Apache Flink is a true streaming-first platform with sub-second latency and exactly-once semantics, while Apache Spark is a batch-processing framework with micro-batching for stream processing. Flink excels for real-time applications requiring low latency, whereas Spark dominates in mixed batch-stream workloads and has broader ecosystem adoption.
Apache Flink vs Apache Spark — Side-by-side comparison on A Versus B
Distributed stream processing engine for real-time analytics with advanced state management and exactly-once semantics
General-purpose distributed computing framework using micro-batching for stream processing
Processing Model. Latency (P99). Ecosystem Maturity. Job Recovery Overhead. SQL Support Completeness. State Management Capability. Industry Adoption (2026)
Choose Apache Flink if you need true real-time stream processing with sub-second latency, exactly-once semantics, and complex stateful operations—ideal for fraud detection, real-time recommendations, and financial trading systems. Choose Apache Spark if you need a versatile platform for batch processing, interactive analytics, and mixed batch-stream workloads with a mature ecosystem, extensive library support, and easier team onboarding.
Apache Flink: 5-7 (JobManager, TaskManagers, StateBackend, Checkpoints) | Apache Spark: N/A
Time to First Correct Result (learning curve)
Apache Flink: Yes (core feature) | Apache Spark: N/A
Apache Flink: 250,000 | Apache Spark: N/A
Apache Flink: 5-50ms | Apache Spark: 500-2000ms
Apache Flink: Exactly-once (native) | Apache Spark: N/A
Apache Flink: 1M-10M | Apache Spark: N/A
Apache Flink: 4-16 GB | Apache Spark: N/A
Apache Flink: 2-3 nodes | Apache Spark: N/A
Apache Flink: 4 (Java, Scala, Python, SQL) | Apache Spark: 5 (Scala, Python, Java, R, SQL)
Apache Flink: Distributed snapshots + checkpointing | Apache Spark: N/A
Apache Flink: Continuous streams (any size) | Apache Spark: N/A
Apache Flink: 50-100ms | Apache Spark: N/A
Apache Flink: Exactly-once | Apache Spark: N/A
Apache Flink: Event-time, processing-time, session windows, custom | Apache Spark: N/A
Apache Flink: Requires cluster with YARN/Kubernetes, moderate DevOps | Apache Spark: N/A
Apache Flink: 8-12 weeks (with Kubernetes ops experience) | Apache Spark: N/A
Apache Flink: 1024 MB | Apache Spark: 2-4 GB
Apache Flink: 10,000+ | Apache Spark: N/A
Apache Flink: 4 (Java, Python, Scala, SQL) | Apache Spark: N/A
Apache Flink: 100-500 ms (tuning dependent) | Apache Spark: N/A
Apache Flink: Terabyte-scale (tested to 10+ TB) | Apache Spark: N/A
Apache Flink: 29,000+ | Apache Spark: N/A
Apache Flink: 23,800+ | Apache Spark: 35,900 stars
Apache Flink: ~1,850 | Apache Spark: N/A
Apache Flink: 1.5-2.5 GB | Apache Spark: N/A
Apache Flink: Native & first-class (core design) | Apache Spark: N/A
Apache Flink: Limited (requires external libraries) | Apache Spark: N/A
Apache Flink: 2-3 seconds (streaming) / 4-6 (batch) | Apache Spark: N/A
Apache Flink: 1-100 ms (sub-second typical) | Apache Spark: N/A
Apache Flink: 100,000-1,000,000 events/sec | Apache Spark: N/A
Apache Flink: PyFlink added in v1.11 (2020); improved in v1.14+ | Apache Spark: N/A
Apache Flink: Exactly-once (configurable per checkpoint) | Apache Spark: N/A
Apache Flink: Basic REST API only (external UI required) | Apache Spark: N/A
Apache Flink: Java 11+ | Apache Spark: N/A
Apache Flink: ~30 native connectors | Apache Spark: 200+ via ecosystem integrations
Apache Flink: ~23,000 | Apache Spark: 40,100 stars
Apache Flink: ~200-400MB (optimized) | Apache Spark: ~400-800MB (GC overhead)
Apache Flink: Full with watermarks, out-of-order handling, allowedLateness | Apache Spark: Partial in Structured Streaming, limited out-of-order support
Apache Flink: ~1-2M events/sec | Apache Spark: ~500K-1M events/sec
Apache Flink: Separate APIs (DataStream vs Batch) | Apache Spark: Unified via Structured Streaming/Dataset API
Apache Flink: 5,000,000+ | Apache Spark: N/A
Apache Flink: 2-4 GB | Apache Spark: N/A
Apache Flink: Memory, RocksDB, External (3 options) | Apache Spark: N/A
Apache Flink: 0 (Open source) | Apache Spark: N/A
Apache Flink: 10,400 | Apache Spark: N/A
Apache Flink: 12 (since 2014) | Apache Spark: N/A
Apache Flink: Millions (1M+) | Apache Spark: N/A
Apache Flink: 1-10ms (true event streaming) | Apache Spark: 100-2,000ms (Spark Streaming micro-batch interval)
Apache Flink: 1,000,000 - 5,000,000 (streaming-optimized) | Apache Spark: 500,000 - 2,000,000 (batch-optimized)
Apache Flink: 6-8GB (efficient state management) | Apache Spark: 8-12GB (caching overhead)
Apache Flink: 1,800,000 (StackOverflow, job postings 2024) | Apache Spark: 7,200,000 (StackOverflow, job postings 2024)
Apache Flink: 2,000+ (Flink SQL, state backends, CEP library) | Apache Spark: 14,000+ (Spark packages, MLlib, SQL, GraphX, etc.)
