software
Apache Spark vs Flink 2026: Streaming Comparison
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).
Apache Spark
General-purpose distributed computing framework using micro-batching for stream processing
Data engineers building ETL pipelines, machine learning platforms, analytical dashboards, and organizations with diverse workloads (batch + some streaming). Best for teams prioritizing ecosystem maturity and skill availability.
Score63%
VS
Apache Flink
Open-source, true streaming platform with event-time processing and sub-second latency
Organizations requiring true real-time streaming (sub-100ms), complex stateful event processing, fraud detection, IoT analytics, and real-time recommendation systems. Ideal when streaming is the primary use case, not a secondary feature.
Score63%
Quick Answer
AI SummaryApache 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).
Our Verdict
AI-assistedChoose 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.
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Apache Spark
7.7/10
Apache Flink
7.3/10
Choose Apache Spark if
Best pickData engineers building ETL pipelines, machine learning platforms, analytical dashboards, and organizations with diverse workloads (batch + some streaming). Best for teams prioritizing ecosystem maturity and skill availability.
Choose Apache Flink if
Organizations requiring true real-time streaming (sub-100ms), complex stateful event processing, fraud detection, IoT analytics, and real-time recommendation systems. Ideal when streaming is the primary use case, not a secondary feature.
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Key Differences at a Glance
- Processing Model:✓ Apache Flink wins(True event-by-event streaming vs Micro-batch (Spark Streaming), Native batch)
- Latency (Event to Result):✓ Apache Flink wins(1ms - 10ms vs 100ms - 2 seconds)
- Ecosystem Maturity & Libraries:✓ Apache Spark wins(14,000+ packages via Spark ecosystem, MLlib, SQL, GraphX vs 2,000+ packages, maturing ML libraries)
Key Facts & Figures
96 numeric metrics compared
| Metric | Apache Spark | Apache Flink | Ratio |
|---|---|---|---|
| Typical Query Latency (1GB dataset)(milliseconds) | 2000-5000ms | — | — |
| Maximum Practical Data Size(GB) | 1,000,000+ GB (petascale) | — | — |
| Memory Required Per Query(MB) | 500-2000MB | — | — |
| Setup Time for Basic Analytics(minutes) | 30-120 minutes | — | — |
| Query Latency (1GB CSV)(milliseconds) | 8,000-15,000ms | — | — |
| Maximum Scalable Dataset Size(GB) | 1,000+ PB | — | — |
| Minimum Memory Requirement(MB) | 2-4 GB | 1024 MB | |
| Setup Time (from scratch)(minutes) | 60-120 (cluster setup) | — | — |
| GitHub Stars (2026)(stars) | 35,900 stars | 23,800+ | |
| Initial Licensing Cost(USD) | $0 | — | — |
| Setup Time to Production(minutes) | 6-12 weeks | — | — |
| SQL Query Performance (TPC-DS Benchmark)(seconds) | 45-120 seconds | — | — |
| Users Per Collaborative Project(concurrent users) | 1-5 (via Jupyter sharing) | — | — |
| Typical Cluster Cost (Monthly)(USD) | $1,500-$5,000+ | — | — |
| Data Processing Speed (1TB dataset)(minutes) | 5-15 minutes | — | — |
| Supported Programming Languages(count) | Python, Scala, Java, R, SQL | — | — |
| Setup Time for Production Deployment(hours) | 40-80 hours | — | — |
| Supported Warehouse Platforms(platforms) | Hadoop, Kubernetes, cloud object storage (3+ classes) | — | — |
| Built-in Data Testing Features(count) | 0 (requires external frameworks) | — | — |
| Minimum Dataset Size for Optimal Use(GB) | 100+ GB | — | — |
| GitHub Community (Stars)(thousands) | 38.5K stars | — | — |
| Query Performance on 1TB Dataset(seconds) | 30-120 seconds | — | — |
| Cluster Setup Time(hours) | 40-80 hours | — | — |
| Cost per Core-Hour(USD) | $0.035-0.15 | — | — |
| Supported Languages/APIs(count) | Python, Scala, Java, SQL, R | — | — |
| Cloud Provider Support(providers) | 4+ (AWS, Azure, GCP, on-prem) | — | — |
