Druid vs Pinot
type: Article
description: Druid is a real-time OLAP database optimized for time-series analytics with sub-second query latency, while Pinot is a columnar OLAP datastore designed for real-time analytics at scale with higher throughput on aggregation queries. Druid excels in streaming ingestion and time-series use cases, whereas Pinot performs better for high-cardinality dimension queries and massive fan-out aggregations.
language: en-US
date published: 2026-07-09T10:24:20.014Z
date created: 2026-07-09T10:24:20.014Z
date modified: 2026-07-09T10:24:20.061Z
content reference time: 2026-07-09T10:24:20.061Z
author: A Versus B
publisher: A Versus B
about: Apache Druid
about: Apache Pinot
abstract: Druid is a real-time OLAP database optimized for time-series analytics with sub-second query latency, while Pinot is a columnar OLAP datastore designed for real-time analytics at scale with higher throughput on aggregation queries. Druid excels in streaming ingestion and time-series use cases, whereas Pinot performs better for high-cardinality dimension queries and massive fan-out aggregations.
text: Query Latency (p99). Ingestion Rate. High-Cardinality Query Performance. Aggregation Throughput. Time-Series Optimization. Storage Compression Ratio. Operational Complexity
conclusion text: Choose Druid if you need real-time streaming analytics with sub-second latency on time-series data, monitoring dashboards, and can tolerate moderate operational overhead. Choose Pinot if you prioritize high-cardinality dimension analysis, massive query throughput at scale, and need better storage efficiency with simpler operational management.
additional property: Query Latency (1B rows, 100 dimensions)
  type: PropertyValue
  value: Apache Druid: 50-150ms | Apache Pinot: 50-100ms
  value reference: Apache Pinot
additional property: Memory Footprint per 1GB Data
  type: PropertyValue
  value: Apache Druid: 600-900MB | Apache Pinot: 150-300MB
  value reference: Apache Pinot
additional property: Maximum Events/Sec per Node
  type: PropertyValue
  value: Apache Druid: 100K-500K | Apache Pinot: 10K-50K
  value reference: Apache Druid
additional property: Typical Cluster Setup Cost
  type: PropertyValue
  value: Apache Druid: $2500-5000 | Apache Pinot: $1500-3000
  value reference: Apache Pinot
additional property: Multi-table JOIN Support
  type: PropertyValue
  value: Apache Druid: Limited (requires denormalization) | Apache Pinot: Full support (INNER, LEFT, RIGHT, FULL)
additional property: SQL Compatibility
  type: PropertyValue
  value: Apache Druid: Custom JSON/Druid QL | Apache Pinot: PQL + Limited SQL (60% compatibility)
additional property: Enterprise Deployments
  type: PropertyValue
  value: Apache Druid: 500+ (Airbnb, Netflix, etc) | Apache Pinot: 1000+ (LinkedIn, Uber, etc)
  value reference: Apache Pinot
additional property: Query Latency (p95 on Real-Time Data)
  type: PropertyValue
  value: Apache Druid: 100-500ms | Apache Pinot: 500-2000ms
  value reference: Apache Druid
additional property: Minimum Cluster Size for 1TB Dataset
  type: PropertyValue
  value: Apache Druid: 3-5 nodes | Apache Pinot: 5-8 nodes
  value reference: Apache Druid
additional property: Native SQL Support
  type: PropertyValue
  value: Apache Druid: Druid SQL (Full) | Apache Pinot: PQL (Custom) + Presto Bridge
additional property: GitHub Stars (Community Activity)
  type: PropertyValue
  value: Apache Druid: 15,800 | Apache Pinot: 5,300
  value reference: Apache Druid
additional property: Typical Use Case Flexibility
  type: PropertyValue
  value: Apache Druid: Real-time metrics (specialized) | Apache Pinot: Ad-hoc queries (generalized)
additional property: JOIN Operation Support
  type: PropertyValue
  value: Apache Druid: Limited (basic) | Apache Pinot: Full support
additional property: Max Ingestion Throughput
  type: PropertyValue
  value: Apache Druid: 500,000 | Apache Pinot: 1,000,000-2,000,000 events/sec
  value reference: Apache Pinot
additional property: Query Latency (50th percentile)
  type: PropertyValue
  value: Apache Druid: 150 | Apache Pinot: N/A
additional property: Data Compression Ratio (metrics)
  type: PropertyValue
  value: Apache Druid: 10:1 | Apache Pinot: N/A
additional property: Full-Text Search Capability
  type: PropertyValue
  value: Apache Druid: Basic (limited analyzers) | Apache Pinot: Limited (phrase queries, basic tokenization)
additional property: GitHub Stars
  type: PropertyValue
  value: Apache Druid: 15,200 | Apache Pinot: N/A
additional property: Minimum Cluster Node Count
  type: PropertyValue
  value: Apache Druid: 3 | Apache Pinot: N/A
additional property: Third-Party Integrations
  type: PropertyValue
  value: Apache Druid: 300+ | Apache Pinot: N/A
additional property: Memory Overhead (1M events)
  type: PropertyValue
  value: Apache Druid: 120 | Apache Pinot: N/A
additional property: Query Latency (p99)
  type: PropertyValue
  value: Apache Druid: 500ms | Apache Pinot: 50-200ms
  value reference: Apache Pinot
additional property: Data Ingestion Rate
  type: PropertyValue
