software
ClickHouse vs DuckDB 2026 | Analytics DB Comparison
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
Distributed column-oriented OLAP database for petabyte-scale analytics
Data engineers managing 1TB+ datasets, enterprises needing real-time analytics infrastructure, teams with DevOps resources for cluster operations
Score63%
VS
DuckDB
Embedded analytical SQL database optimized for in-process OLAP queries
Data scientists, developers building analytical features into applications, prototyping analytics solutions, local/personal analytics projects up to 100GB
Score63%
180 attributes7 differences16 pros/cons
Quick Answer
AI SummaryClickHouse 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.
Our Verdict
AI-assistedChoose 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.
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ClickHouse
7.3/10
DuckDB
7.8/10
Choose ClickHouse if
Data engineers managing 1TB+ datasets, enterprises needing real-time analytics infrastructure, teams with DevOps resources for cluster operations
Choose DuckDB if
Best pickData scientists, developers building analytical features into applications, prototyping analytics solutions, local/personal analytics projects up to 100GB
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Key Differences at a Glance
- Deployment Model:✓ DuckDB wins(Embedded in-process database vs Distributed server architecture (client-server))
- Scalability Range:✓ ClickHouse wins(Petabyte-scale across clusters vs Single machine (up to available RAM/disk))
- Setup Complexity:✓ DuckDB wins(Single file, zero config installation vs Requires cluster management, replication config)
Key Facts & Figures
138 numeric metrics compared
| Metric | ClickHouse | DuckDB | Ratio |
|---|---|---|---|
| P99 Query Latency (1B rows, aggregation)(milliseconds) | 50-200ms | — | — |
| Ingestion Latency (Kafka to query-ready)(seconds) | 5-30 seconds | — | — |
| Maximum Recommended Node Storage(TB) | 2-10TB per node | — | — |
| 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) | 7/10 (moderate-hard) | 7 (requires SQL) | |
| Query Latency (1GB aggregation)(milliseconds) | 500-2000ms | 10-50ms | |
| Compression Ratio (typical)(ratio) | 10:1 to 40:1 | 4:1 to 8:1 | |
| Memory Required (minimal)(MB) | 500-2000MB | 10-50MB | |
| Ingest Throughput(events/second) | 1M+ with batching | 100K-500K single machine | |
| Setup Time to First Query(minutes) | 30-120 minutes | < 1 minute | |
| SQL Standard Compliance(% compatibility) | 70% standard SQL | 95% standard SQL | |
| Query Latency (p99)(milliseconds) | 200-500ms | — | — |
| Ingestion Latency (end-to-end)(milliseconds) | 1000-10000ms | — | — |
| Memory Usage per Query(MB) | 50-200MB | — | — |
| Maximum Cluster Size(nodes) | 100+ | 1 (single machine) | |
| Typical Cost per TB/year(USD) | $800-1500 | — | — |
| Ingestion Latency(seconds) | 10-60 seconds | — | — |
| Query Latency (100M rows)(milliseconds) | 50-500ms | — | — |
| Maximum Cluster Nodes(nodes) | 1000+ nodes tested | — | — |
| GitHub Stars (2026)(stars) | 35,000+ | 18,500+ | |
| Typical Maximum Dataset Size(GB) | ~1,000,000+ GB (1+ PB) | ~100 GB | |
| Idle Memory Usage(MB) | 500-2000 MB | 50-100 MB | |
| Supported Data Formats(formats) | 12+ formats (TSV, Native, Avro, Protobuf, etc.) | 12+ formats | |
| Query Latency (100M rows, simple aggregation)(milliseconds) | 500-1500ms | 50-200ms | |
| 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) | $40-60 | — | — |
| Typical Node Memory(GB) | 8-32GB | — | — |
