You are a seasoned IBM MQ/JMS developer and have recently been told that Apache Kafka is a popular Messaging system with features that make it moe desirable in some situations....the obvious question that comes to mind is that how is it different from other messaging systems?

The intent of this post is to get the existing MQ/JMS users up to speed on differences between the Kafka & typical MQ/JMS messaging systems. Here I am highlighting only the differences not the similarities.

Quick Intro to Kafka

Kafka is a messaging system. From the ground up it has been designed to provide high throughput, fast performance, scalability and high availability. Before we get into the differences between Kafka and MQ, lets quickly go over the basics of Kafka.

Producers of the messages publishes to the Topics

Consumers subscribes to the Topics

Messages are array of bytes. They can be JSON objects, Strings etc

Topics are logs of messages

Kafka is run as a Cluster of servers each of which is called a Broker

For more please go through the documentation available here.

MQ/JMS Versus Kafka

1. No concept of Queue in Kafka i.e., no P2P model

In Kafka there is no concept of Queue and hence no send or receive for putting/getting messages from the queue. Publish-subscribe is the only paradigm available as a messaging model. Producers of the messages Publish a message to the Topic and Consumer receives messages by Subscribing to the topic. This publish-subscribe paradigm is very similar between MQ/JMS and Kafka - the difference is under the covers that we will discuss next.

2. Message Persistence

Typical JMS providers (IBM MQ, Rabbit MQ, Active MQ ..) implement the topics in a such way that the messages published to the topic are sent to a common storage (memory or/and persistent store) from where they are picked up by the subscribers. In MQ/JMS systems once the message is read it is removed from the storage and is no more available. Kafka retains the messages even after all the subscribers have read the message. The rentention period is a configurable parameter.

In a typical MQ/JMS consumer implementation, the message is deleted by the messaging system on receiving an ACK/Commit. If for some reason the message gets processed but fails before the ACK/Commit, it would lead to message being read more than once. This problem has been addressed by Kafka by way of message retention and state management based on the consumer offset.

3. Topic partitioning

Kafka has implemented the topics as partitioned logs. A partition is an ordered, immutable sequence of messages that is continually appended to. This is similar to database log, for that reason the partition is also referred to as the commit-log. This is one of the biggest difference between MQ/JMS and Kafka. The partitioning of the topic leads to its high throughput (and parallelism).

Partitions for the same topic are distributed across multiple brokers in the cluster

Partitions are replicated across multiple servers; number of replicas is a configurable parameter

Each Partition has one server as a leader and a number of servers as followers

Each Server acts a leader for some of its partitions and as a follower for some other

The Producers are responsible for choosing which message to assign to which partition within the topic based on key assigned to message.

5. Message sequencing

In MQ/JMS there is no gurantee that the messages will be received in a sequence in which they were sent. In Kafka though the sequence is maintained at a partition level. In other words if the topic is configured with a single partition then the messages are received in the same order that they were sent in.

6. Message reads

The consumer of the messages in Kafka issues a fetch request to the broker leading the partition it wants to consume. As part of the fetch, consumer specifies the offset from which the message in the log is read from. This is very different from the MQ/JMS messaging system where First In First Out (FIFO) is the way messages are read off the queue/topic. The other thing that happens is that with offset based control, the consumer can re-read the same message which is not possible in MQ/JMS (yes you can do it with browse but that is not what it is intended for).

This rewinding mechanism can be very handy in some situation. E.g., if you received a batch of messages and processed it with buggy code, you may fix the code and re-run the processing on the messages by resetting the offset.

7. Load balancing

In the case of MQ/JMS the load balancing required messaging systems to be designed using some clustering mechanism and the onus of distributing the load across the cluster members was on the producer sending the messages. The Kafka nodes publish the metadata which tells the producer which servers are alive in the cluster, where the leader for the partitions are. This allows the client to send message to the appropriate server (and partition) thus distributing the message load across the cluster members.

8. Automatic failover & High availability

Traditional MQ/JMS implementations did not have the concept of message replication but some systems built it over a period of time; those replication features at most times were not leveraged in favor of simplicity. In Kafka, as decribed earlier the messages are replicated (leader-followers) for each topic's partitions across a configurable number of servers. This inherently leads to an architecture that provides automatic failover to replica thus leading to high availability.

Zookeeper plays a central role in this replication mechanism. The follower servers maintain a session to zookeeper and respond to the heartbeat messages. The slaves/replicas continuously read messages from the leader as fast as they can as to not fall behind. The leader if discovers that the slave is lagging removes it as the replica; this is determined by way of configurable parameters. The message is considered committed when all replicas are in sync with the leader. This sync aspect is also configurable.

The state of the replication is managed by the leader server and leader may drop the replica/slave if the replica is lagging too far behind or is unresponsive.