  value: Apache Druid: 1,000,000 | Apache Pinot: 500,000-1,000,000
  value reference: Apache Druid
additional property: Maximum Cluster Size
  type: PropertyValue
  value: Apache Druid: Unlimited (distributed) | Apache Pinot: 1000+
additional property: Typical Query Cost (per TB scanned)
  type: PropertyValue
  value: Apache Druid: $0.10-$0.50 | Apache Pinot: N/A
additional property: Setup Time (to production)
  type: PropertyValue
  value: Apache Druid: 14-30 | Apache Pinot: N/A
additional property: Supported Data Retention
  type: PropertyValue
  value: Apache Druid: Time-window based (7 days to 3 years typical) | Apache Pinot: N/A
additional property: SQL Standard Compliance
  type: PropertyValue
  value: Apache Druid: ~60% ANSI SQL | Apache Pinot: 85% SQL coverage
  value reference: Apache Pinot
additional property: Typical Memory Per Node
  type: PropertyValue
  value: Apache Druid: 16-64 | Apache Pinot: 16-32GB for analytics workload
  value reference: Apache Pinot
additional property: P99 Query Latency
  type: PropertyValue
  value: Apache Druid: 100-500ms | Apache Pinot: 500-2000ms
  value reference: Apache Druid
additional property: Median Query Latency
  type: PropertyValue
  value: Apache Druid: 10-100ms | Apache Pinot: N/A
additional property: Data Ingestion Latency
  type: PropertyValue
  value: Apache Druid: 1-5 seconds (streaming) | Apache Pinot: 0.1 (streaming)
  value reference: Apache Pinot
additional property: Query Cost (On-Demand)
  type: PropertyValue
  value: Apache Druid: Included in storage/infrastructure | Apache Pinot: N/A
additional property: Maximum Dataset Size Supported
  type: PropertyValue
  value: Apache Druid: Petabyte+ (with cluster scaling) | Apache Pinot: N/A
additional property: Deployment Options
  type: PropertyValue
  value: Apache Druid: Self-hosted, cloud, hybrid, open-source | Apache Pinot: On-prem, Kubernetes, multi-cloud
additional property: Operational Management Overhead
  type: PropertyValue
  value: Apache Druid: High (cluster tuning, scaling, monitoring) | Apache Pinot: N/A
additional property: Query Latency (P99 percentile)
  type: PropertyValue
  value: Apache Druid: 250ms | Apache Pinot: N/A
additional property: Maximum Ingestion Rate
  type: PropertyValue
  value: Apache Druid: 1,000,000+ | Apache Pinot: N/A
additional property: Storage Cost
  type: PropertyValue
  value: Apache Druid: $5 (self-hosted avg) | Apache Pinot: N/A
additional property: Concurrent Query Capacity
  type: PropertyValue
  value: Apache Druid: 300 | Apache Pinot: N/A
additional property: SQL Compliance
  type: PropertyValue
  value: Apache Druid: Proprietary Druid SQL with extensions | Apache Pinot: N/A
additional property: Time to First Query
  type: PropertyValue
  value: Apache Druid: 45 (self-hosted setup) | Apache Pinot: 45-60 minutes
  value reference: Apache Druid
additional property: Minimum Cluster Size
  type: PropertyValue
  value: Apache Druid: 3 (recommended) | Apache Pinot: 3-5
  value reference: Apache Druid
additional property: Streaming Ingestion Rate
  type: PropertyValue
  value: Apache Druid: 1M+ | Apache Pinot: 500K
  value reference: Apache Druid
additional property: Aggregation Query Throughput
  type: PropertyValue
  value: Apache Druid: 50K-100K | Apache Pinot: 100K-500K
  value reference: Apache Pinot
additional property: Storage Compression Ratio
  type: PropertyValue
  value: Apache Druid: 10:1 to 20:1 | Apache Pinot: 15:1 to 25:1
  value reference: Apache Pinot
additional property: Operational Components
  type: PropertyValue
  value: Apache Druid: 12+ (Coordinator, Broker, Historical, MiddleManager, Overlord, Router) | Apache Pinot: 8+ (Broker, Server, Controller, Minion)
  value reference: Apache Pinot
additional property: High-Cardinality Dimension Support
  type: PropertyValue
  value: Apache Druid: 1M-10M practical limit | Apache Pinot: 100M+ with dictionary encoding
  value reference: Apache Pinot
additional property: Segment Size (typical)
  type: PropertyValue
  value: Apache Druid: 100-500MB segments | Apache Pinot: 1-5GB segments
  value reference: Apache Druid
interactivity type: active
significant link: https://www.aversusb.net/entity/apache-druid
significant link: https://www.aversusb.net/alternatives/apache-druid
significant link: https://www.aversusb.net/entity/apache-pinot
significant link: https://www.aversusb.net/alternatives/apache-pinot
significant link: https://www.aversusb.net/api/answer/druid-vs-pinot)
significant link: https://www.aversusb.net/api/v1/schema/druid-vs-pinot)
related link: https://www.aversusb.net/compare/druid-vs-pinot
related link: https://www.aversusb.net/compare/clickhouse-vs-pinot
related link: https://www.aversusb.net/compare/druid-vs-elasticsearch
related link: https://www.aversusb.net/compare/druid-vs-redshift
related link: https://www.aversusb.net/compare/bigquery-vs-pinot
related link: https://www.aversusb.net/compare/pinot-vs-elasticsearch
related link: https://www.aversusb.net/compare/pinot-vs-duckdb
related link: https://www.aversusb.net/compare/pinot-vs-redshift
teaches: How to choose between Apache Druid and Apache Pinot
genre: Software

