software
Druid vs Elasticsearch 2026: Analytics & Search
Druid is a columnar OLAP database optimized for real-time analytics on time-series data with sub-second latency, while Elasticsearch is a distributed search and analytics engine built for full-text search and log analysis. Druid excels at numerical aggregations across massive datasets, whereas Elasticsearch prioritizes flexible querying and text search capabilities.
Druid
Real-time OLAP database for streaming analytics and time-series data
Data engineers and analysts managing high-volume time-series metrics, financial trading platforms, real-time dashboards requiring sub-second aggregations
Score63%
VS
E
Elasticsearch
Open-source distributed search and analytics engine built on Lucene, part of the Elastic Stack.
DevOps teams, security operations, log aggregation, e-commerce search, content platforms requiring flexible full-text search
Score63%
Quick Answer
AI SummaryDruid is a columnar OLAP database optimized for real-time analytics on time-series data with sub-second latency, while Elasticsearch is a distributed search and analytics engine built for full-text search and log analysis. Druid excels at numerical aggregations across massive datasets, whereas Elasticsearch prioritizes flexible querying and text search capabilities.
Our Verdict
AI-assistedChoose Druid if you need sub-second analytics queries on time-series data at massive scale with pre-aggregated rollups and extreme storage efficiency. Choose Elasticsearch if you prioritize full-text search capabilities, flexible JSON document querying, or need a more accessible solution for log analytics with a larger ecosystem and community support.
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Druid
8.1/10
Elasticsearch
6.9/10
E
Choose Druid if
Best pickData engineers and analysts managing high-volume time-series metrics, financial trading platforms, real-time dashboards requiring sub-second aggregations
E
Choose Elasticsearch if
DevOps teams, security operations, log aggregation, e-commerce search, content platforms requiring flexible full-text search
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Key Differences at a Glance
- Primary Use Case:Real-time OLAP analytics on time-series data vs Full-text search and log/event analytics
- Query Latency (typical aggregations):✓ Druid wins(50-500ms for billion-row datasets vs 100-2000ms depending on query complexity)
- Full-Text Search Capability:✓ Elasticsearch wins(Native with advanced analyzers and stemming vs Limited/not native)
Key Facts & Figures
127 numeric metrics compared
| Metric | Druid | Elasticsearch | Ratio |
|---|---|---|---|
| Ingestion Latency(seconds) | 0.1-0.5 seconds | — | — |
| Query Latency (100M rows)(milliseconds) | 100-1000ms | — | — |
| Data Compression Ratio(ratio) | 8:1 to 15:1 | 4:1 to 8:1 | |
| Maximum Cluster Nodes(nodes) | 500+ nodes tested | Unlimited (enterprise only) | |
| GitHub Stars (2026)(stars) | 16,000+ stars | — | — |
| Data Ingestion Latency(seconds) | 100-500ms (real-time) | — | — |
| Query Latency (100M rows, simple aggregation)(milliseconds) | 100-300ms | — | — |
| Compression Ratio(ratio) | 4:1-6:1 | — | — |
| Typical Storage Cost(USD per TB per month) | $50-80 | — | — |
| Minimum Recommended Cluster Size(nodes) | 10 nodes | — | — |
| Max Recommended Dataset Size(terabytes) | 10TB practical limit | — | — |
| SQL Feature Completeness(percentage) | 70% (Druid SQL dialect) | — | — |
| Query Latency (Typical)(milliseconds) | 50-200ms | — | — |
| Data Ingestion Rate(GB/sec) | 1,000,000+ realtime | — | — |
| Enterprise Customers (2025)(count) | ~400 enterprises | — | — |
| Base Setup Cost (Annual)(USD) | $50,000-500,000 (infrastructure) | — | — |
| Time to Insight (Complex Query)(seconds) | 0.2 (pre-aggregated metrics) | — | — |
| Maximum Daily Data Volume(terabytes) | 500+ TB/day sustainable | — | — |
| Operational Complexity (1-10 scale)(complexity score) | 8/10 (high setup & tuning) | — | — |
| Average Query Latency (Standard Aggregation)(milliseconds) | 45ms | — | — |
| Streaming Ingestion Latency(seconds) | 0.1-0.5 seconds | — | — |
| Memory Per 1TB Data(GB) | 150GB | — | — |
| Kafka Integration Latency(seconds) | 0.2-0.5 seconds (real-time) | — | — |
| First Release Year(year) | 2012 (Metamarkets) | — | — |
| Query Latency (Aggregation on 1B rows)(milliseconds) | ~250ms | ~800ms | |
| GitHub Community (Stars)(stars) | 14,000 | 67,000 | |
| Minimum Recommended Heap Memory(GB) | 4-8 GB | 8-16 GB | |
| Real-Time Ingestion Latency(milliseconds) | ~10-100ms | ~100-500ms | |
| Setup Complexity (1-10 scale)(difficulty score) | 8/10 (complex) | 5/10 (moderate) | |
| Maximum Single Query Dataset Size(billion rows) | 100+ billion | 10-50 billion (performance degradation) | |
| Typical Query Latency (100GB Dataset)(milliseconds) | 50-500ms | — | — |