| Minimum Recommended Cluster Size(nodes) | 3-5 nodes | — | — |
| Max Dataset Size (Practical)(TB) | 1000TB+ (unlimited with tiering) | — | — |
| Query Latency (1B row scan, 10 column aggregate)(milliseconds) | 50-100ms | — | — |
| Storage Cost (per TB/month)(USD) | $15-25 | — | — |
| Typical Data Compression Ratio(x) | 10-40x | — | — |
| Minimum Cluster Size (nodes)(nodes) | 1 (can run standalone) | — | — |
| Data Ingestion Latency(seconds) | Microseconds to milliseconds | — | — |
| AWS Service Integration (native)(count) | 5-10 (via third-party) | — | — |
| GitHub Stars (as of 2026)(thousands) | 25000+ | — | — |
| Query Latency (1 billion rows, simple SELECT)(milliseconds) | 150ms | — | — |
| Cost per GB Scanned(USD) | $0.015 | — | — |
| Maximum Ingestion Rate(events/second) | 1,000,000 | — | — |
| Infrastructure Management Overhead(hours per month) | 40-80 hours | — | — |
| Minimum Monthly Cost (basic setup)(USD) | $500 (ClickHouse Cloud starter) | — | — |
| Cloud Provider Support(count) | 4+ (AWS, Azure, GCP, on-premise) | — | — |
| Automatic Scaling Time(seconds) | 60-300 (manual cluster resize required) | — | — |
| Average Query Latency (1 Billion Row Scan)(ms) | 75ms | — | — |
| Monthly Cost per TB Stored(USD) | $0.09 | — | — |
| Time to Production Deployment(days) | 1440 (self-managed) / 60 (managed) | — | — |
| Maximum Concurrent Queries(queries/sec) | 100,000+ | — | — |
| Uptime SLA Guarantee(percent) | 99.0% (self-managed) / 99.95% (managed) | — | — |
| Native AWS Service Integration(count) | 3 (S3, Kinesis via 3rd party, basic) | — | — |
| Data Ingestion Rate(GB/sec) | 1-5 | — | — |
| Average Query Latency (1TB dataset)(milliseconds) | 85ms | — | — |
| Cost per TB Scanned(USD) | $0.01 | — | — |
| Initial Setup Time(minutes) | 14 days | — | — |
| Max Concurrent Queries (single cluster)(queries) | 1,000+ | — | — |
| Enterprise SLA Availability(percent) | 99.5% (self-hosted dependent) | — | — |
| Peak Ingestion Speed(events per second) | 100,000-500,000 | — | — |
| ANSI SQL Compliance(percentage) | 95% | — | — |
| Deployment Components(count) | 1-2 components | — | — |
| Time to First Query(minutes) | 5-10 minutes | — | — |
| Average Query Latency (Standard Aggregation)(milliseconds) | 250ms | — | — |
| Streaming Ingestion Latency(seconds) | 2-5 seconds | — | — |
| Memory Per 1TB Data(GB) | 35GB | — | — |
| Kafka Integration Latency(seconds) | 3-5 seconds (batched) | — | — |
| First Release Year(year) | 2016 (Yandex) | 2019 | |
| Query Latency (1B rows, COUNT aggregation)(milliseconds) | 20-100ms | 80-150ms | |
| Data Compression Ratio(ratio) | 10-40x for analytics | 8-15x for analytics | |
| Community Size (GitHub Stars)(stars) | 35,000+ stars | 20,000+ stars | |
| Maximum Dataset Size(scale) | Petabytes (1000+ TB) | ~1TB (limited by single machine) | — |
| Typical Query Latency (1TB dataset)(seconds) | 1-5 seconds | 0.1-2 seconds | |
| Installation Size(MB) | 300-500 MB | 15-25 MB | |
| SQL Standard Compatibility(percent) | 95% (custom dialect) | 99% (PostgreSQL compatible) | |
| 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(TB) | 1,000 | 1,000 | |
| 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 | |
| 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)(thousands) | 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(count) | 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 | |
| Base Monthly Cost(USD) | Free | Free | |
| Global Edge Locations(cities/regions) | None (local only) | None (local only) | |
| OLAP Query Speed (1GB dataset)(milliseconds) | 50-100ms | 50-100ms | |
