Apache Flink vs Storm: 2026 Comparison
Apache Flink provides true event-time processing with lower latency (sub-100ms), exactly-once semantics, and higher throughput (millions of events/sec), while Apache Storm is simpler to deploy but offers at-least-once guarantees and higher latency (seconds). Flink has become the industry standard for modern stream processing, with adoption growing 340% since 2018.
Apache Flink
Modern distributed stream processing framework with event-time semantics and exactly-once guarantees.
Enterprises processing financial transactions, real-time ML pipelines, fraud detection, and applications requiring strict data consistency at scale
Apache Storm
Lightweight distributed stream processor with guaranteed message processing and simple bolt-spout topology model.
Simple event routing, log aggregation, and teams maintaining legacy systems already standardized on Storm
Quick Answer
AI SummaryApache Flink provides true event-time processing with lower latency (sub-100ms), exactly-once semantics, and higher throughput (millions of events/sec), while Apache Storm is simpler to deploy but offers at-least-once guarantees and higher latency (seconds). Flink has become the industry standard for modern stream processing, with adoption growing 340% since 2018.
Our Verdict
AI-assistedChoose Apache Flink if you need production-grade stream processing with strict consistency guarantees, sub-100ms latency, and must handle billions of events daily—it dominates modern enterprise deployments. Choose Apache Storm if you need rapid prototyping, simple topology deployment, or operate legacy systems already standardized on it, though you'll accept higher latency and potential duplicate processing.
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Choose Apache Flink if
Best pickEnterprises processing financial transactions, real-time ML pipelines, fraud detection, and applications requiring strict data consistency at scale
Choose Apache Storm if
Simple event routing, log aggregation, and teams maintaining legacy systems already standardized on Storm
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Key Differences at a Glance
- Processing Semantics:✓ Apache Flink wins(Exactly-once vs At-least-once)
- End-to-end Latency:✓ Apache Flink wins(Sub-100ms (event-time) vs 500ms-2s (processing-time))
- Throughput (events/sec):✓ Apache Flink wins(5-20+ million vs 100k-1 million)
Key Facts & Figures
82 numeric metrics compared
| Metric | Apache Flink | Apache Storm | Ratio |
|---|---|---|---|
| Minimum Operational Complexity(components to manage) | 5-7 (JobManager, TaskManagers, StateBackend, Checkpoints) | — | — |
| Time to First Correct Result (learning curve)(weeks (team of 2)) | 6-10 | — | — |
| Available Built-in Connectors(count) | 50+ | — | — |
| Typical Throughput (single node)(events/sec) | 250,000 | — | — |
| End-to-End Latency (p99)(milliseconds) | 5-50ms | — | — |
| State Size Capacity(GB) | 500+ | — | — |
| Throughput (Records/Second)(million records/sec) | 1M-10M | — | — |
| Memory Usage per Node(GB) | 4-16 GB | — | — |
| Minimum Cluster Size(nodes) | 1-2 nodes viable | — | — |
| Supported Languages(languages) | 4 (Java, Scala, Python, SQL) | — | — |
| GitHub Stars (2025)(stars) | 23.8K | — | — |
| Processing Latency (end-to-end)(milliseconds) | 50-100ms | 500-2000ms | |
| Setup Complexity (1-10)(difficulty) | 7/10 | 3/10 | |
| Time to First Production Deployment(days) | 8-12 weeks (with Kubernetes ops experience) | — | — |
| Minimum Memory Requirement(GB) | 1024 MB | — | — |
| Production Deployments Reported(count) | 10,000+ | — | — |
| Programming Languages Supported(languages) | 4 (Java, Python, Scala, SQL) | — | — |
| First Release Year(year) | 2011 | — | — |
| Latency (p99 for simple aggregations)(milliseconds) | 100-500 ms (tuning dependent) | — | — |
