Redis Streams vs Kafka: Which Is Better for Real‑Time Event Processing?

Redis Streams vs Kafka

Kafka is renowned for handling large‑scale data processing and is widely deployed in many well‑known companies. In 2015, LinkedIn operated 60 clusters with 1,100 brokers, processing 13 million messages per second.

However, scale is not Kafka’s only strength. Its programming paradigm—partitioned, ordered, event‑driven processing—solves many problems, such as ensuring the latest update is indexed last or handling dependent user actions. This differs from traditional job queues where workers pull tasks concurrently, sacrificing ordering guarantees.

If you need ordered processing but find Kafka heavy or costly, Redis 5.0 introduced a "stream" data structure that aims to address similar use cases.

Kafka Architecture

The core data structure in Kafka is the topic, an append‑only, time‑ordered sequence of records. Topics are partitioned to enable scalability; each partition assigns a monotonically increasing offset to records, uniquely identifying them. Consumers track the last offset they have processed, allowing multiple consumers to read independently while preserving order.

Partitions are distributed across Kafka instances. A consumer group is a set of cooperating processes that collectively consume a topic. Each partition is assigned to a single group member, and when members join or leave, partitions are rebalanced to ensure fair distribution.

Only one consumer in a group processes a given partition, guaranteeing strict ordering within that partition.

Redis Stream Data Structure

Redis Streams provide a persistent, ordered event store similar to a Kafka partition, with a configurable maximum length and key/value pairs akin to a Redis hash. The primary difference lies in the consumer‑group model.

In Redis, a consumer group consists of processes that all read from the same stream, but each event is delivered to only one consumer within the group. This ensures that duplicate processing does not occur.

Groups parallelize the handling of a single stream, resembling a traditional job‑queue architecture, which sacrifices the global ordering guarantee of a true stream processor.

Stream Processing as a Client Library

To build a Kafka‑like stream processing engine on top of Redis, you need to implement:

Event partitioning: create N streams and assign each incoming event to a stream based on a hash or field.

Worker partition assignment: design an algorithm that distributes streams among workers, similar to Kafka’s rebalancing.

Ordered processing with acknowledgments: each worker iterates its assigned partitions, tracks offsets, and uses Redis consumer‑group state to ensure ordered handling and acknowledgment of events.

For robustness, consider error handling such as forwarding failed events to a dead‑letter stream.

The open‑source Runnel library (Python) implements these concepts, providing Redis‑based stream processing with lock‑based coordination, a control stream for communication, and detailed documentation of its architecture and rebalancing algorithm.

Trade‑offs

Redis offers unparalleled processing speed because everything resides in memory, but it is not suitable for storing unlimited amounts of data. Kafka, by contrast, can retain events indefinitely. Using Redis typically means keeping a fixed‑size window of recent events and offloading long‑term storage to external systems (e.g., S3), which adds architectural complexity but reduces cost.

The motivation for exploring Redis streams is the ease of deployment and low operational cost compared to Kafka, while still achieving a distributed stream‑processing paradigm with careful design.