| Supported Languages(languages) | 7 (Python, Node.js, Go, Rust, R, Java, C++) | 7 (Python, Node.js, Go, Rust, R, Java, C++) | |
| Setup Time (fresh install to first query)(minutes) | 2 minutes | 2 minutes | |
| Minimum Memory Requirement(GB) | 0.1GB | 0.1GB | |
| Single Query Latency (10GB dataset)(seconds) | 0.3 seconds | 0.3 seconds | |
| Maximum Practical Data Scale(TB) | 1TB | 1TB | |
| Supported SQL Features(percentage of ANSI SQL) | 95% | 95% | |
| Community Contributors (2025)(active developers) | 280+ contributors | 280+ contributors | |
| GitHub Stars(stars) | 22000 stars | 22000 stars | |
| Time to Learn (for SQL users)(hours) | 4 hours | 4 hours | |
| Ingestion Rate (events/second)(events/sec) | 50,000 | 50,000 | |
| Query Latency (1B rows)(seconds) | 0.5-2 | 0.5-2 | |
| Maximum Recommended Dataset Size(rows) | 1 | 1 | |
| Deployment Time(seconds) | 0.08 | 0.08 | |
| Minimum Cluster Size(nodes) | 1 | 1 | |
| Memory Per Node(GB per 1M events/sec) | 2-64 (varies) | 2-64 (varies) | |
| Query Speed (1GB CSV aggregation)(seconds) | 1.2 seconds | 1.2 seconds | |
| Maximum Practical Dataset Size(petabytes) | 100+ GB | 100+ GB | |
| Memory Usage (1GB CSV load)(MB) | 200 MB (compressed) | 200 MB (compressed) | |
| Built-in Statistical Functions(count) | 200+ | 200+ | |
| Stack Overflow Questions Answered(count) | 3,200 | 3,200 | |
| Query Latency on 100GB Dataset(seconds) | 0.1-0.5 seconds | 0.1-0.5 seconds | |
| Memory Usage for 10GB Query(GB) | 0.5-1GB | 0.5-1GB | |
| Time to First Query (fresh install)(minutes) | 2-5 (download and run) | 2-5 (download and run) | |
| Number of Supported Languages(languages) | 5 (SQL, Python, R, C++, Go) | 5 (SQL, Python, R, C++, Go) | |
| Community GitHub Stars (2026)(stars) | 21,800+ | 21,800+ | |
| Query Processing Throughput (GBps)(GB/s) | 10-100 (vectorized) | 10-100 (vectorized) |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
DuckDB leads2 ties
ClickHouse
Attribute
DuckDB
- Distributed server architecture (client-server)Deployment ModelEmbedded in-process database(winner)
- Petabyte-scale across clusters(winner)Scalability RangeSingle machine (up to available RAM/disk)
- Requires cluster management, replication configSetup ComplexitySingle file, zero config installation(winner)
- 1-5 seconds (distributed)Query Speed (1TB dataset)0.1-2 seconds (single machine)
- Native built-in replication and sharding(winner)Multi-node SupportNot supported; single-machine only
- ClickHouse SQL dialect (95% SQL standard)SQL CompatibilityPostgreSQL-compatible SQL(winner)
- Real-time metrics, logs, time-series at scaleUse Case FocusLocal analytics, data science, prototyping
- Deployment Model
ClickHouse
Distributed server architecture (client-server)
DuckDB
Embedded in-process database(winner)
- Scalability Range
ClickHouse
Petabyte-scale across clusters(winner)
DuckDB
Single machine (up to available RAM/disk)
- Setup Complexity
ClickHouse
Requires cluster management, replication config
DuckDB
Single file, zero config installation(winner)
- Query Speed (1TB dataset)
ClickHouse
1-5 seconds (distributed)
DuckDB
0.1-2 seconds (single machine)
- Multi-node Support
ClickHouse
Native built-in replication and sharding(winner)
DuckDB
Not supported; single-machine only
- SQL Compatibility
ClickHouse
ClickHouse SQL dialect (95% SQL standard)
DuckDB
PostgreSQL-compatible SQL(winner)
- Use Case Focus
ClickHouse
Real-time metrics, logs, time-series at scale
DuckDB
Local analytics, data science, prototyping
Full Comparison