| Storage Cost per TB per Month(USD) | $0.50-$1.00 | — | — |
| Typical Setup Time for Production(days) | 14-30 days | — | — |
| Data Ingestion Freshness(seconds) | 1-5 seconds | — | — |
| Maximum Concurrent Queries (Standard Node)(queries) | 32+ (distributed) | — | — |
| SQL Dialect Compatibility(percentage) | Partial (60-70% ANSI SQL) | — | — |
| Monthly Ingestion Cost per GB(USD) | $0.10 - $0.20 | $0.10 - $0.20 | |
| Free Tier Data Retention(days) | 30+ days (self-hosted) | 30+ days (self-hosted) | |
| Setup Time to Production(minutes) | 40-80 (self-hosted) | 40-80 (self-hosted) | |
| Maximum Data Volume per Cluster(TB) | Petabyte-scale (1000+ TB) | Petabyte-scale (1000+ TB) | |
| Query Language Complexity (1-10 scale)(complexity rating) | 7 (Lucene/DSL - steep learning curve) | 7 (Lucene/DSL - steep learning curve) | |
| Average Customer Onboarding Time(hours) | 30-90 days | 30-90 days | |
| Base Annual Cost (Small Deployment)(USD) | $0 (self-hosted) | $0 (self-hosted) | |
| Per-Gigabyte Ingestion Cost(USD per GB per day) | $0 (unlimited after infrastructure cost) | $0 (unlimited after infrastructure cost) | |
| Query Response Time (1B records)(milliseconds) | 50-200ms | 50-200ms | |
| Built-in Compliance Certifications(count) | 0 (requires custom hardening) | 0 (requires custom hardening) | |
| Machine Learning Use Cases Included(count) | 3 (anomaly detection, forecasting, outlier detection) | 3 (anomaly detection, forecasting, outlier detection) | |
| Community Support Response Time(hours) | 12-48 (community forums) | 12-48 (community forums) | |
| Max Ingestion Throughput(events/second) | 75,000 | 75,000 | |
| Query Latency (50th percentile)(milliseconds) | 300 | 300 | |
| Data Compression Ratio (metrics)(ratio) | 4:1 | 4:1 | |
| GitHub Stars(stars) | 65,800 | 65,800 | |
| Minimum Cluster Node Count(nodes) | 2 | 2 | |
| Third-Party Integrations(count) | 2000+ | 2000+ | |
| Memory Overhead (1M events)(MB per node) | 250 | 250 | |
| P99 Query Latency(milliseconds) | 100-500ms | 100-500ms | |
| Maximum Recommended Cluster Size(nodes) | 1,000+ (tested to 10,000 with advanced tuning) | 1,000+ (tested to 10,000 with advanced tuning) | |
| Time to First Production Query(days) | 1-3 days (schemaless indexing) | 1-3 days (schemaless indexing) | |
| Typical Memory Per Node(GB) | 32-64GB for equivalent throughput | 32-64GB for equivalent throughput | |
| GitHub Stars (as of 2026)(thousands) | 66,000+ | 66,000+ | |
| Total Cost of Ownership (Year 1)(USD) | $0-$10,000 (self-hosted) or $20,000-$60,000 (managed) | $0-$10,000 (self-hosted) or $20,000-$60,000 (managed) | |
| Average Time to Root Cause (MTTR)(minutes) | 45-120 (manual investigation required) | 45-120 (manual investigation required) | |
| Implementation Timeline(months) | 6-12 weeks | 6-12 weeks | |
| Supported Technologies(integrations) | 1000+ via community/Beats | 1000+ via community/Beats | |
| Ingest Rate (Typical)(events per second) | 500,000+ EPS per cluster | 500,000+ EPS per cluster | |
| Average Query Latency(milliseconds) | 47ms | 47ms | |
| Minimum RAM Requirement(GB) | 512MB | 512MB | |
| Enterprise Market Share(%) | 66% | 66% | |
| GitHub Community Size(stars) | 68,000+ stars | 68,000+ stars | |
| Replication Setup Time(minutes) | 5-10 minutes | 5-10 minutes | |
| Bulk Indexing Performance(%) | 65 docs/sec per thread | 65 docs/sec per thread | |
| Annual Commercial Support Cost(USD) | $5,000-$50,000 | $5,000-$50,000 | |
| Time to First Production Deployment(days) | 14-28 days | 14-28 days | |
| Query Latency (p99)(milliseconds) | 50-200ms | 50-200ms | |
| Monthly Cost (100GB index, 1M queries/month)(USD) | $200-500 self-hosted | $200-500 self-hosted | |
| Maximum Practical Index Size(GB) | Petabyte-scale (unlimited) | Petabyte-scale (unlimited) | |
| API Response Time for Simple Search(milliseconds) | 100-300ms | 100-300ms | |
| Customization Depth (1-10 scale)(score) | 9/10 (plugins, analyzers, scripting) | 9/10 (plugins, analyzers, scripting) | |
| Support SLA Response Time(hours) | Community-based or 4+ hours (enterprise) | Community-based or 4+ hours (enterprise) | |
| Time to Production(days) | 14-28 days (self-hosted) | 14-28 days (self-hosted) | |
| Monthly Cost (1TB/day ingestion)(USD) | $3,000-$8,000 | $3,000-$8,000 | |
| Price per GB Ingested(USD/GB) | $0.02-$0.05 | $0.02-$0.05 | |
| Out-of-Box Integrations(count) | ~300 integrations | ~300 integrations | |
| Infrastructure Management Overhead(hours per month) | 1.0-2.0 FTE | 1.0-2.0 FTE | |
| Minimum Memory Requirement(GB) | 2-4 GB | 2-4 GB | |
| Setup Time to First Query(minutes) | 120-240 minutes | 120-240 minutes | |
| Query Latency (p95 on 1M docs)(milliseconds) | 50-200 ms | 50-200 ms | |
| Maximum Recommended Data Scale(documents) | 1+ Billion (across clusters) | 1+ Billion (across clusters) | |