| Machine Learning Algorithms Available(count) | 50+ (MLlib + custom models) | — | — |
| Data Format Support(format types) | 8+ formats (Parquet, ORC, Avro, Delta, Iceberg, HDF5, CSV, JSON) | — | — |
| Processing Speed (Same 1TB dataset)(seconds) | 30-60 seconds (in-memory) | — | — |
| Processing Speed (Average Query)(seconds) | 10-60 seconds | — | — |
| Memory Requirement (Per Node)(GB) | 16-256 GB | — | — |
| Real-time Streaming Capability(latency (ms)) | 500-5000 ms micro-batches | — | — |
| Market Adoption by Fortune 500(percent) | 82% | — | — |
| Typical Cluster Cost (100-node setup)(USD annual) | $450,000-650,000 | — | — |
| End-to-End Latency (p99)(milliseconds) | 500-2000ms | 5-50ms | |
| Native Connectors Available(count) | 200+ via ecosystem integrations | ~30 native connectors | |
| GitHub Stars(stars) | 40,100 stars | 15,400 stars | |
| Memory Overhead (per task)(MB) | ~400-800MB (GC overhead) | ~200-400MB (optimized) | |
| Throughput (events/sec per node)(events/sec) | ~500K-1M events/sec | ~1-2M events/sec | |
| Processing Speed (Iterative Query)(seconds) | 0.5-2 seconds | — | — |
| Memory Requirement(GB) | 8-64 GB per node | — | — |
| Supported Languages(count) | 5 (Scala, Python, Java, R, SQL) | 4 (Java, Scala, Python, SQL) | |
| Real-time Processing(latency (milliseconds)) | 100-500 ms (micro-batch) | — | — |
| Ecosystem Age(years) | 12 years (since 2013) | — | — |
| Enterprise Adoption(companies) | 74% currently use | — | — |
| Event Latency (Processing End-to-End)(milliseconds) | 100-2,000ms (Spark Streaming micro-batch interval) | 1-10ms (true event streaming) | |
| Throughput Capacity(events/second/node) | 500,000 - 2,000,000 (batch-optimized) | 1,000,000 - 5,000,000 (streaming-optimized) | |
| Memory Per Node(GB per 1M events/sec) | 8-12GB (caching overhead) | 6-8GB (efficient state management) | |
| Available Libraries & Integrations(count) | 14,000+ (Spark packages, MLlib, SQL, GraphX, etc.) | 2,000+ (Flink SQL, state backends, CEP library) | |
| Mean Time to Deploy Production Job(weeks) | 2-4 weeks (larger talent pool, more examples) | 6-10 weeks (steeper learning curve, less documentation) | |
| Stateful Window Operations Complexity(lines of code for session windows) | 80-150 lines (custom state handling needed) | 15-30 lines (native session window API) | |
| Minimum Achievable Latency (P99)(milliseconds) | 500-2000ms | 100-500ms | |
| GitHub Stars (Popularity Indicator)(stars) | 32,000 | 2,500 | |
| Market Adoption Rate(percentage of streaming workloads) | 60-65% | 15-20% | |
| Memory Overhead per Task(megabytes (baseline)) | 512-1024MB | 256-512MB | |
| ANSI SQL Compliance(percentage) | 98% | 95% | |
| State Management Capabilities(feature count) | 2 types (RDD state, DataFrame state) | 5 types (keyed, operator, broadcast, queryable, custom) | |
| Production Deployments (2026)(thousands of deployments) | 45,000-55,000 | 8,000-12,000 | |
| Year-over-Year Growth Rate(percentage) | 8% | 25% | |
| Minimum Operational Complexity(components to manage) | 5-7 (JobManager, TaskManagers, StateBackend, Checkpoints) | 5-7 (JobManager, TaskManagers, StateBackend, Checkpoints) | |
| Time to First Correct Result (learning curve)(weeks (team of 2)) | 6-10 | 6-10 | |
| Available Built-in Connectors(count) | 50+ | 50+ | |
| Typical Throughput (single node)(events/sec) | 250,000 | 250,000 | |
| State Size Capacity(GB) | 500+ | 500+ | |
| Throughput (Records/Second)(million records/sec) | 1M-10M | 1M-10M | |
| Memory Usage per Node(GB) | 4-16 GB | 4-16 GB | |
| Minimum Cluster Size(nodes) | 2-3 nodes | 2-3 nodes | |
| GitHub Stars (2025)(stars) | 23.8K | 23.8K | |
| Processing Latency (end-to-end)(milliseconds) | 50-100ms | 50-100ms | |
| Setup Complexity (1-10)(complexity score) | 7/10 | 7/10 | |
| Time to First Production Deployment(days) | 8-12 weeks (with Kubernetes ops experience) | 8-12 weeks (with Kubernetes ops experience) | |
