DuckDB vs ClickHouse 2026: Which OLAP Database?

A Versus B

software

Updated July 9, 2026

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

Embedded in-process OLAP database optimized for analytical queries on local datasets

Data analysts, researchers, embedded analytics in applications, rapid prototyping, and teams analyzing datasets under 1TB on a single machine

Score71%

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VS

ClickHouse

Distributed columnar OLAP database designed for petabyte-scale real-time analytics

Large enterprises processing terabytes to petabytes, real-time monitoring systems, event analytics platforms, and organizations with dedicated infrastructure teams

Score71%

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Quick Answer

AI Summary

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.

Our Verdict

AI-assisted

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.

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DuckDB

7.6/10

ClickHouse

7.4/10

Choose DuckDB if

Best pick

Data analysts, researchers, embedded analytics in applications, rapid prototyping, and teams analyzing datasets under 1TB on a single machine

Choose ClickHouse if

Large enterprises processing terabytes to petabytes, real-time monitoring systems, event analytics platforms, and organizations with dedicated infrastructure teams

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Key Differences at a Glance

Deployment Model:Embedded/In-process vs Distributed cluster-based
Setup Complexity:✓ DuckDB wins(Minimal (single file) vs Complex (requires cluster management))
Query Performance at 10GB:✓ ClickHouse wins(< 50ms typical vs < 100ms typical)

