In industries like finance, e-commerce, cybersecurity, and IoT, detecting meaningful patterns in streams of events — such as fraud attempts, system anomalies, or user journeys — is critical. This is where Complex Event Processing (CEP) comes in.

By combining Apache Pulsar for real-time messaging with Apache Flink for stateful stream processing, you can build powerful CEP pipelines that are scalable, fault-tolerant, and capable of handling massive volumes of data with low latency.

This post explores how to use Pulsar and Flink together for CEP, covering integration methods, pattern recognition, windowing, and deployment tips.

Apache Pulsar provides:

  • High-throughput, low-latency pub-sub messaging
  • Multi-tenancy and geo-replication
  • Tiered storage and decoupled compute-storage

Apache Flink offers:

  • Stateful stream processing with exactly-once semantics
  • Complex event pattern recognition using Flink CEP
  • Time-based windowing, joins, and aggregations

Together, they create a real-time analytics stack capable of consuming, enriching, detecting, and acting on events in milliseconds.

Architecture Overview

[IoT Devices / Web Events / Logs]
↓
[Apache Pulsar Topics]
↓
[Apache Flink + Pulsar Source Connector]
↓
[Flink CEP Pattern Matching / Enrichment / Aggregation]
↓
[Pulsar Sink / Database / Alerts / Dashboards]

Apache Flink supports Pulsar through an official source and sink connector (as of Flink 1.14+).

Add Pulsar Connector Dependency:

<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-pulsar</artifactId>
<version>3.0.0</version>
</dependency>

Example: CEP with Flink + Pulsar

Suppose you want to detect users who add an item to cart but never complete a purchase within 10 minutes.

Define a CEP pattern:
Pattern<Event, ?> pattern = Pattern.<Event>begin("addToCart")
.where(e -> e.getType().equals("ADD_TO_CART"))
.followedBy("noPurchase")
.where(e -> e.getType().equals("PURCHASE"))
.not()
.within(Time.minutes(10));
Apply the pattern to Pulsar stream:
DataStream<Event> eventStream = env
.fromSource(
PulsarSourceBuilder.<Event>builder()
.setServiceUrl("pulsar://localhost:6650")
.setAdminUrl("http://localhost:8080")
.setTopic("user-events")
.setDeserializationSchema(new EventDeserializationSchema())
.build(),
WatermarkStrategy.noWatermarks(),
"PulsarSource"
);

PatternStream<Event> patternStream = CEP.pattern(eventStream, pattern);

patternStream
.select(new PatternSelectFunction<Event, Alert>() {
public Alert select(Map<String, List<Event>> pattern) {
return new Alert("Potential abandoned cart detected!");
}
})
.addSink(new PulsarSink<>());

Use Cases for Pulsar + Flink CEP

  • Fraud Detection: Identify out-of-pattern transaction sequences
  • Cybersecurity: Detect login anomalies or privilege escalations
  • E-Commerce: Track customer funnels and cart abandonment
  • IoT Monitoring: Trigger alerts on abnormal sensor sequences
  • Network Analytics: Detect usage spikes or packet anomalies

Best Practices

  • Use Key_Shared subscriptions in Pulsar for parallel Flink consumption
  • Enable event time processing with proper watermarking
  • Partition Pulsar topics to match Flink parallelism
  • Store Flink checkpoints in DFS or cloud storage for recovery
  • Tune buffer timeout, state backend, and parallelism for low-latency

Monitoring and Observability

Monitor Pulsar and Flink using:

  • Pulsar Metrics (Prometheus + Grafana)
  • Flink Dashboard (job graph, task metrics, throughput)
  • Custom alerts on pattern detection output topics

Ensure that backpressure, latency, and checkpoint health are continuously tracked.

Conclusion

Combining Apache Pulsar and Apache Flink allows you to implement sophisticated real-time complex event processing pipelines with high fault tolerance, scalability, and low operational overhead.

Whether you’re processing financial transactions, IoT telemetry, or e-commerce behavior patterns, this integration offers the tools you need to detect insights, anomalies, and opportunities in motion — not after the fact.

Start building smarter, faster, and more responsive systems today with Pulsar and Flink.