| Production Deployments Reported(count) | 10,000+ | 10,000+ | |
| Programming Languages Supported(languages) | 4 (Java, Python, Scala, SQL) | 4 (Java, Python, Scala, SQL) | |
| First Release Year(year) | 2011 | 2011 | |
| Latency (p99 for simple aggregations)(milliseconds) | 100-500 ms (tuning dependent) | 100-500 ms (tuning dependent) | |
| Maximum Managed State Size(TB) | Terabyte-scale (tested to 10+ TB) | Terabyte-scale (tested to 10+ TB) | |
| GitHub Stars (as of 2026)(stars) | 29,000+ | 29,000+ | |
| Minimum Processing Latency(milliseconds) | <100ms (native streaming) | <100ms (native streaming) | |
| Job Market Demand(postings) | ~1,850 | ~1,850 | |
| Baseline JVM Memory Overhead(GB) | 1.5-2.5 GB | 1.5-2.5 GB | |
| Top-Level Apache Status(year achieved) | 2015 | 2015 | |
| Average Query Execution (1GB dataset)(seconds) | 2-3 seconds (streaming) / 4-6 (batch) | 2-3 seconds (streaming) / 4-6 (batch) | |
| Processing Latency(milliseconds) | 1-100 ms (sub-second typical) | 1-100 ms (sub-second typical) | |
| Maximum Throughput per Node(events/second) | 100,000-1,000,000 events/sec | 100,000-1,000,000 events/sec | |
| Time to Deploy Pipeline(minutes) | 30-90 minutes (requires cluster setup) | 30-90 minutes (requires cluster setup) | |
| Minimum Java Version Required(version) | Java 11+ | Java 11+ | |
| Initial Release Year(year) | 2014 | 2014 | |
| Maximum Throughput(messages/second) | 5,000,000+ | 5,000,000+ | |
| Memory Overhead (idle cluster)(GB) | 2-4 GB | 2-4 GB | |
| Time to Build First Pipeline(hours) | 72 | 72 | |
| Active Contributors (6-month window)(developers) | 180+ | 180+ | |
| Price (Self-Hosted)(USD/month) | 0 (Open source) | 0 (Open source) | |
| Community GitHub Stars(stars) | 10,400 | 10,400 | |
| Years in Production(years) | 12 (since 2014) | 12 (since 2014) | |
| Built-in Connectors(count) | 15+ | 15+ | |
| Max Throughput (Typical Setup)(events/sec) | Millions (1M+) | Millions (1M+) |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
Apache Flink leads
Apache Spark
Attribute
Apache Flink
- Micro-batch (Spark Streaming), Native batchProcessing ModelTrue event-by-event streaming(winner)
- 100ms - 2 secondsLatency (Event to Result)1ms - 10ms(winner)
- 14,000+ packages via Spark ecosystem, MLlib, SQL, GraphX(winner)Ecosystem Maturity & Libraries2,000+ packages, maturing ML libraries
- Basic state managementStateful Operations (Session Windows, Complex State)Advanced state backends, fine-grained control(winner)
- 7.2M+ developers, 65% adoption in enterprises(winner)Community Adoption & Job Market1.8M+ developers, 18% enterprise adoption
- Easier (Python, SQL, Scala, Java APIs)(winner)Ease of LearningSteeper learning curve (Java/Scala dominant)
- Higher memory overhead (caching architecture)Memory Efficiency at Scale20-30% lower memory per node(winner)
- Processing Model
Apache Spark
Micro-batch (Spark Streaming), Native batch
Apache Flink
True event-by-event streaming(winner)
- Latency (Event to Result)
Apache Spark
100ms - 2 seconds
Apache Flink
1ms - 10ms(winner)
- Ecosystem Maturity & Libraries
Apache Spark
14,000+ packages via Spark ecosystem, MLlib, SQL, GraphX(winner)
Apache Flink
2,000+ packages, maturing ML libraries
- Stateful Operations (Session Windows, Complex State)
Apache Spark
Basic state management
Apache Flink
Advanced state backends, fine-grained control(winner)
- Community Adoption & Job Market
Apache Spark
7.2M+ developers, 65% adoption in enterprises(winner)
Apache Flink
1.8M+ developers, 18% enterprise adoption
- Ease of Learning
Apache Spark
Easier (Python, SQL, Scala, Java APIs)(winner)
Apache Flink
Steeper learning curve (Java/Scala dominant)
- Memory Efficiency at Scale
Apache Spark
Higher memory overhead (caching architecture)
Apache Flink
20-30% lower memory per node(winner)
Full Comparison
| Attribute | Apache Spark | Apache Flink |
|---|---|---|
| Typical Query Latency (1GB dataset)(milliseconds) | 2000-5000ms | — |