| Attribute | ClickHouse | DuckDB |
|---|---|---|
| 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 (1GB aggregation)(milliseconds) | 500-2000ms | 10-50ms(winner) |
| Query Latency (p99)(milliseconds) | 200-500ms | — |
Show 35 more attributesIngestion Latency(seconds) 10-60 seconds — Query Latency (100M rows)(milliseconds) 50-500ms — Idle Memory Usage(MB) 500-2000 MB 50-100 MB Query Latency (100M rows, simple aggregation)(milliseconds) 500-1500ms 50-200ms 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 — Query Latency (1B rows, COUNT aggregation)(milliseconds) 20-100ms 80-150ms Typical Query Latency (1TB dataset)(seconds) 1-5 seconds 0.1-2 seconds 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) — 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) — | ||
| 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 | — |
| Setup Time to First Query(minutes) | 30-120 minutes | < 1 minute(winner) |
| Setup Time(minutes) | 30-60 minutes | < 1 minute(winner) |
| Maximum Recommended Node Storage(TB) | 2-10TB per node | — |
| Maximum Cluster Size(nodes) | 100+(winner) | 1 (single machine) |
| Maximum Cluster Nodes(nodes) | 1000+ nodes tested | — |
| Typical Maximum Dataset Size(GB) | ~1,000,000+ GB (1+ PB)(winner) | ~100 GB |
| Max Recommended Dataset Size(terabytes) | 100TB+ efficiently | — |
Show 13 more attributesMax Dataset Size (Practical)(TB) 1000TB+ (unlimited with tiering) — Maximum Concurrent Queries(queries/sec) 100,000+ — Max Concurrent Queries (single cluster)(queries) 1,000+ — Maximum Dataset Size(scale) Petabytes (1000+ TB) ~1TB (limited by single machine) Database File Size Limit(TB) Unlimited — Maximum Practical Data Size(TB) 1,000 — 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) — Maximum Practical Data Scale(TB) 1TB — Minimum Cluster Size(nodes) 1 — Maximum Practical Dataset Size(petabytes) 100+ GB — | ||
| 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) | — |
| Monthly Cost (100 GB compressed)(USD) | $150 | — |
| Storage Cost per TB/month(USD) | $40-60 | — |
| Cost per GB Scanned(USD) | $0.015 | — |
| Minimum Monthly Cost (basic setup)(USD) | $500 (ClickHouse Cloud starter) | — |
| Cost per TB Scanned(USD) | $0.01 | — |
Show 2 more attributesBase Monthly Cost(USD) Free — Free Tier Storage(GB) Unlimited (disk-dependent) — | ||
| 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 | — |
Show 8 more attributesSQL Compatibility(percentage) MySQL-compatible with ClickHouse extensions — Built-in ML Capabilities No (third-party integration required) — 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) — Built-in Statistical Functions(count) 200+ — | ||
| Compression Ratio(ratio) | 8:1-12:1 | — |
| Licensing Model | Open-source (free) + optional support | — |
| Learning Curve (1-10 scale)(difficulty) | 7/10 (moderate-hard) | 7 (requires SQL) |
| Compression Ratio (typical)(ratio) | 10:1 to 40:1(winner) | 4:1 to 8:1 |
| Memory Usage per Query(MB) | 50-200MB | — |
| Memory Usage (1GB CSV load)(MB) | 200 MB (compressed) | — |
| Memory Usage for 10GB Query(GB) | 0.5-1GB | — |
| Memory Required (minimal)(MB) | 500-2000MB | 10-50MB(winner) |
| Ingest Throughput(events/second) | 1M+ with batching(winner) | 100K-500K single machine |
| Max Concurrent Queries (default config)(queries) | Unlimited (resource-based) | — |
| SQL Standard Compliance(% compatibility) | 70% standard SQL | 95% standard SQL(winner) |
| Supported Data Formats(formats) | 12+ formats (TSV, Native, Avro, Protobuf, etc.) | 12+ formats |
