ClickHouse vs DuckDB
ClickHouse
Columnar OLAP database engineered for rapid analytical queries on large historical datasets with extreme compression.
Large enterprises, data warehousing teams, analytics platforms serving 100+ concurrent users
DuckDB
Columnar SQL database engine optimized for OLAP queries and analytics on large datasets
Data scientists, analytics engineers, embedded analytics in applications, local ETL workflows
Short Answer
ClickHouse is a distributed column-store database optimized for analytical queries on massive datasets across multiple servers, while DuckDB is an embedded in-process SQL database designed for analytical workloads on single machines. ClickHouse excels at petabyte-scale OLAP with horizontal scaling, whereas DuckDB prioritizes ease of use and performance for data analysis without infrastructure overhead.
Our Verdict
AI-assistedChoose ClickHouse if you need to analyze petabyte-scale datasets across distributed infrastructure, require fault tolerance, or are building a shared analytics platform serving multiple teams. Choose DuckDB if you're performing local analytical queries, building data science workflows, need instant setup without DevOps overhead, or are embedding analytics in applications.
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Choose ClickHouse if
Large enterprises, data warehousing teams, analytics platforms serving 100+ concurrent users
Choose DuckDB if
Data scientists, analytics engineers, embedded analytics in applications, local ETL workflows
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Key Differences at a Glance
Key Facts & Figures
| Metric | ClickHouse | DuckDB | Diff |
|---|---|---|---|
| Query Latency (1 billion rows)(seconds) | 1.2 seconds | — | — |
| Monthly Cost (100 GB compressed)(USD) | $150 | — | — |
| Ingestion Throughput(events/sec) | 1,000,000 events/sec | — | — |
| Compression Ratio(ratio) | 8:1-12:1 | — | — |
| Learning Curve (1-10 scale)(difficulty score) | 7/10 (moderate-hard) | — | — |
| Query Latency (1GB aggregation)(milliseconds) | 500-2000ms | 10-50ms | +4067% |
| Compression Ratio (typical)(ratio) | 10:1 to 40:1 | 4:1 to 8:1 | +317% |
| Memory Required (minimal)(MB) | 500-2000MB | 10-50MB | +4067% |
| Ingest Throughput(million rows/second) | 1-5 million rows/sec | 10-50 million rows/sec | -90% |
| Setup Time to First Query(days) | 30-120 minutes | < 1 minute | +14900% |
| SQL Standard Compliance(percent) | 70% ANSI SQL | 95% ANSI SQL | -26% |
| Query Latency (P99)(milliseconds) | 50-200ms (historical) | — | — |
| Ingestion Latency (end-to-end)(milliseconds) | 1000-10000ms | — | — |
| Memory Usage per Query(MB) | 50-200MB | — | — |
| Maximum Cluster Size(petabytes) | 1000+ | 1 (single machine) | +99900% |
| Typical Cost per TB/year(USD) | $800-1500 | — | — |
| Ingestion Latency(seconds) | 10-60 seconds | — | — |
| Query Latency (100M rows)(milliseconds) | 50-500ms | — | — |
| Data Compression Ratio(x) | 10-100x | 5-8x | +746% |
| Maximum Cluster Nodes(nodes) | 1000+ nodes tested | — | — |
| GitHub Stars (2026)(count) | 34,000+ | 18,500+ | +84% |
| Typical Maximum Dataset Size(GB) | ~1,000,000+ GB (1+ PB) | ~100 GB | +999900% |
| Idle Memory Usage(MB) | 500-2000 MB | 50-100 MB | +1567% |
| Supported Data Formats(formats) | 12+ formats (TSV, Native, Avro, Protobuf, etc.) | 12+ formats | — |
| Data Ingestion Latency(minutes) | 15-60 minutes (batch) | — | — |