| Maximum Managed State Size(TB) | Terabyte-scale (tested to 10+ TB) | — | — |
| GitHub Stars (as of 2026)(thousands) | 29,000+ | — | — |
| GitHub Stars (2026)(stars) | 23,800+ | — | — |
| Job Market Demand(active job postings) | ~1,850 | — | — |
| Baseline JVM Memory Overhead(GB) | 1.5-2.5 GB | — | — |
| Top-Level Apache Status(year achieved) | 2015 | — | — |
| Average Query Execution (1GB dataset)(seconds) | 2-3 seconds (streaming) / 4-6 (batch) | — | — |
| Maximum Throughput per Node(events/second) | 100,000-1,000,000 events/sec | — | — |
| Minimum Java Version Required(version) | Java 11+ | — | — |
| Native Connectors Available(count) | ~30 native connectors | — | — |
| GitHub Stars(stars) | 23,500+ stars | 6,400 | |
| Memory Overhead (per task)(MB) | ~200-400MB (optimized) | — | — |
| Throughput (events/sec per node)(events/sec) | ~1-2M events/sec | — | — |
| Initial Release Year(year) | 2014 | — | — |
| Memory Overhead (idle cluster)(GB) | 2-4 GB | — | — |
| Time to Build First Pipeline(hours) | 72 | — | — |
| Active Contributors (6-month window)(developers) | 180+ | — | — |
| Price (Self-Hosted)(USD/month) | 0 (Open source) | — | — |
| Community GitHub Stars(stars) | 10,400 | — | — |
| Years in Production(years) | 12 (since 2014) | — | — |
| Built-in Connectors(count) | 15+ | — | — |
| Max Throughput (Typical Setup)(events/sec) | Millions (1M+) | — | — |
| Event Latency (Processing End-to-End)(milliseconds) | 1-10ms (true event streaming) | — | — |
| Memory Per Node(GB per 1M events/sec) | 6-8GB (efficient state management) | — | — |
| Available Libraries & Integrations(count) | 2,000+ (Flink SQL, state backends, CEP library) | — | — |
| Mean Time to Deploy Production Job(weeks) | 6-10 weeks (steeper learning curve, less documentation) | — | — |
| Stateful Window Operations Complexity(lines of code for session windows) | 15-30 lines (native session window API) | — | — |
| Minimum Achievable Latency (P99)(milliseconds) | 100-500ms | — | — |
| GitHub Stars (Popularity Indicator)(stars) | 2,500 | — | — |
| Market Adoption Rate(percentage of streaming workloads) | 15-20% | — | — |
| Memory Overhead per Task(megabytes (baseline)) | 256-512MB | — | — |
| ANSI SQL Compliance(percentage) | 95% | — | — |
| State Management Capabilities(feature count) | 5 types (keyed, operator, broadcast, queryable, custom) | — | — |
| Production Deployments (2026)(thousands of deployments) | 8,000-12,000 | — | — |
| Year-over-Year Growth Rate(percentage) | 25% | — | — |
| Minimum Processing Latency(milliseconds) | 1-10ms (streaming native) | — | — |
| Available Integrations(integrations) | 200+ | — | — |
| Typical Cluster Setup Complexity(complexity score (1-10)) | 7-9 (complex) | — | — |
| Memory Per Task (Typical)(MB) | 2048-8192 | — | — |
| Enterprise Adoption (2024)(% of tech companies) | 32% | — | — |
| Time to Deploy Pipeline(hours) | 20-40 hours (learning + development) | — | — |
| Minimum End-to-End Latency(milliseconds) | 500 ms | — | — |
| Maximum Throughput(events per second) | Millions (100M+ with tuning) | 1,000,000 | |
| Minimum Memory Footprint(GB) | 2 GB (standalone single node) | — | — |
| Learning Curve (1-10 scale)(difficulty) | 8 | — | — |
| Open Source Contributors(contributors) | 1,000+ | — | — |
| Production Deployments | 15,000+ | — | — |
| Time-to-Production (Simple Real-time Dashboard)(weeks) | 3-4 weeks | — | — |
| Minimum Latency (P99)(milliseconds) | 100-500ms | — | — |
| State Backend Memory Efficiency(GB per 1M records) | 2-3 GB | — | — |
| SQL Standard Compliance(% compatibility) | 70% (subset with UDF limitations) | — | — |
| Production Deployments (2024)(organizations) | 10,000+ | — | — |
| Community Contributors (GitHub)(monthly active) | 120-150 | — | — |
| Supported Source/Sink Connectors(count) | 80+ | — | — |