| Query Latency (1GB CSV)(milliseconds) | 8,000-15,000ms | — |
| Minimum Memory Requirement(MB) | 2-4 GB(winner) | 1024 MB |
| SQL Query Performance (TPC-DS Benchmark)(seconds) | 45-120 seconds | — |
| Data Processing Speed (1TB dataset)(minutes) | 5-15 minutes | — |
Show 21 more attributesQuery Performance on 1TB Dataset(seconds) 30-120 seconds — Maximum Dataset Size Supported(GB) Unlimited (depends on storage) — Processing Speed (Same 1TB dataset)(seconds) 30-60 seconds (in-memory) — Processing Speed (Average Query)(seconds) 10-60 seconds — End-to-End Latency (p99)(milliseconds) 500-2000ms 5-50ms Memory Overhead (per task)(MB) ~400-800MB (GC overhead) ~200-400MB (optimized) Throughput (events/sec per node)(events/sec) ~500K-1M events/sec ~1-2M events/sec Processing Speed (Iterative Query)(seconds) 0.5-2 seconds — Event Latency (Processing End-to-End)(milliseconds) 100-2,000ms (Spark Streaming micro-batch interval) 1-10ms (true event streaming) Throughput Capacity(events/second/node) 500,000 - 2,000,000 (batch-optimized) 1,000,000 - 5,000,000 (streaming-optimized) Minimum Achievable Latency (P99)(milliseconds) 500-2000ms 100-500ms Typical Throughput (single node)(events/sec) 250,000 — Throughput (Records/Second)(million records/sec) 1M-10M — Processing Latency (end-to-end)(milliseconds) 50-100ms — Latency (p99 for simple aggregations)(milliseconds) 100-500 ms (tuning dependent) — Minimum Processing Latency(milliseconds) <100ms (native streaming) — Average Query Execution (1GB dataset)(seconds) 2-3 seconds (streaming) / 4-6 (batch) — Processing Latency(milliseconds) 1-100 ms (sub-second typical) — Maximum Throughput per Node(events/second) 100,000-1,000,000 events/sec — Maximum Throughput(messages/second) 5,000,000+ — Max Throughput (Typical Setup)(events/sec) Millions (1M+) — | ||
| Maximum Practical Data Size(GB) | 1,000,000+ GB (petascale) | — |
| Maximum Scalable Dataset Size(GB) | 1,000+ PB | — |
| State Size Capacity(GB) | 500+ | — |
| Maximum Managed State Size(TB) | Terabyte-scale (tested to 10+ TB) | — |
| Memory Required Per Query(MB) | 500-2000MB | — |
| Memory Per Node(GB per 1M events/sec) | 8-12GB (caching overhead) | 6-8GB (efficient state management)(winner) |
| Memory Overhead per Task(megabytes (baseline)) | 512-1024MB | 256-512MB(winner) |
| Memory Usage per Node(GB) | 4-16 GB | — |
| Baseline JVM Memory Overhead(GB) | 1.5-2.5 GB | — |
Show 1 more attributeMemory Overhead (idle cluster)(GB) 2-4 GB — | ||
| Setup Time for Basic Analytics(minutes) | 30-120 minutes | — |
| Setup Time (from scratch)(minutes) | 60-120 (cluster setup) | — |
| Setup Time for Production Deployment(hours) | 40-80 hours | — |
| Setup Complexity (1-10)(complexity score) | 7/10 | — |
| Primary Language Support(count) | Python, Scala, SQL, R, Java | — |
| Time to Deploy Pipeline(minutes) | 30-90 minutes (requires cluster setup) | — |
| Time to Build First Pipeline(hours) | 72 | — |
| GitHub Stars (2026)(stars) | 35,900 stars(winner) | 23,800+ |
| GitHub Stars (as of 2026)(stars) | 29,000+ | — |
| Multi-machine Distributed Computing(capability) | Native support | — |
| Primary Implementation Language | Java | — |
| Fault Tolerance(capability) | Yes (RDD lineage-based) | — |
| Fault Tolerance Method(mechanism) | Lineage-based recovery (RDD parents) | — |
| Data Storage Redundancy(replication factor) | Depends on underlying storage | — |
| Delivery Semantics | Exactly-once (native) | — |
| Fault Tolerance Mechanism | Distributed snapshots + checkpointing | — |
Show 1 more attributeProcessing Semantics Exactly-once — | ||
| Initial Licensing Cost(USD) | $0 | — |
| Typical Cluster Cost (Monthly)(USD) | $1,500-$5,000+ | — |
| Typical Cluster Cost (100-node setup)(USD annual) | $450,000-650,000 | — |
| Price (Self-Hosted)(USD/month) | 0 (Open source) | — |
| Setup Time to Production(minutes) | 6-12 weeks | — |
| Cluster Management Required(hours/month) | 40-80 hours (dedicated DevOps engineer) | — |
