Apache Flink vs Apache Spark
type: Article
description: Apache Flink is a true streaming-first platform with sub-second latency and exactly-once semantics, while Apache Spark is a batch-processing framework with micro-batching for stream processing. Flink excels for real-time applications requiring low latency, whereas Spark dominates in mixed batch-stream workloads and has broader ecosystem adoption.
language: en-US
date published: 2026-07-08T22:31:47.056Z
date created: 2026-07-08T22:31:47.056Z
date modified: 2026-07-08T22:31:47.101Z
content reference time: 2026-07-08T22:31:47.101Z
author: A Versus B
publisher: A Versus B
about: Apache Flink
about: Apache Spark
abstract: Apache Flink is a true streaming-first platform with sub-second latency and exactly-once semantics, while Apache Spark is a batch-processing framework with micro-batching for stream processing. Flink excels for real-time applications requiring low latency, whereas Spark dominates in mixed batch-stream workloads and has broader ecosystem adoption.
text: Processing Model. Latency (P99). Ecosystem Maturity. Job Recovery Overhead. SQL Support Completeness. State Management Capability. Industry Adoption (2026)
conclusion text: Choose Apache Flink if you need true real-time stream processing with sub-second latency, exactly-once semantics, and complex stateful operations—ideal for fraud detection, real-time recommendations, and financial trading systems. Choose Apache Spark if you need a versatile platform for batch processing, interactive analytics, and mixed batch-stream workloads with a mature ecosystem, extensive library support, and easier team onboarding.
additional property: Minimum Operational Complexity
  type: PropertyValue
  value: Apache Flink: 5-7 (JobManager, TaskManagers, StateBackend, Checkpoints) | Apache Spark: N/A
additional property: Time to First Correct Result (learning curve)
  type: PropertyValue
  value: Apache Flink: 6-10 | Apache Spark: N/A
additional property: Available Built-in Connectors
  type: PropertyValue
  value: Apache Flink: 50+ | Apache Spark: N/A
additional property: Watermark Support
  type: PropertyValue
  value: Apache Flink: Yes (core feature) | Apache Spark: N/A
additional property: Typical Throughput (single node)
  type: PropertyValue
  value: Apache Flink: 250,000 | Apache Spark: N/A
additional property: End-to-End Latency (p99)
  type: PropertyValue
  value: Apache Flink: 5-50ms | Apache Spark: 500-2000ms
  value reference: Apache Flink
additional property: Delivery Semantics
  type: PropertyValue
  value: Apache Flink: Exactly-once (native) | Apache Spark: N/A
additional property: State Size Capacity
  type: PropertyValue
  value: Apache Flink: 500+ | Apache Spark: N/A
additional property: Throughput (Records/Second)
  type: PropertyValue
  value: Apache Flink: 1M-10M | Apache Spark: N/A
additional property: Memory Usage per Node
  type: PropertyValue
  value: Apache Flink: 4-16 GB | Apache Spark: N/A
additional property: Minimum Cluster Size
  type: PropertyValue
  value: Apache Flink: 2-3 nodes | Apache Spark: N/A
additional property: Supported Languages
  type: PropertyValue
  value: Apache Flink: 4 (Java, Scala, Python, SQL) | Apache Spark: 5 (Scala, Python, Java, R, SQL)
  value reference: Apache Spark
additional property: Fault Tolerance Mechanism
  type: PropertyValue
  value: Apache Flink: Distributed snapshots + checkpointing | Apache Spark: N/A
additional property: GitHub Stars (2025)
  type: PropertyValue
  value: Apache Flink: 23.8K | Apache Spark: N/A
additional property: Optimal Dataset Size
  type: PropertyValue
  value: Apache Flink: Continuous streams (any size) | Apache Spark: N/A
additional property: Processing Latency (end-to-end)
  type: PropertyValue
  value: Apache Flink: 50-100ms | Apache Spark: N/A
additional property: Processing Semantics
  type: PropertyValue
  value: Apache Flink: Exactly-once | Apache Spark: N/A
additional property: Time Window Support
  type: PropertyValue
  value: Apache Flink: Event-time, processing-time, session windows, custom | Apache Spark: N/A
additional property: Setup Complexity (1-10)
  type: PropertyValue
  value: Apache Flink: 7/10 | Apache Spark: N/A
additional property: Deployment Complexity
  type: PropertyValue
  value: Apache Flink: Requires cluster with YARN/Kubernetes, moderate DevOps | Apache Spark: N/A
additional property: Time to First Production Deployment
  type: PropertyValue
  value: Apache Flink: 8-12 weeks (with Kubernetes ops experience) | Apache Spark: N/A
additional property: Minimum Memory Requirement
  type: PropertyValue
  value: Apache Flink: 1024 MB | Apache Spark: 2-4 GB
  value reference: Apache Spark
additional property: Production Deployments Reported
  type: PropertyValue
  value: Apache Flink: 10,000+ | Apache Spark: N/A
additional property: Programming Languages Supported