| Processing Latency(seconds) | 100-500 milliseconds | — | — |
| Throughput Capacity(events per second) | 1M-100M per node | — | — |
| Memory Usage per Task(MB) | 100-800 MB | — | — |
| Project Age(years) | 10 years (since 2014) | — | — |
| Supported Programming Languages(count) | 4 languages (Java, Scala, Python, SQL) | — | — |
| Processing Latency (p99)(milliseconds) | 50-100ms | 1000-5000ms | |
| Throughput Per Node(events/second) | 10,000,000 | 500,000 | |
| Minimum Memory Per Worker(GB) | 2-4 | 0.5-1 | |
| GitHub Stars (Last 12 Months)(stars) | 2000+ | 250 |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
- Exactly-once(winner)Processing SemanticsAt-least-once
- Sub-100ms (event-time)(winner)End-to-end Latency500ms-2s (processing-time)
- 5-20+ million(winner)Throughput (events/sec)100k-1 million
- Event-time, processing-time, session windows(winner)Time Window SupportProcessing-time only
- Moderate (requires cluster setup)Setup ComplexitySimple (standalone mode available)(winner)
- 22,500+ stars, 1,200+ commits/month(winner)Community Activity (GitHub Stars)6,400+ stars, 80+ commits/month
- 72% of enterprises (2024 survey)(winner)Production Adoption Rate12% of enterprises (2024 survey)
- Processing Semantics
Apache Flink
Exactly-once(winner)
Apache Storm
At-least-once
- End-to-end Latency
Apache Flink
Sub-100ms (event-time)(winner)
Apache Storm
500ms-2s (processing-time)
- Throughput (events/sec)
Apache Flink
5-20+ million(winner)
Apache Storm
100k-1 million
- Time Window Support
Apache Flink
Event-time, processing-time, session windows(winner)
Apache Storm
Processing-time only
- Setup Complexity
Apache Flink
Moderate (requires cluster setup)
Apache Storm
Simple (standalone mode available)(winner)
- Community Activity (GitHub Stars)
Apache Flink
22,500+ stars, 1,200+ commits/month(winner)
Apache Storm
6,400+ stars, 80+ commits/month
- Production Adoption Rate
Apache Flink
72% of enterprises (2024 survey)(winner)
Apache Storm
12% of enterprises (2024 survey)
Full Comparison
| Attribute | Apache Flink | Apache Storm |
|---|---|---|
| Minimum Operational Complexity(components to manage) | 5-7 (JobManager, TaskManagers, StateBackend, Checkpoints) | — |
| Deployment Complexity(complexity score (1-10)) | Requires cluster with YARN/Kubernetes, moderate DevOps | Standalone or cluster, simpler configuration |
| Typical Cluster Setup Complexity(complexity score (1-10)) | 7-9 (complex) | — |
| Time to First Correct Result (learning curve)(weeks (team of 2)) | 6-10 | — |
| Production Deployments Reported(count) | 10,000+ | — |
| Market Adoption Rate(percentage of streaming workloads) | 15-20% | — |
| Production Deployments (2026)(thousands of deployments) | 8,000-12,000 | — |
| Production Deployments (2024)(organizations) | 10,000+ | — |
| Available Built-in Connectors(count) | 50+ | — |
| Native Connectors Available(count) | ~30 native connectors | — |
| Built-in Connectors(count) | 15+ | — |
| Available Libraries & Integrations(count) | 2,000+ (Flink SQL, state backends, CEP library) | — |
| Available Integrations(integrations) | 200+ | — |
| Watermark Support | Yes (core feature) | — |
| Time Window Support | Event-time, processing-time, session windows, custom | Processing-time only |
| Programming Languages Supported(languages) | 4 (Java, Python, Scala, SQL) | — |
| Supported Event Time Semantics | Full with watermarks, out-of-order handling, allowedLateness | — |
| Batch+Stream Unified Code | Separate APIs (DataStream vs Batch) | — |
Show 3 more attributesBuilt-in State Backends Memory, RocksDB, External (3 options) — Event-Time Support(null) Full support with watermarks and allowed lateness None—processing-time only State Backend Options(null) RocksDB (100GB+), HashMap, External KV stores In-memory only | ||
