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 vs Apache Storm — Side-by-side comparison on A Versus B
Modern distributed stream processing framework with event-time semantics and exactly-once guarantees.
Lightweight distributed stream processor with guaranteed message processing and simple bolt-spout topology model.
Processing Semantics. End-to-end Latency. Throughput (events/sec). Time Window Support. Setup Complexity. Community Activity (GitHub Stars). Production Adoption Rate
Choose 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.
Apache Flink: 5-7 (JobManager, TaskManagers, StateBackend, Checkpoints) | Apache Storm: N/A
Time to First Correct Result (learning curve)
Apache Flink: Yes (core feature) | Apache Storm: N/A
Apache Flink: 250,000 | Apache Storm: N/A
Apache Flink: 5-50ms | Apache Storm: N/A
Apache Flink: Exactly-once (native) | Apache Storm: N/A
Apache Flink: 1M-10M | Apache Storm: N/A
Apache Flink: 4-16 GB | Apache Storm: N/A
Apache Flink: 1-2 nodes viable | Apache Storm: N/A
Apache Flink: 4 (Java, Scala, Python, SQL) | Apache Storm: N/A
Apache Flink: Distributed snapshots + checkpointing | Apache Storm: N/A
Apache Flink: Continuous streams (any size) | Apache Storm: N/A
Apache Flink: 50-100ms | Apache Storm: 500-2000ms
Apache Flink: Exactly-once | Apache Storm: At-least-once
Apache Flink: Event-time, processing-time, session windows, custom | Apache Storm: Processing-time only
Apache Flink: Requires cluster with YARN/Kubernetes, moderate DevOps | Apache Storm: Standalone or cluster, simpler configuration
Apache Flink: 8-12 weeks (with Kubernetes ops experience) | Apache Storm: N/A
Apache Flink: 1024 MB | Apache Storm: N/A
Apache Flink: 10,000+ | Apache Storm: N/A
Apache Flink: 4 (Java, Python, Scala, SQL) | Apache Storm: N/A
Apache Flink: 100-500 ms (tuning dependent) | Apache Storm: N/A
Apache Flink: Terabyte-scale (tested to 10+ TB) | Apache Storm: N/A
Apache Flink: 29,000+ | Apache Storm: N/A
Apache Flink: 23,800+ | Apache Storm: N/A
Apache Flink: ~1,850 | Apache Storm: N/A
Apache Flink: 1.5-2.5 GB | Apache Storm: N/A
Apache Flink: Limited (requires external libraries) | Apache Storm: N/A
Apache Flink: 2-3 seconds (streaming) / 4-6 (batch) | Apache Storm: N/A
Apache Flink: 100,000-1,000,000 events/sec | Apache Storm: N/A
Apache Flink: PyFlink added in v1.11 (2020); improved in v1.14+ | Apache Storm: N/A
Apache Flink: Exactly-once (configurable per checkpoint) | Apache Storm: N/A
Apache Flink: Basic REST API only (external UI required) | Apache Storm: N/A
Apache Flink: Java 11+ | Apache Storm: N/A
Apache Flink: ~30 native connectors | Apache Storm: N/A
Apache Flink: 23,500+ stars | Apache Storm: 6,400
Apache Flink: ~200-400MB (optimized) | Apache Storm: N/A
Apache Flink: Full with watermarks, out-of-order handling, allowedLateness | Apache Storm: N/A
Apache Flink: ~1-2M events/sec | Apache Storm: N/A
Apache Flink: Separate APIs (DataStream vs Batch) | Apache Storm: N/A
Apache Flink: 2-4 GB | Apache Storm: N/A
Apache Flink: Memory, RocksDB, External (3 options) | Apache Storm: N/A
Apache Flink: 0 (Open source) | Apache Storm: N/A
Apache Flink: 10,400 | Apache Storm: N/A
Apache Flink: 12 (since 2014) | Apache Storm: N/A
Apache Flink: Millions (1M+) | Apache Storm: N/A
Apache Flink: 1-10ms (true event streaming) | Apache Storm: N/A
Apache Flink: 6-8GB (efficient state management) | Apache Storm: N/A
Apache Flink: 1,800,000 (StackOverflow, job postings 2024) | Apache Storm: N/A
Apache Flink: 2,000+ (Flink SQL, state backends, CEP library) | Apache Storm: N/A
Apache Flink: 18% (Alibaba, Netflix, Uber, Lyft use cases) | Apache Storm: 12%
Apache Flink: 6-10 weeks (steeper learning curve, less documentation) | Apache Storm: N/A
Apache Flink: 15-30 lines (native session window API) | Apache Storm: N/A
Apache Flink: 100-500ms | Apache Storm: N/A
Apache Flink: 15-20% | Apache Storm: N/A
Apache Flink: 256-512MB | Apache Storm: N/A
Apache Flink: 5 types (keyed, operator, broadcast, queryable, custom) | Apache Storm: N/A
Apache Flink: 8,000-12,000 | Apache Storm: N/A
