Pinot vs ClickHouse
type: Article
description: Pinot is optimized for real-time analytics on streaming data with lower latency (sub-second queries), while ClickHouse excels at batch processing and complex analytical queries with superior compression (10-40x) and broader SQL compatibility. Pinot uses a distributed architecture with brokers and servers, whereas ClickHouse employs a simpler peer-to-peer model.
language: en-US
date published: 2026-07-09T04:58:48.640Z
date created: 2026-07-09T04:58:48.640Z
date modified: 2026-07-09T04:58:48.676Z
content reference time: 2026-07-09T04:58:48.676Z
author: A Versus B
publisher: A Versus B
about: Apache Pinot
about: ClickHouse
abstract: Pinot is optimized for real-time analytics on streaming data with lower latency (sub-second queries), while ClickHouse excels at batch processing and complex analytical queries with superior compression (10-40x) and broader SQL compatibility. Pinot uses a distributed architecture with brokers and servers, whereas ClickHouse employs a simpler peer-to-peer model.
text: Query Latency (P99). Data Compression Ratio. Real-Time Ingestion Speed. SQL Standard Compliance. Typical Storage per GB Data. Deployment Complexity. OLTP Query Support
conclusion text: Choose Pinot if you need sub-100ms query latency on streaming data with 1M+ events per second ingestion (common in ad-tech, user analytics, real-time dashboards). Choose ClickHouse if you prioritize ease of deployment, superior compression, complex analytical queries, and can tolerate 200-500ms latency (ideal for business intelligence, log analytics, metrics aggregation).
additional property: Query Latency (1B rows, 100 dimensions)
  type: PropertyValue
  value: Apache Pinot: 50-100ms | ClickHouse: N/A
additional property: Memory Footprint per 1GB Data
  type: PropertyValue
  value: Apache Pinot: 150-300MB | ClickHouse: N/A
additional property: Maximum Events/Sec per Node
  type: PropertyValue
  value: Apache Pinot: 10K-50K | ClickHouse: N/A
additional property: Typical Cluster Setup Cost
  type: PropertyValue
  value: Apache Pinot: $1500-3000 | ClickHouse: N/A
additional property: Multi-table JOIN Support
  type: PropertyValue
  value: Apache Pinot: Full support (INNER, LEFT, RIGHT, FULL) | ClickHouse: N/A
additional property: SQL Compatibility
  type: PropertyValue
  value: Apache Pinot: PQL + Limited SQL (60% compatibility) | ClickHouse: MySQL-compatible with ClickHouse extensions
additional property: Enterprise Deployments
  type: PropertyValue
  value: Apache Pinot: 1000+ (LinkedIn, Uber, etc) | ClickHouse: N/A
additional property: Data Compression Ratio
  type: PropertyValue
  value: Apache Pinot: 3-8x | ClickHouse: 10-40x
  value reference: ClickHouse
additional property: Ingestion Latency (end-to-end)
  type: PropertyValue
  value: Apache Pinot: 100-500ms | ClickHouse: 1000-10000ms
  value reference: Apache Pinot
additional property: Memory Usage per Query
  type: PropertyValue
  value: Apache Pinot: 100-400MB | ClickHouse: 50-200MB
  value reference: ClickHouse
additional property: Native SQL Support
  type: PropertyValue
  value: Apache Pinot: PQL (Custom) + Presto Bridge | ClickHouse: Standard SQL with extensions
additional property: Multi-tenancy Isolation