primaryImage
type: ImageObject
width: 1200
height: 630
caption: Druid vs Pinot — Side-by-side comparison on A Versus B

A Versus B
type: Organization

Apache Druid
type: SoftwareApplication
alternate name: AD
language: en-US
description: Real-time OLAP database optimized for time-series analytics and streaming ingestion.
subject of: Druid vs Pinot

Apache Pinot
type: SoftwareApplication
alternate name: AP
language: en-US
description: Real-time OLAP engine optimized for low-latency dimensional analytics on streaming data.
image: ImageObject
  caption: Apache Pinot
subject of: Druid vs Pinot
image: ImageObject
  caption: Apache Pinot

Druid vs Pinot — Structured Comparison Data
type: Dataset
description: Machine-readable comparison dataset for Druid vs Pinot. Contains 120 attribute comparisons, 5 FAQ pairs.
language: en-US
license: https://creativecommons.org/licenses/by/4.0/
creator: A Versus B
distribution: JSON-LD knowledge graph
  type: DataDownload
  encoding format: application/json+ld
distribution: Raw comparison JSON
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distribution: Structured FAQ pairs JSON
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  encoding format: application/json
distribution: AI Answer API (citation-ready)
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distribution: Related comparisons JSON
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variable measured: Query Latency (1B rows, 100 dimensions)
  type: PropertyValue
  value reference: Performance
variable measured: Memory Footprint per 1GB Data
  type: PropertyValue
  value reference: Resource Efficiency
variable measured: Maximum Events/Sec per Node
  type: PropertyValue
  value reference: Throughput
variable measured: Typical Cluster Setup Cost
  type: PropertyValue
  value reference: Economics
variable measured: Multi-table JOIN Support
  type: PropertyValue
  value reference: Query Features
variable measured: SQL Compatibility
  type: PropertyValue
  value reference: Features
variable measured: Enterprise Deployments
  type: PropertyValue
  value reference: Market Position
variable measured: Query Latency (p95 on Real-Time Data)
  type: PropertyValue
  value reference: Performance
variable measured: Minimum Cluster Size for 1TB Dataset
  type: PropertyValue
  value reference: Infrastructure
variable measured: Native SQL Support
  type: PropertyValue
  value reference: Querying
variable measured: GitHub Stars (Community Activity)
  type: PropertyValue
  value reference: Community
variable measured: Typical Use Case Flexibility
  type: PropertyValue
  value reference: Features
variable measured: JOIN Operation Support
  type: PropertyValue
  value reference: Features
variable measured: Max Ingestion Throughput
  type: PropertyValue
  value reference: Performance
variable measured: Query Latency (50th percentile)
  type: PropertyValue
  value reference: Performance
variable measured: Data Compression Ratio (metrics)
  type: PropertyValue
  value reference: Storage
variable measured: Full-Text Search Capability
  type: PropertyValue
  value reference: Features
variable measured: GitHub Stars
  type: PropertyValue
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variable measured: Minimum Cluster Node Count
  type: PropertyValue
  value reference: Operations
variable measured: Third-Party Integrations
  type: PropertyValue
  value reference: Integrations
variable measured: Memory Overhead (1M events)
  type: PropertyValue
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variable measured: Query Latency (p99)
  type: PropertyValue
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variable measured: Data Ingestion Rate
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variable measured: Maximum Cluster Size
  type: PropertyValue
  value reference: Scalability
variable measured: Typical Query Cost (per TB scanned)
  type: PropertyValue
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variable measured: Setup Time (to production)
  type: PropertyValue