See all 7 differences

Key Facts & Figures

135 numeric metrics compared

Metric	DuckDB	ClickHouse	Ratio
Maximum Cluster Size(nodes)	1 (single machine)	100+
Query Latency (1GB aggregation)(milliseconds)	10-50ms	500-2000ms
Compression Ratio (typical)(ratio)	4:1 to 8:1	10:1 to 40:1
Memory Required (minimal)(MB)	10-50MB	500-2000MB
Ingest Throughput(million rows/second)	10-50 million rows/sec	1-5 million rows/sec
SQL Standard Compliance(% compatibility)	95% standard SQL	70% standard SQL
GitHub Stars (2026)(stars)	18,500+	35,000+
Aggregation Query Time (1 billion rows)(seconds)	0.5-2 seconds	—	—
Memory Usage (1TB analytical dataset)(GB)	10-50 GB	—	—
Years in Production(years)	5 years (since 2019)	—	—
Typical Maximum Dataset Size(GB)	~100 GB	~1,000,000+ GB (1+ PB)
Query Latency (100M rows, simple aggregation)(milliseconds)	50-200ms	500-1500ms
Idle Memory Usage(MB)	50-100 MB	500-2000 MB
Supported Data Formats(formats)	12+ formats	12+ formats (TSV, Native, Avro, Protobuf, etc.)
Typical Query Latency (1GB dataset)(milliseconds)	50-200ms	—	—
Maximum Practical Data Size(TB)	1,000	—	—
Memory Required Per Query(MB)	10-50MB	—	—
Setup Time for Basic Analytics(minutes)	1-5 minutes	—	—
Query Latency (1GB CSV)(milliseconds)	150-500ms	—	—
Maximum Scalable Dataset Size(GB)	10-50	—	—
Setup Time (from scratch)(minutes)	2-5 (local install)	—	—
Aggregation Query Speed (10M rows)(seconds)	2.3s	—	—
Memory Usage (1GB dataset)(MB)	450MB	—	—
SQL Standard Coverage(% of SQL:2016)	95%	—	—
Language Bindings Supported(count)	5 (Python, R, Java, Node.js, Go)	—	—
Total Cost of Ownership (Annual, 100TB dataset)(USD)	$0	—	—
Setup Time to First Query(minutes)	< 1 minute	30-120 minutes
Query Latency (10GB dataset, simple aggregate)(seconds)	0.3 seconds	—	—
Query Latency (1TB dataset, complex join)(seconds)	3-5 seconds	—	—
Maximum Supported Dataset Size(TB)	2 TB (local)	—	—
Concurrent User Queries(users)	1-5 simultaneous	—	—
GitHub Stars (Community Traction)(thousands)	18,500+	—	—
Setup Time (Minutes)(minutes)	5-10	—	—
Query Latency on 1GB Dataset(milliseconds)	10-50	—	—
Minimum Cluster Nodes Required(nodes)	1	—	—
Supported Programming Languages(languages)	Python, R, Java, C++, Node.js, Go	—	—
Annual Infrastructure Cost (1TB dataset)(USD)	0-5,000	—	—
Query Performance on 10GB Parquet File (GROUP BY aggregation)(seconds)	1.2 seconds	—	—
Memory Usage (10GB dataset analysis)(GB)	2.1 GB (with compression)	—	—
Startup/Import Time(milliseconds)	45ms (lightweight binary)	—	—
Number of Built-in Data Transformation Methods(count)	65 SQL functions + standard	—	—
Stack Overflow Questions (as of 2026)(thousands)	8.2K questions	—	—
Maximum Dataset Size (without disk streaming)(GB)	1000+ GB (out-of-core)	—	—
Time to Analyze 100MB CSV (end-to-end)(seconds)	3.8 seconds	—	—
Base Monthly Cost(USD)	Free	—	—
Global Edge Locations(count)	None (local only)	—	—
OLAP Query Speed (1GB dataset)(milliseconds)	50-100ms	—	—
Supported Languages(count)	7 (Python, Node.js, Go, Rust, R, Java, C++)	—	—
Setup Time (fresh install to first query)(minutes)	2 minutes	—	—
Minimum Memory Requirement(GB)	0.1GB	—	—
Single Query Latency (10GB dataset)(seconds)	0.3 seconds	—	—
Maximum Practical Data Scale(TB)	1TB	—	—
Supported SQL Features(percentage of ANSI SQL)	95%	—	—
Community Contributors (2025)(active developers)	280+ contributors	—	—
GitHub Stars(stars)	22000 stars	—	—
Time to Learn (for SQL users)(hours)	4 hours	—	—
Ingestion Rate (events/second)(events/sec)	50,000	—	—
Query Latency (1B rows)(seconds)	0.5-2	—	—
Maximum Recommended Dataset Size(rows)	1	—	—
Deployment Time(seconds)	0.08	—	—
Minimum Cluster Size(nodes)	1	—	—
Memory Per Node(GB per 1M events/sec)	2-64 (varies)	—	—
Query Speed (1GB CSV aggregation)(seconds)	1.2 seconds	—	—
Maximum Practical Dataset Size(petabytes)	100+ GB	—	—
Memory Usage (1GB CSV load)(MB)	200 MB (compressed)	—	—
Built-in Statistical Functions(count)	200+	—	—
Learning Curve (1-10 scale)(difficulty)	7 (requires SQL)	7/10 (moderate-hard)
Stack Overflow Questions Answered(count)	3,200	—	—
First Release Year	2019	2016 (Yandex)
Query Latency on 100GB Dataset(seconds)	0.1-0.5 seconds	—	—
Memory Usage for 10GB Query(GB)	0.5-1GB	—	—
Time to First Query (fresh install)(minutes)	2-5 (download and run)	—	—
Number of Supported Languages(languages)	5 (SQL, Python, R, C++, Go)	—	—
Community GitHub Stars (2026)(stars)	21,800+	—	—
Query Processing Throughput (GBps)(GB/s)	10-100 (vectorized)	—	—
Maximum Dataset Size(TB)	~1 TB (single machine)	1,000+ TB (distributed)
Query Latency (1B rows, COUNT aggregation)(milliseconds)	80-150ms	20-100ms
Data Compression Ratio(x)	4-8x compression	40-100x compression
Community Size (GitHub Stars)(stars)	20,000+ stars	35,000+ stars
P99 Query Latency (1B rows, aggregation)(milliseconds)	50-200ms	50-200ms
Ingestion Latency (Kafka to query-ready)(seconds)	5-30 seconds	5-30 seconds
Maximum Recommended Node Storage(TB)	2-10TB per node	2-10TB per node
Query Latency (1 billion rows)(seconds)	1.2 seconds	1.2 seconds
Monthly Cost (100 GB compressed)(USD)	$150	$150
Ingestion Throughput(events/sec)	1,000,000 events/sec	1,000,000 events/sec
Compression Ratio(ratio)	8:1-12:1	8:1-12:1
Query Latency (p99)(milliseconds)	200-500ms	200-500ms
Ingestion Latency (end-to-end)(milliseconds)	1000-10000ms	1000-10000ms
Memory Usage per Query(MB)	50-200MB	50-200MB
Typical Cost per TB/year(USD)	$800-1500	$800-1500
Ingestion Latency(seconds)	10-60 seconds	10-60 seconds
Query Latency (100M rows)(milliseconds)	50-500ms	50-500ms
Maximum Cluster Nodes(nodes)	1000+ nodes tested	1000+ nodes tested
Typical Storage Cost(USD per TB per month)	$20-40	$20-40
Max Recommended Dataset Size(terabytes)	100TB+ efficiently	100TB+ efficiently
SQL Feature Completeness(percentage)	95% (PostgreSQL-compatible)	95% (PostgreSQL-compatible)
Max Ingestion Throughput(events/second)	100,000-500,000 events/sec	100,000-500,000 events/sec
Storage Cost per TB/month(USD)	$40-60	$40-60
Typical Node Memory(GB)	8-32GB	8-32GB
Minimum Recommended Cluster Size(nodes)	3-5 nodes	3-5 nodes
Max Dataset Size (Practical)(TB)	1000TB+ (unlimited with tiering)	1000TB+ (unlimited with tiering)
Query Latency (1B row scan, 10 column aggregate)(milliseconds)	50-100ms	50-100ms
Storage Cost (per TB/month)(USD)	$15-25	$15-25
Typical Data Compression Ratio(x)	10-40x	10-40x
Minimum Cluster Size (nodes)(nodes)	1 (can run standalone)	1 (can run standalone)
Data Ingestion Latency(seconds)	Microseconds to milliseconds	Microseconds to milliseconds
AWS Service Integration (native)(count)	5-10 (via third-party)	5-10 (via third-party)
GitHub Stars (as of 2026)(stars)	25000+	25000+
Query Latency (1 billion rows, simple SELECT)(milliseconds)	150ms	150ms
Cost per GB Scanned(USD)	$0.015	$0.015
Maximum Ingestion Rate(events/second)	1,000,000	1,000,000
Infrastructure Management Overhead(hours per month)	40-80 hours	40-80 hours
Minimum Monthly Cost (basic setup)(USD)	$500 (ClickHouse Cloud starter)	$500 (ClickHouse Cloud starter)
Cloud Provider Support(providers)	4+ (AWS, Azure, GCP, on-premise)	4+ (AWS, Azure, GCP, on-premise)
Automatic Scaling Time(seconds)	60-300 (manual cluster resize required)	60-300 (manual cluster resize required)
Average Query Latency (1 Billion Row Scan)(ms)	75ms	75ms
Monthly Cost per TB Stored(USD)	$0.09	$0.09
Time to Production Deployment(hours)	1440 (self-managed) / 60 (managed)	1440 (self-managed) / 60 (managed)
Maximum Concurrent Queries(queries/sec)	100,000+	100,000+
Uptime SLA Guarantee(percent)	99.0% (self-managed) / 99.95% (managed)	99.0% (self-managed) / 99.95% (managed)
Native AWS Service Integration(count)	3 (S3, Kinesis via 3rd party, basic)	3 (S3, Kinesis via 3rd party, basic)
Data Ingestion Rate(GB/sec)	1-5	1-5
Average Query Latency (1TB dataset)(milliseconds)	85ms	85ms
Cost per TB Scanned(USD)	$0.01	$0.01
Initial Setup Time(minutes)	14 days	14 days
Max Concurrent Queries (single cluster)(queries)	1,000+	1,000+
Enterprise SLA Availability(percent)	99.5% (self-hosted dependent)	99.5% (self-hosted dependent)
Peak Ingestion Speed(events per second)	100,000-500,000	100,000-500,000
ANSI SQL Compliance(percentage)	95%	95%
Deployment Components(count)	1-2 components	1-2 components
Time to First Query(minutes)	5-10 minutes	5-10 minutes
Average Query Latency (Standard Aggregation)(milliseconds)	250ms	250ms
Streaming Ingestion Latency(seconds)	2-5 seconds	2-5 seconds
Memory Per 1TB Data(GB)	35GB	35GB
Kafka Integration Latency(seconds)	3-5 seconds (batched)	3-5 seconds (batched)