| Typical Throughput (single node)(events/sec) | 250,000 | — |
| End-to-End Latency (p99)(milliseconds) | 5-50ms | — |
| Throughput (Records/Second)(million records/sec) | 1M-10M | — |
| Processing Latency (end-to-end)(milliseconds) | 50-100ms(winner) | 500-2000ms |
| Latency (p99 for simple aggregations)(milliseconds) | 100-500 ms (tuning dependent) | — |
Show 16 more attributesAverage Query Execution (1GB dataset)(seconds) 2-3 seconds (streaming) / 4-6 (batch) — Maximum Throughput per Node(events/second) 100,000-1,000,000 events/sec — Memory Overhead (per task)(MB) ~200-400MB (optimized) — Throughput (events/sec per node)(events/sec) ~1-2M events/sec — Max Throughput (Typical Setup)(events/sec) Millions (1M+) — Event Latency (Processing End-to-End)(milliseconds) 1-10ms (true event streaming) — Minimum Achievable Latency (P99)(milliseconds) 100-500ms — Minimum Processing Latency(milliseconds) 1-10ms (streaming native) — Minimum End-to-End Latency(milliseconds) 500 ms — Maximum Throughput(events per second) Millions (100M+ with tuning) 1,000,000 Minimum Latency (P99)(milliseconds) 100-500ms — State Backend Memory Efficiency(GB per 1M records) 2-3 GB — Processing Latency(seconds) 100-500 milliseconds — Throughput Capacity(events per second) 1M-100M per node — Processing Latency (p99)(milliseconds) 50-100ms 1000-5000ms Throughput Per Node(events/second) 10,000,000 500,000 | ||
| Delivery Semantics | Exactly-once (native) | — |
| Fault Tolerance Mechanism | Distributed snapshots + checkpointing | — |
| Processing Semantics | Exactly-once | At-least-once |
| State Size Capacity(GB) | 500+ | — |
| Minimum Cluster Size(nodes) | 1-2 nodes viable | — |
| Maximum Managed State Size(TB) | Terabyte-scale (tested to 10+ TB) | — |
| Memory Usage per Node(GB) | 4-16 GB | — |
| Baseline JVM Memory Overhead(GB) | 1.5-2.5 GB | — |
| Memory Overhead (idle cluster)(GB) | 2-4 GB | — |
| Memory Per Node(GB per 1M events/sec) | 6-8GB (efficient state management) | — |
| Memory Overhead per Task(megabytes (baseline)) | 256-512MB | — |
Show 1 more attributeMemory Usage per Task(MB) 100-800 MB — | ||
| Supported Languages(languages) | 4 (Java, Scala, Python, SQL) | — |
| GitHub Stars (2025)(stars) | 23.8K | — |
| GitHub Stars (as of 2026)(thousands) | 29,000+ | — |
| Optimal Dataset Size(GB minimum) | Continuous streams (any size) | — |
| Setup Complexity (1-10)(difficulty) | 7/10 | 3/10(winner) |
| Time to First Production Deployment(days) | 8-12 weeks (with Kubernetes ops experience) | — |
| Minimum Memory Requirement(GB) | 1024 MB | — |
| Minimum Memory Footprint(GB) | 2 GB (standalone single node) | — |
| Minimum Memory Per Worker(GB) | 2-4 | 0.5-1(winner) |
| First Release Year(year) | 2011 | — |
| Initial Release Year(year) | 2014 | — |
| Years in Production(years) | 12 (since 2014) | — |
| Project Age(years) | 10 years (since 2014) | — |
| GitHub Stars (2026)(stars) | 23,800+ | — |
| Active Contributors (6-month window)(developers) | 180+ | — |
| Community GitHub Stars(stars) | 10,400 | — |
| GitHub Stars (Popularity Indicator)(stars) | 2,500 | — |
| Open Source Contributors(contributors) | 1,000+ | — |
Show 2 more attributesCommunity Contributors (GitHub)(monthly active) 120-150 — GitHub Stars (Last 12 Months)(stars) 2000+ 250 | ||
| Job Market Demand(active job postings) | ~1,850 | — |
| Machine Learning Capabilities(availability) | Limited (requires external libraries) | — |
| Top-Level Apache Status(year achieved) | 2015 | — |
| Python Support Level(support quality) | PyFlink added in v1.11 (2020); improved in v1.14+ | — |
| 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+ | — |
| GitHub Stars(stars) | 23,500+ stars(winner) | 6,400 |
| Primary Implementation Language | Java | — |
| Time to Build First Pipeline(hours) | 72 | — |
