Scaling Kafka With Consumer Groups

Kevin Feasel

2018-05-15

Hadoop

Suhita Goswami explains how to use consumer groups to scale processing from Apache Kafka:

Kafka builds on the publish-subscribe model with the advantages of a message queuing system. It achieves this with:

  • the use of consumer groups
  • message retention by brokers

When consumers join a group and subscribe to a topic, only one consumer from the group actually consumes each message from the topic. The messages are also retained by the brokers in their topic partitions, unlike traditional message queues.

Multiple consumer groups can read from the same set of topics, and at different times catering to different logical application domains. Thus, Kafka provides both the advantage of high scalability via consumers belonging to the same consumer group and the ability to serve multiple independent downstream applications simultaneously.

Consumer groups are a great solution to the problem of long-running consumers when items to process are independent and can run concurrently.

Related Posts

Generating Load For Kafka With JMeter

Anup Shirolkar shows us a way to use JMeter to generate load for Apache Kafka clusters: The Anomalia Machina is going to require (at least!) one more thing as stated in the intro, loading with lots of data! Kafka is a log aggregation system and operates on a¬†publish-subscribe mechanism. The Kafka cluster in Anomalia Machina […]

Read More

Data Science And Data Engineering In HDP 3.0

Saumitra Buragohain, et al, show off some of the things added to the Hortonworks Data Platform for data scientists and data engineers: We leverage the power of HDP 3.0 from efficient storage (erasure coding), GPU pooling to containerized TensorFlow and Zeppelin to enable this use case. We will the save the details for a different […]

Read More

Categories

May 2018
MTWTFSS
« Apr Jun »
 123456
78910111213
14151617181920
21222324252627
28293031