| Aggregation Types Supported(count) | 40+ aggregation types | 40+ aggregation types | |
| GitHub Stars (Community Size)(stars) | ~60,000 stars | ~60,000 stars | |
| Annual Infrastructure Cost (100M docs)(USD) | $50,000-150,000 | $50,000-150,000 | |
| Enterprise Market Share (2024)(%) | 70% | 70% | |
| Time to First Production Insight(days) | 14-30 days | 14-30 days | |
| Automatic Instrumentation Coverage(technologies) | 30-40 (via Beats) | 30-40 (via Beats) | |
| Starting Annual Cost(USD) | $0 (open-source) or $12,000 | $0 (open-source) or $12,000 | |
| Community Size & Resources(active contributors) | 1M+ active developers | 1M+ active developers | |
| Average Time to Value(days) | 14-30 days | 14-30 days | |
| Total Cost of Ownership (3-year, 500GB/month ingestion)(USD) | $120,000 (self-hosted licensing + 2 FTEs infrastructure) | $120,000 (self-hosted licensing + 2 FTEs infrastructure) | |
| Number of Pre-built Integrations(count) | 150+ | 150+ | |
| Query Performance on 1TB Index(milliseconds) | 100-2000ms (depends on tuning) | 100-2000ms (depends on tuning) | |
| Required Infrastructure Team Size (100 users)(FTEs) | 2-3 (cluster management + optimization) | 2-3 (cluster management + optimization) | |
| Memory Usage (Baseline Configuration)(MB) | 512 MB | 512 MB | |
| Search Latency (99th Percentile)(ms) | 150 ms | 150 ms | |
| Maximum Dataset Size (Practical Limit)(TB) | Unlimited (petabyte-scale) | Unlimited (petabyte-scale) | |
| Setup Time (Basic Deployment)(minutes) | 60-120 minutes | 60-120 minutes | |
| Available Integrations(count) | 1000+ plugins/integrations | 1000+ plugins/integrations | |
| Full-Text Search Languages Supported(languages) | 30+ languages | 30+ languages | |
| Production Deployments (Estimated)(count) | 500,000+ companies worldwide | 500,000+ companies worldwide | |
| Typical Query Latency (1B rows, GROUP BY)(milliseconds) | 500-2000ms | 500-2000ms | |
| Index Size to Data Ratio(multiplier) | 0.5-2x | 0.5-2x | |
| GitHub Stars (Community Size Proxy)(stars) | 68,000+ | 68,000+ | |
| Typical Deployment Complexity(relative score) | Low-Medium (simpler operations) | Low-Medium (simpler operations) | |
| Maximum Practical Dataset Size(petabytes) | 5+ PB (operational limit) | 5+ PB (operational limit) | |
| Annual Cost for 500GB/day Ingestion(USD) | $180,000-$240,000 | $180,000-$240,000 | |
| Minimum Required DevOps FTE(people) | 2-4 full-time engineers | 2-4 full-time engineers | |
| Data Retention Cost per GB/month(USD) | $0.50-$1.50 | $0.50-$1.50 | |
| Base Monthly Cost (Small Team)(USD) | $0 (self-hosted) / $884 (cloud) | $0 (self-hosted) / $884 (cloud) | |
| Initial Setup Time(minutes) | 40-80 hours for production cluster | 40-80 hours for production cluster | |
| Supported Languages/Frameworks(count) | 45+ via Logstash and Beats | 45+ via Logstash and Beats | |
| Annual Cloud Subscription (Large Team)(USD) | $10,600-$40,000 (Elastic Cloud) | $10,600-$40,000 (Elastic Cloud) | |
| Annual Licensing Cost (Small Deployment)(USD) | $0 (self-managed) | $0 (self-managed) | |
| Data Ingestion Capacity(events/second) | 1,000,000+ | 1,000,000+ | |
| Initial Deployment Time(weeks) | 2-4 weeks | 2-4 weeks | |
| Pre-Built Integrations(count) | 300+ | 300+ | |
| Storage Compression Ratio(ratio) | 4:1 | 4:1 | |
| Search Query Latency (1B docs)(milliseconds) | 50-200ms | 50-200ms | |
| GitHub Community Stars(stars) | 150,000+ | 150,000+ |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
Elasticsearch leads2 ties
E
3ElasticsearchDruid
Attribute
Elasticsearch
- Real-time OLAP analytics on time-series dataPrimary Use CaseFull-text search and log/event analytics
- 50-500ms for billion-row datasets(winner)Query Latency (typical aggregations)100-2000ms depending on query complexity
- Limited/not nativeFull-Text Search CapabilityNative with advanced analyzers and stemming(winner)
- Columnar storage with rollup pre-aggregationData ModelDocument-based JSON with inverted indexing
- 8:1 to 15:1 (highly compressible numeric data)(winner)Typical Storage Efficiency (compression ratio)4:1 to 8:1 (document storage overhead)
- ~14,000 starsCommunity Size (GitHub stars as of 2026)~67,000 stars(winner)
- High - requires careful segment management and deep configurationSetup ComplexityMedium - relatively straightforward single-node setup, harder at scale(winner)
- Primary Use Case
Druid
Real-time OLAP analytics on time-series data
Elasticsearch
Full-text search and log/event analytics
- Query Latency (typical aggregations)
Druid
50-500ms for billion-row datasets(winner)
Elasticsearch
100-2000ms depending on query complexity
- Full-Text Search Capability
Druid
Limited/not native
Elasticsearch
Native with advanced analyzers and stemming(winner)
- Data Model
Druid
Columnar storage with rollup pre-aggregation
Elasticsearch
Document-based JSON with inverted indexing
- Typical Storage Efficiency (compression ratio)