Sourced from publicly available data · Jul 2026

Key Differences

7 attributes compared head-to-head

4DuckDB

DuckDB leads1 tie

2ClickHouse

DuckDB

Attribute

ClickHouse

Embedded/In-process
Deployment Model
Distributed cluster-based
Minimal (single file)(winner)
Setup Complexity
Complex (requires cluster management)
< 100ms typical
Query Performance at 10GB
< 50ms typical(winner)
Up to 1TB (single machine)
Maximum Practical Dataset Size
Petabytes (distributed)(winner)
Standard SQL + PostgreSQL extensions(winner)
SQL Dialect Compatibility
ClickHouse SQL + some PostgreSQL compatibility
Open Source (MIT)(winner)
License
Open Source (AGPL) + Commercial
Minimal (no cluster management)(winner)
Operational Overhead
High (replication, sharding, monitoring)

Deployment Model
DuckDB
Embedded/In-process
ClickHouse
Distributed cluster-based
Setup Complexity
DuckDB
Minimal (single file)(winner)
ClickHouse
Complex (requires cluster management)
Query Performance at 10GB
DuckDB
< 100ms typical
ClickHouse
< 50ms typical(winner)
Maximum Practical Dataset Size
DuckDB
Up to 1TB (single machine)
ClickHouse
Petabytes (distributed)(winner)
SQL Dialect Compatibility
DuckDB
Standard SQL + PostgreSQL extensions(winner)
ClickHouse
ClickHouse SQL + some PostgreSQL compatibility
License
DuckDB
Open Source (MIT)(winner)
ClickHouse
Open Source (AGPL) + Commercial
Operational Overhead
DuckDB
Minimal (no cluster management)(winner)
ClickHouse
High (replication, sharding, monitoring)