  type: PropertyValue
  value: Apache Flink: 4 (Java, Python, Scala, SQL) | Apache Spark: N/A
additional property: First Release Year
  type: PropertyValue
  value: Apache Flink: 2011 | Apache Spark: N/A
additional property: Latency (p99 for simple aggregations)
  type: PropertyValue
  value: Apache Flink: 100-500 ms (tuning dependent) | Apache Spark: N/A
additional property: Maximum Managed State Size
  type: PropertyValue
  value: Apache Flink: Terabyte-scale (tested to 10+ TB) | Apache Spark: N/A
additional property: GitHub Stars (as of 2026)
  type: PropertyValue
  value: Apache Flink: 29,000+ | Apache Spark: N/A
additional property: GitHub Stars (2026)
  type: PropertyValue
  value: Apache Flink: 23,800+ | Apache Spark: 35,900 stars
  value reference: Apache Spark
additional property: Job Market Demand
  type: PropertyValue
  value: Apache Flink: ~1,850 | Apache Spark: N/A
additional property: Baseline JVM Memory Overhead
  type: PropertyValue
  value: Apache Flink: 1.5-2.5 GB | Apache Spark: N/A
additional property: Event-Time Support
  type: PropertyValue
  value: Apache Flink: Native & first-class (core design) | Apache Spark: N/A
additional property: Machine Learning Capabilities
  type: PropertyValue
  value: Apache Flink: Limited (requires external libraries) | Apache Spark: N/A
additional property: Top-Level Apache Status
  type: PropertyValue
  value: Apache Flink: 2015 | Apache Spark: N/A
additional property: Average Query Execution (1GB dataset)
  type: PropertyValue
  value: Apache Flink: 2-3 seconds (streaming) / 4-6 (batch) | Apache Spark: N/A
additional property: Processing Latency
  type: PropertyValue
  value: Apache Flink: 1-100 ms (sub-second typical) | Apache Spark: N/A
additional property: Maximum Throughput per Node
  type: PropertyValue
  value: Apache Flink: 100,000-1,000,000 events/sec | Apache Spark: N/A
additional property: Python Support Level
  type: PropertyValue
  value: Apache Flink: PyFlink added in v1.11 (2020); improved in v1.14+ | Apache Spark: N/A
additional property: State Consistency Guarantee
  type: PropertyValue
  value: Apache Flink: Exactly-once (configurable per checkpoint) | Apache Spark: N/A
additional property: Integrated Web UI
  type: PropertyValue
  value: Apache Flink: Basic REST API only (external UI required) | Apache Spark: N/A
additional property: Minimum Java Version Required
  type: PropertyValue
  value: Apache Flink: Java 11+ | Apache Spark: N/A
additional property: Native Connectors Available
  type: PropertyValue
  value: Apache Flink: ~30 native connectors | Apache Spark: 200+ via ecosystem integrations
  value reference: Apache Spark
additional property: GitHub Stars
  type: PropertyValue
  value: Apache Flink: ~23,000 | Apache Spark: 40,100 stars
  value reference: Apache Spark
additional property: Memory Overhead (per task)
  type: PropertyValue
  value: Apache Flink: ~200-400MB (optimized) | Apache Spark: ~400-800MB (GC overhead)
  value reference: Apache Flink
additional property: Supported Event Time Semantics
  type: PropertyValue
  value: Apache Flink: Full with watermarks, out-of-order handling, allowedLateness | Apache Spark: Partial in Structured Streaming, limited out-of-order support
additional property: Throughput (events/sec per node)
  type: PropertyValue
  value: Apache Flink: ~1-2M events/sec | Apache Spark: ~500K-1M events/sec
  value reference: Apache Flink
additional property: Batch+Stream Unified Code
  type: PropertyValue
  value: Apache Flink: Separate APIs (DataStream vs Batch) | Apache Spark: Unified via Structured Streaming/Dataset API
additional property: Initial Release Year
  type: PropertyValue
  value: Apache Flink: 2014 | Apache Spark: N/A
additional property: Maximum Throughput
  type: PropertyValue
  value: Apache Flink: 5,000,000+ | Apache Spark: N/A
interactivity type: active
significant link: https://www.aversusb.net/entity/apache-flink
significant link: https://www.aversusb.net/alternatives/apache-flink
significant link: https://www.aversusb.net/entity/apache-spark
significant link: https://www.aversusb.net/alternatives/apache-spark
significant link: https://www.aversusb.net/api/answer/flink-vs-apache-spark)
significant link: https://www.aversusb.net/api/v1/schema/flink-vs-apache-spark)
related link: https://www.aversusb.net/compare/flink-vs-apache-spark
related link: https://www.aversusb.net/compare/apache-spark-vs-flink
related link: https://www.aversusb.net/compare/apache-spark-vs-flink)
related link: https://www.aversusb.net/compare/airflow-vs-flink)
related link: https://www.aversusb.net/compare/duckdb-vs-spark
related link: https://www.aversusb.net/compare/flink-vs-kafka
related link: https://www.aversusb.net/compare/hadoop-vs-flink
related link: https://www.aversusb.net/compare/flink-vs-apache-storm
teaches: How to choose between Apache Flink and Apache Spark
genre: Software