Druid
8:1 to 15:1 (highly compressible numeric data)(winner)
Elasticsearch
4:1 to 8:1 (document storage overhead)
- Community Size (GitHub stars as of 2026)
Druid
~14,000 stars
Elasticsearch
~67,000 stars(winner)
- Setup Complexity
Druid
High - requires careful segment management and deep configuration
Elasticsearch
Medium - relatively straightforward single-node setup, harder at scale(winner)
Full Comparison
| Attribute | Druid | Elasticsearch |
|---|---|---|
| Ingestion Latency(seconds) | 0.1-0.5 seconds | — |
| Query Latency (100M rows)(milliseconds) | 100-1000ms | — |
| Data Ingestion Latency(seconds) | 100-500ms (real-time) | — |
| Query Latency (100M rows, simple aggregation)(milliseconds) | 100-300ms | — |
| Query Latency (Typical)(milliseconds) | 50-200ms | — |
Show 26 more attributesData Ingestion Rate(GB/sec) 1,000,000+ realtime — Time to Insight (Complex Query)(seconds) 0.2 (pre-aggregated metrics) — Average Query Latency (Standard Aggregation)(milliseconds) 45ms — Query Latency (Aggregation on 1B rows)(milliseconds) ~250ms ~800ms Real-Time Ingestion Latency(milliseconds) ~10-100ms ~100-500ms Typical Query Latency (100GB Dataset)(milliseconds) 50-500ms — Data Ingestion Freshness(seconds) 1-5 seconds — Maximum Concurrent Queries (Standard Node)(queries) 32+ (distributed) — Free Tier Data Retention(days) 30+ days (self-hosted) — Query Response Time (1B records)(milliseconds) 50-200ms — Max Ingestion Throughput(events/second) 75,000 — Query Latency (50th percentile)(milliseconds) 300 — P99 Query Latency(milliseconds) 100-500ms — Average Time to Root Cause (MTTR)(minutes) 45-120 (manual investigation required) — Average Query Latency(milliseconds) 47ms — Bulk Indexing Performance(%) 65 docs/sec per thread — Query Latency (p99)(milliseconds) 50-200ms — API Response Time for Simple Search(milliseconds) 100-300ms — Default Data Retention(days) Unlimited (configurable) — Query Latency (p95 on 1M docs)(milliseconds) 50-200 ms — Query Performance on 1TB Index(milliseconds) 100-2000ms (depends on tuning) — Memory Usage (Baseline Configuration)(MB) 512 MB — Search Latency (99th Percentile)(ms) 150 ms — Typical Query Latency (1B rows, GROUP BY)(milliseconds) 500-2000ms — Data Ingestion Capacity(events/second) 1,000,000+ — Search Query Latency (1B docs)(milliseconds) 50-200ms — | ||
| Data Compression Ratio(ratio) | 8:1 to 15:1(winner) | 4:1 to 8:1 |
| Index Size to Data Ratio(multiplier) | 0.5-2x | — |
| Maximum Cluster Nodes(nodes) | 500+ nodes tested | Unlimited (enterprise only)(winner) |
| Max Recommended Dataset Size(terabytes) | 10TB practical limit | — |
| Maximum Daily Data Volume(terabytes) | 500+ TB/day sustainable | — |
| Maximum Single Query Dataset Size(billion rows) | 100+ billion(winner) | 10-50 billion (performance degradation) |
| Horizontal Scalability(text) | Linear scaling with added nodes | — |
Show 7 more attributesMaximum Data Volume per Cluster(TB) Petabyte-scale (1000+ TB) — Maximum Recommended Cluster Size(nodes) 1,000+ (tested to 10,000 with advanced tuning) — Ingest Rate (Typical)(events per second) 500,000+ EPS per cluster — Maximum Practical Index Size(GB) Petabyte-scale (unlimited) — Maximum Recommended Data Scale(documents) 1+ Billion (across clusters) — Maximum Dataset Size (Practical Limit)(TB) Unlimited (petabyte-scale) — Maximum Practical Dataset Size(petabytes) 5+ PB (operational limit) — | ||
| SQL Compatibility(percentage) | Druid SQL (subset of ANSI) | — |
| Streaming Integration | Native (Kafka, Kinesis, Pulsar) | — |
| Transaction Support(consistency level) | No ACID (eventual consistency) | — |
| SQL Feature Completeness(percentage) | 70% (Druid SQL dialect) | — |
| Time-Series Aggregation Support(native features) | Native time_floor, granular rollups, datasource-level aggregations | — |
Show 11 more attributesFull-Text Search Native Support Limited (no native implementation) Native with advanced analyzers Supported Programming Languages(count) Unlimited (via client libraries) — Full-Text Search Capability Native (analyzers, relevance tuning, fuzzy matching) — Faceted Search Native Support Via aggregations (requires custom queries) — Out-of-Box Integrations(count) ~300 integrations — Default Typo Tolerance Manual configuration required — Available Integrations(count) 1000+ plugins/integrations — Full-Text Search Languages Supported(languages) 30+ languages — Typo Tolerance (Out-of-Box)(null) Requires configuration/plugin — Pre-Built Integrations(count) 300+ — SQL Language Support(native support) Via plugin (not native) — | ||
| GitHub Stars (2026)(stars) | 16,000+ stars | — |
| GitHub Community (Stars)(stars) | 14,000 | 67,000(winner) |
| GitHub Stars(stars) | 65,800 | — |
| GitHub Community Size(stars) | 68,000+ stars | — |
| Community Size(members) | 180,000+ (open-source) | — |