| SQL Standard Compatibility(percent) | 95% (custom dialect) | 99% (PostgreSQL compatible)(winner) |
| Primary Language Support | Python, SQL, C++, R, Julia, Node.js | — |
| Supported Programming Languages(count) | Python, R, Java, C++, Node.js, Go | — |
Show 1 more attributeNumber of Supported Languages(languages) 5 (SQL, Python, R, C++, Go) — | ||
| Ingestion Latency (end-to-end)(milliseconds) | 1000-10000ms | — |
| Native SQL Support | Standard SQL with extensions | — |
| Multi-tenancy Isolation | Limited/requires custom logic | — |
| Multi-machine Distributed Computing(capability) | Not supported | — |
| Multi-node Support(boolean) | No (single-node only) | — |
| Typical Cost per TB/year(USD) | $800-1500 | — |
| GitHub Stars (2026)(stars) | 35,000+(winner) | 18,500+ |
| Community Size (GitHub Stars)(stars) | 35,000+ stars(winner) | 20,000+ stars |
| GitHub Stars (Community Traction)(thousands) | 18,500+ | — |
| Community Contributors (2025)(active developers) | 280+ contributors | — |
| Stack Overflow Questions Answered(count) | 3,200 | — |
Show 1 more attributeCommunity GitHub Stars (2026)(stars) 21,800+ — | ||
| 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 | — |
| 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 Cluster Size (nodes)(nodes) | 1 (can run standalone) | — |
| Cloud Provider Support(count) | 4+ (AWS, Azure, GCP, on-premise) | — |
| Minimum Cluster Nodes Required(nodes) | 1 | — |
| Global Edge Locations(cities/regions) | None (local only) | — |
Show 1 more attributeMinimum Hardware Requirements(GB RAM) 512MB standalone — | ||
| Minimum Recommended Cluster Size(nodes) | 3-5 nodes | — |
| Typical Data Compression Ratio(x) | 10-40x | — |
| Data Compression Ratio(ratio) | 10-40x for analytics(winner) | 8-15x for analytics |
| AWS Service Integration (native)(count) | 5-10 (via third-party) | — |
| Language Bindings Supported(count) | 5 (Python, R, Java, Node.js, Go) | — |
| GitHub Stars (as of 2026)(thousands) | 25000+ | — |
| Maximum Ingestion Rate(events/second) | 1,000,000 | — |
| Peak Ingestion Speed(events per second) | 100,000-500,000 | — |
| Infrastructure Management Overhead(hours per month) | 40-80 hours | — |
| Operational Complexity(setup hours) | 40-100 (cluster setup) | 0.5-1 (embed library)(winner) |
| Time to Production Deployment(days) | 1440 (self-managed) / 60 (managed) | — |
| Uptime SLA Guarantee(percent) | 99.0% (self-managed) / 99.95% (managed) | — |
| Enterprise SLA Availability(percent) | 99.5% (self-hosted dependent) | — |
| ACID Compliance Level | Partial (batch insert-optimized) | — |
| Fault Tolerance(capability) | No (single machine) | — |
| 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) | — |
| First Release Year(year) | 2016 (Yandex)(winner) | 2019 |
| Years in Production(years) | 5 years (since 2019) | — |
| License Restrictions(commercial use) | AGPL - Source disclosure required | MIT - No restrictions |
| Installation Size(MB) | 300-500 MB | 15-25 MB(winner) |
| Multi-node Replication(native support) | Native (Zookeeper/Keeper) | Not supported |
| 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) | — |
| Concurrent Write Support | Single-threaded writes only | — |
| 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 | — |
| SQL Standard Coverage(% of SQL:2016) | 95% | — |
| ACID Transactions | Fully supported | — |
| Core Language | C++ (Rust bindings available) | — |
| Latest Stable Version | v0.10.0 (2024) | — |
| 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 | — |
| Free Tier Row Reads/Month(millions) | Unlimited | — |
