Backpressure is a significant challenge in distributed systems, particularly when the production of messages exceeds the consumer's ability to process them. The author provides a clear definition of backpressure, emphasizing its second aspect: the risk of overwhelming consumers with too many messages. When backpressure isn't managed, it can lead to issues like out-of-memory errors, dropped messages, wasted resources, and increased latency. The article outlines a system with three core components: the producer that sends messages, a messaging system that forwards them, and the consumer that processes them. To manage backpressure effectively, the author presents four strategies. The first is to slow down the producer by having the consumer send feedback when it needs a reduced message rate. This approach can be implemented in Go using channels to signal the producer. The second strategy involves dropping existing messages if they are less critical than new ones. This method was used in the author’s real-time leaderboard project, prioritizing the final state over intermediate states. The third option is to drop incoming messages entirely when the channel is full, which is straightforward but may not be viable for critical data. Finally, increasing the number of consumers can help scale the system, particularly when tasks can be processed in parallel, though this can complicate message ordering. Each method comes with tradeoffs. Slowing the producer adds complexity but helps manage flow. Dropping messages can lead to data loss, especially if the messages are important. Ignoring incoming messages simplifies the process but risks losing critical information. Scaling consumers works well for parallel tasks but can complicate ordering. The author reinforces the importance of understanding these strategies to maintain efficient communication within distributed systems.