How does Redis Streams support data streaming?

Redis Streams enables data streaming by acting as a log-like structure that records and orders events in real time. Each entry in a Redis Stream is a key-value pair with a unique, auto-generated timestamp-based ID, ensuring strict ordering. Producers append messages to the stream using the XADD command, and consumers read messages sequentially or by querying a range of IDs. For example, a sensor application might use XADD temperatures:stream * sensor_id 1234 temp 98.6 to log temperature data, where * auto-generates the entry’s ID. Redis maintains the stream in memory, allowing fast writes and reads, and supports persistence via snapshots or append-only files (AOF) for durability.

Redis Streams supports consumer groups to manage parallel processing. A consumer group lets multiple consumers split the workload by claiming different portions of the stream. For instance, a group of three workers processing payment transactions could use XGROUP CREATE payments:stream mygroup $ to create a group, then read messages with XREADGROUP GROUP mygroup consumer1. Each consumer acknowledges processed messages with XACK, ensuring messages aren’t reprocessed unless explicitly retried. If a consumer fails, pending messages can be reassigned to others in the group. This design prevents data loss and enables scalable handling of high-throughput streams without manual coordination.

Redis Streams also provides flexibility for different access patterns. Consumers can read new messages in real time using blocking operations (e.g., XREAD BLOCK 5000 STREAMS mystream $ waits up to 5 seconds for new data) or replay historical data using IDs (e.g., XRANGE mystream 1526569415632-0 +). The stream’s capped length (configurable with XTRIM or MAXLEN in XADD) prevents unbounded memory usage. Because Redis is single-threaded, stream operations are atomic, avoiding race conditions. These features make Redis Streams suitable for use cases like event sourcing, real-time analytics, and activity feeds, where ordered, reliable, and low-latency message handling is critical.