Apache Pulsar is not just a powerful messaging system—it also comes with native stream processing capabilities via Pulsar Functions. These lightweight, serverless functions allow developers to write and deploy real-time transformations, enrichments, and analytics directly within the Pulsar ecosystem—without needing external processing engines like Flink or Spark.

In this post, we’ll explore how to create stream processing applications using Pulsar Functions, understand their architecture, and walk through real-world use cases and best practices.

What Are Pulsar Functions?

Pulsar Functions are small, event-driven pieces of code that consume messages from Pulsar topics, process them, and publish the results to another topic.

Key benefits:

  • Serverless: No need to manage streaming engines
  • Language support: Write functions in Java, Python, or Go
  • Low latency: Ideal for lightweight, fast processing
  • Easy deployment: Use CLI or YAML for function management

Pulsar Function Architecture

[Input Topic]
↓
[Pulsar Function]
↓
[Output Topic]

Functions can also:

  • Write to external systems (via sink connectors)
  • Trigger alerts
  • Maintain lightweight state via state stores

Hello World: Pulsar Function Example in Python

Create a simple function that filters sensor data:

sensor_filter.py

def process(input, context):
data = json.loads(input)
if data["temperature"] > 75:
return json.dumps(data)
else:
return None

Deploy it using the Pulsar CLI:

bin/pulsar-admin functions create \
--name high-temp-filter \
--inputs sensor-data \
--output high-temp-alerts \
--py sensor_filter.py \
--classname sensor_filter.process

Stateful Functions

Pulsar Functions support state management via:

  • Key/value state stores
  • APIs for incremental counters, lookup, and update

Example (Java):

context.putState("count", currentCount + 1);
long total = context.getState("count");

Useful for:

  • Aggregation
  • Rate limiting
  • Stateful enrichments

Use Cases

  1. IoT Analytics
    • Filter and route sensor data in real-time
    • Enrich messages with device metadata
  2. Fraud Detection
    • Trigger alerts on suspicious transaction patterns
    • Combine multiple input streams for correlation
  3. Log Transformation
    • Normalize and enrich application logs
    • Forward to downstream analytics systems
  4. ML Inference at Edge
    • Call model APIs or lightweight classifiers on incoming messages

Deploying at Scale

Use Kubernetes with the Pulsar Operator or Helm charts to deploy Functions in production:

  • Enable Function Workers in standalone or cluster mode
  • Scale function instances with the --parallelism flag
  • Monitor using Prometheus, Grafana, or Pulsar Manager UI

Best Practices

  • Keep function logic lightweight — offload heavy compute elsewhere
  • Use parallelism for throughput scaling
  • Handle serialization errors gracefully
  • Isolate state per key to avoid race conditions
  • Log important metrics and exceptions

Conclusion

Apache Pulsar Functions make it easy to build and deploy streaming logic inside your messaging layer, enabling fast, serverless, and distributed processing for real-time data. Whether you’re building IoT filters, real-time fraud detection, or data enrichment pipelines, Pulsar Functions provide a simple and scalable way to move beyond just messaging — and into processing.

Start building your stream-native apps today with just a few lines of code and the power of Apache Pulsar.