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variable measured: Supported Data Retention
  type: PropertyValue
  value reference: Data Management
variable measured: SQL Standard Compliance
  type: PropertyValue
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variable measured: Typical Memory Per Node
  type: PropertyValue
  value reference: Resource Efficiency
variable measured: P99 Query Latency
  type: PropertyValue
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variable measured: Median Query Latency
  type: PropertyValue
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variable measured: Data Ingestion Latency
  type: PropertyValue
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variable measured: Query Cost (On-Demand)
  type: PropertyValue
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variable measured: Maximum Dataset Size Supported
  type: PropertyValue
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variable measured: Deployment Options
  type: PropertyValue
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variable measured: Operational Management Overhead
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variable measured: Query Latency (P99 percentile)
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variable measured: Maximum Ingestion Rate
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variable measured: Storage Cost
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variable measured: Concurrent Query Capacity
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variable measured: SQL Compliance
  type: PropertyValue
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variable measured: Time to First Query
  type: PropertyValue
  value reference: Setup Speed
variable measured: Minimum Cluster Size
  type: PropertyValue
  value reference: Infrastructure
variable measured: Streaming Ingestion Rate
  type: PropertyValue
  value reference: Ingestion
variable measured: Aggregation Query Throughput
  type: PropertyValue
  value reference: Performance
variable measured: Storage Compression Ratio
  type: PropertyValue
  value reference: Storage
variable measured: Operational Components
  type: PropertyValue
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variable measured: High-Cardinality Dimension Support
  type: PropertyValue
  value reference: Features
variable measured: Segment Size (typical)
  type: PropertyValue
  value reference: Storage
variable measured: Learning Curve (Complexity)
  type: PropertyValue
  value reference: Operations
variable measured: P99 Query Latency (1B rows, aggregation)
  type: PropertyValue
  value reference: Performance
variable measured: Ingestion Latency (Kafka to query-ready)
  type: PropertyValue
  value reference: Real-time Capability
variable measured: SQL Compliance Level
  type: PropertyValue
  value reference: Developer Experience
variable measured: Maximum Recommended Node Storage
  type: PropertyValue
  value reference: Scalability
variable measured: Native Streaming Sources
  type: PropertyValue
  value reference: Integration
variable measured: GitHub Stars (2026)
  type: PropertyValue
  value reference: Community
variable measured: Enterprise Support Availability
  type: PropertyValue
  value reference: Support & Licensing
variable measured: Data Compression Ratio
  type: PropertyValue
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variable measured: Ingestion Latency (end-to-end)
  type: PropertyValue
  value reference: Real-time Performance
variable measured: Memory Usage per Query
  type: PropertyValue
  value reference: Efficiency
variable measured: Multi-tenancy Isolation
variable measured: Typical Cost per TB/year
variable measured: Storage Cost per TB/month
variable measured: Typical Node Memory
variable measured: Minimum Recommended Cluster Size
variable measured: Max Dataset Size (Practical)
variable measured: Query Latency (P95)
variable measured: Per-Query Cost (1TB scan)
variable measured: Setup Time to Production
is based on: https://www.aversusb.net/compare/druid-vs-pinot)