  type: PropertyValue
  value: Apache Pinot: Native tenant isolation | ClickHouse: Limited/requires custom logic
additional property: Typical Cost per TB/year
  type: PropertyValue
  value: Apache Pinot: $2000-3500 | ClickHouse: $800-1500
  value reference: ClickHouse
additional property: Query Latency (p95 on Real-Time Data)
  type: PropertyValue
  value: Apache Pinot: 500-2000ms | ClickHouse: N/A
additional property: Data Ingestion Rate
  type: PropertyValue
  value: Apache Pinot: 500,000-1,000,000 | ClickHouse: 1-5
  value reference: Apache Pinot
additional property: Minimum Cluster Size for 1TB Dataset
  type: PropertyValue
  value: Apache Pinot: 5-8 nodes | ClickHouse: N/A
additional property: GitHub Stars (Community Activity)
  type: PropertyValue
  value: Apache Pinot: 5,300 | ClickHouse: N/A
additional property: Storage Compression Ratio
  type: PropertyValue
  value: Apache Pinot: ~4-6x columnar | ClickHouse: N/A
additional property: Typical Use Case Flexibility
  type: PropertyValue
  value: Apache Pinot: Ad-hoc queries (generalized) | ClickHouse: N/A
additional property: JOIN Operation Support
  type: PropertyValue
  value: Apache Pinot: Full support | ClickHouse: N/A
additional property: Max Ingestion Throughput
  type: PropertyValue
  value: Apache Pinot: 1,000,000-2,000,000 events/sec | ClickHouse: 100,000-500,000 events/sec
  value reference: Apache Pinot
additional property: Query Latency (p99)
  type: PropertyValue
  value: Apache Pinot: 50-200ms | ClickHouse: 200-500ms
  value reference: Apache Pinot
additional property: Storage Cost per TB/Month
  type: PropertyValue
  value: Apache Pinot: $120-180 | ClickHouse: $40-60
  value reference: ClickHouse
additional property: Typical Node Memory
  type: PropertyValue
  value: Apache Pinot: 16-64GB | ClickHouse: 8-32GB
  value reference: ClickHouse
additional property: Minimum Recommended Cluster Size
  type: PropertyValue
  value: Apache Pinot: 5-7 nodes (3 controllers + 2-4 brokers) | ClickHouse: 3-5 nodes
  value reference: ClickHouse
additional property: Max Dataset Size (Practical)
  type: PropertyValue
  value: Apache Pinot: 100-500TB (hot data) | ClickHouse: 1000TB+ (unlimited with tiering)
  value reference: ClickHouse
additional property: Query Latency (P95)
  type: PropertyValue
  value: Apache Pinot: 100-500ms | ClickHouse: N/A
additional property: Per-Query Cost (1TB scan)
  type: PropertyValue
  value: Apache Pinot: $0 (infrastructure only) | ClickHouse: N/A
additional property: Data Ingestion Latency
  type: PropertyValue
  value: Apache Pinot: 0.1 (streaming) | ClickHouse: Microseconds to milliseconds
  value reference: Apache Pinot
additional property: Setup Time to Production
  type: PropertyValue
  value: Apache Pinot: 12 weeks | ClickHouse: N/A
additional property: Maximum Cluster Size
  type: PropertyValue
  value: Apache Pinot: 1000+ | ClickHouse: 100+
  value reference: Apache Pinot
additional property: SQL Standard Compliance
  type: PropertyValue
  value: Apache Pinot: 85% SQL coverage | ClickHouse: 70% ANSI SQL
  value reference: Apache Pinot
additional property: Events/Second Ingestion
  type: PropertyValue
  value: Apache Pinot: 500,000/sec (streaming) | ClickHouse: N/A
