Apache Spark vs Apache Flink
type: Article
description: Apache Spark excels at batch processing and general-purpose data analytics with broader ecosystem support, while Apache Flink is purpose-built for real-time stream processing with lower latency and more sophisticated stateful operations. Spark processes data in micro-batches (100ms minimum latency), whereas Flink processes individual events with true streaming (1-10ms latency).
language: en-US
date published: 2026-07-08T21:13:28.501Z
date created: 2026-07-08T21:13:28.501Z
date modified: 2026-07-08T21:13:28.539Z
content reference time: 2026-07-08T21:13:28.539Z
author: A Versus B
publisher: A Versus B
about: Apache Spark
about: Apache Flink
abstract: Apache Spark excels at batch processing and general-purpose data analytics with broader ecosystem support, while Apache Flink is purpose-built for real-time stream processing with lower latency and more sophisticated stateful operations. Spark processes data in micro-batches (100ms minimum latency), whereas Flink processes individual events with true streaming (1-10ms latency).
text: Processing Model. Latency (Event to Result). Ecosystem Maturity & Libraries. Stateful Operations (Session Windows, Complex State). Community Adoption & Job Market. Ease of Learning. Memory Efficiency at Scale
conclusion text: Choose Apache Spark if you need a versatile platform for batch analytics, machine learning, SQL queries, and some streaming—especially if team expertise and ecosystem breadth matter. Choose Apache Flink if you require true real-time processing with sub-100ms latency, complex stateful computations, or are building event-driven architectures where streaming is the primary workload.
additional property: Typical Query Latency (1GB dataset)
  type: PropertyValue
  value: Apache Spark: 2000-5000ms | Apache Flink: N/A
additional property: Maximum Practical Data Size
  type: PropertyValue
  value: Apache Spark: 1,000,000+ GB (petascale) | Apache Flink: N/A
additional property: Memory Required Per Query
  type: PropertyValue
  value: Apache Spark: 500-2000MB | Apache Flink: N/A
additional property: Setup Time for Basic Analytics
  type: PropertyValue
  value: Apache Spark: 30-120 minutes | Apache Flink: N/A
additional property: Primary Language Support
  type: PropertyValue
  value: Apache Spark: Python, Scala, SQL, R, Java | Apache Flink: N/A
additional property: Query Latency (1GB CSV)
  type: PropertyValue
  value: Apache Spark: 8,000-15,000ms | Apache Flink: N/A
additional property: Maximum Scalable Dataset Size
  type: PropertyValue
  value: Apache Spark: 1,000+ PB | Apache Flink: N/A
additional property: Minimum Memory Requirement
  type: PropertyValue
  value: Apache Spark: 2-4 GB | Apache Flink: 1024 MB
  value reference: Apache Spark
additional property: Setup Time (from scratch)
  type: PropertyValue
  value: Apache Spark: 60-120 (cluster setup) | Apache Flink: N/A
additional property: GitHub Stars (2026)
  type: PropertyValue
  value: Apache Spark: 35,900 stars | Apache Flink: 23,800+
  value reference: Apache Spark
additional property: Multi-machine Distributed Computing
  type: PropertyValue
  value: Apache Spark: Native support | Apache Flink: N/A
additional property: Fault Tolerance
  type: PropertyValue
  value: Apache Spark: Yes (RDD lineage-based) | Apache Flink: N/A
additional property: Initial Licensing Cost
  type: PropertyValue
  value: Apache Spark: $0 | Apache Flink: N/A
additional property: Setup Time to Production
  type: PropertyValue
  value: Apache Spark: 6-12 weeks | Apache Flink: N/A
additional property: Cluster Management Required
  type: PropertyValue
  value: Apache Spark: 40-80 hours (dedicated DevOps engineer) | Apache Flink: N/A
additional property: SQL Query Performance (TPC-DS Benchmark)
  type: PropertyValue
  value: Apache Spark: 45-120 seconds | Apache Flink: N/A
additional property: Users Per Collaborative Project
  type: PropertyValue
  value: Apache Spark: 1-5 (via Jupyter sharing) | Apache Flink: N/A
additional property: Built-in Security Features
  type: PropertyValue
  value: Apache Spark: 0 (manual implementation required) | Apache Flink: N/A
additional property: Supported Data Formats
  type: PropertyValue
  value: Apache Spark: Parquet, ORC, JSON, CSV, Avro, Delta (via library) | Apache Flink: N/A
additional property: Community Size
  type: PropertyValue
  value: Apache Spark: 25,000+ questions | Apache Flink: N/A
additional property: Typical Cluster Cost (Monthly)
  type: PropertyValue
  value: Apache Spark: $1,500-$5,000+ | Apache Flink: N/A
additional property: Data Processing Speed (1TB dataset)
  type: PropertyValue
