Chapter 12: Python in the Pulsar-Ray-Flink Pipeline

Introduction

In large-scale AI workflows, data preprocessing, model training, and inference need to be efficiently orchestrated. In a Ray-Pulsar-Flink setup, Python-based Ray processes interact with Pulsar to consume preprocessed data from Flink, ensuring smooth and scalable AI pipelines.

💡 Real-world analogy: Python processes in Ray don’t talk to Flink directly—instead, Pulsar acts as a messenger, delivering clean, preprocessed data from Flink to Ray.

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Why Use Pulsar Between Flink and Ray?

Pulsar serves as a high-speed, distributed messaging system that enables seamless communication between different components.

✅ Decouples data preprocessing (Flink) from model training (Ray). ✅ Handles real-time streaming and batch workloads efficiently. ✅ Ensures fault tolerance and message persistence.

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How Python-Based Ray Processes Interact with Pulsar

Python-based Ray processes consume AI-ready data from Pulsar and use it for training, inference, or analytics.

1️⃣ Producing Preprocessed Data from Flink to Pulsar

Flink preprocesses raw data and sends it to Pulsar:

from pulsar import Client

client = Client('pulsar://localhost:6650')
producer = client.create_producer('preprocessed-data')

# Sending preprocessed data
producer.send(b'{"features": [0.2, 0.8, 0.5], "label": 1}')
client.close()

💡 Use Case: Flink cleans, normalizes, and structures raw data before publishing it to Pulsar.

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2️⃣ Consuming Data from Pulsar into Ray

Ray processes subscribe to Pulsar topics to retrieve preprocessed data.

import ray
from pulsar import Client

ray.init()

@ray.remote
def process_data():
    client = Client('pulsar://localhost:6650')
    consumer = client.subscribe('preprocessed-data', 'ray-group')

    msg = consumer.receive()
    print(f"Received: {msg.data().decode('utf-8')}")
    consumer.acknowledge(msg)
    client.close()

ray.get(process_data.remote())

💡 Use Case: Ray consumes structured data and triggers AI model training or inference.

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Optimizing AI Pipelines in a Ray-Pulsar Setup

3️⃣ Handling Large-Scale AI Workflows Efficiently

✅ Use multiple Ray workers to process data concurrently. ✅ Partition topics in Pulsar to distribute data across multiple Ray actors. ✅ Use Pulsar’s message retention to avoid data loss during failures.

4️⃣ Example: Distributed AI Training with Pulsar & Ray

@ray.remote
def train_model(data):
    print(f"Training model with {data}")
    return "Training complete"

# Simulating batch processing
messages = ["data1", "data2", "data3"]
jobs = [train_model.remote(msg) for msg in messages]
results = ray.get(jobs)
print(results)

💡 Use Case: Parallel model training triggered by new data arriving in Pulsar.

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Conclusion

By integrating Flink (preprocessing), Pulsar (messaging), and Ray (AI processing), we create a highly efficient, scalable AI pipeline. Python-based Ray processes can consume and process AI-ready data from Pulsar, enabling real-time and batch AI workloads.

In the next chapter, we will explore further optimizations and monitoring strategies for AI workflows in production.