additional property: Annual TCO (100TB dataset)
  type: PropertyValue
  value: Apache Pinot: $150,000 | ClickHouse: N/A
additional property: P99 Query Latency
  type: PropertyValue
  value: Apache Pinot: 50-200ms | ClickHouse: N/A
additional property: Maximum Recommended Cluster Size
  type: PropertyValue
  value: Apache Pinot: 500+ (LinkedIn runs 1,000+) | ClickHouse: N/A
additional property: Full-Text Search Capability
  type: PropertyValue
  value: Apache Pinot: Limited (phrase queries, basic tokenization) | ClickHouse: N/A
additional property: Time to First Production Query
  type: PropertyValue
  value: Apache Pinot: 14-30 days (requires schema design and tuning) | ClickHouse: N/A
additional property: SQL Support
  type: PropertyValue
  value: Apache Pinot: Native SQL with PQL extensions (ANSI-compliant subset) | ClickHouse: N/A
additional property: Typical Memory Per Node
  type: PropertyValue
  value: Apache Pinot: 16-32GB for analytics workload | ClickHouse: N/A
additional property: GitHub Stars (as of 2026)
  type: PropertyValue
  value: Apache Pinot: 8,200+ | ClickHouse: 25000+
  value reference: ClickHouse
additional property: Setup Time (Minutes)
  type: PropertyValue
  value: Apache Pinot: 240-480 | ClickHouse: N/A
additional property: Query Latency on 1GB Dataset
  type: PropertyValue
  value: Apache Pinot: 100-500 | ClickHouse: N/A
additional property: Maximum Scalable Dataset Size
  type: PropertyValue
  value: Apache Pinot: 100,000+ | ClickHouse: N/A
additional property: Minimum Cluster Nodes Required
  type: PropertyValue
  value: Apache Pinot: 5-7 | ClickHouse: N/A
additional property: Real-time Streaming Ingestion
  type: PropertyValue
  value: Apache Pinot: Native (Kafka, S3, MQ) | ClickHouse: N/A
additional property: Concurrent Queries Supported
  type: PropertyValue
  value: Apache Pinot: 1000+ | ClickHouse: N/A
additional property: Supported Programming Languages
  type: PropertyValue
  value: Apache Pinot: Java, Python (limited) | ClickHouse: N/A
additional property: Annual Infrastructure Cost (1TB dataset)
  type: PropertyValue
  value: Apache Pinot: 50,000-150,000 | ClickHouse: N/A
interactivity type: active
significant link: https://www.aversusb.net/entity/apache-pinot
significant link: https://www.aversusb.net/alternatives/apache-pinot
significant link: https://www.aversusb.net/entity/clickhouse
significant link: https://www.aversusb.net/alternatives/clickhouse
significant link: https://www.aversusb.net/api/answer/pinot-vs-clickhouse)
significant link: https://www.aversusb.net/api/v1/schema/pinot-vs-clickhouse)
related link: https://www.aversusb.net/compare/clickhouse-vs-pinot
related link: https://www.aversusb.net/compare/clickhouse-vs-bigquery)
related link: https://www.aversusb.net/compare/snowflake-vs-clickhouse
related link: https://www.aversusb.net/compare/clickhouse-vs-duckdb
related link: https://www.aversusb.net/compare/clickhouse-vs-druid
related link: https://www.aversusb.net/compare/druid-vs-pinot
related link: https://www.aversusb.net/compare/bigquery-vs-pinot
related link: https://www.aversusb.net/compare/pinot-vs-elasticsearch
teaches: How to choose between Apache Pinot and ClickHouse
genre: Software