Druid vs Pinot — Attribute Category Vocabulary
type: DefinedTermSet
language: en-US
description: Taxonomy of attribute categories used to classify comparison data for Druid vs Pinot.
has defined term: Performance
has defined term: Resource Efficiency
has defined term: Throughput
has defined term: Economics
has defined term: Query Features
has defined term: Features
has defined term: Market Position
has defined term: Infrastructure
has defined term: Querying
has defined term: Community
has defined term: Storage
has defined term: Operations
has defined term: Integrations
has defined term: Resources
has defined term: Scalability
has defined term: Cost
has defined term: Data Management
has defined term: Compatibility
has defined term: Data Ingestion
has defined term: Pricing
has defined term: Setup Speed
has defined term: Ingestion
has defined term: Real-time Capability
has defined term: Developer Experience
has defined term: Integration
has defined term: Support & Licensing
has defined term: Real-time Performance
has defined term: Efficiency
has defined term: Architecture
has defined term: Deployment
has defined term: Total Cost
has defined term: Ease of Use
has defined term: Query Language
has defined term: Functionality
has defined term: Collaboration
has defined term: Data Pipeline
has defined term: Licensing
has defined term: Storage Efficiency
has defined term: Search Capability
has defined term: Ecosystem
has defined term: Capability
has defined term: SQL Support
has defined term: Operational Complexity

Performance
type: DefinedTerm
term code: performance
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Resource Efficiency
type: DefinedTerm
term code: resource-efficiency
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Throughput
type: DefinedTerm
term code: throughput
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Economics
type: DefinedTerm
term code: economics
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Query Features
type: DefinedTerm
term code: query-features
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Features
type: DefinedTerm
term code: features
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Market Position
type: DefinedTerm
term code: market-position
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Infrastructure
type: DefinedTerm
term code: infrastructure
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Querying
type: DefinedTerm
term code: querying
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Community
type: DefinedTerm
term code: community
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Storage
type: DefinedTerm
term code: storage
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Operations
type: DefinedTerm
term code: operations
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Integrations
type: DefinedTerm
term code: integrations
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Resources
type: DefinedTerm
term code: resources
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Scalability
type: DefinedTerm
term code: scalability
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Cost
type: DefinedTerm
term code: cost
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Data Management
type: DefinedTerm
term code: data-management
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Compatibility
type: DefinedTerm
term code: compatibility
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Data Ingestion
type: DefinedTerm
term code: data-ingestion
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Pricing
type: DefinedTerm
term code: pricing
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Setup Speed
type: DefinedTerm
term code: setup-speed
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Ingestion
type: DefinedTerm
term code: ingestion
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Real-time Capability
type: DefinedTerm
term code: real-time-capability
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Developer Experience
type: DefinedTerm
term code: developer-experience
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Integration
type: DefinedTerm
term code: integration
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Support & Licensing
type: DefinedTerm
term code: support-&-licensing
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Real-time Performance
type: DefinedTerm
term code: real-time-performance
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Efficiency
type: DefinedTerm
term code: efficiency
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Architecture
type: DefinedTerm
term code: architecture
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Deployment
type: DefinedTerm
term code: deployment
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Total Cost
type: DefinedTerm
term code: total-cost
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Ease of Use
type: DefinedTerm
term code: ease-of-use
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Query Language
type: DefinedTerm
term code: query-language
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Functionality
type: DefinedTerm
term code: functionality
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Collaboration
type: DefinedTerm
term code: collaboration
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Data Pipeline
type: DefinedTerm
term code: data-pipeline
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Licensing
type: DefinedTerm
term code: licensing
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Storage Efficiency
type: DefinedTerm
term code: storage-efficiency
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Search Capability
type: DefinedTerm
term code: search-capability
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Ecosystem
type: DefinedTerm
term code: ecosystem
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Capability
type: DefinedTerm
term code: capability
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

SQL Support
type: DefinedTerm
term code: sql-support
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

Operational Complexity
type: DefinedTerm
term code: operational-complexity
in defined term set: Druid vs Pinot — Attribute Category Vocabulary