  value: Apache Spark: 5-15 minutes | Apache Flink: N/A
additional property: Supported Programming Languages
  type: PropertyValue
  value: Apache Spark: Python, Scala, Java, R, SQL | Apache Flink: N/A
additional property: Setup Time for Production Deployment
  type: PropertyValue
  value: Apache Spark: 40-80 hours | Apache Flink: N/A
additional property: Supported Warehouse Platforms
  type: PropertyValue
  value: Apache Spark: Hadoop, Kubernetes, cloud object storage (3+ classes) | Apache Flink: N/A
additional property: Built-in Data Testing Features
  type: PropertyValue
  value: Apache Spark: 0 (requires external frameworks) | Apache Flink: N/A
additional property: Minimum Dataset Size for Optimal Use
  type: PropertyValue
  value: Apache Spark: 100+ GB | Apache Flink: N/A
additional property: GitHub Community (Stars)
  type: PropertyValue
  value: Apache Spark: 38.5K stars | Apache Flink: N/A
additional property: Query Performance on 1TB Dataset
  type: PropertyValue
  value: Apache Spark: 30-120 seconds | Apache Flink: N/A
additional property: Cluster Setup Time
  type: PropertyValue
  value: Apache Spark: 40-80 hours | Apache Flink: N/A
additional property: Cost per Core-Hour
  type: PropertyValue
  value: Apache Spark: $0.035-0.15 | Apache Flink: N/A
additional property: Supported Languages/APIs
  type: PropertyValue
  value: Apache Spark: Python, Scala, Java, SQL, R | Apache Flink: N/A
additional property: Maximum Dataset Size Supported
  type: PropertyValue
  value: Apache Spark: Unlimited (depends on storage) | Apache Flink: N/A
additional property: Cloud Provider Support
  type: PropertyValue
  value: Apache Spark: 4+ (AWS, Azure, GCP, on-prem) | Apache Flink: N/A
additional property: Machine Learning Algorithms Available
  type: PropertyValue
  value: Apache Spark: 50+ (MLlib + custom models) | Apache Flink: N/A
additional property: Data Format Support
  type: PropertyValue
  value: Apache Spark: 8+ formats (Parquet, ORC, Avro, Delta, Iceberg, HDF5, CSV, JSON) | Apache Flink: N/A
additional property: Processing Speed (Same 1TB dataset)
  type: PropertyValue
  value: Apache Spark: 30-60 seconds (in-memory) | Apache Flink: N/A
additional property: Processing Speed (Average Query)
  type: PropertyValue
  value: Apache Spark: 10-60 seconds | Apache Flink: N/A
additional property: Memory Requirement (Per Node)
  type: PropertyValue
  value: Apache Spark: 16-256 GB | Apache Flink: N/A
additional property: First Release
  type: PropertyValue
  value: Apache Spark: 2014 | Apache Flink: N/A
additional property: Real-time Streaming Capability
  type: PropertyValue
  value: Apache Spark: 500-5000 ms micro-batches | Apache Flink: N/A
additional property: Market Adoption by Fortune 500
  type: PropertyValue
  value: Apache Spark: 82% | Apache Flink: N/A
additional property: Typical Cluster Cost (100-node setup)
  type: PropertyValue
  value: Apache Spark: $450,000-650,000 | Apache Flink: N/A
additional property: Fault Tolerance Method
  type: PropertyValue
  value: Apache Spark: Lineage-based recovery (RDD parents) | Apache Flink: N/A
additional property: End-to-End Latency (p99)
  type: PropertyValue
  value: Apache Spark: 500-2000ms | Apache Flink: 5-50ms
  value reference: Apache Flink
additional property: Native Connectors Available
  type: PropertyValue
  value: Apache Spark: 200+ via ecosystem integrations | Apache Flink: ~30 native connectors
  value reference: Apache Spark
additional property: GitHub Stars
  type: PropertyValue
  value: Apache Spark: 40,100 stars | Apache Flink: ~23,000
  value reference: Apache Spark
additional property: Enterprise Adoption Rate
  type: PropertyValue
  value: Apache Spark: 65% (Databricks, AWS, Google, Meta deployments) | Apache Flink: 18% (Alibaba, Netflix, Uber, Lyft use cases)
  value reference: Apache Spark
additional property: Memory Overhead (per task)
  type: PropertyValue
  value: Apache Spark: ~400-800MB (GC overhead) | Apache Flink: ~200-400MB (optimized)
  value reference: Apache Flink
interactivity type: active
significant link: https://www.aversusb.net/entity/apache-spark
significant link: https://www.aversusb.net/alternatives/apache-spark
significant link: https://www.aversusb.net/entity/apache-flink
significant link: https://www.aversusb.net/alternatives/apache-flink
significant link: https://www.aversusb.net/api/answer/apache-spark-vs-flink)
significant link: https://www.aversusb.net/api/v1/schema/apache-spark-vs-flink)
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/flink-vs-apache-spark)
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 Spark and Apache Flink
genre: Software