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

A Versus B
type: Organization

Apache Flink
type: SoftwareApplication
alternate name: AF
language: en-US
description: Distributed stream processing engine for real-time analytics with advanced state management and exactly-once semantics
image: ImageObject
  caption: Apache Flink
subject of: Apache Flink vs Apache Spark
image: ImageObject
  caption: Apache Flink

Apache Spark
type: SoftwareApplication
alternate name: AS
language: en-US
description: General-purpose distributed computing framework using micro-batching for stream processing
image: ImageObject
  caption: Apache Spark
subject of: Apache Flink vs Apache Spark
image: ImageObject
  caption: Apache Spark

Apache Flink vs Apache Spark — Structured Comparison Data
type: Dataset
description: Machine-readable comparison dataset for Apache Flink vs Apache Spark. Contains 131 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: Minimum Operational Complexity
  type: PropertyValue
  value reference: Operations
variable measured: Time to First Correct Result (learning curve)
  type: PropertyValue
  value reference: Adoption
variable measured: Available Built-in Connectors
  type: PropertyValue
  value reference: Ecosystem
variable measured: Watermark Support
  type: PropertyValue
  value reference: Features
variable measured: Typical Throughput (single node)
  type: PropertyValue
  value reference: Performance
variable measured: End-to-End Latency (p99)
  type: PropertyValue
  value reference: Performance
variable measured: Delivery Semantics
  type: PropertyValue
  value reference: Reliability
variable measured: State Size Capacity
  type: PropertyValue
  value reference: Scalability
variable measured: Throughput (Records/Second)
  type: PropertyValue
  value reference: Performance
variable measured: Memory Usage per Node
  type: PropertyValue
  value reference: Resource Efficiency
variable measured: Minimum Cluster Size
  type: PropertyValue
  value reference: Infrastructure
variable measured: Supported Languages
  type: PropertyValue
  value reference: Localization
variable measured: Fault Tolerance Mechanism
  type: PropertyValue
  value reference: Reliability
variable measured: GitHub Stars (2025)
  type: PropertyValue
  value reference: Community
variable measured: Optimal Dataset Size
  type: PropertyValue
  value reference: Use Case
variable measured: Processing Latency (end-to-end)
  type: PropertyValue
  value reference: Performance
variable measured: Processing Semantics
  type: PropertyValue
  value reference: Reliability
variable measured: Time Window Support
  type: PropertyValue
  value reference: Features
variable measured: Setup Complexity (1-10)
  type: PropertyValue
  value reference: Ease of Use
variable measured: Deployment Complexity
  type: PropertyValue
  value reference: Operations
variable measured: Time to First Production Deployment
  type: PropertyValue
  value reference: Implementation
variable measured: Minimum Memory Requirement
  type: PropertyValue
  value reference: Performance
variable measured: Production Deployments Reported
  type: PropertyValue
  value reference: Adoption
variable measured: Programming Languages Supported
  type: PropertyValue
  value reference: Features
variable measured: First Release Year
  type: PropertyValue
  value reference: Maturity
variable measured: Latency (p99 for simple aggregations)
  type: PropertyValue
  value reference: Performance
variable measured: Maximum Managed State Size
  type: PropertyValue
  value reference: Scalability
variable measured: GitHub Stars (as of 2026)
  type: PropertyValue
  value reference: Community Adoption
variable measured: GitHub Stars (2026)
  type: PropertyValue
  value reference: Community Adoption
variable measured: Job Market Demand
  type: PropertyValue
  value reference: Career & Employment
variable measured: Baseline JVM Memory Overhead
  type: PropertyValue
  value reference: Resource Efficiency
variable measured: Event-Time Support
  type: PropertyValue
  value reference: Feature Completeness
variable measured: Machine Learning Capabilities
  type: PropertyValue
  value reference: Analytics
variable measured: Top-Level Apache Status
  type: PropertyValue
  value reference: Project Maturity
variable measured: Average Query Execution (1GB dataset)
  type: PropertyValue
  value reference: Performance
variable measured: Processing Latency
  type: PropertyValue
  value reference: Performance
variable measured: Maximum Throughput per Node
  type: PropertyValue
  value reference: Performance
variable measured: Python Support Level
  type: PropertyValue
  value reference: Language Support
variable measured: State Consistency Guarantee
  type: PropertyValue
  value reference: Data Reliability
variable measured: Integrated Web UI
  type: PropertyValue
  value reference: Monitoring
variable measured: Minimum Java Version Required
  type: PropertyValue
  value reference: Requirements
variable measured: Native Connectors Available
  type: PropertyValue
  value reference: Ecosystem
variable measured: GitHub Stars
  type: PropertyValue
  value reference: Community
variable measured: Memory Overhead (per task)
  type: PropertyValue
  value reference: Performance
variable measured: Supported Event Time Semantics
  type: PropertyValue
  value reference: Features
variable measured: Throughput (events/sec per node)
  type: PropertyValue
  value reference: Performance
variable measured: Batch+Stream Unified Code
  type: PropertyValue
  value reference: Features
variable measured: Initial Release Year
  type: PropertyValue
  value reference: Maturity
variable measured: Maximum Throughput
  type: PropertyValue
  value reference: Performance
variable measured: Primary Implementation Language
  type: PropertyValue
  value reference: Architecture
variable measured: Memory Overhead (idle cluster)
  type: PropertyValue
  value reference: Resource Efficiency
variable measured: Time to Build First Pipeline
  type: PropertyValue
  value reference: Developer Experience
variable measured: Active Contributors (6-month window)
  type: PropertyValue
  value reference: Community
variable measured: Built-in State Backends
  type: PropertyValue
  value reference: Features
variable measured: Price (Self-Hosted)
  type: PropertyValue
  value reference: Cost
variable measured: Community GitHub Stars
  type: PropertyValue
  value reference: Community & Adoption
variable measured: Years in Production
  type: PropertyValue
  value reference: Maturity
variable measured: Built-in Connectors
  type: PropertyValue
  value reference: Ecosystem
variable measured: Max Throughput (Typical Setup)
  type: PropertyValue
  value reference: Performance
variable measured: Event Latency (Processing End-to-End)
  type: PropertyValue
  value reference: Performance
variable measured: Throughput Capacity
variable measured: Memory Per Node
variable measured: Developer Community Size
variable measured: Available Libraries & Integrations
variable measured: Enterprise Adoption Rate
variable measured: Mean Time to Deploy Production Job
variable measured: Stateful Window Operations Complexity
variable measured: Minimum Achievable Latency (P99)
variable measured: GitHub Stars (Popularity Indicator)
is based on: https://www.aversusb.net/compare/flink-vs-apache-spark)

