Cloud Native Apache Pulsar Development 202 with Python
summary
Shows a working pipeline that uses Python Pulsar functions (packaged with requirements) to run VADER sentiment on live JSON messages from websockets and MQTT, and publish results to an output topic; demo connects a web chat → Pulsar (port 6650/REST), function adds sentiment and returns results sub‑second on a laptop. Uses Kafka‑compatible clients, Avro/JSON schemas, NiFi integration, and examples for Flink/Spark. real NLP in Python, Pulsar functions + websockets, sub‑second feedback
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chapters (17)
- 0:00Introduction to talk
- 1:08Streaming app components
- 3:15Python Pulsar client options
- 5:23Pulsar functions in Python
- 11:03Sending messages in Python
- 14:15Using schemas with Python
- 17:23Consuming messages in Python
- 19:31Database lookup in functions
- 20:57MQTT integration with Python
- 22:01WebSocket communication
- 22:48Kafka protocol with Python
- 23:31Deploying Pulsar functions
- 25:17Stock data streaming example
- 26:47Web page real-time updates
- 29:56Apache NiFi Python processors
- 32:52Live chat app demo
- 40:31Q&A and closing
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