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caption: Apache Spark vs Apache Flink — Side-by-side comparison on A Versus B

A Versus B
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Apache Spark
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description: General-purpose distributed computing framework using micro-batching for stream processing
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Apache Flink
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Apache Spark vs Apache Flink — Structured Comparison Data
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description: Machine-readable comparison dataset for Apache Spark vs Apache Flink. Contains 131 attribute comparisons, 5 FAQ pairs.
language: en-US
license: https://creativecommons.org/licenses/by/4.0/
creator: A Versus B
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is based on: https://www.aversusb.net/compare/apache-spark-vs-flink)

Apache Spark vs Apache Flink — Attribute Category Vocabulary
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has defined term: Performance
has defined term: Scalability
has defined term: Resource Efficiency
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has defined term: Developer Experience
has defined term: Community Adoption
has defined term: Architecture
has defined term: Reliability
has defined term: Cost
has defined term: Deployment
has defined term: Operations
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has defined term: Security
has defined term: Compatibility
has defined term: Community
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has defined term: Use Case
has defined term: Implementation
has defined term: Pricing
has defined term: ML Capabilities
has defined term: Flexibility
has defined term: Infrastructure
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has defined term: Industry
has defined term: Ecosystem
has defined term: Adoption
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has defined term: Localization
has defined term: Capability
has defined term: Maturity
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has defined term: Development Velocity
has defined term: Functionality
has defined term: Language Support
has defined term: Trend
has defined term: Career & Employment
has defined term: Feature Completeness
has defined term: Analytics
has defined term: Project Maturity
has defined term: Data Reliability
has defined term: Monitoring
has defined term: Requirements
has defined term: Community & Adoption
has defined term: Resource Requirements
has defined term: Market Share
has defined term: Time to Production

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

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

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

Ease of Use
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term code: ease-of-use
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Developer Experience
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term code: developer-experience
in defined term set: Apache Spark vs Apache Flink — Attribute Category Vocabulary

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

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

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

Cost
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term code: cost
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Deployment
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term code: deployment
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Operations
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term code: operations
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Collaboration
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term code: collaboration
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Security
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term code: security
in defined term set: Apache Spark vs Apache Flink — Attribute Category Vocabulary

Compatibility
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Community
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Pricing
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ML Capabilities
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Flexibility
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Infrastructure
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History
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Industry
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Ecosystem
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Adoption
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Resources
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term code: resources
in defined term set: Apache Spark vs Apache Flink — Attribute Category Vocabulary

Localization
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term code: localization
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Capability
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Development Velocity
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Functionality
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Language Support
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Trend
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Career & Employment
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term code: career-&-employment
in defined term set: Apache Spark vs Apache Flink — Attribute Category Vocabulary

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

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

Project Maturity
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term code: project-maturity
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Data Reliability
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term code: data-reliability
in defined term set: Apache Spark vs Apache Flink — Attribute Category Vocabulary

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

Requirements
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term code: requirements
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Community & Adoption
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in defined term set: Apache Spark vs Apache Flink — Attribute Category Vocabulary

