Understanding Data Flow in a Reactive Pipeline

A reactive data pipeline processes data as it arrives, passing it through a sequence of well-defined steps. Each step can filter, transform, or consume the data. In Java, you can use libraries like java.util.stream for basic reactive-style processing, but for true reactive programming, frameworks such as Project Reactor or RxJava are often used. Here, you will see how data flows through a reactive pipeline using core Java and principles common to all reactive systems.

Step 1: Data Source

You start with a data source, such as a list of user actions, sensor readings, or API responses. In a real-world scenario, this data could come from a database, a web service, or a message queue.

package com.example;

import java.util.List;
import java.util.stream.Stream;

public class ReactivePipelineExample {
    public static void main(String[] args) {
        // Simulate a stream of incoming temperature readings (Celsius)
        List<Integer> temperatureReadings = List.of(18, 21, 25, 29, 15, 32, 27);
        
        // Create a reactive-like pipeline using Java Streams
        Stream<Integer> pipeline = temperatureReadings.stream()
            // Step 2: Filter - Only process readings above 20°C
            .filter(temp -> temp > 20)
            // Step 3: Transform - Convert Celsius to Fahrenheit
            .map(temp -> temp * 9 / 5 + 32)
            // Step 4: Consume - Print each processed value
            .peek(fahrenheit -> System.out.println("Processed: " + fahrenheit + "°F"));

        // Trigger the pipeline
        pipeline.forEach(fahrenheit -> {});
    }
}

Step 2: Filtering Data

• Only allow relevant data to continue through the pipeline;
• In the example, filter out temperature readings that are 20°C or lower;
• This ensures you only process data that meets your criteria.

Step 3: Transforming Data

• Convert or enrich each item as needed;
• In the example, convert Celsius to Fahrenheit for each reading;
• Transformation prepares data for downstream consumers.

Step 4: Consuming Data

• Take action on each processed item, such as saving to a database, sending a notification, or displaying results;
• In the example, print each processed temperature to the console;
• This is the final step in the pipeline where data is actually used.

Real-World Applications

• Filtering and transforming user input before storing it in a database;
• Processing sensor data in IoT applications;
• Handling event streams in web applications.

By breaking the pipeline into clear steps, you can build systems that are flexible, maintainable, and responsive to real-time data.