| Query Latency (100M rows, simple aggregation)(milliseconds) | 500-1500ms | 50-200ms | +700% |
| Typical Storage Cost(USD per TB per month) | $20-40 | — | — |
| Max Recommended Dataset Size(terabytes) | 100TB+ efficiently | — | — |
| SQL Feature Completeness(percentage) | 95% (PostgreSQL-compatible) | — | — |
| Max Ingestion Throughput(events/second) | 100,000-500,000 events/sec | — | — |
| Storage Cost per TB/Month(USD) | $50-150 | — | — |
| Typical Node Memory(GB) | 8-32GB | — | — |
| Minimum Recommended Cluster Size(nodes) | 3-5 nodes | — | — |
| Max Dataset Size (Practical)(TB) | 1000TB+ (unlimited with tiering) | — | — |
| Aggregation Query Time (1 billion rows)(seconds) | 0.5-2 seconds | 0.5-2 seconds | — |
| Memory Usage (1TB analytical dataset)(GB) | 10-50 GB | 10-50 GB | — |
| Years in Production(years) | 5 years (since 2019) | 5 years (since 2019) | — |
| Typical Query Latency (1GB dataset)(milliseconds) | 50-200ms | 50-200ms | — |
| Maximum Practical Data Size(GB) | 256GB | 256GB | — |
| Memory Required Per Query(MB) | 10-50MB | 10-50MB | — |
| Setup Time for Basic Analytics(minutes) | 1-5 minutes | 1-5 minutes | — |
| Query Latency (1GB CSV)(milliseconds) | 150-500ms | 150-500ms | — |
| Maximum Scalable Dataset Size(GB) | 10-50 | 10-50 | — |
| Minimum Memory Requirement(GB) | 0.1-0.5 GB | 0.1-0.5 GB | — |
| Setup Time (from scratch)(minutes) | 2-5 (local install) | 2-5 (local install) | — |
| Aggregation Query Speed (10M rows)(seconds) | 2.3s | 2.3s | — |
| Memory Usage (1GB dataset)(MB) | 450MB | 450MB | — |
| SQL Standard Coverage(% of SQL:2016) | 95% | 95% | — |
| Language Bindings Supported(count) | 5 (Python, R, Java, Node.js, Go) | 5 (Python, R, Java, Node.js, Go) | — |
| Total Cost of Ownership (Annual, 100TB dataset)(USD) | $0 | $0 | — |
| Query Latency (10GB dataset, simple aggregate)(seconds) | 0.3 seconds | 0.3 seconds | — |
| Query Latency (1TB dataset, complex join)(seconds) | 3-5 seconds | 3-5 seconds | — |
| Maximum Supported Dataset Size(TB) | 2 TB (local) | 2 TB (local) | — |
| Concurrent User Queries(users) | 1-5 simultaneous | 1-5 simultaneous | — |
| GitHub Stars (Community Traction)(stars) | 18,500+ | 18,500+ | — |
| Setup Time (Minutes)(minutes) | 5-10 | 5-10 | — |
| Query Latency on 1GB Dataset(milliseconds) | 10-50 | 10-50 | — |
| Minimum Cluster Nodes Required(nodes) | 1 | 1 | — |
| Supported Programming Languages(languages) | Python, R, Java, C++, Node.js, Go | Python, R, Java, C++, Node.js, Go | — |
| Annual Infrastructure Cost (1TB dataset)(USD) | 0-5,000 | 0-5,000 | — |
| Query Performance on 10GB Parquet File (GROUP BY aggregation)(seconds) | 1.2 seconds | 1.2 seconds | — |
| Memory Usage (10GB dataset analysis)(GB) | 2.1 GB (with compression) | 2.1 GB (with compression) | — |
| Startup/Import Time(milliseconds) | 45ms (lightweight binary) | 45ms (lightweight binary) | — |
| Number of Built-in Data Transformation Methods(count) | 65 SQL functions + standard | 65 SQL functions + standard | — |
| Stack Overflow Questions (as of 2026)(thousands) | 8.2K questions | 8.2K questions | — |
| Maximum Dataset Size (without disk streaming)(GB) | 1000+ GB (out-of-core) | 1000+ GB (out-of-core) | — |
| Time to Analyze 100MB CSV (end-to-end)(seconds) | 3.8 seconds | 3.8 seconds | — |
All figures sourced from publicly available data. Last updated Jun 2026.