Resource Requirements
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Market Share
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faq
type: FAQPage
language: en-US
is accessible for free: true
conditions of access: Free
date modified: 2026-07-08T21:13:28.539Z
author: A Versus B
publisher: A Versus B
is part of: apache-spark-vs-flink)
main entity: When should I use Spark Streaming vs Flink Streaming?
  type: Question
  accepted answer: Answer
    text: Use Spark Streaming if you have mixed batch and streaming workloads, need strong ML capabilities, or have a team familiar with Spark—it's acceptable for use cases with 100ms+ latency tolerance (dashboards, hourly aggregations). Use Flink if streaming is your primary workload and you need sub-50ms latency (fraud detection, algorithmic trading, real-time anomaly detection, IoT). Flink's event-time semantics and state backends also make it superior for complex temporal operations.
    language: en-US
main entity: Which is easier to learn and deploy?
  type: Question
  accepted answer: Answer
    text: Spark is significantly easier: Python support via PySpark is extensive, SQL-first development is possible, and documentation is abundant. Spark also has a larger talent pool—finding engineers is 3x faster. Flink requires strong Java/Scala expertise and has a steeper API learning curve, adding 4-6 weeks to typical deployment timelines compared to Spark.
    language: en-US
main entity: What's the difference in costs between Spark and Flink?
  type: Question
  accepted answer: Answer
    text: Spark typically costs 20-40% more in infrastructure due to higher memory footprint and caching overhead. However, this is offset by faster development time and lower labor costs (larger, cheaper talent pool). Flink can run leaner on compute but has higher hiring/training costs. For large organizations, total cost of ownership (TCO) is often comparable—the choice should be based on latency and workload type, not just compute costs.
    language: en-US
main entity: Can Spark and Flink work together in the same architecture?
  type: Question
  accepted answer: Answer
    text: Yes. Many organizations use Spark for batch ETL and analytics (data warehouse jobs, ML training) and Flink for real-time streaming (event processing, alerts). They can read/write from the same data sources (Kafka, S3, databases) and complement each other. However, managing two frameworks increases operational complexity; only adopt both if you have distinct use cases that justify the overhead.
    language: en-US
main entity: Which has better fault tolerance and exactly-once semantics?
  type: Question
  accepted answer: Answer
    text: Both support exactly-once semantics, but Flink's approach is more transparent. Flink uses distributed checkpointing (default every 10 seconds) with configurable state backends (RocksDB for large state). Spark Structured Streaming uses WAL + idempotent sinks. Flink is clearer about failure scenarios and provides more control; Spark abstracts complexity but offers less fine-grained tuning. For mission-critical systems (financial transactions), Flink's explicit semantics are preferable.
    language: en-US

How to Choose Between Apache Spark and Apache Flink
type: HowTo
description: Apache Spark excels at batch processing and general-purpose data analytics with broader ecosystem support, while Apache Flink is purpose-built for real-time stream processing with lower latency and more sophisticated stateful operations. Spark processes data in micro-batches (100ms minimum latency), whereas Flink processes individual events with true streaming (1-10ms latency).
language: en-US
total time: PT3M
step: Consider: Processing Model
step: Consider: Latency (Event to Result)
step: Consider: Ecosystem Maturity & Libraries
step: Consider: Stateful Operations (Session Windows, Complex State)
step: Consider: Community Adoption & Job Market
step: Consider: Ease of Learning
step: Consider: Memory Efficiency at Scale
tool: A Versus B Comparison Guide
tip: HowToTip
  text: Choose Apache Spark if you need a versatile platform for batch analytics, machine learning, SQL queries, and some streaming—especially if team expertise and ecosystem breadth matter. Choose Apache Flink if you require true real-time processing with sub-100ms latency, complex stateful computations, or are building event-driven architectures where streaming is the primary workload.

Consider: Processing Model
type: HowToStep
position: 1
text: Processing Model: Apache Spark has Micro-batch (Spark Streaming), Native batch, while Apache Flink has True event-by-event streaming.

Consider: Latency (Event to Result)
type: HowToStep
position: 2
text: Latency (Event to Result): Apache Spark has 100ms - 2 seconds, while Apache Flink has 1ms - 10ms.

Consider: Ecosystem Maturity & Libraries
type: HowToStep
position: 3
text: Ecosystem Maturity & Libraries: Apache Spark has 14,000+ packages via Spark ecosystem, MLlib, SQL, GraphX, while Apache Flink has 2,000+ packages, maturing ML libraries.