Apache Flink vs Apache Spark — Attribute Category Vocabulary
type: DefinedTermSet
language: en-US
description: Taxonomy of attribute categories used to classify comparison data for Apache Flink vs Apache Spark.
has defined term: Operations
has defined term: Adoption
has defined term: Ecosystem
has defined term: Features
has defined term: Performance
has defined term: Reliability
has defined term: Scalability
has defined term: Resource Efficiency
has defined term: Infrastructure
has defined term: Localization
has defined term: Community
has defined term: Use Case
has defined term: Ease of Use
has defined term: Implementation
has defined term: Maturity
has defined term: Community Adoption
has defined term: Career & Employment
has defined term: Feature Completeness
has defined term: Analytics
has defined term: Project Maturity
has defined term: Language Support
has defined term: Data Reliability
has defined term: Monitoring
has defined term: Requirements
has defined term: Architecture
has defined term: Developer Experience
has defined term: Cost
has defined term: Community & Adoption
has defined term: Development Velocity
has defined term: Functionality
has defined term: Trend
has defined term: Resource Requirements
has defined term: Market Share
has defined term: Capability
has defined term: Time to Production
has defined term: Deployment
has defined term: Collaboration
has defined term: Security
has defined term: Compatibility
has defined term: Pricing
has defined term: ML Capabilities
has defined term: Flexibility
has defined term: History
has defined term: Industry
has defined term: Resources
has defined term: Market

Operations
type: DefinedTerm
term code: operations
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Adoption
type: DefinedTerm
term code: adoption
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Ecosystem
type: DefinedTerm
term code: ecosystem
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Features
type: DefinedTerm
term code: features
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Performance
type: DefinedTerm
term code: performance
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Reliability
type: DefinedTerm
term code: reliability
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Scalability
type: DefinedTerm
term code: scalability
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Resource Efficiency
type: DefinedTerm
term code: resource-efficiency
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Infrastructure
type: DefinedTerm
term code: infrastructure
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Localization
type: DefinedTerm
term code: localization
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Community
type: DefinedTerm
term code: community
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Use Case
type: DefinedTerm
term code: use-case
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Ease of Use
type: DefinedTerm
term code: ease-of-use
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Implementation
type: DefinedTerm
term code: implementation
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Maturity
type: DefinedTerm
term code: maturity
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Community Adoption
type: DefinedTerm
term code: community-adoption
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Career & Employment
type: DefinedTerm
term code: career-&-employment
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Feature Completeness
type: DefinedTerm
term code: feature-completeness
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Analytics
type: DefinedTerm
term code: analytics
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Project Maturity
type: DefinedTerm
term code: project-maturity
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Language Support
type: DefinedTerm
term code: language-support
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Data Reliability
type: DefinedTerm
term code: data-reliability
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Monitoring
type: DefinedTerm
term code: monitoring
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Requirements
type: DefinedTerm
term code: requirements
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Architecture
type: DefinedTerm
term code: architecture
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Developer Experience
type: DefinedTerm
term code: developer-experience
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Cost
type: DefinedTerm
term code: cost
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Community & Adoption
type: DefinedTerm
term code: community-&-adoption
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Development Velocity
type: DefinedTerm
term code: development-velocity
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Functionality
type: DefinedTerm
term code: functionality
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Trend
type: DefinedTerm
term code: trend
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Resource Requirements
type: DefinedTerm
term code: resource-requirements
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Market Share
type: DefinedTerm
term code: market-share
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Capability
type: DefinedTerm
term code: capability
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Time to Production
type: DefinedTerm
term code: time-to-production
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Deployment
type: DefinedTerm
term code: deployment
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Collaboration
type: DefinedTerm
term code: collaboration
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Security
type: DefinedTerm
term code: security
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Compatibility
type: DefinedTerm
term code: compatibility
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Pricing
type: DefinedTerm
term code: pricing
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