Key Differences
ClickHouse
Distributed client-server with horizontal scaling🏆
DuckDB
Embedded in-process single-instance
ClickHouse
Petabyte-scale (1000+ TB)🏆
DuckDB
Terabyte-scale (up to ~100 TB practical)
ClickHouse
Requires cluster configuration and DevOps
DuckDB
Zero setup - embedded library🏆
ClickHouse
~500-2000ms for aggregation queries
DuckDB
~10-50ms for aggregation queries🏆
ClickHouse
Large-scale distributed analytics infrastructure
DuckDB
Local data science and analytics workflows
ClickHouse
500MB-2GB per node minimum
DuckDB
10-50MB for typical operations🏆
ClickHouse
Custom dialect (ClickHouse SQL)
DuckDB
Standard SQL (ANSI-compliant)🏆
Full Comparison
| Attribute |  ClickHouse |  DuckDB |
|---|---|---|
| Query Latency (1 billion rows)(seconds) | 1.2 seconds | — |
| Ingestion Throughput(events/sec) | 1,000,000 events/sec | — |
| Query Latency (1GB aggregation)(milliseconds) | 500-2000ms | 10-50ms |
| Ingest Throughput(million rows/second) | 1-5 million rows/sec | 10-50 million rows/sec |
| Query Latency (P99)(milliseconds) | 50-200ms (historical) | — |
Show 14 more attributesIngestion Latency(seconds) 10-60 seconds — Query Latency (100M rows)(milliseconds) 50-500ms — Query Latency (100M rows, simple aggregation)(milliseconds) 500-1500ms 50-200ms Max Ingestion Throughput(events/second) 100,000-500,000 events/sec — Aggregation Query Time (1 billion rows)(seconds) 0.5-2 seconds — Typical Query Latency (1GB dataset)(milliseconds) 50-200ms — Query Latency (1GB CSV)(milliseconds) 150-500ms — Aggregation Query Speed (10M rows)(seconds) 2.3s — Query Latency (10GB dataset, simple aggregate)(seconds) 0.3 seconds — Query Latency (1TB dataset, complex join)(seconds) 3-5 seconds — Query Latency on 1GB Dataset(milliseconds) 10-50 — Concurrent Queries Supported(queries) Limited by single machine — Query Performance on 10GB Parquet File (GROUP BY aggregation)(seconds) 1.2 seconds — Startup/Import Time(milliseconds) 45ms (lightweight binary) — | ||
| Monthly Cost (100 GB compressed)(USD) | $150 | — |
| Setup Time(minutes) | 240 minutes | — |
| Learning Curve (1-10 scale)(difficulty score) | 7/10 (moderate-hard) | — |
| Setup Time for Basic Analytics(minutes) | 1-5 minutes | — |
| Setup Time (from scratch)(minutes) | 2-5 (local install) | — |
| Data Retention for Time-Travel(days) | Not native | — |
| Native SQL Support | Standard SQL with extensions | — |
| Streaming Integration | Limited (Kafka via TableEngine) | — |
| Transaction Support(consistency level) | No ACID (eventual consistency) | — |
| SQL Feature Completeness(percentage) | 95% (PostgreSQL-compatible) | — |
Show 5 more attributesTime-Series Aggregation Support(native features) Standard SQL; requires manual time bucketing — Native Format Support Parquet, CSV, JSON, Iceberg, Hugging Face — Built-in Machine Learning Capabilities No (requires external integration) — Real-time Streaming Ingestion Batch-focused only — Supported Programming Languages(languages) Python, R, Java, C++, Node.js, Go — | ||
| Compression Ratio(ratio) | 8:1-12:1 | — |
| Licensing Model | Open-source (free) + optional support | — |
| Typical Cost per TB/year(USD) | $800-1500 | — |
| Compression Ratio (typical)(ratio) | 10:1 to 40:1 | 4:1 to 8:1 |
| Memory Usage per Query(MB) | 50-200MB | — |
| Data Compression Ratio(x) | 10-100x | 5-8x |
| Memory Required (minimal)(MB) | 500-2000MB | 10-50MB |
| Setup Time to First Query(days) | 30-120 minutes | < 1 minute |
| Core Language | C++ (Rust bindings available) | — |
| SQL Standard Compliance(percent) | 70% ANSI SQL | 95% ANSI SQL |
| Supported Data Formats(formats) | 12+ formats (TSV, Native, Avro, Protobuf, etc.) | 12+ formats |
| Ingestion Latency (end-to-end)(milliseconds) | 1000-10000ms | — |
| Maximum Cluster Size(petabytes) | 1000+ | 1 (single machine) |
| Maximum Cluster Nodes(nodes) | 1000+ nodes tested | — |
| Typical Maximum Dataset Size(GB) | ~1,000,000+ GB (1+ PB) | ~100 GB |
| Max Recommended Dataset Size(terabytes) | 100TB+ efficiently | — |
| Max Dataset Size (Practical)(TB) | 1000TB+ (unlimited with tiering) | — |