Consider: Stateful Operations (Session Windows, Complex State)
type: HowToStep
position: 4
text: Stateful Operations (Session Windows, Complex State): Apache Spark has Basic state management, while Apache Flink has Advanced state backends, fine-grained control.

Consider: Community Adoption & Job Market
type: HowToStep
position: 5
text: Community Adoption & Job Market: Apache Spark has 7.2M+ developers, 65% adoption in enterprises, while Apache Flink has 1.8M+ developers, 18% enterprise adoption.

Consider: Ease of Learning
type: HowToStep
position: 6
text: Ease of Learning: Apache Spark has Easier (Python, SQL, Scala, Java APIs), while Apache Flink has Steeper learning curve (Java/Scala dominant).

Consider: Memory Efficiency at Scale
type: HowToStep
position: 7
text: Memory Efficiency at Scale: Apache Spark has Higher memory overhead (caching architecture), while Apache Flink has 20-30% lower memory per node.

claimreview
type: ClaimReview
is accessible for free: true
conditions of access: Free
claim reviewed: Apache Spark vs Apache Flink
review body: Apache Spark excels at batch processing and general-purpose data analytics with broader ecosystem support, while Apache Flink is purpose-built for real-time stream processing with lower latency and more sophisticated stateful operations. Spark processes data in micro-batches (100ms minimum latency), whereas Flink processes individual events with true streaming (1-10ms latency).
review rating: Rating
  rating: 5
  worst rating: 1
  best rating: 5
  alternate name: Verified Comparison
author: A Versus B
item reviewed: Apache Spark vs Apache Flink

Supported Event Time Semantics
type: PropertyValue
value: Apache Spark: Partial in Structured Streaming, limited out-of-order support | Apache Flink: Full with watermarks, out-of-order handling, allowedLateness

Throughput (events/sec per node)
type: PropertyValue
value: Apache Spark: ~500K-1M events/sec | Apache Flink: ~1-2M events/sec
value reference: Apache Flink

Batch+Stream Unified Code
type: PropertyValue
value: Apache Spark: Unified via Structured Streaming/Dataset API | Apache Flink: Separate APIs (DataStream vs Batch)

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

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

Supported Languages
type: PropertyValue
value: Apache Spark: 5 (Scala, Python, Java, R, SQL) | Apache Flink: 4 (Java, Scala, Python, SQL)
value reference: Apache Spark

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Minimum Operational Complexity
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: 5-7 (JobManager, TaskManagers, StateBackend, Checkpoints)

Time to First Correct Result (learning curve)
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: 6-10

Available Built-in Connectors
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: 50+

Watermark Support
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: Yes (core feature)

Typical Throughput (single node)
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: 250,000

Delivery Semantics
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: Exactly-once (native)

State Size Capacity
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: 500+

Throughput (Records/Second)
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: 1M-10M

Memory Usage per Node
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: 4-16 GB

Minimum Cluster Size
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: 2-3 nodes

Fault Tolerance Mechanism
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: Distributed snapshots + checkpointing

GitHub Stars (2025)
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: 23.8K

Optimal Dataset Size
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: Continuous streams (any size)

Processing Latency (end-to-end)
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: 50-100ms

Processing Semantics
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: Exactly-once

Time Window Support
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: Event-time, processing-time, session windows, custom

Setup Complexity (1-10)
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: 7/10

Deployment Complexity
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: Requires cluster with YARN/Kubernetes, moderate DevOps

Time to First Production Deployment
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: 8-12 weeks (with Kubernetes ops experience)

Production Deployments Reported
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: 10,000+

Programming Languages Supported
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: 4 (Java, Python, Scala, SQL)

First Release Year
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: 2011

Latency (p99 for simple aggregations)
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: 100-500 ms (tuning dependent)

Maximum Managed State Size
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: Terabyte-scale (tested to 10+ TB)

GitHub Stars (as of 2026)
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: 29,000+

Job Market Demand
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: ~1,850

Baseline JVM Memory Overhead
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: 1.5-2.5 GB

Event-Time Support
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: Native & first-class (core design)

Machine Learning Capabilities
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: Limited (requires external libraries)

Top-Level Apache Status
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: 2015

Average Query Execution (1GB dataset)
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: 2-3 seconds (streaming) / 4-6 (batch)