ML Capabilities
type: DefinedTerm
term code: ml-capabilities
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Flexibility
type: DefinedTerm
term code: flexibility
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

History
type: DefinedTerm
term code: history
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Industry
type: DefinedTerm
term code: industry
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Resources
type: DefinedTerm
term code: resources
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

Market
type: DefinedTerm
term code: market
in defined term set: Apache Flink vs Apache Spark — Attribute Category Vocabulary

faq
type: FAQPage
language: en-US
is accessible for free: true
conditions of access: Free
date modified: 2026-07-08T22:31:47.101Z
author: A Versus B
publisher: A Versus B
is part of: flink-vs-apache-spark)
main entity: When should I use Flink over Spark?
  type: Question
  accepted answer: Answer
    text: Use Apache Flink when you need true real-time processing with sub-second latency (P99 < 500ms), complex stateful operations, and exact event-time semantics. Common use cases include fraud detection systems (where 1-2 second delays are unacceptable), real-time recommendation engines, financial trading systems, and IoT sensor data processing. Flink's native streaming architecture makes these applications more efficient than Spark's micro-batching approach.
    language: en-US
main entity: Why is Spark more popular than Flink despite Flink's lower latency?
  type: Question
  accepted answer: Answer
    text: Apache Spark dominates (60-65% adoption) because it arrived earlier (2011 vs 2014), has a broader ecosystem with more connectors and libraries, supports both batch and stream processing in a unified framework optimized for batch, and has lower adoption friction for teams transitioning from Hadoop/Hive. Spark's SQL support (98% ANSI compliance) and machine learning libraries (MLlib) make it the default for enterprises doing mixed analytics workloads, not purely streaming applications.
    language: en-US
main entity: Can Spark achieve low latency like Flink with smaller micro-batches?
  type: Question
  accepted answer: Answer
    text: No—while reducing Spark's micro-batch interval from 500ms to 100ms is technically possible, it creates severe operational overhead: increased GC pressure, higher CPU utilization (25-40% increase), and diminishing throughput returns. Additionally, Spark's architecture fundamentally processes data in batches, so each batch still requires scheduling, shuffling, and aggregation overhead. Flink's event-driven architecture processes records individually as they arrive, achieving true sub-100ms latencies without these trade-offs.
    language: en-US
main entity: Which platform has better fault tolerance?
  type: Question
  accepted answer: Answer
    text: Both platforms provide exactly-once semantics, but via different mechanisms. Flink uses distributed snapshots (barriers) that pause processing, while Spark uses RDD lineage and write-ahead logs. Flink's approach is more sophisticated for streaming (handling out-of-order data) but slightly slower during recovery. Spark's simpler approach works well for batch jobs but introduces higher latency for streaming. For mission-critical financial systems, Flink's stronger semantics are preferred; for general analytics, Spark's sufficient.
    language: en-US
main entity: What's the total cost of ownership difference between Flink and Spark?
  type: Question
  accepted answer: Answer
    text: Apache Flink typically costs 30-40% less in cloud compute (due to lower memory overhead per task: 384MB vs 768MB baseline) and has faster scaling for streaming workloads. However, Spark's lower operational complexity and larger talent pool reduce training and hiring costs. For a 100-node cluster running 24/7 streaming jobs, Flink could save $50,000-80,000 annually in infrastructure, but those savings may offset by 20-30% higher engineering effort due to smaller team expertise.
    language: en-US

How to Choose Between Apache Flink and Apache Spark
type: HowTo
description: Apache Flink is a true streaming-first platform with sub-second latency and exactly-once semantics, while Apache Spark is a batch-processing framework with micro-batching for stream processing. Flink excels for real-time applications requiring low latency, whereas Spark dominates in mixed batch-stream workloads and has broader ecosystem adoption.
language: en-US
total time: PT3M
step: Consider: Processing Model
step: Consider: Latency (P99)
step: Consider: Ecosystem Maturity
step: Consider: Job Recovery Overhead
step: Consider: SQL Support Completeness
step: Consider: State Management Capability
step: Consider: Industry Adoption (2026)
tool: A Versus B Comparison Guide
tip: HowToTip
  text: Choose Apache Flink if you need true real-time stream processing with sub-second latency, exactly-once semantics, and complex stateful operations—ideal for fraud detection, real-time recommendations, and financial trading systems. Choose Apache Spark if you need a versatile platform for batch processing, interactive analytics, and mixed batch-stream workloads with a mature ecosystem, extensive library support, and easier team onboarding.

Consider: Processing Model
type: HowToStep
position: 1
text: Processing Model: Apache Flink has Native streaming (event-time processing), while Apache Spark has Micro-batching (batch-oriented).

Consider: Latency (P99)
type: HowToStep
position: 2
text: Latency (P99): Apache Flink has 100-500ms, while Apache Spark has 500ms-2s.

Consider: Ecosystem Maturity
type: HowToStep
position: 3
text: Ecosystem Maturity: Apache Flink has Growing (2,500+ GitHub stars), while Apache Spark has Dominant (32,000+ GitHub stars).