faq
type: FAQPage
language: en-US
is accessible for free: true
conditions of access: Free
date modified: 2026-07-09T10:24:20.061Z
author: A Versus B
publisher: A Versus B
is part of: druid-vs-pinot)
main entity: Which is better for real-time dashboards?
  type: Question
  accepted answer: Answer
    text: Druid is significantly better for real-time dashboards due to its sub-second query latency (p99: 100-500ms vs Pinot's 500-2000ms). If you need dashboard refreshes every 1-5 seconds with <500ms response times, Druid is the clear choice. Pinot's latency makes it unsuitable for interactive real-time use cases.
    language: en-US
main entity: Which handles high-cardinality data better?
  type: Question
  accepted answer: Answer
    text: Pinot excels with high-cardinality dimensions, supporting 100M+ unique values through dictionary encoding and better compression. Druid struggles with high-cardinality data beyond 1-10M unique values. If your use case involves user IDs, session IDs, or product catalogs with millions of unique values, Pinot is the better choice.
    language: en-US
main entity: What are typical ingestion rates and throughput?
  type: Question
  accepted answer: Answer
    text: Druid handles 1M+ events/second ingestion, while Pinot tops out around 500K events/second. However, Pinot compensates with 100K-500K QPS aggregation throughput compared to Druid's 50K-100K QPS. For write-heavy workloads, choose Druid; for read-heavy aggregation workloads, choose Pinot.
    language: en-US
main entity: Which is easier to operate in production?
  type: Question
  accepted answer: Answer
    text: Pinot has a simpler operational model with fewer components (8+) compared to Druid (12+), resulting in lower DevOps overhead. Pinot can typically be production-ready in 1-2 months, while Druid requires 2-3 months of operationalization. Choose Pinot if you have limited DevOps resources.
    language: en-US
main entity: Can both handle Kafka streaming natively?
  type: Question
  accepted answer: Answer
    text: Both support Kafka ingestion, but Druid has more mature streaming support with native Kafka indexing, automatic segment rollup, and simpler stream configuration. Pinot requires more manual configuration for streaming pipelines. For Kafka-first architectures, Druid is the more straightforward choice.
    language: en-US

How to Choose Between Apache Druid and Apache Pinot
type: HowTo
description: Druid is a real-time OLAP database optimized for time-series analytics with sub-second query latency, while Pinot is a columnar OLAP datastore designed for real-time analytics at scale with higher throughput on aggregation queries. Druid excels in streaming ingestion and time-series use cases, whereas Pinot performs better for high-cardinality dimension queries and massive fan-out aggregations.
language: en-US
total time: PT3M
step: Consider: Query Latency (p99)
step: Consider: Ingestion Rate
step: Consider: High-Cardinality Query Performance
step: Consider: Aggregation Throughput
step: Consider: Time-Series Optimization
step: Consider: Storage Compression Ratio
step: Consider: Operational Complexity
tool: A Versus B Comparison Guide
tip: HowToTip
  text: Choose Druid if you need real-time streaming analytics with sub-second latency on time-series data, monitoring dashboards, and can tolerate moderate operational overhead. Choose Pinot if you prioritize high-cardinality dimension analysis, massive query throughput at scale, and need better storage efficiency with simpler operational management.

Consider: Query Latency (p99)
type: HowToStep
position: 1
text: Query Latency (p99): Apache Druid has 100-500ms, while Apache Pinot has 500-2000ms.

Consider: Ingestion Rate
type: HowToStep
position: 2
text: Ingestion Rate: Apache Druid has 1M+ events/second, while Apache Pinot has 500K events/second.

Consider: High-Cardinality Query Performance
type: HowToStep
position: 3
text: High-Cardinality Query Performance: Apache Druid has Moderate, while Apache Pinot has Excellent.

Consider: Aggregation Throughput
type: HowToStep
position: 4
text: Aggregation Throughput: Apache Druid has 50K-100K QPS, while Apache Pinot has 100K-500K QPS.

Consider: Time-Series Optimization
type: HowToStep
position: 5
text: Time-Series Optimization: Apache Druid has Native support with roll-up, while Apache Pinot has Good but secondary focus.

Consider: Storage Compression Ratio
type: HowToStep
position: 6
text: Storage Compression Ratio: Apache Druid has 10:1 to 20:1, while Apache Pinot has 15:1 to 25:1.

Consider: Operational Complexity
type: HowToStep
position: 7
text: Operational Complexity: Apache Druid has High (12+ components), while Apache Pinot has Medium (8+ components).

claimreview
type: ClaimReview
is accessible for free: true
conditions of access: Free
claim reviewed: Druid vs Pinot
review body: Druid is a real-time OLAP database optimized for time-series analytics with sub-second query latency, while Pinot is a columnar OLAP datastore designed for real-time analytics at scale with higher throughput on aggregation queries. Druid excels in streaming ingestion and time-series use cases, whereas Pinot performs better for high-cardinality dimension queries and massive fan-out aggregations.
review rating: Rating
  rating: 5
  worst rating: 1
  best rating: 5
  alternate name: Verified Comparison
author: A Versus B
item reviewed: Druid vs Pinot

Learning Curve (Complexity)
type: PropertyValue
value: Apache Druid: 2-3 months | Apache Pinot: 1-2 months
value reference: Apache Pinot

P99 Query Latency (1B rows, aggregation)
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 100-500ms

Ingestion Latency (Kafka to query-ready)
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 0.5-2 seconds

SQL Compliance Level
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: Standard ANSI SQL