| Minimum Operational Complexity(components to manage) | 5-7 (JobManager, TaskManagers, StateBackend, Checkpoints) | — |
| Deployment Complexity | Requires cluster with YARN/Kubernetes, moderate DevOps | — |
| Users Per Collaborative Project(concurrent users) | 1-5 (via Jupyter sharing) | — |
| Built-in Security Features | 0 (manual implementation required) | — |
| Supported Data Formats(formats) | Parquet, ORC, JSON, CSV, Avro, Delta (via library) | — |
| Supported Warehouse Platforms(platforms) | Hadoop, Kubernetes, cloud object storage (3+ classes) | — |
| Supported Languages/APIs(count) | Python, Scala, Java, SQL, R | — |
| Community Size(GitHub stars) | 25,000+ questions | — |
| GitHub Community (Stars)(thousands) | 38.5K stars | — |
| GitHub Stars(stars) | 40,100 stars(winner) | 15,400 stars |
| GitHub Stars (Popularity Indicator)(stars) | 32,000(winner) | 2,500 |
| GitHub Stars (2025)(stars) | 23.8K | — |
Show 1 more attributeActive Contributors (6-month window)(developers) 180+ — | ||
| Supported Programming Languages(count) | Python, Scala, Java, R, SQL | — |
| Built-in Data Testing Features(count) | 0 (requires external frameworks) | — |
| Cloud Provider Support(providers) | 4+ (AWS, Azure, GCP, on-prem) | — |
| Real-time Streaming Capability(latency (ms)) | 500-5000 ms micro-batches | — |
| Supported Event Time Semantics | Partial in Structured Streaming, limited out-of-order support | Full with watermarks, out-of-order handling, allowedLateness |
| Batch+Stream Unified Code | Unified via Structured Streaming/Dataset API | Separate APIs (DataStream vs Batch) |
Show 5 more attributesMachine Learning Capability(native support) Full MLlib with algorithms, pipelines — Watermark Support Yes (core feature) — Time Window Support Event-time, processing-time, session windows, custom — Programming Languages Supported(languages) 4 (Java, Python, Scala, SQL) — Built-in State Backends Memory, RocksDB, External (3 options) — | ||
| Minimum Dataset Size for Optimal Use(GB) | 100+ GB | — |
| Optimal Dataset Size(GB minimum) | Continuous streams (any size) | — |
| Cluster Setup Time(hours) | 40-80 hours | — |
| Time to First Production Deployment(days) | 8-12 weeks (with Kubernetes ops experience) | — |
| Cost per Core-Hour(USD) | $0.035-0.15 | — |
| Machine Learning Algorithms Available(count) | 50+ (MLlib + custom models) | — |
| Data Format Support(format types) | 8+ formats (Parquet, ORC, Avro, Delta, Iceberg, HDF5, CSV, JSON) | — |
| Memory Requirement (Per Node)(GB) | 16-256 GB | — |
| Minimum Cluster Size(nodes) | 2-3 nodes | — |
| First Release(year) | 2014 | — |
| Market Adoption by Fortune 500(percent) | 82% | — |
| Native Connectors Available(count) | 200+ via ecosystem integrations(winner) | ~30 native connectors |
| Developer Community Size(active developers) | 7,200,000 (StackOverflow, job postings 2024)(winner) | 1,800,000 (StackOverflow, job postings 2024) |
| Available Libraries & Integrations(count) | 14,000+ (Spark packages, MLlib, SQL, GraphX, etc.)(winner) | 2,000+ (Flink SQL, state backends, CEP library) |
| Available Built-in Connectors(count) | 50+ | — |
| Built-in Connectors(count) | 15+ | — |
| Enterprise Adoption Rate(% of Fortune 500) | 65% (Databricks, AWS, Google, Meta deployments)(winner) | 18% (Alibaba, Netflix, Uber, Lyft use cases) |
| Market Adoption Rate(percentage of streaming workloads) | 60-65%(winner) | 15-20% |
| Production Deployments (2026)(thousands of deployments) | 45,000-55,000(winner) | 8,000-12,000 |
| Time to First Correct Result (learning curve)(weeks (team of 2)) | 6-10 | — |
| Production Deployments Reported(count) | 10,000+ | — |
| Memory Requirement(GB) | 8-64 GB per node | — |
| Supported Languages(count) | 5 (Scala, Python, Java, R, SQL)(winner) | 4 (Java, Scala, Python, SQL) |
| Real-time Processing(latency (milliseconds)) | 100-500 ms (micro-batch) | — |
| Ecosystem Age(years) | 12 years (since 2013) | — |