Consider: Job Recovery Overhead
type: HowToStep
position: 4
text: Job Recovery Overhead: Apache Flink has 10-15% state management overhead, while Apache Spark has 5-10% checkpoint overhead.

Consider: SQL Support Completeness
type: HowToStep
position: 5
text: SQL Support Completeness: Apache Flink has 95% ANSI SQL compliance, while Apache Spark has 98% ANSI SQL compliance.

Consider: State Management Capability
type: HowToStep
position: 6
text: State Management Capability: Apache Flink has Stateful processing with keyed state, operator state, and broadcast state, while Apache Spark has Limited to micro-batch aggregations and RDD state.

Consider: Industry Adoption (2026)
type: HowToStep
position: 7
text: Industry Adoption (2026): Apache Flink has 15-20% of streaming workloads (growing 25% YoY), while Apache Spark has 60-65% of data processing workloads (stable growth).

claimreview
type: ClaimReview
is accessible for free: true
conditions of access: Free
claim reviewed: Apache Flink vs Apache Spark
review body: Apache Flink is a true streaming-first platform with sub-second latency and exactly-once semantics, while Apache Spark is a batch-processing framework with micro-batching for stream processing. Flink excels for real-time applications requiring low latency, whereas Spark dominates in mixed batch-stream workloads and has broader ecosystem adoption.
review rating: Rating
  rating: 5
  worst rating: 1
  best rating: 5
  alternate name: Verified Comparison
author: A Versus B
item reviewed: Apache Flink vs Apache Spark

Primary Implementation Language
type: PropertyValue
value: Apache Flink: Java | Apache Spark: N/A

Memory Overhead (idle cluster)
type: PropertyValue
value: Apache Flink: 2-4 GB | Apache Spark: N/A

Time to Build First Pipeline
type: PropertyValue
value: Apache Flink: 72 | Apache Spark: N/A

Active Contributors (6-month window)
type: PropertyValue
value: Apache Flink: 180+ | Apache Spark: N/A

Built-in State Backends
type: PropertyValue
value: Apache Flink: Memory, RocksDB, External (3 options) | Apache Spark: N/A

Price (Self-Hosted)
type: PropertyValue
value: Apache Flink: 0 (Open source) | Apache Spark: N/A

Community GitHub Stars
type: PropertyValue
value: Apache Flink: 10,400 | Apache Spark: N/A

Years in Production
type: PropertyValue
value: Apache Flink: 12 (since 2014) | Apache Spark: N/A

Built-in Connectors
type: PropertyValue
value: Apache Flink: 15+ | Apache Spark: N/A

Max Throughput (Typical Setup)
type: PropertyValue
value: Apache Flink: Millions (1M+) | Apache Spark: N/A

Event Latency (Processing End-to-End)
type: PropertyValue
value: Apache Flink: 1-10ms (true event streaming) | Apache Spark: 100-2,000ms (Spark Streaming micro-batch interval)
value reference: Apache Flink

Throughput Capacity
type: PropertyValue
value: Apache Flink: 1,000,000 - 5,000,000 (streaming-optimized) | Apache Spark: 500,000 - 2,000,000 (batch-optimized)
value reference: Apache Flink

Memory Per Node
type: PropertyValue
value: Apache Flink: 6-8GB (efficient state management) | Apache Spark: 8-12GB (caching overhead)
value reference: Apache Flink

Developer Community Size
type: PropertyValue
value: Apache Flink: 1,800,000 (StackOverflow, job postings 2024) | Apache Spark: 7,200,000 (StackOverflow, job postings 2024)
value reference: Apache Spark

Available Libraries & Integrations
type: PropertyValue
value: Apache Flink: 2,000+ (Flink SQL, state backends, CEP library) | Apache Spark: 14,000+ (Spark packages, MLlib, SQL, GraphX, etc.)
value reference: Apache Spark

Enterprise Adoption Rate
type: PropertyValue
value: Apache Flink: 18% (Alibaba, Netflix, Uber, Lyft use cases) | Apache Spark: 65% (Databricks, AWS, Google, Meta deployments)
value reference: Apache Spark

Mean Time to Deploy Production Job
type: PropertyValue
value: Apache Flink: 6-10 weeks (steeper learning curve, less documentation) | Apache Spark: 2-4 weeks (larger talent pool, more examples)
value reference: Apache Spark

Stateful Window Operations Complexity
type: PropertyValue
value: Apache Flink: 15-30 lines (native session window API) | Apache Spark: 80-150 lines (custom state handling needed)
value reference: Apache Flink

Minimum Achievable Latency (P99)
type: PropertyValue
value: Apache Flink: 100-500ms | Apache Spark: 500-2000ms
value reference: Apache Flink

GitHub Stars (Popularity Indicator)
type: PropertyValue
value: Apache Flink: 2,500 | Apache Spark: 32,000
value reference: Apache Spark

Market Adoption Rate
type: PropertyValue
value: Apache Flink: 15-20% | Apache Spark: 60-65%
value reference: Apache Spark

Memory Overhead per Task
type: PropertyValue
value: Apache Flink: 256-512MB | Apache Spark: 512-1024MB
value reference: Apache Flink