Show 1 more attributeGitHub Community Stars(stars) 150,000+ — | ||
| Compression Ratio(ratio) | 4:1-6:1 | — |
| Columnar Compression Ratio(ratio (data reduction %)) | 4:1 to 10:1 | — |
| Data Compression Ratio (metrics)(ratio) | 4:1 | — |
| Data Retention Default(months) | Unlimited (storage-dependent) | — |
| Typical Storage Cost(USD per TB per month) | $50-80 | — |
| Storage Cost per TB per Month(USD) | $0.50-$1.00 | — |
| Total Cost of Ownership (Year 1)(USD) | $0-$10,000 (self-hosted) or $20,000-$60,000 (managed) | — |
| Annual Commercial Support Cost(USD) | $5,000-$50,000 | — |
| Annual Infrastructure Cost (100M docs)(USD) | $50,000-150,000 | — |
Show 3 more attributesTotal Cost of Ownership (3-year, 500GB/month ingestion)(USD) $120,000 (self-hosted licensing + 2 FTEs infrastructure) — Annual Cost for 500GB/day Ingestion(USD) $180,000-$240,000 — Data Retention Cost per GB/month(USD) $0.50-$1.50 — | ||
| Minimum Recommended Cluster Size(nodes) | 10 nodes | — |
| Setup Time to Production(minutes) | 40-80 (self-hosted) | — |
| Implementation Timeline(months) | 6-12 weeks | — |
| Time to Production(days) | 14-28 days (self-hosted) | — |
| Setup Time (Basic Deployment)(minutes) | 60-120 minutes | — |
| Enterprise Customers (2025)(count) | ~400 enterprises | — |
| Production Deployments (Estimated)(count) | 500,000+ companies worldwide | — |
| Base Setup Cost (Annual)(USD) | $50,000-500,000 (infrastructure) | — |
| Monthly Ingestion Cost per GB(USD) | $0.10 - $0.20 | — |
| Base Annual Cost (Small Deployment)(USD) | $0 (self-hosted) | — |
| Per-Gigabyte Ingestion Cost(USD per GB per day) | $0 (unlimited after infrastructure cost) | — |
| Monthly Cost (100GB index, 1M queries/month)(USD) | $200-500 self-hosted | — |
Show 6 more attributesMonthly Cost (1TB/day ingestion)(USD) $3,000-$8,000 — Price per GB Ingested(USD/GB) $0.02-$0.05 — Starting Annual Cost(USD) $0 (open-source) or $12,000 — Base Monthly Cost (Small Team)(USD) $0 (self-hosted) / $884 (cloud) — Annual Cloud Subscription (Large Team)(USD) $10,600-$40,000 (Elastic Cloud) — Annual Licensing Cost (Small Deployment)(USD) $0 (self-managed) — | ||
| Operational Complexity (1-10 scale)(complexity score) | 8/10 (high setup & tuning) | — |
| Streaming Ingestion Latency(seconds) | 0.1-0.5 seconds | — |
| Memory Per 1TB Data(GB) | 150GB | — |
| Minimum Recommended Heap Memory(GB) | 4-8 GB(winner) | 8-16 GB |
| SQL Standard Compliance(% compatibility) | Druid SQL dialect (proprietary subset) | — |
| SQL Dialect Compatibility(percentage) | Partial (60-70% ANSI SQL) | — |
| Supported Technologies(integrations) | 1000+ via community/Beats | — |
| Supported Languages/Frameworks(count) | 45+ via Logstash and Beats | — |
| Cluster Node Types Required | Coordinator, Data, Query, Broker (4+ types) | — |
| Kafka Integration Latency(seconds) | 0.2-0.5 seconds (real-time) | — |
| First Release Year(year) | 2012 (Metamarkets) | — |
| Setup Complexity (1-10 scale)(difficulty score) | 8/10 (complex) | 5/10 (moderate)(winner) |
| Query Language Complexity (1-10 scale)(complexity rating) | 7 (Lucene/DSL - steep learning curve) | — |
| Time to First Production Query(days) | 1-3 days (schemaless indexing) | — |
| Typical Setup Time for Production(days) | 14-30 days | — |
| Deployment Options | Self-hosted, SaaS (Elastic Cloud), or Kubernetes | — |
| Average Customer Onboarding Time(hours) | 30-90 days | — |
| Built-in Compliance Certifications(count) | 0 (requires custom hardening) | — |
| Machine Learning Use Cases Included(count) | 3 (anomaly detection, forecasting, outlier detection) | — |
| Aggregation Types Supported(count) | 40+ aggregation types | — |
| Community Support Response Time(hours) | 12-48 (community forums) | — |
| Support SLA Response Time(hours) | Community-based or 4+ hours (enterprise) | — |
| Minimum Cluster Node Count(nodes) | 2 | — |
| Replication Setup Time(minutes) | 5-10 minutes | — |
| Infrastructure Management Requirement | Requires DevOps expertise | — |
| Infrastructure Management Overhead(hours per month) | 1.0-2.0 FTE | — |
| Infrastructure Setup Complexity(DevOps hours) | 80-200 hours (extensive) | — |
Show 3 more attributesRequired Infrastructure Team Size (100 users)(FTEs) 2-3 (cluster management + optimization) — Typical Deployment Complexity(relative score) Low-Medium (simpler operations) — Minimum Required DevOps FTE(people) 2-4 full-time engineers — | ||
| Third-Party Integrations(count) | 2000+ | — |
| Memory Overhead (1M events)(MB per node) | 250 | — |
| Minimum Memory Requirement(GB) | 2-4 GB | — |
| SQL Support | SQL via plugin (Elastic SQL), primary is Query DSL | — |
| Typical Memory Per Node(GB) | 32-64GB for equivalent throughput | — |
| GitHub Stars (as of 2026)(thousands) | 66,000+ | — |
| Data Retention (Default)(months) | Unlimited (storage-dependent) | — |
| Data Retention (Standard)(months) | Unlimited (user-configured) | — |