| First Release Year(year) | 2011 | — |
| Initial Release Year(year) | 2014 | — |
| Years in Production(years) | 12 (since 2014) | — |
| Enterprise Adoption(companies) | 74% currently use | — |
| Mean Time to Deploy Production Job(weeks) | 2-4 weeks (larger talent pool, more examples)(winner) | 6-10 weeks (steeper learning curve, less documentation) |
| Stateful Window Operations Complexity(lines of code for session windows) | 80-150 lines (custom state handling needed) | 15-30 lines (native session window API)(winner) |
| State Management Capabilities(feature count) | 2 types (RDD state, DataFrame state) | 5 types (keyed, operator, broadcast, queryable, custom)(winner) |
| ANSI SQL Compliance(percentage) | 98%(winner) | 95% |
| Python Support Level(support quality) | PyFlink added in v1.11 (2020); improved in v1.14+ | — |
| Year-over-Year Growth Rate(percentage) | 8% | 25%(winner) |
| Job Market Demand(postings) | ~1,850 | — |
| Event-Time Support | Native & first-class (core design) | — |
| Machine Learning Capabilities(availability) | Limited (requires external libraries) | — |
| Top-Level Apache Status(year achieved) | 2015 | — |
| State Consistency Guarantee(semantic level) | Exactly-once (configurable per checkpoint) | — |
| Integrated Web UI(rating) | Basic REST API only (external UI required) | — |
| Minimum Java Version Required(version) | Java 11+ | — |
| Community GitHub Stars(stars) | 10,400 | — |
Apache SparkApache FlinkTypical Query Latency (1GB dataset)(milliseconds)
2000-5000ms
—
Query Latency (1GB CSV)(milliseconds)
8,000-15,000ms
—
Minimum Memory Requirement(MB)
2-4 GB
1024 MB
SQL Query Performance (TPC-DS Benchmark)(seconds)
45-120 seconds
—
Data Processing Speed (1TB dataset)(minutes)
5-15 minutes
—
Show 21 more attributes
Query Performance on 1TB Dataset(seconds)
30-120 seconds
—
Maximum Dataset Size Supported(GB)
Unlimited (depends on storage)
—
Processing Speed (Same 1TB dataset)(seconds)
30-60 seconds (in-memory)
—
Processing Speed (Average Query)(seconds)
10-60 seconds
—
End-to-End Latency (p99)(milliseconds)
500-2000ms
5-50ms
Memory Overhead (per task)(MB)
~400-800MB (GC overhead)
~200-400MB (optimized)
Throughput (events/sec per node)(events/sec)
~500K-1M events/sec
~1-2M events/sec
Processing Speed (Iterative Query)(seconds)
0.5-2 seconds
—
Event Latency (Processing End-to-End)(milliseconds)
100-2,000ms (Spark Streaming micro-batch interval)
1-10ms (true event streaming)
Throughput Capacity(events/second/node)
500,000 - 2,000,000 (batch-optimized)
1,000,000 - 5,000,000 (streaming-optimized)
Minimum Achievable Latency (P99)(milliseconds)
500-2000ms
100-500ms
Typical Throughput (single node)(events/sec)
250,000
—
Throughput (Records/Second)(million records/sec)
1M-10M
—
Processing Latency (end-to-end)(milliseconds)
50-100ms
—
Latency (p99 for simple aggregations)(milliseconds)
100-500 ms (tuning dependent)
—
Minimum Processing Latency(milliseconds)
<100ms (native streaming)
—
Average Query Execution (1GB dataset)(seconds)
2-3 seconds (streaming) / 4-6 (batch)
—
Processing Latency(milliseconds)
1-100 ms (sub-second typical)
—
Maximum Throughput per Node(events/second)
100,000-1,000,000 events/sec
—
Maximum Throughput(messages/second)
5,000,000+
—
Max Throughput (Typical Setup)(events/sec)
Millions (1M+)
—
Maximum Practical Data Size(GB)
1,000,000+ GB (petascale)
—
Maximum Scalable Dataset Size(GB)
1,000+ PB
—
State Size Capacity(GB)
500+
—
Maximum Managed State Size(TB)
Terabyte-scale (tested to 10+ TB)
—
Memory Required Per Query(MB)
500-2000MB
—
Memory Per Node(GB per 1M events/sec)
8-12GB (caching overhead)
6-8GB (efficient state management)
Memory Overhead per Task(megabytes (baseline))
512-1024MB
256-512MB
Memory Usage per Node(GB)
4-16 GB
—
Baseline JVM Memory Overhead(GB)
1.5-2.5 GB
—
Show 1 more attribute
Memory Overhead (idle cluster)(GB)
2-4 GB
—
Setup Time for Basic Analytics(minutes)
30-120 minutes
—
Setup Time (from scratch)(minutes)