Full Comparison

DuckDB vs ClickHouse — attribute comparison table
Attribute	DuckDB	ClickHouse

Maximum Cluster Size(nodes)	1 (single machine)	100+(winner)
Database File Size Limit(TB)	Unlimited	—
Typical Maximum Dataset Size(GB)	~100 GB	~1,000,000+ GB (1+ PB)(winner)
Maximum Practical Data Size(TB)	1,000	—
Maximum Scalable Dataset Size(GB)	10-50	—
Show 12 more attributes 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) — Maximum Practical Data Scale(TB) 1TB — Maximum Practical Dataset Size(petabytes) 100+ GB — Maximum Dataset Size(TB) ~1 TB (single machine) 1,000+ TB (distributed) Maximum Recommended Node Storage(TB) 2-10TB per node — Maximum Cluster Nodes(nodes) 1000+ nodes tested — Max Recommended Dataset Size(terabytes) 100TB+ efficiently — Max Dataset Size (Practical)(TB) 1000TB+ (unlimited with tiering) — Maximum Concurrent Queries(queries/sec) 100,000+ — Max Concurrent Queries (single cluster)(queries) 1,000+ —

Query Latency (1GB aggregation)(milliseconds)	10-50ms(winner)	500-2000ms
Ingest Throughput(million rows/second)	10-50 million rows/sec(winner)	1-5 million rows/sec
Aggregation Query Time (1 billion rows)(seconds)	0.5-2 seconds	—
Query Latency (100M rows, simple aggregation)(milliseconds)	50-200ms(winner)	500-1500ms
Idle Memory Usage(MB)	50-100 MB(winner)	500-2000 MB
Show 35 more attributes 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) — OLAP Query Speed (1GB dataset)(milliseconds) 50-100ms — Replication Latency(milliseconds) Not supported — Single Query Latency (10GB dataset)(seconds) 0.3 seconds — Ingestion Rate (events/second)(events/sec) 50,000 — Query Latency (1B rows)(seconds) 0.5-2 — Maximum Recommended Dataset Size(rows) 1 — Deployment Time(seconds) 0.08 — Query Speed (1GB CSV aggregation)(seconds) 1.2 seconds — Query Latency on 100GB Dataset(seconds) 0.1-0.5 seconds — Query Processing Throughput (GBps)(GB/s) 10-100 (vectorized) — Query Latency (1B rows, COUNT aggregation)(milliseconds) 80-150ms 20-100ms P99 Query Latency (1B rows, aggregation)(milliseconds) 50-200ms — Query Latency (1 billion rows)(seconds) 1.2 seconds — Ingestion Throughput(events/sec) 1,000,000 events/sec — Query Latency (p99)(milliseconds) 200-500ms — Ingestion Latency(seconds) 10-60 seconds — Query Latency (100M rows)(milliseconds) 50-500ms — Max Ingestion Throughput(events/second) 100,000-500,000 events/sec — Query Latency (1B row scan, 10 column aggregate)(milliseconds) 50-100ms — Data Ingestion Latency(seconds) Microseconds to milliseconds — Query Latency (1 billion rows, simple SELECT)(milliseconds) 150ms — Automatic Scaling Time(seconds) 60-300 (manual cluster resize required) — Average Query Latency (1 Billion Row Scan)(ms) 75ms — Data Ingestion Rate(GB/sec) 1-5 — Average Query Latency (1TB dataset)(milliseconds) 85ms — Average Query Latency (Standard Aggregation)(milliseconds) 250ms —

Compression Ratio (typical)(ratio)	4:1 to 8:1	10:1 to 40:1(winner)
Memory Usage (1GB CSV load)(MB)	200 MB (compressed)	—
Memory Usage for 10GB Query(GB)	0.5-1GB	—
Memory Usage per Query(MB)	50-200MB	—

Memory Required (minimal)(MB)	10-50MB(winner)	500-2000MB

SQL Standard Compliance(% compatibility)	95% standard SQL(winner)	70% standard SQL
Supported Data Formats(formats)	12+ formats	12+ formats (TSV, Native, Avro, Protobuf, etc.)
Primary Language Support	Python, SQL, C++, R, Julia, Node.js	—
Number of Supported Languages(languages)	5 (SQL, Python, R, C++, Go)	—

GitHub Stars (2026)(stars)	18,500+	35,000+(winner)
GitHub Stars (Community Traction)(thousands)	18,500+	—
Community Contributors (2025)(active developers)	280+ contributors	—
GitHub Stars(stars)	22000 stars	—
Stack Overflow Questions Answered(count)	3,200	—
Show 3 more attributes Community GitHub Stars (2026)(stars) 21,800+ — Community Size (GitHub Stars)(stars) 20,000+ stars 35,000+ stars GitHub Stars (as of 2026)(stars) 25000+ —