| Learning Curve(difficulty rating) | 3-6 months | — |
| Query Language | Query DSL (complex, steep learning curve) | — |
| API Complexity(learning effort) | Complex Query DSL requiring technical expertise | — |
| Setup Time to First Query(minutes) | 120-240 minutes | — |
| Minimum RAM Requirement(GB) | 512MB | — |
| Enterprise Market Share(%) | 66% | — |
| Time to First Production Deployment(days) | 14-28 days | — |
| Average Time to Value(days) | 14-30 days | — |
| Initial Deployment Time(weeks) | 2-4 weeks | — |
| Customization Depth (1-10 scale)(score) | 9/10 (plugins, analyzers, scripting) | — |
| Query Language Complexity | Advanced EQL with full customization | — |
| GitHub Stars (Community Size)(stars) | ~60,000 stars | — |
| Enterprise Market Share (2024)(%) | 70% | — |
| Time to First Production Insight(days) | 14-30 days | — |
| Automatic Instrumentation Coverage(technologies) | 30-40 (via Beats) | — |
| AI-Powered Root Cause Analysis(native capability) | Requires third-party integrations | — |
| AI Anomaly Detection | Requires ML plugins or third-party tools | — |
| Query Language Flexibility(flexibility score) | Full Elasticsearch Query DSL and custom scripts | — |
| Community Size & Resources(active contributors) | 1M+ active developers | — |
| Mean Time to Resolution (MTTR) Improvement(percentage reduction) | Depends on manual investigation | — |
| Number of Pre-built Integrations(count) | 150+ | — |
| Supported Data Types(types) | Logs, metrics (via plugins), custom JSON data | — |
| Query Language Expressiveness(languages supported) | Lucene, KQL, SQL, JavaScript | — |
| Full-Text Search Optimization | Native, highly optimized (primary feature) | — |
| GitHub Stars (Community Size Proxy)(stars) | 68,000+ | — |
| Real-Time Ingestion Support | Via Beats and Logstash (indirect) | — |
| SQL Query Support | SQL plugin available (limited JOIN support) | — |
| SLA Uptime Guarantee(percent) | Varies by deployment (self-hosted: customer responsibility) | — |
| Open-Source | Yes (SSPL/Elastic License) | — |
| Initial Setup Time(minutes) | 40-80 hours for production cluster | — |
| Storage Compression Ratio(ratio) | 4:1 | — |
DruidEElasticsearch
Ingestion Latency(seconds)
0.1-0.5 seconds
—
Query Latency (100M rows)(milliseconds)
100-1000ms
—
Data Ingestion Latency(seconds)
100-500ms (real-time)
—
Query Latency (100M rows, simple aggregation)(milliseconds)
100-300ms
—
Query Latency (Typical)(milliseconds)
50-200ms
—
Show 26 more attributes
Data Ingestion Rate(GB/sec)
1,000,000+ realtime
—
Time to Insight (Complex Query)(seconds)
0.2 (pre-aggregated metrics)
—
Average Query Latency (Standard Aggregation)(milliseconds)
45ms
—
Query Latency (Aggregation on 1B rows)(milliseconds)
~250ms
~800ms
Real-Time Ingestion Latency(milliseconds)
~10-100ms
~100-500ms
Typical Query Latency (100GB Dataset)(milliseconds)
50-500ms
—
Data Ingestion Freshness(seconds)
1-5 seconds
—
Maximum Concurrent Queries (Standard Node)(queries)
32+ (distributed)
—
Free Tier Data Retention(days)
30+ days (self-hosted)
—
Query Response Time (1B records)(milliseconds)
50-200ms
—
Max Ingestion Throughput(events/second)
75,000
—
Query Latency (50th percentile)(milliseconds)
300
—
P99 Query Latency(milliseconds)
100-500ms
—
Average Time to Root Cause (MTTR)(minutes)
45-120 (manual investigation required)
—
Average Query Latency(milliseconds)
47ms
—
Bulk Indexing Performance(%)
65 docs/sec per thread
—
Query Latency (p99)(milliseconds)
50-200ms
—
API Response Time for Simple Search(milliseconds)
100-300ms
—
Default Data Retention(days)
Unlimited (configurable)
—
Query Latency (p95 on 1M docs)(milliseconds)
50-200 ms
—
Query Performance on 1TB Index(milliseconds)
100-2000ms (depends on tuning)
—
Memory Usage (Baseline Configuration)(MB)
512 MB
—
Search Latency (99th Percentile)(ms)
150 ms
—
Typical Query Latency (1B rows, GROUP BY)(milliseconds)
500-2000ms
—
Data Ingestion Capacity(events/second)
1,000,000+
—
Search Query Latency (1B docs)(milliseconds)
50-200ms
—
Data Compression Ratio(ratio)
8:1 to 15:1
4:1 to 8:1
Index Size to Data Ratio(multiplier)
0.5-2x
—
Maximum Cluster Nodes(nodes)
500+ nodes tested
Unlimited (enterprise only)
Max Recommended Dataset Size(terabytes)
10TB practical limit
—
Maximum Daily Data Volume(terabytes)
500+ TB/day sustainable
—
Maximum Single Query Dataset Size(billion rows)
100+ billion
10-50 billion (performance degradation)
Horizontal Scalability(text)
Linear scaling with added nodes
—
Show 7 more attributes
Maximum Data Volume per Cluster(TB)
Petabyte-scale (1000+ TB)
—
Maximum Recommended Cluster Size(nodes)
1,000+ (tested to 10,000 with advanced tuning)
—
Ingest Rate (Typical)(events per second)
500,000+ EPS per cluster
—
Maximum Practical Index Size(GB)
Petabyte-scale (unlimited)
—
Maximum Recommended Data Scale(documents)
