What Are Reactive Streams?

Reactive Streams is a standard for asynchronous stream processing with non-blocking back pressure. This specification defines how data flows between components in a reactive system, ensuring that producers (publishers) do not overwhelm consumers (subscribers) with more data than they can handle. Reactive Streams provides a set of interfaces and rules that enable smooth, predictable data transfer, even when the rate of data production and consumption varies.

Why Reactive Streams Matter

Reactive Streams address many challenges in modern software systems:

• Enable efficient use of system resources by handling data asynchronously;
• Prevent system overload by applying back pressure, allowing consumers to signal how much data they can process;
• Improve scalability and responsiveness in applications that deal with large or variable data volumes;
• Provide a foundation for building robust, maintainable, and resilient data pipelines.

By adopting Reactive Streams in Java, you gain a powerful toolset for building systems that remain responsive under load, adapt to changing conditions, and handle data efficiently. This approach is especially valuable in cloud-native, distributed, and event-driven architectures.

Publisher in Reactive Streams

A Publisher is a core interface in the Reactive Streams specification. Its main role is to produce and deliver data to one or more Subscribers in a controlled, asynchronous manner. The Publisher does not push data to subscribers immediately; instead, it waits for subscribers to signal how much data they are ready to receive. This process is called backpressure.

How a Publisher Emits Data

• A Subscriber subscribes to a Publisher by calling the subscribe() method;
• The Publisher creates a connection and sends a Subscription to the Subscriber;
• The Subscriber requests a specific number of items using the request(n) method on the Subscription;
• The Publisher emits up to n items to the Subscriber using the onNext() method;
• When the Publisher has no more data, it calls onComplete(); if an error occurs, it calls onError().

This approach ensures that subscribers are never overwhelmed with more data than they can handle, making data flow predictable and robust.

The Role of a Subscriber in Reactive Streams

A Subscriber is a key participant in the reactive streams model. Its main job is to receive and react to data and signals sent by a Publisher. You implement a Subscriber to define how your application handles incoming data, errors, and completion events.

A Subscriber reacts to four main signals from a Publisher:

• onSubscribe: Receives a Subscription object, which you use to request data items from the Publisher;
• onNext: Receives each data item emitted by the Publisher as it becomes available;
• onError: Handles errors if something goes wrong during data processing;
• onComplete: Signals that all data has been sent and no more items will be emitted.

By controlling how many items you request and how you handle each signal, you can efficiently process data streams, manage backpressure, and respond to errors or completion events in a predictable way.

Backpressure in Reactive Streams

Backpressure is a crucial concept in reactive systems that ensures data flows smoothly between components, even when they operate at different speeds. It prevents a fast data producer (Publisher) from overwhelming a slower data consumer (Subscriber), which could otherwise lead to memory issues, dropped messages, or system crashes.

Why Backpressure Matters

• Prevents data loss caused by buffer overflows;
• Protects system resources by avoiding excessive memory usage;
• Maintains stability and responsiveness in high-throughput or bursty environments.

Without backpressure, a Publisher might emit items faster than a Subscriber can process them. This imbalance can cause queues to grow uncontrollably, leading to performance degradation or out-of-memory errors.

Backpressure in Java Reactive Streams

The Reactive Streams specification in Java addresses backpressure by defining a contract between the Publisher and Subscriber:

• The Subscriber signals how many items it is ready to receive by calling the request(n) method on the Subscription;
• The Publisher only sends up to n items, waiting for further requests before emitting more;
• This pull-based approach lets the Subscriber control the pace of data flow, ensuring it is never overwhelmed.

Example Sequence

1. The Subscriber subscribes to the Publisher and receives a Subscription;
2. The Subscriber calls request(5) to signal it can handle 5 items;
3. The Publisher sends up to 5 items, then waits for the next request() call.

This mechanism allows you to build robust, resilient reactive applications that automatically adapt to changing workloads and processing speeds.