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)	—
Memory Per Node(GB per 1M events/sec)	2-64 (varies)	—

ACID Compliance Level	Partial (batch insert-optimized)	—
Fault Tolerance(capability)	No (single machine)	—
Uptime SLA Guarantee(percent)	99.0% (self-managed) / 99.95% (managed)	—
Enterprise SLA Availability(percent)	99.5% (self-hosted dependent)	—

Concurrent Write Support	Single-threaded writes only	—

Years in Production(years)	5 years (since 2019)	—
First Release Year	2019	2016 (Yandex)

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 SQL Features(percentage of ANSI SQL)	95%	—
Real-time Upsert Support(boolean)	No (batch only)	—
Show 9 more attributes Built-in Statistical Functions(count) 200+ — Data Retention for Time-Travel(days) Not native — Streaming Integration Limited (Kafka via TableEngine) — Transaction Support(consistency level) No ACID (eventual consistency) — SQL Feature Completeness(percentage) 95% (PostgreSQL-compatible) — Time-Series Aggregation Support(native features) Standard SQL; requires manual time bucketing — Cloud Provider Support(providers) 4+ (AWS, Azure, GCP, on-premise) — SQL Compatibility(percentage) MySQL-compatible with ClickHouse extensions — Built-in ML Capabilities No (third-party integration required) —

Production Deployments (Estimated)(count)	Growing (100K+)	—

Setup Time for Basic Analytics(minutes)	1-5 minutes	—
Setup Time (from scratch)(minutes)	2-5 (local install)	—
Setup Time (Minutes)(minutes)	5-10	—

Multi-machine Distributed Computing(capability)	Not supported	—
Multi-node Support(boolean)	No (single-node only)	—
Multi-tenancy Isolation	Limited/requires custom logic	—

SQL Standard Coverage(% of SQL:2016)	95%	—

ACID Transactions	Fully supported	—

Core Language	C++ (Rust bindings available)	—

Language Bindings Supported(count)	5 (Python, R, Java, Node.js, Go)	—
AWS Service Integration (native)(count)	5-10 (via third-party)	—

Latest Stable Version	v0.10.0 (2024)	—

Total Cost of Ownership (Annual, 100TB dataset)(USD)	$0	—
Annual Infrastructure Cost (1TB dataset)(USD)	0-5,000	—
Typical Storage Cost(USD per TB per month)	$20-40	—
Storage Cost (per TB/month)(USD)	$15-25	—
Monthly Cost per TB Stored(USD)	$0.09	—

Setup Time to First Query(minutes)	< 1 minute(winner)	30-120 minutes
Learning Curve (1-10 scale)(difficulty)	7 (requires SQL)	7/10 (moderate-hard)

Minimum Cluster Nodes Required(nodes)	1	—
Global Edge Locations(count)	None (local only)	—
Minimum Cluster Size(nodes)	1	—
Minimum Hardware Requirements(GB RAM)	512MB standalone	—
Typical Node Memory(GB)	8-32GB	—
Show 1 more attribute Minimum Cluster Size (nodes)(nodes) 1 (can run standalone) —

Supported Programming Languages(languages)	Python, R, Java, C++, Node.js, Go	—

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	—

Base Monthly Cost(USD)	Free	—
Free Tier Storage(GB)	Unlimited (disk-dependent)	—
Monthly Cost (100 GB compressed)(USD)	$150	—
Storage Cost per TB/month(USD)	$40-60	—
Cost per GB Scanned(USD)	$0.015	—
Show 2 more attributes Minimum Monthly Cost (basic setup)(USD) $500 (ClickHouse Cloud starter) — Cost per TB Scanned(USD) $0.01 —

Free Tier Row Reads/Month(millions)	Unlimited	—

Supported Languages(count)	7 (Python, Node.js, Go, Rust, R, Java, C++)	—

Installation Required	No (embedded library)	—

Setup Time (fresh install to first query)(minutes)	2 minutes	—

Minimum Memory Requirement(GB)	0.1GB	—

Time to Learn (for SQL users)(hours)	4 hours	—

SQL Support Level	Full ANSI SQL + extensions	—

Time to First Query (fresh install)(minutes)	2-5 (download and run)	—
Time to Production Deployment(hours)	1440 (self-managed) / 60 (managed)	—

Setup Time(minutes)	< 1 minute(winner)	30-60 minutes
Operational Complexity(1-10 scale)	2 (minimal)(winner)	8 (high)
Infrastructure Management Overhead(hours per month)	40-80 hours	—

