouse SQL dialect (95% SQL standard), while DuckDB has PostgreSQL-compatible SQL.
Use Case Focus: ClickHouse has Real-time metrics, logs, time-series at scale, while DuckDB has Local analytics, data science, prototyping.