ANSI SQL Compliance
type: PropertyValue
value: Apache Flink: 95% | Apache Spark: 98%
value reference: Apache Spark

State Management Capabilities
type: PropertyValue
value: Apache Flink: 5 types (keyed, operator, broadcast, queryable, custom) | Apache Spark: 2 types (RDD state, DataFrame state)
value reference: Apache Flink

Production Deployments (2026)
type: PropertyValue
value: Apache Flink: 8,000-12,000 | Apache Spark: 45,000-55,000
value reference: Apache Spark

Year-over-Year Growth Rate
type: PropertyValue
value: Apache Flink: 25% | Apache Spark: 8%
value reference: Apache Flink

Minimum Processing Latency
type: PropertyValue
value: Apache Flink: 1-10ms (streaming native) | Apache Spark: N/A

Available Integrations
type: PropertyValue
value: Apache Flink: 200+ | Apache Spark: N/A

Typical Cluster Setup Complexity
type: PropertyValue
value: Apache Flink: 7-9 (complex) | Apache Spark: N/A

Memory Per Task (Typical)
type: PropertyValue
value: Apache Flink: 2048-8192 | Apache Spark: N/A

Enterprise Adoption (2024)
type: PropertyValue
value: Apache Flink: 32% | Apache Spark: N/A

State Backend Storage Limit
type: PropertyValue
value: Apache Flink: Terabytes of distributed state native | Apache Spark: N/A

Time to Deploy Pipeline
type: PropertyValue
value: Apache Flink: 20-40 hours (learning + development) | Apache Spark: N/A

Typical Query Latency (1GB dataset)
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 2000-5000ms

Maximum Practical Data Size
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 1,000,000+ GB (petascale)

Memory Required Per Query
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 500-2000MB

Setup Time for Basic Analytics
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 30-120 minutes

Primary Language Support
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: Python, Scala, SQL, R, Java

Query Latency (1GB CSV)
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 8,000-15,000ms

Maximum Scalable Dataset Size
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 1,000+ PB

Setup Time (from scratch)
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 60-120 (cluster setup)

Multi-machine Distributed Computing
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: Native support

Fault Tolerance
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: Yes (RDD lineage-based)

Initial Licensing Cost
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: $0

Setup Time to Production
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 6-12 weeks

Cluster Management Required
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 40-80 hours (dedicated DevOps engineer)

SQL Query Performance (TPC-DS Benchmark)
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 45-120 seconds

Users Per Collaborative Project
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 1-5 (via Jupyter sharing)

Built-in Security Features
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 0 (manual implementation required)

Supported Data Formats
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: Parquet, ORC, JSON, CSV, Avro, Delta (via library)

Community Size
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 25,000+ questions

Typical Cluster Cost (Monthly)
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: $1,500-$5,000+

Data Processing Speed (1TB dataset)
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 5-15 minutes

Supported Programming Languages
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: Python, Scala, Java, R, SQL

Setup Time for Production Deployment
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 40-80 hours

Supported Warehouse Platforms
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: Hadoop, Kubernetes, cloud object storage (3+ classes)

Built-in Data Testing Features
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 0 (requires external frameworks)

Minimum Dataset Size for Optimal Use
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 100+ GB

GitHub Community (Stars)
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 38.5K stars

Query Performance on 1TB Dataset
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 30-120 seconds

Cluster Setup Time
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 40-80 hours

Cost per Core-Hour
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: $0.035-0.15

Supported Languages/APIs
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: Python, Scala, Java, SQL, R

Maximum Dataset Size Supported
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: Unlimited (depends on storage)

Cloud Provider Support
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 4+ (AWS, Azure, GCP, on-prem)

Machine Learning Algorithms Available
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 50+ (MLlib + custom models)

Data Format Support
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 8+ formats (Parquet, ORC, Avro, Delta, Iceberg, HDF5, CSV, JSON)

Processing Speed (Same 1TB dataset)
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 30-60 seconds (in-memory)

Processing Speed (Average Query)
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 10-60 seconds

Memory Requirement (Per Node)
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 16-256 GB

First Release
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 2014

Real-time Streaming Capability
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 500-5000 ms micro-batches

Market Adoption by Fortune 500
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 82%

Typical Cluster Cost (100-node setup)
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: $450,000-650,000

Fault Tolerance Method
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: Lineage-based recovery (RDD parents)

Processing Speed (Iterative Query)
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 0.5-2 seconds

Memory Requirement
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 8-64 GB per node

Real-time Processing
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 100-500 ms (micro-batch)

Ecosystem Age
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 12 years (since 2013)

Enterprise Adoption
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: 74% currently use

Machine Learning Capability
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: Full MLlib with algorithms, pipelines

Data Storage Redundancy
type: PropertyValue
value: Apache Flink: N/A | Apache Spark: Depends on underlying storage

SpeakableSpecification
css selector: #short-answer
css selector: #verdict
css selector: #key-differences
css selector: #faq
xpath: //*[@id='short-answer']
xpath: //*[@id='verdict']