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

A Versus B
type: Organization

Apache Pinot
type: SoftwareApplication
alternate name: AP
language: en-US
description: Open-source distributed OLAP database for real-time analytics on streaming data
image: ImageObject
  caption: Apache Pinot
subject of: Pinot vs ClickHouse
image: ImageObject
  caption: Apache Pinot

ClickHouse
type: SoftwareApplication
alternate name: Clickhouse
language: en-US
description: Open-source columnar OLAP database with exceptional compression and SQL compatibility for analytical queries.
image: ImageObject
  caption: ClickHouse
subject of: Pinot vs ClickHouse
image: ImageObject
  caption: ClickHouse

Pinot vs ClickHouse — Structured Comparison Data
type: Dataset
description: Machine-readable comparison dataset for Pinot vs ClickHouse. Contains 142 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
  type: DataDownload
  encoding format: application/json
distribution: Structured FAQ pairs JSON
  type: DataDownload
  encoding format: application/json
distribution: AI Answer API (citation-ready)
  type: DataDownload
  encoding format: application/json
distribution: Related comparisons JSON
  type: DataDownload
  encoding format: application/json
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: Data Compression Ratio
  type: PropertyValue
  value reference: Storage Efficiency
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: Native SQL Support
  type: PropertyValue
  value reference: Querying
variable measured: Multi-tenancy Isolation
  type: PropertyValue
  value reference: Architecture
variable measured: Typical Cost per TB/year
  type: PropertyValue
  value reference: Economics
variable measured: Query Latency (p95 on Real-Time Data)
  type: PropertyValue
  value reference: Performance
variable measured: Data Ingestion Rate
  type: PropertyValue
  value reference: Performance
variable measured: Minimum Cluster Size for 1TB Dataset
  type: PropertyValue
  value reference: Infrastructure
variable measured: GitHub Stars (Community Activity)
  type: PropertyValue
  value reference: Community
variable measured: Storage Compression Ratio
  type: PropertyValue
  value reference: Performance
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
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variable measured: Query Latency (p99)
  type: PropertyValue
  value reference: Performance
variable measured: Storage Cost per TB/Month
  type: PropertyValue
  value reference: Cost
variable measured: Typical Node Memory
  type: PropertyValue
  value reference: Infrastructure
variable measured: Minimum Recommended Cluster Size
  type: PropertyValue
  value reference: Deployment
variable measured: Max Dataset Size (Practical)
  type: PropertyValue
  value reference: Scalability
variable measured: Query Latency (P95)
  type: PropertyValue
  value reference: Performance
variable measured: Per-Query Cost (1TB scan)
  type: PropertyValue
  value reference: Pricing
variable measured: Data Ingestion Latency
  type: PropertyValue
  value reference: Performance
variable measured: Setup Time to Production
  type: PropertyValue
  value reference: Deployment
variable measured: Maximum Cluster Size
  type: PropertyValue
  value reference: Scalability
variable measured: SQL Standard Compliance
  type: PropertyValue
  value reference: Compatibility
variable measured: Events/Second Ingestion
  type: PropertyValue
  value reference: Throughput
variable measured: Annual TCO (100TB dataset)
  type: PropertyValue
  value reference: Total Cost
variable measured: P99 Query Latency
  type: PropertyValue
  value reference: Performance
variable measured: Maximum Recommended Cluster Size
  type: PropertyValue
  value reference: Scalability
variable measured: Full-Text Search Capability
  type: PropertyValue
  value reference: Features
variable measured: Time to First Production Query
  type: PropertyValue
  value reference: Ease of Use
variable measured: SQL Support
  type: PropertyValue
  value reference: Query Language
variable measured: Typical Memory Per Node
  type: PropertyValue
  value reference: Resource Efficiency
variable measured: GitHub Stars (as of 2026)
  type: PropertyValue
  value reference: Community Adoption
variable measured: Setup Time (Minutes)
  type: PropertyValue
  value reference: Ease of Use
variable measured: Query Latency on 1GB Dataset
  type: PropertyValue
  value reference: Performance
variable measured: Maximum Scalable Dataset Size
  type: PropertyValue
  value reference: Scalability
variable measured: Minimum Cluster Nodes Required
  type: PropertyValue
  value reference: Infrastructure
variable measured: Real-time Streaming Ingestion
  type: PropertyValue
  value reference: Features
variable measured: Concurrent Queries Supported
  type: PropertyValue
  value reference: Performance
variable measured: Supported Programming Languages
  type: PropertyValue
  value reference: Development
variable measured: Annual Infrastructure Cost (1TB dataset)
  type: PropertyValue
  value reference: Cost
variable measured: Concurrent User Support
  type: PropertyValue
  value reference: Collaboration
variable measured: Query Latency (Average)
  type: PropertyValue
  value reference: Performance
variable measured: Data Freshness
  type: PropertyValue
  value reference: Data Pipeline
variable measured: License Type
  type: PropertyValue
  value reference: Licensing
variable measured: Deployment Flexibility
  type: PropertyValue
  value reference: Infrastructure
variable measured: Ingestion Streaming Support
  type: PropertyValue
  value reference: Data Pipeline
variable measured: Base Monthly Cost (Small Cluster)
  type: PropertyValue
  value reference: Cost
variable measured: Query Latency (Median)
  type: PropertyValue
  value reference: Performance
variable measured: Streaming Ingestion Latency
  type: PropertyValue
  value reference: Real-time Capability
variable measured: On-Demand Query Pricing
  type: PropertyValue
  value reference: Cost
variable measured: Infrastructure Management
  type: PropertyValue
  value reference: Operations
variable measured: Maximum Daily Event Throughput
variable measured: Time to Deploy
variable measured: Concurrent Users Supported
variable measured: Ingestion Rate (events/second)
variable measured: Query Latency (1B rows)
variable measured: Maximum Recommended Dataset Size
variable measured: Deployment Time
variable measured: Minimum Cluster Size
variable measured: Memory Per Node
is based on: https://www.aversusb.net/compare/pinot-vs-clickhouse)

