Apache ActiveMQ and Kafka are an open-source and multi-protocol messaging server created on Java. These can support multiple messaging protocols such as AMQP, STOMP, and MQTT and they support the Java Message Service API. Messages between applications and services are frequently sent using it. Kafka is a platform for event streaming, whereas ActiveMQ is a messaging broker. These are two well-liked open-source messaging platforms that support data management and monitoring.
In this article, we will be discussing Apache ActiveMQ vs Kafka.
Apache Active MQ
Active MQ is a safe and dependable data sharing between applications is the aim of one of the classic message brokers. It is focused on transactional messaging and clearly defined message formats because it handles little data. There is another edition available than this “classic” one: Active MQ Artemis. This next-generation broker is built on top of HornetQ, which source code RedHat released to the Apache Foundation in 2015.
Use Cases of Active MQ
- Handle a limited quantity of mail each day.
- High degree of transactions and dependability
- Real-time data conversions and ETL tasks
Scalability in Apache Active MQ
- Apache ActiveMQ can process enormous amounts of messages and is very scalable.
- Scalability can be attained with a Replicated Message Store configuration by spreading the message store among several ActiveMQ instances.
- Every communication is kept on several brokers, thus even in the event of a broker failure, it will still be accessible on other brokers.
Handle Messaging in Apache Active MQ
The JMS API message selector is used to manage messages in ActiveMQ. Through the usage of a push-type platform, service providers can send customers messages.
ActiveMQ has the ability to filter messages, unlike Kafka, so users only see the messages they are interested in. Message acknowledgment is another feature that ActiveMQ offers to ensure that messages are received. It is important to understand that messages may not always arrive in the same sequence that they were delivered using ActiveMQ. Messages can be repeated in case of an error and will always be received.
Kafka
Kafka is a distributed system designed to handle massive volumes of data. It can be utilized for conventional communications in addition to metrics for tracking website activity and log aggregation, commit logs, stream processing, and event sourcing. Since typical cloud architectures built with microservices have emerged, these needs have become increasingly important.
Use Cases of Kafka
- Handle a large volume of data.
- Delivery of messages without data transformation is feasible, but difficult.
- Delivery of messages without assurances about transportation (it would be possible, but not easy)
Scalability in Kafka
- There are no hard constraints to the scalability or flexibility of Kafka’s topic support.
- A lot of factors, including as hardware resources, cluster configuration, and usage habits, affect how many topics Kafka can manage efficiently.
- It is compatible with horizontal scaling, which is the practice of expanding a Kafka cluster’s processing capacity by adding more nodes.
Handle Messaging in Kafka
Kafka can store messages or events indefinitely for use in a variety of applications in addition to transferring them. Although permanent storage is an option, the retention period for a particular topic can be customized to the exact millisecond, if necessary, based on the use case. Setting the retention period to as short a duration as the use case permits is the accepted best practice for preventing needless data retention. Whether a partition key is found, and the partitioner method chosen will determine this.
Difference between Apache ActiveMQ and Kafka
|
Characteristics |
Apache ActiveMQ |
Kafka |
|---|---|---|
|
License |
Apache ActiveMQ has Apache License 2.0 |
Kafka has Apache License 2.0 |
|
Scalability |
Scalable but not built or optimized to manage situations at extreme scales, such as Kafka. |
Incredibly scalable. When dispersed across hundreds of brokers, Kafka can manage petabytes of data and trillions of messages daily. |
|
Message consumption |
ActiveMQ from Apache has push and pull. |
Kafka has pull, or extended polling. |
|
Performance |
Low latency and good throughput (with medium workloads). ActiveMQ Artemis is intended to provide enhanced performance in contrast to ActiveMQ Classic. |
Incredibly low latencies (in the millisecond range) combined with high throughput workloads (millions of messages per second). |
|
Persistence |
To persist data, ActiveMQ Classic employs a database that complies with JDBC standards or KahaDB (file-based storage). |
The disc is where messages are kept it has infinite capacity to store info. |