Redis Streams vs Kafka: Choosing the Right Event Streaming Architecture

Your team is debating whether to add Kafka to your stack for event streaming — or whether Redis Streams can handle it. Both process millions of messages per second, but they solve different problems. Here's the architectural breakdown to make the right call for your workload.

How Redis Streams Work

Redis Streams (introduced in Redis 5.0) is an append-only log data structure with consumer group support. Each message gets an auto-generated ID based on the millisecond timestamp plus a sequence number. Consumer groups allow multiple consumers to process the same stream with at-least-once delivery guarantees.

# Produce a message
XADD orders * user_id 12345 product_id 999 quantity 2

# Create consumer group
XGROUP CREATE orders processing-group $ MKSTREAM

# Consume messages (read up to 10, block 2s if empty)
XREADGROUP GROUP processing-group worker-1 COUNT 10 BLOCK 2000 STREAMS orders >

# Acknowledge processed messages
XACK orders processing-group 1722000000000-0

# Check pending (unacknowledged) messages
XPENDING orders processing-group - + 10

Redis Streams Consumer Group Pattern

import redis

r = redis.Redis(host='redis.internal', port=6379)

# Create consumer group
try:
    r.xgroup_create('orders', 'processors', id='$', mkstream=True)
except redis.ResponseError:
    pass  # group already exists

while True:
    messages = r.xreadgroup(
        'processors', 'worker-1',
        {'orders': '>'},
        count=10, block=2000
    )
    for stream, msgs in (messages or []):
        for msg_id, data in msgs:
            process_order(data)
            r.xack('orders', 'processors', msg_id)

How Apache Kafka Works

Kafka stores messages in immutable, ordered partitions within topics. Each partition is a durable log replicated across brokers. Consumers track their position (offset) independently, enabling replay, time-travel queries, and multiple independent consumer groups reading the same topic at different rates.

from confluent_kafka import Producer, Consumer

# Producer
p = Producer({'bootstrap.servers': 'kafka.internal:9092'})
p.produce('orders', key='12345', value=json.dumps({'user_id': 12345, 'product_id': 999}))
p.flush()

# Consumer with exactly-once processing
c = Consumer({
    'bootstrap.servers': 'kafka.internal:9092',
    'group.id': 'order-processors',
    'auto.offset.reset': 'earliest',
    'enable.auto.commit': False  # manual commit for exactly-once
})
c.subscribe(['orders'])

while True:
    msg = c.poll(timeout=1.0)
    if msg and not msg.error():
        process_order(json.loads(msg.value()))
        c.commit(asynchronous=False)  # commit after processing

Redis Streams vs Kafka: Side-by-Side

When to Use Redis Streams

• Low-latency job queues (task processing, webhooks, real-time notifications)
• Short-lived event pipelines where you don't need weeks of history
• Teams already running Redis who want to avoid Kafka's operational complexity
• Event volumes under ~100M messages/day where memory retention is feasible

When to Use Kafka

• Event sourcing and audit logs requiring indefinite retention
• Cross-team event backbone shared by 10+ services
• Exactly-once semantics for financial transactions
• Stream processing with Flink, Spark Streaming, or Kafka Streams

Working with JusDB on Redis and Event Streaming

JusDB architects event streaming solutions using Redis Streams and Kafka for engineering teams at any scale. We tune Redis memory allocation, configure consumer groups for reliable processing, and design Kafka topologies for cross-service event backbones.

Explore JusDB Redis Services →  |  Talk to a DBA

Related reading:

• Redis 8 vs Valkey 8
• ScyllaDB 6.0 vs Cassandra