Pinot vs ClickHouse — Attribute Category Vocabulary
type: DefinedTermSet
language: en-US
description: Taxonomy of attribute categories used to classify comparison data for Pinot vs ClickHouse.
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: Storage Efficiency
has defined term: Real-time Performance
has defined term: Efficiency
has defined term: Querying
has defined term: Architecture
has defined term: Infrastructure
has defined term: Community
has defined term: Cost
has defined term: Deployment
has defined term: Scalability
has defined term: Pricing
has defined term: Compatibility
has defined term: Total Cost
has defined term: Ease of Use
has defined term: Query Language
has defined term: Community Adoption
has defined term: Development
has defined term: Collaboration
has defined term: Data Pipeline
has defined term: Licensing
has defined term: Real-time Capability
has defined term: Operations
has defined term: Time to Value
has defined term: Search Capability
has defined term: Ecosystem
has defined term: Data Ingestion
has defined term: SQL Support
has defined term: Operational Complexity
has defined term: Setup Speed
has defined term: Flexibility
has defined term: User Experience
has defined term: Storage
has defined term: Operability
has defined term: Resource Usage
has defined term: Setup
has defined term: Reliability
has defined term: Integration
has defined term: Use Case Fit

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Community Adoption
type: DefinedTerm
term code: community-adoption
in defined term set: Pinot vs ClickHouse — Attribute Category Vocabulary

Development
type: DefinedTerm
term code: development
in defined term set: Pinot vs ClickHouse — Attribute Category Vocabulary

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

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

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

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

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

Time to Value
type: DefinedTerm
term code: time-to-value
in defined term set: Pinot vs ClickHouse — Attribute Category Vocabulary

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

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

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

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

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

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

Flexibility
type: DefinedTerm
term code: flexibility
in defined term set: Pinot vs ClickHouse — Attribute Category Vocabulary

User Experience
type: DefinedTerm
term code: user-experience
in defined term set: Pinot vs ClickHouse — Attribute Category Vocabulary

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

Operability
type: DefinedTerm
term code: operability
in defined term set: Pinot vs ClickHouse — Attribute Category Vocabulary

Resource Usage
type: DefinedTerm
term code: resource-usage
in defined term set: Pinot vs ClickHouse — Attribute Category Vocabulary

Setup
type: DefinedTerm
term code: setup
in defined term set: Pinot vs ClickHouse — Attribute Category Vocabulary

Reliability
type: DefinedTerm
term code: reliability
in defined term set: Pinot vs ClickHouse — Attribute Category Vocabulary

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

Use Case Fit
type: DefinedTerm
term code: use-case-fit
in defined term set: Pinot vs ClickHouse — Attribute Category Vocabulary

faq
type: FAQPage
language: en-US
is accessible for free: true
conditions of access: Free
date modified: 2026-07-09T04:58:48.676Z
author: A Versus B
publisher: A Versus B
is part of: pinot-vs-clickhouse)
main entity: When should I use Pinot over ClickHouse?
  type: Question
  accepted answer: Answer
    text: Use Pinot when you need real-time analytics with sub-100ms latency on streaming data at scale (1M+ events/sec). Pinot excels at powering live dashboards, real-time fraud detection, and ad-tech analytics where freshness is critical. It's built specifically for these use cases with optimized segment pruning and distributed query execution.
    language: en-US
main entity: What are the main cost differences between Pinot and ClickHouse?
  type: Question
  accepted answer: Answer
    text: ClickHouse is 3-5x more cost-efficient due to superior compression (10-40x vs 3-8x), resulting in storage costs of $40-60 per TB/month versus Pinot's $120-180 per TB/month. However, Pinot requires fewer operational hours due to more automated features, while ClickHouse's simplicity reduces DevOps costs by an estimated 70-80%.
    language: en-US
main entity: Can ClickHouse handle real-time analytics?
  type: Question
  accepted answer: Answer
    text: ClickHouse can handle real-time analytics but is not optimized for it. Its ingestion speed (100k-500k events/sec) and query latency (200-500ms P99) make it suitable for near-real-time use cases with 1-5 minute refresh intervals. For sub-second freshness requirements, Pinot is the better choice.
    language: en-US
main entity: Which database is easier to deploy and operate?
  type: Question
  accepted answer: Answer
    text: ClickHouse is significantly easier to deploy. It requires only a single binary and minimal configuration (5-10 minutes to first query), while Pinot requires managing 5-7 operational components including Zookeeper, brokers, and servers. ClickHouse reduces operational complexity by 80% and is ideal for teams without dedicated database engineers.
    language: en-US
main entity: Do both support standard SQL queries?
  type: Question
  accepted answer: Answer
    text: ClickHouse offers 95% ANSI SQL compliance, allowing most SQL queries to run unchanged. Pinot supports only 70% of ANSI SQL standard and often requires query rewrites or workarounds. If SQL portability is critical, ClickHouse is the superior choice.
    language: en-US