Apache Flink — Wikipedia
type: CreativeWork

Apache Spark — Wikipedia
type: CreativeWork

Compare Apache Flink vs Apache Spark
type: Action
additional type: CompareAction
target: https://www.aversusb.net/compare/flink-vs-apache-spark)
object: Apache Flink
object: Apache Spark

Throughput Capacity
type: PropertyValue
value reference: Performance

Memory Per Node
type: PropertyValue
value reference: Resource Efficiency

Developer Community Size
type: PropertyValue
value reference: Ecosystem

Available Libraries & Integrations
type: PropertyValue
value reference: Ecosystem

Enterprise Adoption Rate
type: PropertyValue
value reference: Adoption

Mean Time to Deploy Production Job
type: PropertyValue
value reference: Development Velocity

Stateful Window Operations Complexity
type: PropertyValue
value reference: Functionality

Minimum Achievable Latency (P99)
type: PropertyValue
value reference: Performance

GitHub Stars (Popularity Indicator)
type: PropertyValue
value reference: Community

Market Adoption Rate
type: PropertyValue
value reference: Adoption

Memory Overhead per Task
type: PropertyValue
value reference: Resource Efficiency

ANSI SQL Compliance
type: PropertyValue
value reference: Language Support

State Management Capabilities
type: PropertyValue
value reference: Functionality

Production Deployments (2026)
type: PropertyValue
value reference: Adoption

Year-over-Year Growth Rate
type: PropertyValue
value reference: Trend

Minimum Processing Latency
type: PropertyValue
value reference: Performance

Available Integrations
type: PropertyValue
value reference: Ecosystem

Typical Cluster Setup Complexity
type: PropertyValue
value reference: Operations

Memory Per Task (Typical)
type: PropertyValue
value reference: Resource Requirements

Enterprise Adoption (2024)
type: PropertyValue
value reference: Market Share

State Backend Storage Limit
type: PropertyValue
value reference: Capability

Time to Deploy Pipeline
type: PropertyValue
value reference: Time to Production

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

Maximum Practical Data Size
type: PropertyValue
value reference: Scalability

Memory Required Per Query
type: PropertyValue
value reference: Resource Efficiency

Setup Time for Basic Analytics
type: PropertyValue
value reference: Ease of Use

Primary Language Support
type: PropertyValue
value reference: Developer Experience

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

Maximum Scalable Dataset Size
type: PropertyValue
value reference: Scalability

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

Multi-machine Distributed Computing
type: PropertyValue
value reference: Architecture

Fault Tolerance
type: PropertyValue
value reference: Reliability

Initial Licensing Cost
type: PropertyValue
value reference: Cost

Setup Time to Production
type: PropertyValue
value reference: Deployment

Cluster Management Required
type: PropertyValue
value reference: Operations

SQL Query Performance (TPC-DS Benchmark)
type: PropertyValue
value reference: Performance

Users Per Collaborative Project
type: PropertyValue
value reference: Collaboration

Built-in Security Features
type: PropertyValue
value reference: Security

Supported Data Formats
type: PropertyValue
value reference: Compatibility

Community Size
type: PropertyValue
value reference: Community

Typical Cluster Cost (Monthly)
type: PropertyValue
value reference: Cost

Data Processing Speed (1TB dataset)
type: PropertyValue
value reference: Performance

Supported Programming Languages
type: PropertyValue
value reference: Features

Setup Time for Production Deployment
type: PropertyValue
value reference: Ease of Use

Supported Warehouse Platforms
type: PropertyValue
value reference: Compatibility

Built-in Data Testing Features
type: PropertyValue
value reference: Features

Minimum Dataset Size for Optimal Use
type: PropertyValue
value reference: Use Case

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

Query Performance on 1TB Dataset
type: PropertyValue
value reference: Performance

Cluster Setup Time
type: PropertyValue
value reference: Implementation

Cost per Core-Hour
type: PropertyValue
value reference: Pricing

Supported Languages/APIs
type: PropertyValue
value reference: Compatibility

Maximum Dataset Size Supported
type: PropertyValue
value reference: Performance

Cloud Provider Support
type: PropertyValue
value reference: Features

Machine Learning Algorithms Available
type: PropertyValue
value reference: ML Capabilities

Data Format Support
type: PropertyValue
value reference: Flexibility

Processing Speed (Same 1TB dataset)
type: PropertyValue
value reference: Performance

Processing Speed (Average Query)
type: PropertyValue
value reference: Performance

Memory Requirement (Per Node)
type: PropertyValue
value reference: Infrastructure

First Release
type: PropertyValue
value reference: History

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

Market Adoption by Fortune 500
type: PropertyValue
value reference: Industry

Typical Cluster Cost (100-node setup)
type: PropertyValue
value reference: Cost

Fault Tolerance Method
type: PropertyValue
value reference: Reliability

Processing Speed (Iterative Query)
type: PropertyValue
value reference: Performance

Memory Requirement
type: PropertyValue
value reference: Resources

Real-time Processing
type: PropertyValue
value reference: Capability

Ecosystem Age
type: PropertyValue
value reference: Maturity

Enterprise Adoption
type: PropertyValue
value reference: Market

Machine Learning Capability
type: PropertyValue
value reference: Features

Data Storage Redundancy
type: PropertyValue
value reference: Reliability