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 vs Apache Flink — Side-by-side comparison on A Versus B
General-purpose distributed computing framework using micro-batching for stream processing
Distributed stream processing engine for real-time analytics with advanced state management and exactly-once semantics
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
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.
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: 2-4 GB | Apache Flink: 1024 MB
Apache Spark: 60-120 (cluster setup) | Apache Flink: N/A
Apache Spark: 35,900 stars | Apache Flink: 23,800+
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) | Apache Flink: N/A
SQL Query Performance (TPC-DS Benchmark)
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: 500-2000ms | Apache Flink: 5-50ms
Apache Spark: 200+ via ecosystem integrations | Apache Flink: ~30 native connectors
Apache Spark: 40,100 stars | Apache Flink: ~23,000
Apache Spark: 65% (Databricks, AWS, Google, Meta deployments) | Apache Flink: 18% (Alibaba, Netflix, Uber, Lyft use cases)
Apache Spark: ~400-800MB (GC overhead) | Apache Flink: ~200-400MB (optimized)
Apache Spark: Partial in Structured Streaming, limited out-of-order support | Apache Flink: Full with watermarks, out-of-order handling, allowedLateness
Apache Spark: ~500K-1M events/sec | Apache Flink: ~1-2M events/sec
Apache Spark: Unified via Structured Streaming/Dataset API | Apache Flink: Separate APIs (DataStream vs Batch)
Apache Spark: 0.5-2 seconds | Apache Flink: N/A
Apache Spark: 8-64 GB per node | Apache Flink: N/A
Apache Spark: 5 (Scala, Python, Java, R, SQL) | Apache Flink: 4 (Java, Scala, Python, SQL)
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 | Apache Flink: N/A
Apache Spark: 100-2,000ms (Spark Streaming micro-batch interval) | Apache Flink: 1-10ms (true event streaming)
Apache Spark: 500,000 - 2,000,000 (batch-optimized) | Apache Flink: 1,000,000 - 5,000,000 (streaming-optimized)
Apache Spark: 8-12GB (caching overhead) | Apache Flink: 6-8GB (efficient state management)
Apache Spark: 7,200,000 (StackOverflow, job postings 2024) | Apache Flink: 1,800,000 (StackOverflow, job postings 2024)
Apache Spark: 14,000+ (Spark packages, MLlib, SQL, GraphX, etc.) | Apache Flink: 2,000+ (Flink SQL, state backends, CEP library)
Apache Spark: 2-4 weeks (larger talent pool, more examples) | Apache Flink: 6-10 weeks (steeper learning curve, less documentation)
Apache Spark: 80-150 lines (custom state handling needed) | Apache Flink: 15-30 lines (native session window API)
Apache Spark: 500-2000ms | Apache Flink: 100-500ms
Apache Spark: 32,000 | Apache Flink: 2,500
Apache Spark: 60-65% | Apache Flink: 15-20%
Apache Spark: 512-1024MB | Apache Flink: 256-512MB
Apache Spark: 2 types (RDD state, DataFrame state) | Apache Flink: 5 types (keyed, operator, broadcast, queryable, custom)
Apache Spark: N/A | Apache Flink: 5-7 (JobManager, TaskManagers, StateBackend, Checkpoints)
Time to First Correct Result (learning curve)
Apache Spark: N/A | Apache Flink: Yes (core feature)
Apache Spark: N/A | Apache Flink: 250,000
Apache Spark: N/A | 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: 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: 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: ~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: 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 (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)
How to choose between Apache Spark and Apache Flink
Apache Spark vs Apache Flink — Structured Comparison Data
Machine-readable comparison dataset for Apache Spark vs Apache Flink. Contains 131 attribute comparisons, 5 FAQ pairs.
Apache Spark vs Apache Flink — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Apache Spark vs Apache Flink.
When should I use Spark Streaming vs Flink Streaming?
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.
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.
What's the difference in costs between Spark and Flink?
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.
Can Spark and Flink work together in the same architecture?
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.
Which has better fault tolerance and exactly-once semantics?
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.
How to Choose Between Apache Spark and Apache Flink
Processing Model: Apache Spark has Micro-batch (Spark Streaming), Native batch, while Apache Flink has True event-by-event streaming.
Latency (Event to Result): Apache Spark has 100ms - 2 seconds, while Apache Flink has 1ms - 10ms.
Consider: Ecosystem Maturity & Libraries
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)
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
Community Adoption & Job Market: Apache Spark has 7.2M+ developers, 65% adoption in enterprises, while Apache Flink has 1.8M+ developers, 18% enterprise adoption.
Ease of Learning: Apache Spark has Easier (Python, SQL, Scala, Java APIs), while Apache Flink has Steeper learning curve (Java/Scala dominant).
Memory Efficiency at Scale: Apache Spark has Higher memory overhead (caching architecture), while Apache Flink has 20-30% lower memory per node.