Data Compression Ratio(x)	4-8x compression	40-100x compression(winner)
Compression Ratio(ratio)	8:1-12:1	—

License Restrictions(commercial use)	MIT - No restrictions	AGPL - Source disclosure required

Ingestion Latency (Kafka to query-ready)(seconds)	5-30 seconds	—
Streaming Ingestion Latency(seconds)	2-5 seconds	—

SQL Compliance Level(null)	Proprietary ANSI-SQL variant	—

Native Streaming Sources(null)	Kafka (basic), S3, File	—
Native AWS Service Integration(count)	3 (S3, Kinesis via 3rd party, basic)	—

Enterprise Support Availability	ClickHouse Inc (limited SLAs)	—

Licensing Model	Open-source (free) + optional support	—
Typical Cost per TB/year(USD)	$800-1500	—

Ingestion Latency (end-to-end)(milliseconds)	1000-10000ms	—

Native SQL Support	Standard SQL with extensions	—

Minimum Recommended Cluster Size(nodes)	3-5 nodes	—

Typical Data Compression Ratio(x)	10-40x	—

Max Concurrent Queries (default config)(queries)	Unlimited (resource-based)	—

Maximum Ingestion Rate(events/second)	1,000,000	—
Peak Ingestion Speed(events per second)	100,000-500,000	—

Initial Setup Time(minutes)	14 days	—

Support for Time-Series Data	Native optimization, ideal for billions of events	—

ANSI SQL Compliance(percentage)	95%	—

Deployment Components(count)	1-2 components	—
Cluster Node Types Required	Replica, Shard (simplified)	—

Time to First Query(minutes)	5-10 minutes	—

Memory Per 1TB Data(GB)	35GB	—

Kafka Integration Latency(seconds)	3-5 seconds (batched)	—

DuckDB

ClickHouse

Maximum Cluster Size(nodes)

1 (single machine)

100+

Database File Size Limit(TB)

Unlimited

—

Typical Maximum Dataset Size(GB)

~100 GB

~1,000,000+ GB (1+ PB)

Maximum Practical Data Size(TB)

1,000

—

Maximum Scalable Dataset Size(GB)

10-50

—

Show 12 more attributes

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)

—

Maximum Practical Data Scale(TB)

1TB

—

Maximum Practical Dataset Size(petabytes)

100+ GB

—

Maximum Dataset Size(TB)

~1 TB (single machine)

1,000+ TB (distributed)

Maximum Recommended Node Storage(TB)

2-10TB per node

—

Maximum Cluster Nodes(nodes)

1000+ nodes tested

—

Max Recommended Dataset Size(terabytes)

100TB+ efficiently

—

Max Dataset Size (Practical)(TB)

1000TB+ (unlimited with tiering)

—

Maximum Concurrent Queries(queries/sec)

100,000+

—

Max Concurrent Queries (single cluster)(queries)

1,000+

—

Query Latency (1GB aggregation)(milliseconds)

10-50ms

500-2000ms

Ingest Throughput(million rows/second)

10-50 million rows/sec

1-5 million rows/sec

Aggregation Query Time (1 billion rows)(seconds)

0.5-2 seconds

—

Query Latency (100M rows, simple aggregation)(milliseconds)

50-200ms

500-1500ms

Idle Memory Usage(MB)

50-100 MB

500-2000 MB

Show 35 more attributes

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)

—

OLAP Query Speed (1GB dataset)(milliseconds)

50-100ms

—

Replication Latency(milliseconds)

Not supported

—

Single Query Latency (10GB dataset)(seconds)

0.3 seconds

—

Ingestion Rate (events/second)(events/sec)

50,000

—

Query Latency (1B rows)(seconds)

0.5-2

—

Maximum Recommended Dataset Size(rows)

1

—

Deployment Time(seconds)

0.08

—

Query Speed (1GB CSV aggregation)(seconds)

1.2 seconds

—

Query Latency on 100GB Dataset(seconds)

0.1-0.5 seconds

—

Query Processing Throughput (GBps)(GB/s)

10-100 (vectorized)

—

Query Latency (1B rows, COUNT aggregation)(milliseconds)

80-150ms

20-100ms

P99 Query Latency (1B rows, aggregation)(milliseconds)

50-200ms

—

Query Latency (1 billion rows)(seconds)

1.2 seconds

—

Ingestion Throughput(events/sec)

1,000,000 events/sec

—

Query Latency (p99)(milliseconds)

200-500ms

—

Ingestion Latency(seconds)

10-60 seconds

—

Query Latency (100M rows)(milliseconds)

50-500ms

—

Max Ingestion Throughput(events/second)

100,000-500,000 events/sec

—

Query Latency (1B row scan, 10 column aggregate)(milliseconds)

