You’ve decided to use microservices; now is a good time to think about which messaging system you’ll use to connect your services. Apache Kafka and KubeMQ are the two options available to you.
About Apache Kafka
LinkedIn engineers originally designed Kafka as a software bus for monitoring LinkedIn user behavior. It was eventually released as an open-source offering, and the Apache Software Foundation now develops and manages Kafka.
Despite being open-source, it is well-known for being a highly scalable framework capable of connecting to a diverse set of event consumers and producers. It can be set up to handle complex operations with data streams and operate well even in low-bandwidth network environments.
KubeMQ is a messaging service designed from the ground up for Kubernetes. KubeMQ is designed to be stateless and fleeting, as per container design best practices. That is, for the duration of its lifecycle, a KubeMQ node will stay unchanged, foreseeable, and reproducible. Nodes are taken down and replaced if setup changes are required.
This reproducibility implies that, unlike Kafka, KubeMQ emerges with a zero-configuration layout that requires no configuration tinkering after installation. KubeMQ was created to support a wide variety of message patterns.
Kafka vs. KubeMQ: Which Performs Better?
When using container-based microservices, Kubernetes is usually used as the orchestration platform, so your messaging system choice should take into account its capacity to reside within Kubernetes and function effectively as a docker container. In the past, people would choose Kafka for this purpose. But now they have a more modern alternative to it: KubeMQ. Here, we discuss some of the things that set KubeMQ apart from Kafka.
Because Kafka was not designed with Kubernetes implementation in mind, deploying a production-ready cluster requires considerable effort and is often difficult.
On the other hand, KubeMQ is supplied by default in a preset Kubernetes cluster, making it extremely simple and quick to implement a fully functional cluster to production.
It takes approximately five minutes to implement. KubeMQ provides a wizard that produces an implementation-ready HELM or YAML chart to standardize and speed up implementation in Kubernetes. Docker visuals are also accessible for those who want to test KubeMQ before implementing a cluster.
Upgrading from a development environment to a production environment is a big challenge for businesses. The KubeMQ upgrade from development to production is seamless, requiring no additional setup or code changes. This saves money and reduces bugs.
- Size of Cluster
Each container is 30MB in size, making it ideal for use in a microservice layout. A cluster of three containers is 90 MB in size. Also, you are not required to install extra components like Zookeeper in Kafka.
When working with Kafka, to create a cluster, both Zookeeper and Kafka must be installed. Every Kafka container measures about 600 MB, which is twenty times greater than the KubeMQ container, while every Zookeeper container is about 100 MB in size, for a sum of 2.1GB (3 ZooKeeper containers+3 Kafka containers), which is significantly greater than KubeMQ’s90 MB.
KubeMQ was written in GO to make the best use of computer resources and to work seamlessly with Kubernetes. KubeMQ and Kafka were compared using a cluster with a 1k message size and by delivering one million messages using the same hardware specifications. In terms of speed, KubeMQ outperformed Kafka by 20%.
- Ease of Use
To support development with Kafka, the technical team requires a mixture of Java and Scala expertise, as well as substantial Java technology in-house. Kafka necessitates the administrator to define any service that wishes to join a channel in advance.
Furthermore, creating a new channel necessitates additional admin settings, which are not available on the fly. To set up Kafka, various layers of configurations and complexity are needed, and to use it efficiently requires consultants and substantial time from development teams. ZooKeeper is necessary and needs extra effort, setup, and knowledge to maintain.
KubeMQ was created with ease of use in mind, to increase the speed and efficiency of development teams. It is extremely simple for developers to get started with KubeMQ.
KubeMQ, which can be used by developers as well as administrators, does not necessitate the creation of any objects other than the channel. There is no need for clustering, exchanges, brokerages, or any other extra settings because KubeMQ handles everything automatically. Raft is used by KubeMQ, which eliminates the need to retain a ZooKeeper cluster.
- Patterns of Messaging
With perseverance and broadcasting, Kafka supports Pub / Sub. However, Request and RPC response patterns are not assisted.
On the other hand, KubeMQ is a message queue and a message broker designed for a wide variety of use cases. It facilitates Pub / Sub with or without perseverance, Request / Reply (sync, async), minimum one delivery, broadcasting patterns, and RPC.
- Cloud-Native Integrations
Kafka is not a native component of the CNCF landscape, and connections with CNCF instruments are accessible on a case-by-case basis and must be configured individually. Some tools have “shortcuts” (created by the community) for establishing integration, but each one necessitates developers to work with different manuals that include numerous steps to perform the integration procedure and lack support.
On the other hand, KubeMQ is a cloud-native message queue and broker that is merged with CNCF-leading initiatives. Open Tracing and Jaeger for tracing integration, Elastic and Fluentd for logging, and Grafana and Prometheus for monitoring are some of the apps merged with KubeMQ.
Zipkin, DataDog, Stackdriver, Loggly, Honeycomb, and AWS are among the other integrations available. Furthermore, using the KubeMQ plugin system, businesses can connect additional tools.
Final Word for KubeMQ: A Modern Alternative to Kafka
Overall, if we compare Kafka and KubeMQ, the latter is extremely simple to deploy while deployment with Kafka often necessitates taking the help of an expert. In terms of cluster size, Kafka is heavy and demanding whereas KubeMQ is lightweight and has a small cluster footprint.
When it comes to ease of use, Kafka is complex and necessitates the involvement of dedicated personnel. On the other hand, KubeMQ is developer-oriented and can be navigated effortlessly. KubeMQ also has the edge over Kafka in terms of messaging patterns as it supports all patterns, unlike the latter which only supports Async patterns.
Finally, Kafka requires extra adjustments and configurations for cloud-native integrations while KubeMQ offers CNFC-oriented and off-the-shelf integrations. All of this makes KubeMQ Alternative to Kafka the clear winner!
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