Apache Flink: 1-10ms (streaming native) | Apache Storm: N/A
Apache Flink: 7-9 (complex) | Apache Storm: N/A
Apache Flink: 2048-8192 | Apache Storm: N/A
Apache Flink: Terabytes of distributed state native | Apache Storm: N/A
Apache Flink: 20-40 hours (learning + development) | Apache Storm: N/A
Apache Flink: 500 ms | Apache Storm: N/A
Apache Flink: Millions (100M+ with tuning) | Apache Storm: 1,000,000
Apache Flink: 2 GB (standalone single node) | Apache Storm: N/A
Apache Flink: 1,000+ | Apache Storm: N/A
Apache Flink: 15,000+ | Apache Storm: N/A
Time-to-Production (Simple Real-time Dashboard)
Apache Flink: 3-4 weeks | Apache Storm: N/A
Apache Flink: 2-3 GB | Apache Storm: N/A
Apache Flink: 70% (subset with UDF limitations) | Apache Storm: N/A
Apache Flink: 120-150 | Apache Storm: N/A
Apache Flink: 100-500 milliseconds | Apache Storm: N/A
Apache Flink: 1M-100M per node | Apache Storm: N/A
Apache Flink: 100-800 MB | Apache Storm: N/A
Apache Flink: 10 years (since 2014) | Apache Storm: N/A
Apache Flink: 4 languages (Java, Scala, Python, SQL) | Apache Storm: N/A
Apache Flink: 50-100ms | Apache Storm: 1000-5000ms
Apache Flink: 10,000,000 | Apache Storm: 500,000
Apache Flink: Native with distributed snapshots | Apache Storm: Not supported (at-least-once only)
Apache Flink: Full support with watermarks and allowed lateness | Apache Storm: None—processing-time only
Apache Flink: RocksDB (100GB+), HashMap, External KV stores | Apache Storm: In-memory only
Apache Flink: 60+ core parameters | Apache Storm: 15-20 core parameters
How to choose between Apache Flink and Apache Storm
Apache Flink vs Apache Storm — Structured Comparison Data
Machine-readable comparison dataset for Apache Flink vs Apache Storm. Contains 104 attribute comparisons, 5 FAQ pairs.
Apache Flink vs Apache Storm — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Apache Flink vs Apache Storm.
What's the main difference between Flink and Storm?
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.
Which is faster for real-time processing?
Apache Flink is significantly faster with sub-100ms end-to-end latency compared to Storm's 500ms-2s. Flink also handles 20x higher throughput (20M vs 1M events/sec), making it the clear choice for low-latency applications like fraud detection and real-time trading.
Does Storm support event-time windows like Flink?
No. Apache Storm only supports processing-time windowing, meaning it cannot correctly handle out-of-order or late-arriving data. Flink's event-time semantics with watermarks solve this problem, making Flink essential for applications where event arrival order matters.
Can Storm guarantee exactly-once processing?
Storm only offers at-least-once semantics, meaning events may be processed multiple times in failure scenarios, resulting in duplicate data. Apache Flink guarantees exactly-once processing semantics, ensuring each event is processed exactly one time regardless of failures.
Apache Storm receives maintenance updates but has significantly lower activity (80+ commits/month vs Flink's 1,200+). With 72% of enterprises now using Flink vs 12% using Storm (2024), Flink has become the industry standard for stream processing.
How to Choose Between Apache Flink and Apache Storm
Processing Semantics: Apache Flink has Exactly-once, while Apache Storm has At-least-once.
End-to-end Latency: Apache Flink has Sub-100ms (event-time), while Apache Storm has 500ms-2s (processing-time).
Throughput (events/sec): Apache Flink has 5-20+ million, while Apache Storm has 100k-1 million.
Time Window Support: Apache Flink has Event-time, processing-time, session windows, while Apache Storm has Processing-time only.
Setup Complexity: Apache Flink has Moderate (requires cluster setup), while Apache Storm has Simple (standalone mode available).
Consider: Community Activity (GitHub Stars)
Community Activity (GitHub Stars): Apache Flink has 22,500+ stars, 1,200+ commits/month, while Apache Storm has 6,400+ stars, 80+ commits/month.
Production Adoption Rate: Apache Flink has 72% of enterprises (2024 survey), while Apache Storm has 12% of enterprises (2024 survey).