Show 6 more attributesDatabase File Size Limit(TB) Unlimited — Maximum Practical Data Size(GB) 256GB — Maximum Scalable Dataset Size(GB) 10-50 — Maximum Supported Dataset Size(TB) 2 TB (local) — Concurrent User Queries(users) 1-5 simultaneous — Maximum Dataset Size (without disk streaming)(GB) 1000+ GB (out-of-core) — | ||
| Multi-tenancy Isolation | Limited/requires custom logic | — |
| Multi-machine Distributed Computing(capability) | Not supported | — |
| SQL Compatibility | Full ANSI SQL (95%+ compatibility) | — |
| Primary Language Support | Python, SQL, C++, R, Julia, Node.js | — |
| GitHub Stars (2026)(count) | 34,000+ | 18,500+ |
| Idle Memory Usage(MB) | 500-2000 MB | 50-100 MB |
| Memory Usage (1TB analytical dataset)(GB) | 10-50 GB | — |
| Memory Required Per Query(MB) | 10-50MB | — |
| Memory Usage (1GB dataset)(MB) | 450MB | — |
| Memory Usage (10GB dataset analysis)(GB) | 2.1 GB (with compression) | — |
| Data Ingestion Latency(minutes) | 15-60 minutes (batch) | — |
| Typical Storage Cost(USD per TB per month) | $20-40 | — |
| Storage Cost per TB/Month(USD) | $50-150 | — |
| Total Cost of Ownership (Annual, 100TB dataset)(USD) | $0 | — |
| Annual Infrastructure Cost (1TB dataset)(USD) | 0-5,000 | — |
| Typical Node Memory(GB) | 8-32GB | — |
| Minimum Memory Requirement(GB) | 0.1-0.5 GB | — |
| Minimum Cluster Nodes Required(nodes) | 1 | — |
| Minimum Recommended Cluster Size(nodes) | 3-5 nodes | — |
| Setup Time (Minutes)(minutes) | 5-10 | — |
| ACID Compliance Level | Partial (batch insert-optimized) | — |
| Fault Tolerance(capability) | No (single machine) | — |
| Concurrent Write Support | Single-threaded writes only | — |
| Years in Production(years) | 5 years (since 2019) | — |
| Latest Stable Version | v0.10.0 (2024) | — |
| Production Deployments (Estimated)(organizations) | Growing (100K+) | — |
| SQL Standard Coverage(% of SQL:2016) | 95% | — |
| ACID Transactions | Fully supported | — |
| Language Bindings Supported(count) | 5 (Python, R, Java, Node.js, Go) | — |
| GitHub Stars (Community Traction)(stars) | 18,500+ | — |
| Number of Built-in Data Transformation Methods(count) | 65 SQL functions + standard | — |
| Stack Overflow Questions (as of 2026)(thousands) | 8.2K questions | — |
| SQL Window Function Support(yes/no) | Yes (ROW_NUMBER, LAG, LEAD, RANK, etc.) | — |
| Time to Analyze 100MB CSV (end-to-end)(seconds) | 3.8 seconds | — |
ClickHouseDuckDBShow 14 more attributes
Show 5 more attributes
Show 6 more attributes
Visual Comparison
Side-by-side comparison of numeric attributes
Pros & Cons
ClickHouse
Pros
- Handles petabyte-scale datasets with linear horizontal scaling across 1000+ nodes
- Compression ratios of 10:1 to 40:1 reduce storage costs significantly
- Real-time inserts with immediate query visibility (microsecond latency)
- Advanced TTL management and automatic data tiering reduce operational costs
- Battle-tested at scale by Yandex, Uber, and major tech companies handling >1 trillion events/day
Cons
- Requires significant DevOps expertise and infrastructure investment to operate
- Non-standard SQL dialect requires query translation and learning curve
DuckDB
Pros
- Zero setup - runs as in-process library in Python, R, C++, or Node.js
- 10-100x faster than pandas for analytical queries on local datasets
- Native Parquet and CSV support with automatic type inference
- Minimal memory footprint (10-50MB) with intelligent memory management
- ANSI SQL-compliant with familiar PostgreSQL dialect
Cons
- Limited to single-machine deployments with practical ceiling around 100TB
- No built-in replication, clustering, or fault tolerance capabilities
Frequently Asked Questions
Use ClickHouse for multi-terabyte datasets requiring 24/7 uptime, distributed query processing, and multiple concurrent users. Use DuckDB for local analytical workflows, data science projects, datasets under 100TB, and applications where simplicity matters more than clustering.
Resources & Learn More
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Where to Buy
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