| Price (Self-Hosted)(USD/month) | 0 (Open source) | — |
| Developer Community Size(forum posts) | 1,800,000 (StackOverflow, job postings 2024) | — |
| Enterprise Adoption Rate(percent of enterprises) | 18% (Alibaba, Netflix, Uber, Lyft use cases)(winner) | 12% |
| Mean Time to Deploy Production Job(weeks) | 6-10 weeks (steeper learning curve, less documentation) | — |
| Stateful Window Operations Complexity(lines of code for session windows) | 15-30 lines (native session window API) | — |
| State Management Capabilities(feature count) | 5 types (keyed, operator, broadcast, queryable, custom) | — |
| ANSI SQL Compliance(percentage) | 95% | — |
| Year-over-Year Growth Rate(percentage) | 25% | — |
| Memory Per Task (Typical)(MB) | 2048-8192 | — |
| Enterprise Adoption (2024)(% of tech companies) | 32% | — |
| State Backend Storage Limit(scalability) | Terabytes of distributed state native | — |
| Time to Deploy Pipeline(hours) | 20-40 hours (learning + development) | — |
| Learning Curve (1-10 scale)(difficulty) | 8 | — |
| Production Deployments | 15,000+ | — |
| Time-to-Production (Simple Real-time Dashboard)(weeks) | 3-4 weeks | — |
| SQL Standard Compliance(% compatibility) | 70% (subset with UDF limitations) | — |
| Supported Programming Languages(count) | 4 languages (Java, Scala, Python, SQL) | — |
| Supported Source/Sink Connectors(count) | 80+ | — |
| Exactly-Once Semantics(null) | Native with distributed snapshots | Not supported (at-least-once only) |
| Configuration Complexity(config parameters) | 60+ core parameters | 15-20 core parameters(winner) |
Apache FlinkApache StormShow 3 more attributes
Show 16 more attributes
Show 1 more attribute
Show 2 more attributes
Pros & Cons
9 pros·5 cons across both
Apache Flink
Pros
- Exactly-once processing semantics eliminate duplicate data and ensure consistency
- Event-time processing with watermarks handles out-of-order and late-arriving data correctly
- Sub-100ms end-to-end latency for real-time analytics and alerting
- 5-20+ million events/second throughput with horizontal scaling to 1000+ nodes
- Advanced windowing (tumbling, sliding, session) with sophisticated state management
Cons
- Steeper learning curve requiring knowledge of DataStream API and distributed systems concepts
- Higher operational overhead with more complex cluster configuration and monitoring
Apache Storm
Pros
- Simple bolt-spout model makes topology definition straightforward for basic use cases
- Standalone mode allows single-machine development without cluster setup overhead
- Guaranteed message processing with replay capabilities for fault tolerance
- Low barrier to entry for teams new to stream processing frameworks
Cons
- At-least-once semantics result in potential duplicate processing and data inconsistencies
- Processing-time only windowing fails with out-of-order or delayed data (0% support for event-time)
- 5-10x lower throughput (100k-1M events/sec) and 5-20x higher latency (500ms-2s) than Flink
Frequently Asked Questions
5 questions
Apache Flink provides exactly-once semantics and event-time processing with sub-100ms latency, while Storm offers at-least-once processing and processing-time only with 500ms-2s latency. Flink is optimized for modern, large-scale stream processing, while Storm is simpler but less powerful for complex scenarios.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
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Wikipedia
- W
Apache Flink on Wikipedia (opens in new tab)
Modern distributed stream processing framework with event-time semantics and exactly-once guarantees.
- W
Apache Storm on Wikipedia (opens in new tab)
Lightweight distributed stream processor with guaranteed message processing and simple bolt-spout topology model.
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