60-120 (cluster setup)
—
Setup Time for Production Deployment(hours)
40-80 hours
—
Setup Complexity (1-10)(complexity score)
7/10
—
Primary Language Support(count)
Python, Scala, SQL, R, Java
—
Time to Deploy Pipeline(minutes)
30-90 minutes (requires cluster setup)
—
Time to Build First Pipeline(hours)
72
—
GitHub Stars (2026)(stars)
35,900 stars
23,800+
GitHub Stars (as of 2026)(stars)
29,000+
—
Multi-machine Distributed Computing(capability)
Native support
—
Primary Implementation Language
Java
—
Fault Tolerance(capability)
Yes (RDD lineage-based)
—
Fault Tolerance Method(mechanism)
Lineage-based recovery (RDD parents)
—
Data Storage Redundancy(replication factor)
Depends on underlying storage
—
Delivery Semantics
Exactly-once (native)
—
Fault Tolerance Mechanism
Distributed snapshots + checkpointing
—
Show 1 more attribute
Processing Semantics
Exactly-once
—
Initial Licensing Cost(USD)
$0
—
Typical Cluster Cost (Monthly)(USD)
$1,500-$5,000+
—
Typical Cluster Cost (100-node setup)(USD annual)
$450,000-650,000
—
Price (Self-Hosted)(USD/month)
0 (Open source)
—
Setup Time to Production(minutes)
6-12 weeks
—
Cluster Management Required(hours/month)
40-80 hours (dedicated DevOps engineer)
—
Minimum Operational Complexity(components to manage)
5-7 (JobManager, TaskManagers, StateBackend, Checkpoints)
—
Deployment Complexity
Requires cluster with YARN/Kubernetes, moderate DevOps
—
Users Per Collaborative Project(concurrent users)
1-5 (via Jupyter sharing)
—
Built-in Security Features
0 (manual implementation required)
—
Supported Data Formats(formats)
Parquet, ORC, JSON, CSV, Avro, Delta (via library)
—
Supported Warehouse Platforms(platforms)
Hadoop, Kubernetes, cloud object storage (3+ classes)
—
Supported Languages/APIs(count)
Python, Scala, Java, SQL, R
—
Community Size(GitHub stars)
25,000+ questions
—
GitHub Community (Stars)(thousands)
38.5K stars
—
GitHub Stars(stars)
40,100 stars
15,400 stars
GitHub Stars (Popularity Indicator)(stars)
32,000
2,500
GitHub Stars (2025)(stars)
23.8K
—
Show 1 more attribute
Active Contributors (6-month window)(developers)
180+
—
Supported Programming Languages(count)
Python, Scala, Java, R, SQL
—
Built-in Data Testing Features(count)
0 (requires external frameworks)
—
Cloud Provider Support(providers)
4+ (AWS, Azure, GCP, on-prem)
—
Real-time Streaming Capability(latency (ms))
500-5000 ms micro-batches
—
Supported Event Time Semantics
Partial in Structured Streaming, limited out-of-order support
Full with watermarks, out-of-order handling, allowedLateness
Batch+Stream Unified Code
Unified via Structured Streaming/Dataset API
Separate APIs (DataStream vs Batch)
Show 5 more attributes
Machine Learning Capability(native support)
Full MLlib with algorithms, pipelines
—
Watermark Support
Yes (core feature)
—
Time Window Support
Event-time, processing-time, session windows, custom
—
Programming Languages Supported(languages)
4 (Java, Python, Scala, SQL)
—
Built-in State Backends
Memory, RocksDB, External (3 options)
—
Minimum Dataset Size for Optimal Use(GB)
100+ GB
—
Optimal Dataset Size(GB minimum)
Continuous streams (any size)
—
Cluster Setup Time(hours)
40-80 hours
—
Time to First Production Deployment(days)
8-12 weeks (with Kubernetes ops experience)
—
Cost per Core-Hour(USD)
$0.035-0.15
—
Machine Learning Algorithms Available(count)
50+ (MLlib + custom models)
—
Data Format Support(format types)
8+ formats (Parquet, ORC, Avro, Delta, Iceberg, HDF5, CSV, JSON)
—
Memory Requirement (Per Node)(GB)
16-256 GB
—
Minimum Cluster Size(nodes)
2-3 nodes
—
First Release(year)
2014
—
Market Adoption by Fortune 500(percent)
82%
—
Native Connectors Available(count)
200+ via ecosystem integrations
~30 native connectors
Developer Community Size(active developers)
7,200,000 (StackOverflow, job postings 2024)
1,800,000 (StackOverflow, job postings 2024)
Available Libraries & Integrations(count)
14,000+ (Spark packages, MLlib, SQL, GraphX, etc.)