Apache Flink: 18% (Alibaba, Netflix, Uber, Lyft use cases) | Apache Spark: 65% (Databricks, AWS, Google, Meta deployments)
Apache Flink: 6-10 weeks (steeper learning curve, less documentation) | Apache Spark: 2-4 weeks (larger talent pool, more examples)
Apache Flink: 15-30 lines (native session window API) | Apache Spark: 80-150 lines (custom state handling needed)
Apache Flink: 100-500ms | Apache Spark: 500-2000ms
Apache Flink: 2,500 | Apache Spark: 32,000
Apache Flink: 15-20% | Apache Spark: 60-65%
Apache Flink: 256-512MB | Apache Spark: 512-1024MB
Apache Flink: 5 types (keyed, operator, broadcast, queryable, custom) | Apache Spark: 2 types (RDD state, DataFrame state)
Apache Flink: 1-10ms (streaming native) | Apache Spark: N/A
Apache Flink: 7-9 (complex) | Apache Spark: N/A
Apache Flink: 2048-8192 | Apache Spark: N/A
Apache Flink: Terabytes of distributed state native | Apache Spark: N/A
Apache Flink: 20-40 hours (learning + development) | Apache Spark: N/A
Apache Flink: N/A | Apache Spark: 2000-5000ms
Apache Flink: N/A | Apache Spark: 1,000,000+ GB (petascale)
Apache Flink: N/A | Apache Spark: 500-2000MB
Apache Flink: N/A | Apache Spark: 30-120 minutes
Apache Flink: N/A | Apache Spark: Python, Scala, SQL, R, Java
Apache Flink: N/A | Apache Spark: 8,000-15,000ms
Apache Flink: N/A | Apache Spark: 1,000+ PB
Apache Flink: N/A | Apache Spark: 60-120 (cluster setup)
Apache Flink: N/A | Apache Spark: Native support
Apache Flink: N/A | Apache Spark: Yes (RDD lineage-based)
Apache Flink: N/A | Apache Spark: 6-12 weeks
Apache Flink: N/A | Apache Spark: 40-80 hours (dedicated DevOps engineer)
SQL Query Performance (TPC-DS Benchmark)
Apache Flink: N/A | Apache Spark: 45-120 seconds
Apache Flink: N/A | Apache Spark: 1-5 (via Jupyter sharing)
Apache Flink: N/A | Apache Spark: 0 (manual implementation required)
Apache Flink: N/A | Apache Spark: Parquet, ORC, JSON, CSV, Avro, Delta (via library)
Apache Flink: N/A | Apache Spark: 25,000+ questions
Apache Flink: N/A | Apache Spark: $1,500-$5,000+
Apache Flink: N/A | Apache Spark: 5-15 minutes
Apache Flink: N/A | Apache Spark: Python, Scala, Java, R, SQL
Apache Flink: N/A | Apache Spark: 40-80 hours
Apache Flink: N/A | Apache Spark: Hadoop, Kubernetes, cloud object storage (3+ classes)
Apache Flink: N/A | Apache Spark: 0 (requires external frameworks)
Apache Flink: N/A | Apache Spark: 100+ GB
Apache Flink: N/A | Apache Spark: 38.5K stars
Apache Flink: N/A | Apache Spark: 30-120 seconds
Apache Flink: N/A | Apache Spark: $0.035-0.15
Apache Flink: N/A | Apache Spark: Python, Scala, Java, SQL, R
Apache Flink: N/A | Apache Spark: Unlimited (depends on storage)
Apache Flink: N/A | Apache Spark: 4+ (AWS, Azure, GCP, on-prem)
Apache Flink: N/A | Apache Spark: 50+ (MLlib + custom models)
Apache Flink: N/A | Apache Spark: 8+ formats (Parquet, ORC, Avro, Delta, Iceberg, HDF5, CSV, JSON)
Apache Flink: N/A | Apache Spark: 30-60 seconds (in-memory)
Apache Flink: N/A | Apache Spark: 10-60 seconds
Apache Flink: N/A | Apache Spark: 16-256 GB
Apache Flink: N/A | Apache Spark: 500-5000 ms micro-batches
Apache Flink: N/A | Apache Spark: $450,000-650,000
Apache Flink: N/A | Apache Spark: Lineage-based recovery (RDD parents)
Apache Flink: N/A | Apache Spark: 0.5-2 seconds
Apache Flink: N/A | Apache Spark: 8-64 GB per node
Apache Flink: N/A | Apache Spark: 100-500 ms (micro-batch)
Apache Flink: N/A | Apache Spark: 12 years (since 2013)
Apache Flink: N/A | Apache Spark: 74% currently use
Apache Flink: N/A | Apache Spark: Full MLlib with algorithms, pipelines
Apache Flink: N/A | Apache Spark: Depends on underlying storage
How to choose between Apache Flink and Apache Spark
Apache Flink vs Apache Spark — Structured Comparison Data
Machine-readable comparison dataset for Apache Flink vs Apache Spark. Contains 131 attribute comparisons, 5 FAQ pairs.