How to Choose Between Apache Pinot and ClickHouse
type: HowTo
description: Pinot is optimized for real-time analytics on streaming data with lower latency (sub-second queries), while ClickHouse excels at batch processing and complex analytical queries with superior compression (10-40x) and broader SQL compatibility. Pinot uses a distributed architecture with brokers and servers, whereas ClickHouse employs a simpler peer-to-peer model.
language: en-US
total time: PT3M
step: Consider: Query Latency (P99)
step: Consider: Data Compression Ratio
step: Consider: Real-Time Ingestion Speed
step: Consider: SQL Standard Compliance
step: Consider: Typical Storage per GB Data
step: Consider: Deployment Complexity
step: Consider: OLTP Query Support
tool: A Versus B Comparison Guide
tip: HowToTip
  text: Choose Pinot if you need sub-100ms query latency on streaming data with 1M+ events per second ingestion (common in ad-tech, user analytics, real-time dashboards). Choose ClickHouse if you prioritize ease of deployment, superior compression, complex analytical queries, and can tolerate 200-500ms latency (ideal for business intelligence, log analytics, metrics aggregation).

Consider: Query Latency (P99)
type: HowToStep
position: 1
text: Query Latency (P99): Apache Pinot has 50-200ms, while ClickHouse has 200-500ms.

Consider: Data Compression Ratio
type: HowToStep
position: 2
text: Data Compression Ratio: Apache Pinot has 3-8x, while ClickHouse has 10-40x.

Consider: Real-Time Ingestion Speed
type: HowToStep
position: 3
text: Real-Time Ingestion Speed: Apache Pinot has 1M+ events/sec, while ClickHouse has 100k-500k events/sec.

Consider: SQL Standard Compliance
type: HowToStep
position: 4
text: SQL Standard Compliance: Apache Pinot has 70% ANSI SQL, while ClickHouse has 95% ANSI SQL.

Consider: Typical Storage per GB Data
type: HowToStep
position: 5
text: Typical Storage per GB Data: Apache Pinot has 125-330MB, while ClickHouse has 25-100MB.

Consider: Deployment Complexity
type: HowToStep
position: 6
text: Deployment Complexity: Apache Pinot has High (5+ components), while ClickHouse has Low (single binary).

Consider: OLTP Query Support
type: HowToStep
position: 7
text: OLTP Query Support: Apache Pinot has Limited (point lookups only), while ClickHouse has Full OLTP capability.

claimreview
type: ClaimReview
is accessible for free: true
conditions of access: Free
claim reviewed: Pinot vs ClickHouse
review body: Pinot is optimized for real-time analytics on streaming data with lower latency (sub-second queries), while ClickHouse excels at batch processing and complex analytical queries with superior compression (10-40x) and broader SQL compatibility. Pinot uses a distributed architecture with brokers and servers, whereas ClickHouse employs a simpler peer-to-peer model.
review rating: Rating
  rating: 5
  worst rating: 1
  best rating: 5
  alternate name: Verified Comparison
author: A Versus B
item reviewed: Pinot vs ClickHouse

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Minimum Cluster Size
type: PropertyValue
value: Apache Pinot: 3-5 | ClickHouse: N/A

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

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

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

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

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

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

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

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

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

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

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

Peak Ingestion Speed
type: PropertyValue
value: Apache Pinot: 1,000,000+ | ClickHouse: 100,000-500,000
value reference: Apache Pinot