Processing Latency
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: 1-100 ms (sub-second typical)

Maximum Throughput per Node
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: 100,000-1,000,000 events/sec

Python Support Level
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: PyFlink added in v1.11 (2020); improved in v1.14+

State Consistency Guarantee
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: Exactly-once (configurable per checkpoint)

Integrated Web UI
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: Basic REST API only (external UI required)

Minimum Java Version Required
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: Java 11+

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

Maximum Throughput
type: PropertyValue
value: Apache Spark: N/A | Apache Flink: 5,000,000+

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

SpeakableSpecification
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Apache Spark — Wikipedia
type: CreativeWork

Apache Flink — Wikipedia
type: CreativeWork

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

Event Latency (Processing End-to-End)
type: PropertyValue
value reference: Performance

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

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 Operational Complexity
type: PropertyValue
value reference: Operations

Time to First Correct Result (learning curve)
type: PropertyValue
value reference: Adoption

Available Built-in Connectors
type: PropertyValue
value reference: Ecosystem

Watermark Support
type: PropertyValue
value reference: Features

Typical Throughput (single node)
type: PropertyValue
value reference: Performance

Delivery Semantics
type: PropertyValue
value reference: Reliability

State Size Capacity
type: PropertyValue
value reference: Scalability

Throughput (Records/Second)
type: PropertyValue
value reference: Performance

Memory Usage per Node
type: PropertyValue
value reference: Resource Efficiency

Minimum Cluster Size
type: PropertyValue
value reference: Infrastructure

Fault Tolerance Mechanism
type: PropertyValue
value reference: Reliability

GitHub Stars (2025)
type: PropertyValue
value reference: Community

Optimal Dataset Size
type: PropertyValue
value reference: Use Case

Processing Latency (end-to-end)
type: PropertyValue
value reference: Performance

Processing Semantics
type: PropertyValue
value reference: Reliability

Time Window Support
type: PropertyValue
value reference: Features

Setup Complexity (1-10)
type: PropertyValue
value reference: Ease of Use

Deployment Complexity
type: PropertyValue
value reference: Operations

Time to First Production Deployment
type: PropertyValue
value reference: Implementation

Production Deployments Reported
type: PropertyValue
value reference: Adoption

Programming Languages Supported
type: PropertyValue
value reference: Features

First Release Year
type: PropertyValue
value reference: Maturity

Latency (p99 for simple aggregations)
type: PropertyValue
value reference: Performance

Maximum Managed State Size
type: PropertyValue
value reference: Scalability

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

Job Market Demand
type: PropertyValue
value reference: Career & Employment

Baseline JVM Memory Overhead
type: PropertyValue
value reference: Resource Efficiency

Event-Time Support
type: PropertyValue
value reference: Feature Completeness

Machine Learning Capabilities
type: PropertyValue
value reference: Analytics

Top-Level Apache Status
type: PropertyValue
value reference: Project Maturity

Average Query Execution (1GB dataset)
type: PropertyValue
value reference: Performance

Processing Latency
type: PropertyValue
value reference: Performance

Maximum Throughput per Node
type: PropertyValue
value reference: Performance

Python Support Level
type: PropertyValue
value reference: Language Support

State Consistency Guarantee
type: PropertyValue
value reference: Data Reliability

Integrated Web UI
type: PropertyValue
value reference: Monitoring

Minimum Java Version Required
type: PropertyValue
value reference: Requirements

Initial Release Year
type: PropertyValue
value reference: Maturity

Maximum Throughput
type: PropertyValue
value reference: Performance

Primary Implementation Language
type: PropertyValue
value reference: Architecture

Memory Overhead (idle cluster)
type: PropertyValue
value reference: Resource Efficiency

Time to Build First Pipeline
type: PropertyValue
value reference: Developer Experience

Active Contributors (6-month window)
type: PropertyValue
value reference: Community

Built-in State Backends
type: PropertyValue
value reference: Features

Price (Self-Hosted)
type: PropertyValue
value reference: Cost

Community GitHub Stars
type: PropertyValue
value reference: Community & Adoption

Years in Production
type: PropertyValue
value reference: Maturity

Built-in Connectors
type: PropertyValue
value reference: Ecosystem

Max Throughput (Typical Setup)
type: PropertyValue
value reference: Performance

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