Apache Flink vs Apache Spark — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Apache Flink vs Apache Spark.
Use Apache Flink when you need true real-time processing with sub-second latency (P99 < 500ms), complex stateful operations, and exact event-time semantics. Common use cases include fraud detection systems (where 1-2 second delays are unacceptable), real-time recommendation engines, financial trading systems, and IoT sensor data processing. Flink's native streaming architecture makes these applications more efficient than Spark's micro-batching approach.
Why is Spark more popular than Flink despite Flink's lower latency?
Apache Spark dominates (60-65% adoption) because it arrived earlier (2011 vs 2014), has a broader ecosystem with more connectors and libraries, supports both batch and stream processing in a unified framework optimized for batch, and has lower adoption friction for teams transitioning from Hadoop/Hive. Spark's SQL support (98% ANSI compliance) and machine learning libraries (MLlib) make it the default for enterprises doing mixed analytics workloads, not purely streaming applications.
Can Spark achieve low latency like Flink with smaller micro-batches?
No—while reducing Spark's micro-batch interval from 500ms to 100ms is technically possible, it creates severe operational overhead: increased GC pressure, higher CPU utilization (25-40% increase), and diminishing throughput returns. Additionally, Spark's architecture fundamentally processes data in batches, so each batch still requires scheduling, shuffling, and aggregation overhead. Flink's event-driven architecture processes records individually as they arrive, achieving true sub-100ms latencies without these trade-offs.
Which platform has better fault tolerance?
Both platforms provide exactly-once semantics, but via different mechanisms. Flink uses distributed snapshots (barriers) that pause processing, while Spark uses RDD lineage and write-ahead logs. Flink's approach is more sophisticated for streaming (handling out-of-order data) but slightly slower during recovery. Spark's simpler approach works well for batch jobs but introduces higher latency for streaming. For mission-critical financial systems, Flink's stronger semantics are preferred; for general analytics, Spark's sufficient.
What's the total cost of ownership difference between Flink and Spark?
Apache Flink typically costs 30-40% less in cloud compute (due to lower memory overhead per task: 384MB vs 768MB baseline) and has faster scaling for streaming workloads. However, Spark's lower operational complexity and larger talent pool reduce training and hiring costs. For a 100-node cluster running 24/7 streaming jobs, Flink could save $50,000-80,000 annually in infrastructure, but those savings may offset by 20-30% higher engineering effort due to smaller team expertise.
How to Choose Between Apache Flink and Apache Spark
Processing Model: Apache Flink has Native streaming (event-time processing), while Apache Spark has Micro-batching (batch-oriented).
Latency (P99): Apache Flink has 100-500ms, while Apache Spark has 500ms-2s.
Ecosystem Maturity: Apache Flink has Growing (2,500+ GitHub stars), while Apache Spark has Dominant (32,000+ GitHub stars).
Job Recovery Overhead: Apache Flink has 10-15% state management overhead, while Apache Spark has 5-10% checkpoint overhead.
SQL Support Completeness: Apache Flink has 95% ANSI SQL compliance, while Apache Spark has 98% ANSI SQL compliance.
State Management Capability: Apache Flink has Stateful processing with keyed state, operator state, and broadcast state, while Apache Spark has Limited to micro-batch aggregations and RDD state.
Industry Adoption (2026): Apache Flink has 15-20% of streaming workloads (growing 25% YoY), while Apache Spark has 60-65% of data processing workloads (stable growth).