Flink vs Spark: What's the Difference?
Both process large-scale data, but with different models. Flink is a true streaming engine — each event processed individually with sub-second latency and native stateful APIs. Spark uses micro-batch streaming — buffering events for simpler operations at the cost of seconds of latency. The choice depends on your latency requirements, not technical preference.
Side-by-Side Comparison
Apache Flink
- • True per-event streaming (sub-second latency)
- • Native event-time + watermarks for late data
- • Rich state APIs: ValueState, MapState, ListState
- • Exactly-once via distributed Chandy-Lamport snapshots
- • Flink SQL for declarative stream queries
- • Kubernetes Operator for production deployment
Apache Spark
- • Micro-batch streaming (1–30 seconds latency)
- • Mature DataFrame / SQL API for batch + stream
- • MLlib, Spark ML for training on large datasets
- • Deep Delta Lake, Iceberg, and Hudi integration
- • Larger community, more tutorials, easier hiring
- • Databricks manages ops on the cloud
Mental Model
Think of Flink as a conveyor belt in a factory — each item is processed the instant it arrives, no waiting. Think of Spark Streaming as a loading dock — items are collected in batches and processed together every 10 seconds. If you need to react in milliseconds (fraud detection, live bidding), you need the conveyor belt. If you just need fresh dashboards every 30 seconds, the loading dock is simpler to run.
When to Use Each
Choose Flink when:
- • You need sub-second event detection (fraud, anomalies)
- • You need complex stateful joins across streams
- • Your pipeline uses Kafka as the primary source
- • Event-time ordering and late data handling are critical
- • You need exactly-once from source to sink
Choose Spark when:
- • Your workload is primarily batch ETL or SQL analytics
- • You need MLlib for distributed model training
- • Your team already uses Databricks
- • You read from Delta Lake, Iceberg, or Hudi tables
- • Seconds of latency is acceptable
How They Work Together
Many modern data platforms run both engines. Flink handles real-time ingestion and stream processing; Spark handles batch reprocessing, ML training, and analytics queries on the same data lake.
# Common pattern: Flink writes to Iceberg, Spark reads for analytics
# Flink: real-time fraud scoring → write to Iceberg sink
transactions
.key_by(lambda t: t.customer_id)
.window(TumblingEventTimeWindows.of(Time.minutes(1)))
.process(FraudScoringFunction())
.sink_to(iceberg_sink) # write scored events to Iceberg
# Spark: batch analytics on the same Iceberg table
df = spark.read.format("iceberg")\
.load("catalog.db.fraud_scores")
df.groupBy("merchant_id")\
.agg(sum("fraud_amount"), count(*))\
.write.saveAsTable("merchant_risk_daily")
Feature Comparison
| Feature | Flink | Spark Streaming |
|---|---|---|
| Streaming model | True event-by-event | Micro-batch |
| Streaming latency | Sub-second | 1–30 seconds |
| Event-time / watermarks | ✓ native | ✓ with limits |
| Stateful APIs | ✓ rich (Value/Map/List) | ✓ limited |
| Exactly-once | ✓ distributed snapshots | ✓ micro-batch idempotent |
| Batch processing | ✓ unified API | ✓ best in class |
| SQL support | ✓ Flink SQL | ✓ Spark SQL (more mature) |
| ML / analytics | ✗ limited | ✓ MLlib, pandas-on-Spark |
Common Mistakes
Choosing based on familiarity, not latency needs
The latency requirement is the deciding factor. If your use case needs sub-second response (fraud, live bidding, anomaly detection), Flink. If seconds are fine, Spark is simpler to operate. Don't pick Flink just because it sounds more advanced.
Running Flink for batch-heavy workloads
Flink can run batch jobs, but Spark's DataFrame API, SQL optimizer, and Delta Lake integration are more mature for batch ETL. Use the right tool: Flink for streaming, Spark for batch.
Assuming Spark's micro-batch is 'close enough'
For fraud detection or live pricing, 10-second batches mean 10-second windows of exposure. In high-frequency transaction environments this is not acceptable. Know your latency SLO before choosing.
FAQ
- What is the difference between Flink and Spark?
- Flink is a true streaming engine with sub-second latency and rich stateful APIs. Spark Structured Streaming uses micro-batch processing — simpler to operate but adds seconds of latency. Choose based on your latency SLO.
- Can Flink replace Spark?
- For streaming yes, but not for batch and ML. Many orgs run both: Flink for real-time ingestion/processing and Spark for batch ETL, SQL analytics, and ML training.
- Should I learn Flink or Spark first?
- Spark first — it has a gentler curve, larger job market, and covers batch + streaming. Add Flink once you have a use case that requires sub-second latency or complex stateful stream processing.