2,000+ (Flink SQL, state backends, CEP library)
Available Built-in Connectors(count)
50+
—
Built-in Connectors(count)
15+
—
Enterprise Adoption Rate(% of Fortune 500)
65% (Databricks, AWS, Google, Meta deployments)
18% (Alibaba, Netflix, Uber, Lyft use cases)
Market Adoption Rate(percentage of streaming workloads)
60-65%
15-20%
Production Deployments (2026)(thousands of deployments)
45,000-55,000
8,000-12,000
Time to First Correct Result (learning curve)(weeks (team of 2))
6-10
—
Production Deployments Reported(count)
10,000+
—
Memory Requirement(GB)
8-64 GB per node
—
Supported Languages(count)
5 (Scala, Python, Java, R, SQL)
4 (Java, Scala, Python, SQL)
Real-time Processing(latency (milliseconds))
100-500 ms (micro-batch)
—
Ecosystem Age(years)
12 years (since 2013)
—
First Release Year(year)
2011
—
Initial Release Year(year)
2014
—
Years in Production(years)
12 (since 2014)
—
Enterprise Adoption(companies)
74% currently use
—
Mean Time to Deploy Production Job(weeks)
2-4 weeks (larger talent pool, more examples)
6-10 weeks (steeper learning curve, less documentation)
Stateful Window Operations Complexity(lines of code for session windows)
80-150 lines (custom state handling needed)
15-30 lines (native session window API)
State Management Capabilities(feature count)
2 types (RDD state, DataFrame state)
5 types (keyed, operator, broadcast, queryable, custom)
ANSI SQL Compliance(percentage)
98%
95%
Python Support Level(support quality)
PyFlink added in v1.11 (2020); improved in v1.14+
—
Year-over-Year Growth Rate(percentage)
8%
25%
Job Market Demand(postings)
~1,850
—
Event-Time Support
Native & first-class (core design)
—
Machine Learning Capabilities(availability)
Limited (requires external libraries)
—
Top-Level Apache Status(year achieved)
2015
—
State Consistency Guarantee(semantic level)
Exactly-once (configurable per checkpoint)
—
Integrated Web UI(rating)
Basic REST API only (external UI required)
—
Minimum Java Version Required(version)
Java 11+
—
Community GitHub Stars(stars)
10,400
—
Pros & Cons
10 pros·6 cons across both
Apache Spark
+5-3
Pros
- Mature ecosystem with 14,000+ third-party packages and strong MLlib for machine learning
- Unified API: Spark SQL, DataFrames, Datasets work across batch, streaming, and ML seamlessly
- 7.2M+ developers and 65% enterprise adoption—largest hiring demand in data engineering
- Python API (PySpark) enables rapid development and lower barrier to entry
- In-memory caching (RDD persistence) accelerates iterative algorithms by 100x
Cons
- Micro-batch architecture introduces 100ms-2s latency, unsuitable for ultra-low-latency requirements
- Higher memory footprint due to caching and serialization overhead—can cost 30-40% more infrastructure
- Stateful stream processing less sophisticated than Flink; window operations and state management require workarounds
Apache Flink
+5-3
Pros
- True event-by-event streaming with 1-10ms latency—critical for fraud detection, algorithmic trading, and real-time alerts
- Advanced state management with pluggable backends (RocksDB, in-memory); supports complex sessionization and temporal joins
- Superior event-time processing: exactly-once semantics, watermarking, and out-of-order event handling
- Memory efficient: 20-30% lower per-node memory consumption than Spark for equivalent throughput
- Flexible deployment: standalone, YARN, Kubernetes, and hybrid cloud with checkpoint-based fault tolerance
Cons
- Smaller community (1.8M developers vs Spark's 7.2M); fewer job postings and less readily available expertise
- Java/Scala dominant—limited Python support compared to Spark; requires more boilerplate code
- Fewer pre-built libraries: Flink ML is less mature than Spark MLlib; requires custom implementations for many ML tasks
Frequently Asked Questions
5 questions
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.
Resources & Learn More
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