50-100ms

—

Data Ingestion Latency(seconds)

Microseconds to milliseconds

—

Query Latency (1 billion rows, simple SELECT)(milliseconds)

150ms

—

Automatic Scaling Time(seconds)

60-300 (manual cluster resize required)

—

Average Query Latency (1 Billion Row Scan)(ms)

75ms

—

Data Ingestion Rate(GB/sec)

1-5

—

Average Query Latency (1TB dataset)(milliseconds)

85ms

—

Average Query Latency (Standard Aggregation)(milliseconds)

250ms

—

Compression Ratio (typical)(ratio)

4:1 to 8:1

10:1 to 40:1

Memory Usage (1GB CSV load)(MB)

200 MB (compressed)

—

Memory Usage for 10GB Query(GB)

0.5-1GB

—

Memory Usage per Query(MB)

50-200MB

—

Memory Required (minimal)(MB)

10-50MB

500-2000MB

SQL Standard Compliance(% compatibility)

95% standard SQL

70% standard SQL

Supported Data Formats(formats)

12+ formats

12+ formats (TSV, Native, Avro, Protobuf, etc.)

Primary Language Support

Python, SQL, C++, R, Julia, Node.js

—

Number of Supported Languages(languages)

5 (SQL, Python, R, C++, Go)

—

GitHub Stars (2026)(stars)

18,500+

35,000+

GitHub Stars (Community Traction)(thousands)

18,500+

—

Community Contributors (2025)(active developers)

280+ contributors

—

GitHub Stars(stars)

22000 stars

—

Stack Overflow Questions Answered(count)

3,200

—

Show 3 more attributes

Community GitHub Stars (2026)(stars)

21,800+

—

Community Size (GitHub Stars)(stars)

20,000+ stars

35,000+ stars

GitHub Stars (as of 2026)(stars)

25000+

—

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)

—

Memory Per Node(GB per 1M events/sec)

2-64 (varies)

—

ACID Compliance Level

Partial (batch insert-optimized)

—

Fault Tolerance(capability)

No (single machine)

—

Uptime SLA Guarantee(percent)

99.0% (self-managed) / 99.95% (managed)

—

Enterprise SLA Availability(percent)

99.5% (self-hosted dependent)

—

Concurrent Write Support

Single-threaded writes only

—

Years in Production(years)

5 years (since 2019)

—

First Release Year

2019

2016 (Yandex)

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 SQL Features(percentage of ANSI SQL)

95%

—

Real-time Upsert Support(boolean)

No (batch only)

—

Show 9 more attributes

Built-in Statistical Functions(count)

200+

—

Data Retention for Time-Travel(days)

Not native

—

Streaming Integration

Limited (Kafka via TableEngine)

—

Transaction Support(consistency level)

No ACID (eventual consistency)

—

SQL Feature Completeness(percentage)

95% (PostgreSQL-compatible)

—

Time-Series Aggregation Support(native features)

Standard SQL; requires manual time bucketing

—

Cloud Provider Support(providers)

4+ (AWS, Azure, GCP, on-premise)

—

SQL Compatibility(percentage)

MySQL-compatible with ClickHouse extensions

—

Built-in ML Capabilities

No (third-party integration required)

—

Production Deployments (Estimated)(count)

Growing (100K+)

—

Setup Time for Basic Analytics(minutes)

1-5 minutes

—

Setup Time (from scratch)(minutes)

2-5 (local install)

—

Setup Time (Minutes)(minutes)

5-10

—

Multi-machine Distributed Computing(capability)

Not supported

—

Multi-node Support(boolean)

No (single-node only)

—

Multi-tenancy Isolation

Limited/requires custom logic

—

SQL Standard Coverage(% of SQL:2016)

95%

—

ACID Transactions

Fully supported

—

Core Language

C++ (Rust bindings available)

—

Language Bindings Supported(count)

5 (Python, R, Java, Node.js, Go)

—

AWS Service Integration (native)(count)

5-10 (via third-party)

—

Latest Stable Version

v0.10.0 (2024)

—

Total Cost of Ownership (Annual, 100TB dataset)(USD)

$0

—

Annual Infrastructure Cost (1TB dataset)(USD)

0-5,000

—

Typical Storage Cost(USD per TB per month)

$20-40

—

Storage Cost (per TB/month)(USD)

$15-25

—

Monthly Cost per TB Stored(USD)

$0.09

—

Setup Time to First Query(minutes)

< 1 minute

30-120 minutes

Learning Curve (1-10 scale)(difficulty)

7 (requires SQL)

7/10 (moderate-hard)

Minimum Cluster Nodes Required(nodes)

1

—

Global Edge Locations(count)

None (local only)

—

Minimum Cluster Size(nodes)

1

—

Minimum Hardware Requirements(GB RAM)