1+ Billion (across clusters)
—
Maximum Dataset Size (Practical Limit)(TB)
Unlimited (petabyte-scale)
—
Maximum Practical Dataset Size(petabytes)
5+ PB (operational limit)
—
SQL Compatibility(percentage)
Druid SQL (subset of ANSI)
—
Streaming Integration
Native (Kafka, Kinesis, Pulsar)
—
Transaction Support(consistency level)
No ACID (eventual consistency)
—
SQL Feature Completeness(percentage)
70% (Druid SQL dialect)
—
Time-Series Aggregation Support(native features)
Native time_floor, granular rollups, datasource-level aggregations
—
Show 11 more attributes
Full-Text Search Native Support
Limited (no native implementation)
Native with advanced analyzers
Supported Programming Languages(count)
Unlimited (via client libraries)
—
Full-Text Search Capability
Native (analyzers, relevance tuning, fuzzy matching)
—
Faceted Search Native Support
Via aggregations (requires custom queries)
—
Out-of-Box Integrations(count)
~300 integrations
—
Default Typo Tolerance
Manual configuration required
—
Available Integrations(count)
1000+ plugins/integrations
—
Full-Text Search Languages Supported(languages)
30+ languages
—
Typo Tolerance (Out-of-Box)(null)
Requires configuration/plugin
—
Pre-Built Integrations(count)
300+
—
SQL Language Support(native support)
Via plugin (not native)
—
GitHub Stars (2026)(stars)
16,000+ stars
—
GitHub Community (Stars)(stars)
14,000
67,000
GitHub Stars(stars)
65,800
—
GitHub Community Size(stars)
68,000+ stars
—
Community Size(members)
180,000+ (open-source)
—
Show 1 more attribute
GitHub Community Stars(stars)
150,000+
—
Compression Ratio(ratio)
4:1-6:1
—
Columnar Compression Ratio(ratio (data reduction %))
4:1 to 10:1
—
Data Compression Ratio (metrics)(ratio)
4:1
—
Data Retention Default(months)
Unlimited (storage-dependent)
—
Typical Storage Cost(USD per TB per month)
$50-80
—
Storage Cost per TB per Month(USD)
$0.50-$1.00
—
Total Cost of Ownership (Year 1)(USD)
$0-$10,000 (self-hosted) or $20,000-$60,000 (managed)
—
Annual Commercial Support Cost(USD)
$5,000-$50,000
—
Annual Infrastructure Cost (100M docs)(USD)
$50,000-150,000
—
Show 3 more attributes
Total Cost of Ownership (3-year, 500GB/month ingestion)(USD)
$120,000 (self-hosted licensing + 2 FTEs infrastructure)
—
Annual Cost for 500GB/day Ingestion(USD)
$180,000-$240,000
—
Data Retention Cost per GB/month(USD)
$0.50-$1.50
—
Minimum Recommended Cluster Size(nodes)
10 nodes
—
Setup Time to Production(minutes)
40-80 (self-hosted)
—
Implementation Timeline(months)
6-12 weeks
—
Time to Production(days)
14-28 days (self-hosted)
—
Setup Time (Basic Deployment)(minutes)
60-120 minutes
—
Enterprise Customers (2025)(count)
~400 enterprises
—
Production Deployments (Estimated)(count)
500,000+ companies worldwide
—
Base Setup Cost (Annual)(USD)
$50,000-500,000 (infrastructure)
—
Monthly Ingestion Cost per GB(USD)
$0.10 - $0.20
—
Base Annual Cost (Small Deployment)(USD)
$0 (self-hosted)
—
Per-Gigabyte Ingestion Cost(USD per GB per day)
$0 (unlimited after infrastructure cost)
—
Monthly Cost (100GB index, 1M queries/month)(USD)
$200-500 self-hosted
—
Show 6 more attributes
Monthly Cost (1TB/day ingestion)(USD)
$3,000-$8,000
—
Price per GB Ingested(USD/GB)
$0.02-$0.05
—
Starting Annual Cost(USD)
$0 (open-source) or $12,000
—
Base Monthly Cost (Small Team)(USD)
$0 (self-hosted) / $884 (cloud)
—
Annual Cloud Subscription (Large Team)(USD)
$10,600-$40,000 (Elastic Cloud)
—
Annual Licensing Cost (Small Deployment)(USD)
$0 (self-managed)
—
Operational Complexity (1-10 scale)(complexity score)
8/10 (high setup & tuning)
—
Streaming Ingestion Latency(seconds)
0.1-0.5 seconds
—
Memory Per 1TB Data(GB)
150GB
—
Minimum Recommended Heap Memory(GB)
4-8 GB
8-16 GB
SQL Standard Compliance(% compatibility)
Druid SQL dialect (proprietary subset)
—
SQL Dialect Compatibility(percentage)
Partial (60-70% ANSI SQL)
—
Supported Technologies(integrations)
1000+ via community/Beats
—
Supported Languages/Frameworks(count)
45+ via Logstash and Beats
—
Cluster Node Types Required
Coordinator, Data, Query, Broker (4+ types)
—
Kafka Integration Latency(seconds)
0.2-0.5 seconds (real-time)
—
First Release Year(year)
2012 (Metamarkets)
—
Setup Complexity (1-10 scale)(difficulty score)
8/10 (complex)
5/10 (moderate)
Query Language Complexity (1-10 scale)(complexity rating)
7 (Lucene/DSL - steep learning curve)
—
Time to First Production Query(days)
1-3 days (schemaless indexing)
—
Typical Setup Time for Production(days)
14-30 days
—
Deployment Options
Self-hosted, SaaS (Elastic Cloud), or Kubernetes
—
Average Customer Onboarding Time(hours)
30-90 days
—
Built-in Compliance Certifications(count)
0 (requires custom hardening)
—
Machine Learning Use Cases Included(count)
3 (anomaly detection, forecasting, outlier detection)
—
Aggregation Types Supported(count)
40+ aggregation types
—
Community Support Response Time(hours)
