In reactive systems, data flow refers to the movement of data through a series of processing stages, often called a pipeline. Each stage can transform, filter, or consume data as it arrives, allowing you to build flexible, event-driven applications.

Backpressure is a critical concept that helps manage situations where data is produced faster than it can be consumed. Without backpressure, fast producers can overwhelm slower consumers, leading to resource exhaustion and unpredictable system behavior. By applying backpressure, you ensure that each component in your data pipeline processes information at a sustainable rate, resulting in more reliable and efficient asynchronous data streams.

Data Flow in Reactive Streams

Reactive streams use a clear pattern to move data from a source (publisher) to a destination (subscriber). Understanding this flow helps you write efficient, robust reactive programs.

1. Publisher Emits Data

• The publisher is the source of data;
• It produces and sends data items, such as numbers, events, or database records;
• The publisher does not push data indiscriminately; it waits for a request from a subscriber.

2. Subscriber Requests Data

• The subscriber expresses interest in receiving data from the publisher;
• It subscribes to the publisher and specifies how many items it can handle at a time;
• This initial request is called a demand.

3. Data Moves from Publisher to Subscriber

• The publisher sends data items to the subscriber in response to the demand;
• Each item is delivered in order, one at a time, up to the number requested by the subscriber;
• The subscriber processes each item as it arrives.

4. Backpressure: Flow Control

• Backpressure is the mechanism that allows the subscriber to control the rate of data flow;
• If the subscriber is slow, it can request fewer items or pause requests altogether;
• The publisher will not send more data than requested, preventing the subscriber from being overwhelmed.

5. Dynamic Demand

• After processing some items, the subscriber can request more data as needed;
• This creates a feedback loop: the subscriber pulls data at its own pace, and the publisher adapts to this pace.

6. Completion and Error Signals

• When all data is sent, the publisher signals completion;
• If an error occurs, the publisher notifies the subscriber immediately;
• The subscriber can then handle completion or error events appropriately.

Key principles:

• Publishers are responsible for responding to subscriber demand;
• Subscribers control the flow by requesting data as they are ready;
• Backpressure ensures consumers are never overwhelmed by fast producers.

This approach enables scalable, robust systems where each component works at its optimal speed without being overloaded.