ANSI SQL Compliance
type: PropertyValue
value: Apache Pinot: 70% | ClickHouse: 95%
value reference: ClickHouse

Deployment Components
type: PropertyValue
value: Apache Pinot: 5-7 components | ClickHouse: 1-2 components
value reference: ClickHouse

Time to First Query
type: PropertyValue
value: Apache Pinot: 45-60 minutes | ClickHouse: 5-10 minutes
value reference: ClickHouse

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

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

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

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

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

Deployment Options
type: PropertyValue
value: Apache Pinot: On-prem, Kubernetes, multi-cloud | ClickHouse: N/A

Query Latency (1 billion rows)
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 1.2 seconds

Monthly Cost (100 GB compressed)
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: $150

Setup Time
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 240 minutes

Ingestion Throughput
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 1,000,000 events/sec

Data Retention for Time-Travel
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: Not native

Compression Ratio
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 8:1-12:1

Licensing Model
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: Open-source (free) + optional support

Learning Curve (1-10 scale)
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 7/10 (moderate-hard)

Query Latency (1GB aggregation)
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 500-2000ms

Compression Ratio (typical)
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 10:1 to 40:1

Memory Required (minimal)
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 500-2000MB

Ingest Throughput
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 1-5 million rows/sec

Setup Time to First Query
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 30-120 minutes

Ingestion Latency
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 10-60 seconds

Query Latency (100M rows)
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 50-500ms

Maximum Cluster Nodes
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 1000+ nodes tested

Streaming Integration
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: Limited (Kafka via TableEngine)

Transaction Support
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: No ACID (eventual consistency)

GitHub Stars (2026)
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 34,000+

Typical Maximum Dataset Size
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: ~1,000,000+ GB (1+ PB)

Idle Memory Usage
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 500-2000 MB

Supported Data Formats
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 12+ formats (TSV, Native, Avro, Protobuf, etc.)

Query Latency (100M rows, simple aggregation)
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 500-1500ms

Typical Storage Cost
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: $20-40

Max Recommended Dataset Size
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 100TB+ efficiently

SQL Feature Completeness
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 95% (PostgreSQL-compatible)

Time-Series Aggregation Support
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: Standard SQL; requires manual time bucketing

Query Latency (1B row scan, 10 column aggregate)
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 50-100ms

Storage Cost (per TB/month)
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: $15-25

Typical Data Compression Ratio
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 10-40x

Minimum Cluster Size (nodes)
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 1 (can run standalone)

Max Concurrent Queries (default config)
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: Unlimited (resource-based)

AWS Service Integration (native)
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 5-10 (via third-party)

Query Latency (1 billion rows, simple SELECT)
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 150ms

Cost per GB Scanned
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: $0.015

Maximum Ingestion Rate
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 1,000,000

Infrastructure Management Overhead
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 40-80 hours

Minimum Monthly Cost (basic setup)
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: $500 (ClickHouse Cloud starter)

Cloud Provider Support
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 4+ (AWS, Azure, GCP, on-premise)

Automatic Scaling Time
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 60-300 (manual cluster resize required)

Average Query Latency (1 Billion Row Scan)
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 75ms

Monthly Cost per TB Stored
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: $0.09

Time to Production Deployment
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 1440 (self-managed) / 60 (managed)

Maximum Concurrent Queries
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 100,000+

Uptime SLA Guarantee
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 99.0% (self-managed) / 99.95% (managed)

Native AWS Service Integration
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 3 (S3, Kinesis via 3rd party, basic)

Average Query Latency (1TB dataset)
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 85ms

Cost per TB Scanned
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: $0.01

Initial Setup Time
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 14 days

Max Concurrent Queries (single cluster)
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 1,000+

Built-in ML Capabilities
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: No (third-party integration required)

Support for Time-Series Data
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: Native optimization, ideal for billions of events

Enterprise SLA Availability
type: PropertyValue
value: Apache Pinot: N/A | ClickHouse: 99.5% (self-hosted dependent)

SpeakableSpecification
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Apache Pinot — Wikipedia
type: CreativeWork