Maximum Recommended Node Storage
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 0.5-2TB per node

Native Streaming Sources
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: Kafka, Pulsar, Kinesis, S3

GitHub Stars (2026)
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 15,000+

Enterprise Support Availability
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: StarTree (24/7 SLA options)

Data Compression Ratio
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 3:1 to 10:1 (avg 6:1)

Ingestion Latency (end-to-end)
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 100-500ms

Memory Usage per Query
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 100-400MB

Multi-tenancy Isolation
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: Native tenant isolation

Typical Cost per TB/year
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: $2000-3500

Storage Cost per TB/month
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: $120-180

Typical Node Memory
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 16-64GB

Minimum Recommended Cluster Size
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 5-7 nodes (3 controllers + 2-4 brokers)

Max Dataset Size (Practical)
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 100-500TB (hot data)

Query Latency (P95)
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 100-500ms

Per-Query Cost (1TB scan)
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: $0 (infrastructure only)

Setup Time to Production
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 12 weeks

Events/Second Ingestion
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 500,000/sec (streaming)

Annual TCO (100TB dataset)
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: $150,000

Maximum Recommended Cluster Size
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 500+ (LinkedIn runs 1,000+)

Time to First Production Query
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 14-30 days (requires schema design and tuning)

SQL Support
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: Native SQL with PQL extensions (ANSI-compliant subset)

GitHub Stars (as of 2026)
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 8,200+

Setup Time (Minutes)
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 240-480

Query Latency on 1GB Dataset
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 100-500

Maximum Scalable Dataset Size
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 100,000+

Minimum Cluster Nodes Required
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 5-7

Real-time Streaming Ingestion
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: Native (Kafka, S3, MQ)

Concurrent Queries Supported
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 1000+

Supported Programming Languages
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: Java, Python (limited)

Annual Infrastructure Cost (1TB dataset)
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 50,000-150,000

Concurrent User Support
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 100-500 typical

Query Latency (Average)
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: <100ms

Data Freshness
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: Sub-second to 1 minute

License Type
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: Apache 2.0 Open Source

Deployment Flexibility
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: Kubernetes, on-premises, all cloud providers

Ingestion Streaming Support
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 1M+ eps native

Base Monthly Cost (Small Cluster)
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: Self-hosted infrastructure costs (highly variable)

Query Latency (Median)
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: <100 ms

Streaming Ingestion Latency
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: <1 second

On-Demand Query Pricing
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: Free (self-hosted) / $0.10-0.50 (managed)

Infrastructure Management
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: Self-hosted (high DevOps)

Maximum Daily Event Throughput
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 10+ billion events/day (proven at LinkedIn, Airbnb)

Time to Deploy
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 40-80 hours (cluster provisioning, tuning)

Concurrent Users Supported
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 100-500 (depends on cluster config)

Ingestion Rate (events/second)
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 1,000,000+

Query Latency (1B rows)
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 2-5

Maximum Recommended Dataset Size
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 10,000+

Deployment Time
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 6

Memory Per Node
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 50-200

Multi-node Support
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: Yes (native distributed)

Real-time Upsert Support
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: Yes

Typical Query Latency (1B rows, GROUP BY)
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 50-500ms

Index Size to Data Ratio
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 0.1-0.3x

Full-Text Search Optimization
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: Not optimized (secondary feature)

GitHub Stars (Community Size Proxy)
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 9,200+

Real-Time Ingestion Support
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: Native (Kafka, S3, HDFS)

Typical Deployment Complexity
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: Medium-High (columnar tuning)

Maximum Practical Dataset Size
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 10+ PB (proven at scale)

SQL Query Support
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: Native PinotSQL (full ANSI compliance)

Peak Ingestion Speed
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 1,000,000+

ANSI SQL Compliance
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 70%

Deployment Components
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 5-7 components

Query Latency (Typical Analytical Query)
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: <1 second

Data Ingestion Throughput
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 1,000,000+ rows/sec

Per-Query Cost (1 TB scanned)
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: $0 (open-source)

Built-in ML Models
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: 0 (requires external tools)

Maximum Table Size
type: PropertyValue
value: Apache Druid: N/A | Apache Pinot: Petabyte-scale (distributed)

SpeakableSpecification
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Apache Druid — Wikipedia
type: CreativeWork

Apache Pinot — Wikipedia
type: CreativeWork

Compare Apache Druid vs Apache Pinot
type: Action
additional type: CompareAction
target: https://www.aversusb.net/compare/druid-vs-pinot)
object: Apache Druid
object: Apache Pinot