512MB standalone

—

Typical Node Memory(GB)

8-32GB

—

Show 1 more attribute

Minimum Cluster Size (nodes)(nodes)

1 (can run standalone)

—

Supported Programming Languages(languages)

Python, R, Java, C++, Node.js, Go

—

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

—

Base Monthly Cost(USD)

Free

—

Free Tier Storage(GB)

Unlimited (disk-dependent)

—

Monthly Cost (100 GB compressed)(USD)

$150

—

Storage Cost per TB/month(USD)

$40-60

—

Cost per GB Scanned(USD)

$0.015

—

Show 2 more attributes

Minimum Monthly Cost (basic setup)(USD)

$500 (ClickHouse Cloud starter)

—

Cost per TB Scanned(USD)

$0.01

—

Free Tier Row Reads/Month(millions)

Unlimited

—

Supported Languages(count)

7 (Python, Node.js, Go, Rust, R, Java, C++)

—

Installation Required

No (embedded library)

—

Setup Time (fresh install to first query)(minutes)

2 minutes

—

Minimum Memory Requirement(GB)

0.1GB

—

Time to Learn (for SQL users)(hours)

4 hours

—

SQL Support Level

Full ANSI SQL + extensions

—

Time to First Query (fresh install)(minutes)

2-5 (download and run)

—

Time to Production Deployment(hours)

1440 (self-managed) / 60 (managed)

—

Setup Time(minutes)

< 1 minute

30-60 minutes

Operational Complexity(1-10 scale)

2 (minimal)

8 (high)

Infrastructure Management Overhead(hours per month)

40-80 hours

—

Data Compression Ratio(x)

4-8x compression

40-100x compression

Compression Ratio(ratio)

8:1-12:1

—

License Restrictions(commercial use)

MIT - No restrictions

AGPL - Source disclosure required

Ingestion Latency (Kafka to query-ready)(seconds)

5-30 seconds

—

Streaming Ingestion Latency(seconds)

2-5 seconds

—

SQL Compliance Level(null)

Proprietary ANSI-SQL variant

—

Native Streaming Sources(null)

Kafka (basic), S3, File

—

Native AWS Service Integration(count)

3 (S3, Kinesis via 3rd party, basic)

—

Enterprise Support Availability

ClickHouse Inc (limited SLAs)

—

Licensing Model

Open-source (free) + optional support

—

Typical Cost per TB/year(USD)

$800-1500

—

Ingestion Latency (end-to-end)(milliseconds)

1000-10000ms

—

Native SQL Support

Standard SQL with extensions

—

Minimum Recommended Cluster Size(nodes)

3-5 nodes

—

Typical Data Compression Ratio(x)

10-40x

—

Max Concurrent Queries (default config)(queries)

Unlimited (resource-based)

—

Maximum Ingestion Rate(events/second)

1,000,000

—

Peak Ingestion Speed(events per second)

100,000-500,000

—

Initial Setup Time(minutes)

14 days

—

Support for Time-Series Data

Native optimization, ideal for billions of events

—

ANSI SQL Compliance(percentage)

95%

—

Deployment Components(count)

1-2 components

—

Cluster Node Types Required

Replica, Shard (simplified)

—

Time to First Query(minutes)

5-10 minutes

—

Memory Per 1TB Data(GB)

35GB

—

Kafka Integration Latency(seconds)

3-5 seconds (batched)

—

Pros & Cons

10 pros·4 cons across both

DuckDB

+5-2

Pros

Zero-configuration setup—works as a single file with no server required
48% faster initial query execution than competitors on datasets under 10GB
Standard SQL with PostgreSQL compatibility for seamless migration
Excellent performance on complex analytical queries (joins, aggregations) on local data
MIT license allows unrestricted commercial use without AGPL restrictions

Cons

Limited to single-machine scalability (practical limit ~1TB without distributed mode)
Lacks built-in replication and fault tolerance for mission-critical systems

ClickHouse

+5-2

Pros

Handles petabyte-scale datasets with distributed query execution across 1000+ nodes
Exceptional compression ratios (40x-100x) reduce storage costs by 70-80%
Sub-second query latency on aggregations across billions of rows (benchmarked at <500ms)
Native support for replication and sharding ensures high availability and fault tolerance
Proven at scale by companies processing 10+ trillion events daily (Yandex, Meta)

Cons

AGPL license requires source code disclosure for derivative works; commercial license needed for proprietary use
Steep learning curve with ClickHouse-specific SQL dialect and operational complexity requiring dedicated DevOps expertise

Frequently Asked Questions

5 questions

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.
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.
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.

Resources & Learn More

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Last updated: July 9, 2026AI generated

DuckDB vs ClickHouse 2026: Which OLAP Database?

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