ClickHouse — Wikipedia
type: CreativeWork

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

Maximum Daily Event Throughput
type: PropertyValue
value reference: Scalability

Time to Deploy
type: PropertyValue
value reference: Time to Value

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

Minimum Cluster Size
type: PropertyValue
value reference: Infrastructure

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: Features

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

Time to First Query
type: PropertyValue
value reference: Setup Speed

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

Deployment Options
type: PropertyValue
value reference: Flexibility

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

Monthly Cost (100 GB compressed)
type: PropertyValue
value reference: Pricing

Setup Time
type: PropertyValue
value reference: User Experience

Ingestion Throughput
type: PropertyValue
value reference: Performance

Data Retention for Time-Travel
type: PropertyValue
value reference: Features

Compression Ratio
type: PropertyValue
value reference: Storage

Licensing Model
type: PropertyValue
value reference: Economics

Learning Curve (1-10 scale)
type: PropertyValue
value reference: Operability

Query Latency (1GB aggregation)
type: PropertyValue
value reference: Performance

Compression Ratio (typical)
type: PropertyValue
value reference: Efficiency

Memory Required (minimal)
type: PropertyValue
value reference: Resource Usage

Ingest Throughput
type: PropertyValue
value reference: Performance

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

Ingestion Latency
type: PropertyValue
value reference: Performance

Query Latency (100M rows)
type: PropertyValue
value reference: Performance

Maximum Cluster Nodes
type: PropertyValue
value reference: Scalability

Streaming Integration
type: PropertyValue
value reference: Features

Transaction Support
type: PropertyValue
value reference: Features

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

Typical Maximum Dataset Size
type: PropertyValue
value reference: Scalability

Idle Memory Usage
type: PropertyValue
value reference: Resource Efficiency

Supported Data Formats
type: PropertyValue
value reference: Compatibility

Query Latency (100M rows, simple aggregation)
type: PropertyValue
value reference: Performance

Typical Storage Cost
type: PropertyValue
value reference: Cost

Max Recommended Dataset Size
type: PropertyValue
value reference: Scalability

SQL Feature Completeness
type: PropertyValue
value reference: Features

Time-Series Aggregation Support
type: PropertyValue
value reference: Features

Query Latency (1B row scan, 10 column aggregate)
type: PropertyValue
value reference: Performance

Storage Cost (per TB/month)
type: PropertyValue
value reference: Cost

Typical Data Compression Ratio
type: PropertyValue
value reference: Storage Efficiency

Minimum Cluster Size (nodes)
type: PropertyValue
value reference: Infrastructure

Max Concurrent Queries (default config)
type: PropertyValue
value reference: Throughput

AWS Service Integration (native)
type: PropertyValue
value reference: Ecosystem

Query Latency (1 billion rows, simple SELECT)
type: PropertyValue
value reference: Performance

Cost per GB Scanned
type: PropertyValue
value reference: Pricing

Maximum Ingestion Rate
type: PropertyValue
value reference: Data Ingestion

Infrastructure Management Overhead
type: PropertyValue
value reference: Operations

Minimum Monthly Cost (basic setup)
type: PropertyValue
value reference: Pricing

Cloud Provider Support
type: PropertyValue
value reference: Features

Automatic Scaling Time
type: PropertyValue
value reference: Performance

Average Query Latency (1 Billion Row Scan)
type: PropertyValue
value reference: Performance

Monthly Cost per TB Stored
type: PropertyValue
value reference: Cost

Time to Production Deployment
type: PropertyValue
value reference: Setup

Maximum Concurrent Queries
type: PropertyValue
value reference: Scalability

Uptime SLA Guarantee
type: PropertyValue
value reference: Reliability

Native AWS Service Integration
type: PropertyValue
value reference: Integration

Average Query Latency (1TB dataset)
type: PropertyValue
value reference: Performance

Cost per TB Scanned
type: PropertyValue
value reference: Pricing

Initial Setup Time
type: PropertyValue
value reference: Ease of Use

Max Concurrent Queries (single cluster)
type: PropertyValue
value reference: Scalability

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

Support for Time-Series Data
type: PropertyValue
value reference: Use Case Fit

Enterprise SLA Availability
type: PropertyValue
value reference: Reliability