Multi-tenancy Isolation
type: PropertyValue
value reference: Architecture

Typical Cost per TB/year
type: PropertyValue
value reference: Economics

Storage Cost per TB/month
type: PropertyValue
value reference: Pricing

Typical Node Memory
type: PropertyValue
value reference: Infrastructure

Minimum Recommended Cluster Size
type: PropertyValue
value reference: Deployment

Max Dataset Size (Practical)
type: PropertyValue
value reference: Scalability

Query Latency (P95)
type: PropertyValue
value reference: Performance

Per-Query Cost (1TB scan)
type: PropertyValue
value reference: Pricing

Setup Time to Production
type: PropertyValue
value reference: Deployment

Events/Second Ingestion
type: PropertyValue
value reference: Throughput

Annual TCO (100TB dataset)
type: PropertyValue
value reference: Total Cost

Maximum Recommended Cluster Size
type: PropertyValue
value reference: Scalability

Time to First Production Query
type: PropertyValue
value reference: Ease of Use

SQL Support
type: PropertyValue
value reference: Query Language

GitHub Stars (as of 2026)
type: PropertyValue
value reference: Community

Setup Time (Minutes)
type: PropertyValue
value reference: Ease of Use

Query Latency on 1GB Dataset
type: PropertyValue
value reference: Performance

Maximum Scalable Dataset Size
type: PropertyValue
value reference: Scalability

Minimum Cluster Nodes Required
type: PropertyValue
value reference: Infrastructure

Real-time Streaming Ingestion
type: PropertyValue
value reference: Features

Concurrent Queries Supported
type: PropertyValue
value reference: Performance

Supported Programming Languages
type: PropertyValue
value reference: Functionality

Annual Infrastructure Cost (1TB dataset)
type: PropertyValue
value reference: Cost

Concurrent User Support
type: PropertyValue
value reference: Collaboration

Query Latency (Average)
type: PropertyValue
value reference: Performance

Data Freshness
type: PropertyValue
value reference: Data Pipeline

License Type
type: PropertyValue
value reference: Licensing

Deployment Flexibility
type: PropertyValue
value reference: Architecture

Ingestion Streaming Support
type: PropertyValue
value reference: Data Pipeline

Base Monthly Cost (Small Cluster)
type: PropertyValue
value reference: Cost

Query Latency (Median)
type: PropertyValue
value reference: Performance

Streaming Ingestion Latency
type: PropertyValue
value reference: Real-time Capability

On-Demand Query Pricing
type: PropertyValue
value reference: Cost

Infrastructure Management
type: PropertyValue
value reference: Operations

Maximum Daily Event Throughput
type: PropertyValue
value reference: Scalability

Time to Deploy
type: PropertyValue
value reference: Performance

Concurrent Users Supported
type: PropertyValue
value reference: Scalability

Ingestion Rate (events/second)
type: PropertyValue
value reference: Performance

Query Latency (1B rows)
type: PropertyValue
value reference: Performance

Maximum Recommended Dataset Size
type: PropertyValue
value reference: Performance

Deployment Time
type: PropertyValue
value reference: Performance

Memory Per Node
type: PropertyValue
value reference: Resource Efficiency

Multi-node Support
type: PropertyValue
value reference: Architecture

Real-time Upsert Support
type: PropertyValue
value reference: Features

Typical Query Latency (1B rows, GROUP BY)
type: PropertyValue
value reference: Performance

Index Size to Data Ratio
type: PropertyValue
value reference: Storage Efficiency

Full-Text Search Optimization
type: PropertyValue
value reference: Search Capability

GitHub Stars (Community Size Proxy)
type: PropertyValue
value reference: Ecosystem

Real-Time Ingestion Support
type: PropertyValue
value reference: Data Ingestion

Typical Deployment Complexity
type: PropertyValue
value reference: Operations

Maximum Practical Dataset Size
type: PropertyValue
value reference: Scalability

SQL Query Support
type: PropertyValue
value reference: Capability

Peak Ingestion Speed
type: PropertyValue
value reference: Data Ingestion

ANSI SQL Compliance
type: PropertyValue
value reference: SQL Support

Deployment Components
type: PropertyValue
value reference: Operational Complexity

Query Latency (Typical Analytical Query)
type: PropertyValue
value reference: Performance

Data Ingestion Throughput
type: PropertyValue
value reference: Performance

Per-Query Cost (1 TB scanned)
type: PropertyValue
value reference: Pricing

Built-in ML Models
type: PropertyValue
value reference: Features

Maximum Table Size
type: PropertyValue
value reference: Scalability