12-48 (community forums)
—
Support SLA Response Time(hours)
Community-based or 4+ hours (enterprise)
—
Minimum Cluster Node Count(nodes)
2
—
Replication Setup Time(minutes)
5-10 minutes
—
Infrastructure Management Requirement
Requires DevOps expertise
—
Infrastructure Management Overhead(hours per month)
1.0-2.0 FTE
—
Infrastructure Setup Complexity(DevOps hours)
80-200 hours (extensive)
—
Show 3 more attributes
Required Infrastructure Team Size (100 users)(FTEs)
2-3 (cluster management + optimization)
—
Typical Deployment Complexity(relative score)
Low-Medium (simpler operations)
—
Minimum Required DevOps FTE(people)
2-4 full-time engineers
—
Third-Party Integrations(count)
2000+
—
Memory Overhead (1M events)(MB per node)
250
—
Minimum Memory Requirement(GB)
2-4 GB
—
SQL Support
SQL via plugin (Elastic SQL), primary is Query DSL
—
Typical Memory Per Node(GB)
32-64GB for equivalent throughput
—
GitHub Stars (as of 2026)(thousands)
66,000+
—
Data Retention (Default)(months)
Unlimited (storage-dependent)
—
Data Retention (Standard)(months)
Unlimited (user-configured)
—
Learning Curve(difficulty rating)
3-6 months
—
Query Language
Query DSL (complex, steep learning curve)
—
API Complexity(learning effort)
Complex Query DSL requiring technical expertise
—
Setup Time to First Query(minutes)
120-240 minutes
—
Minimum RAM Requirement(GB)
512MB
—
Enterprise Market Share(%)
66%
—
Time to First Production Deployment(days)
14-28 days
—
Average Time to Value(days)
14-30 days
—
Initial Deployment Time(weeks)
2-4 weeks
—
Customization Depth (1-10 scale)(score)
9/10 (plugins, analyzers, scripting)
—
Query Language Complexity
Advanced EQL with full customization
—
GitHub Stars (Community Size)(stars)
~60,000 stars
—
Enterprise Market Share (2024)(%)
70%
—
Time to First Production Insight(days)
14-30 days
—
Automatic Instrumentation Coverage(technologies)
30-40 (via Beats)
—
AI-Powered Root Cause Analysis(native capability)
Requires third-party integrations
—
AI Anomaly Detection
Requires ML plugins or third-party tools
—
Query Language Flexibility(flexibility score)
Full Elasticsearch Query DSL and custom scripts
—
Community Size & Resources(active contributors)
1M+ active developers
—
Mean Time to Resolution (MTTR) Improvement(percentage reduction)
Depends on manual investigation
—
Number of Pre-built Integrations(count)
150+
—
Supported Data Types(types)
Logs, metrics (via plugins), custom JSON data
—
Query Language Expressiveness(languages supported)
Lucene, KQL, SQL, JavaScript
—
Full-Text Search Optimization
Native, highly optimized (primary feature)
—
GitHub Stars (Community Size Proxy)(stars)
68,000+
—
Real-Time Ingestion Support
Via Beats and Logstash (indirect)
—
SQL Query Support
SQL plugin available (limited JOIN support)
—
SLA Uptime Guarantee(percent)
Varies by deployment (self-hosted: customer responsibility)
—
Open-Source
Yes (SSPL/Elastic License)
—
Initial Setup Time(minutes)
40-80 hours for production cluster
—
Storage Compression Ratio(ratio)
4:1
—
Pros & Cons
10 pros·6 cons across both
E
Druid
+5-3
Pros
- Sub-500ms query latency on billion-row datasets via columnar compression
- 8:1 to 15:1 compression ratios with numeric-optimized storage
- Native rollup functionality for pre-aggregated metrics at ingest time
- Horizontal scalability with segment replication and distributed query execution
- Real-time ingestion from Kafka, S3, and other sources with millisecond latency
Cons
- Steep learning curve with complex segment management and deep configuration requirements
- Limited full-text search capabilities without external integrations
- Smaller community (14K GitHub stars) resulting in fewer third-party tools
E
Elasticsearch
+5-3
Pros
- Native full-text search with advanced analyzers, stemming, and fuzzy matching
- Larger ecosystem (67K GitHub stars) with extensive plugins, Beats, and Logstash integration
- Lower barrier to entry for single-node deployments and prototyping
- Rich query DSL supporting complex boolean logic and text-relevance scoring
- Excellent for log analysis, APM, and security monitoring via Elastic Stack
Cons
- Higher memory overhead and storage requirements compared to columnar databases
- Query latency increases significantly with dataset size (100-2000ms at scale)
- Document model less efficient for purely numeric aggregate queries
Frequently Asked Questions
5 questions
Use Druid when you need sub-second analytics on massive time-series datasets (100B+ rows), require extreme storage compression, perform frequent numerical aggregations, or need real-time metric pre-computation via rollups. Druid's columnar architecture excels at OLAP workloads with predictable query patterns.
Resources & Learn More
Curated sources to dive deeper
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Wikipedia
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