| Supported Languages(languages) | 7 (Python, Node.js, Go, Rust, R, Java, C++) | — |
| Installation Required(yes/no) | No (embedded library) | — |
| Time to First Query (fresh install)(minutes) | 2-5 (download and run) | — |
| Setup Time (fresh install to first query)(minutes) | 2 minutes | — |
| Minimum Memory Requirement(GB) | 0.1GB | — |
| GitHub Stars(stars) | 22000 stars | — |
| Time to Learn (for SQL users)(hours) | 4 hours | — |
| SQL Support Level | Full ANSI SQL + extensions | — |
ClickHouseDuckDBP99 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 (1GB aggregation)(milliseconds)
500-2000ms
10-50ms
Query Latency (p99)(milliseconds)
200-500ms
—
Show 35 more attributes
Ingestion Latency(seconds)
10-60 seconds
—
Query Latency (100M rows)(milliseconds)
50-500ms
—
Idle Memory Usage(MB)
500-2000 MB
50-100 MB
Query Latency (100M rows, simple aggregation)(milliseconds)
500-1500ms
50-200ms
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
—
Query Latency (1B rows, COUNT aggregation)(milliseconds)
20-100ms
80-150ms
Typical Query Latency (1TB dataset)(seconds)
1-5 seconds
0.1-2 seconds
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)
—
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)
—
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
—
Setup Time to First Query(minutes)
30-120 minutes
< 1 minute
Setup Time(minutes)
30-60 minutes
< 1 minute
Maximum Recommended Node Storage(TB)
2-10TB per node
—
Maximum Cluster Size(nodes)
100+
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
—
Show 13 more attributes
Max Dataset Size (Practical)(TB)
1000TB+ (unlimited with tiering)
—
Maximum Concurrent Queries(queries/sec)
100,000+
—
Max Concurrent Queries (single cluster)(queries)
1,000+
—
Maximum Dataset Size(scale)
Petabytes (1000+ TB)
~1TB (limited by single machine)
Database File Size Limit(TB)
Unlimited
—
Maximum Practical Data Size(TB)
1,000
—
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)
—
Maximum Practical Data Scale(TB)
1TB
—
Minimum Cluster Size(nodes)
1
—
Maximum Practical Dataset Size(petabytes)
100+ GB
—
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)
—
Monthly Cost (100 GB compressed)(USD)
$150
—
Storage Cost per TB/month(USD)
$40-60
—
Cost per GB Scanned(USD)
$0.015
—
Minimum Monthly Cost (basic setup)(USD)
$500 (ClickHouse Cloud starter)
—
Cost per TB Scanned(USD)
$0.01
—
Show 2 more attributes
Base Monthly Cost(USD)
Free
—
Free Tier Storage(GB)
Unlimited (disk-dependent)
—
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
—
Show 8 more attributes
SQL Compatibility(percentage)
MySQL-compatible with ClickHouse extensions
—
Built-in ML Capabilities
No (third-party integration required)
—
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)
—
Built-in Statistical Functions(count)
200+
—
Compression Ratio(ratio)
8:1-12:1
—
Licensing Model
Open-source (free) + optional support
—
Learning Curve (1-10 scale)(difficulty)
7/10 (moderate-hard)
7 (requires SQL)
Compression Ratio (typical)(ratio)
10:1 to 40:1
4:1 to 8:1
Memory Usage per Query(MB)
50-200MB
—
Memory Usage (1GB CSV load)(MB)
200 MB (compressed)
—
Memory Usage for 10GB Query(GB)
0.5-1GB
—
Memory Required (minimal)(MB)
500-2000MB
10-50MB
Ingest Throughput(events/second)
1M+ with batching
100K-500K single machine
Max Concurrent Queries (default config)(queries)
Unlimited (resource-based)
—
SQL Standard Compliance(% compatibility)
70% standard SQL
95% standard SQL
Supported Data Formats(formats)
12+ formats (TSV, Native, Avro, Protobuf, etc.)
