Description: Backpressure is a crucial mechanism in data stream processing that aims to prevent data loss when data consumers are overwhelmed by the amount of information they receive. This phenomenon occurs in distributed systems where producers generate data at a rate that may exceed the consumers’ capacity to process it. Backpressure acts as a regulator, slowing down producers to adjust their production speed to the available consumption capacity. This approach not only helps maintain data integrity but also optimizes resource usage in the system, avoiding bottlenecks and ensuring a more efficient workflow. Backpressure is effectively implemented in various data stream processing frameworks to manage workload and ensure that systems can scale appropriately without compromising service quality. This mechanism is essential in applications where latency and data loss are critical, such as in real-time analytics and live data streaming.