12+ formats
SQL Standard Compatibility(percent)
95% (custom dialect)
99% (PostgreSQL compatible)
Primary Language Support
Python, SQL, C++, R, Julia, Node.js
—
Supported Programming Languages(count)
Python, R, Java, C++, Node.js, Go
—
Show 1 more attribute
Number of Supported Languages(languages)
5 (SQL, Python, R, C++, Go)
—
Ingestion Latency (end-to-end)(milliseconds)
1000-10000ms
—
Native SQL Support
Standard SQL with extensions
—
Multi-tenancy Isolation
Limited/requires custom logic
—
Multi-machine Distributed Computing(capability)
Not supported
—
Multi-node Support(boolean)
No (single-node only)
—
Typical Cost per TB/year(USD)
$800-1500
—
GitHub Stars (2026)(stars)
35,000+
18,500+
Community Size (GitHub Stars)(stars)
35,000+ stars
20,000+ stars
GitHub Stars (Community Traction)(thousands)
18,500+
—
Community Contributors (2025)(active developers)
280+ contributors
—
Stack Overflow Questions Answered(count)
3,200
—
Show 1 more attribute
Community GitHub Stars (2026)(stars)
21,800+
—
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
—
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 Cluster Size (nodes)(nodes)
1 (can run standalone)
—
Cloud Provider Support(count)
4+ (AWS, Azure, GCP, on-premise)
—
Minimum Cluster Nodes Required(nodes)
1
—
Global Edge Locations(cities/regions)
None (local only)
—
Show 1 more attribute
Minimum Hardware Requirements(GB RAM)
512MB standalone
—
Minimum Recommended Cluster Size(nodes)
3-5 nodes
—
Typical Data Compression Ratio(x)
10-40x
—
Data Compression Ratio(ratio)
10-40x for analytics
8-15x for analytics
AWS Service Integration (native)(count)
5-10 (via third-party)
—
Language Bindings Supported(count)
5 (Python, R, Java, Node.js, Go)
—
GitHub Stars (as of 2026)(thousands)
25000+
—
Maximum Ingestion Rate(events/second)
1,000,000
—
Peak Ingestion Speed(events per second)
100,000-500,000
—
Infrastructure Management Overhead(hours per month)
40-80 hours
—
Operational Complexity(setup hours)
40-100 (cluster setup)
0.5-1 (embed library)
Time to Production Deployment(days)
1440 (self-managed) / 60 (managed)
—
Uptime SLA Guarantee(percent)
99.0% (self-managed) / 99.95% (managed)
—
Enterprise SLA Availability(percent)
99.5% (self-hosted dependent)
—
ACID Compliance Level
Partial (batch insert-optimized)
—
Fault Tolerance(capability)
No (single machine)
—
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)
—
First Release Year(year)
2016 (Yandex)
2019
Years in Production(years)
5 years (since 2019)
—
License Restrictions(commercial use)
AGPL - Source disclosure required
MIT - No restrictions
Installation Size(MB)
300-500 MB
15-25 MB
Multi-node Replication(native support)
Native (Zookeeper/Keeper)
Not supported
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)
—
Concurrent Write Support
Single-threaded writes only
—
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
—
SQL Standard Coverage(% of SQL:2016)
95%
—
ACID Transactions
Fully supported
—
Core Language
C++ (Rust bindings available)
—
Latest Stable Version
v0.10.0 (2024)
—
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
—
Free Tier Row Reads/Month(millions)
Unlimited
—
Supported Languages(languages)
7 (Python, Node.js, Go, Rust, R, Java, C++)
—
Installation Required(yes/no)
No (embedded library)
—
Time to First Query (fresh install)(minutes)
2-5 (download and run)
—
Setup Time (fresh install to first query)(minutes)
2 minutes
—
Minimum Memory Requirement(GB)
0.1GB
—
GitHub Stars(stars)
22000 stars
—
Time to Learn (for SQL users)(hours)
4 hours
—
SQL Support Level
Full ANSI SQL + extensions
—
Pros & Cons
10 pros·6 cons across both
ClickHouse
+5-3
Pros
- Handles petabyte-scale datasets across distributed clusters
- Native horizontal scalability with built-in replication and sharding
- Exceptional compression (10-40x for analytics workloads)
- Real-time data ingestion at 1M+ events/second
- Proven at Yandex, Uber, Spotify with 10+ PB datasets
Cons
- Steeper learning curve with proprietary SQL dialect and cluster complexity
- Requires significant operational overhead for cluster management, monitoring, backups
- Less suitable for transactional OLTP workloads (no ACID in traditional sense)
DuckDB
+5-3
Pros
- Zero setup—install as library, query immediately without server configuration
- PostgreSQL-compatible SQL with wider ecosystem tool support
- Excellent performance on single machines (vectorized execution)
- Minimal memory footprint and resource consumption
- Seamless integration with Python, R, C++, and other languages
Cons
- Cannot scale beyond single machine capacity—limited to available RAM/disk
- No built-in replication, clustering, or high-availability features
- Smaller ecosystem and fewer third-party integrations vs ClickHouse
Frequently Asked Questions
5 questions
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.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
As an affiliate, we may earn a commission from qualifying purchases at no extra cost to you. Learn more about our affiliate disclosure
Wikipedia
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