Apache Kafka is an open-source stream processing platform developed by the Apache Software Foundation written in Scala and Java. The Kafka event streaming platform is used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.
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Red Hat JBoss EAP
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Apache Kafka
Red Hat JBoss Enterprise Application Platform
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Apache Kafka
Red Hat JBoss EAP
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Apache Kafka
Red Hat JBoss Enterprise Application Platform
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Apache Kafka
Red Hat JBoss Enterprise Application Platform
Application Servers
Comparison of Application Servers features of Product A and Product B
For brokering messages, Confluent Kafka is well suited since it offers a managed solution ready to use. Scenarios where the solution is not very well suited are for example, where pricing is an issue. The solution costs quite a lot for basic usage (for example: for 3 clusters, pricing is above 100k$ a year).
Red Hat JBoss Enterprise Application Platform (JBoss EAP) is well suited for deploying high transaction Java EE based applications. It supports many popular Java EE web-based frameworks such as Spring, Angular JS, jQuery Mobile, and Google Web Toolkit.
Apache Kafka is able to handle a large number of I/Os (writes) using 3-4 cheap servers.
It scales very well over large workloads and can handle extreme-scale deployments (eg. Linkedin with 300 billion user events each day).
The same Kafka setup can be used as a messaging bus, storage system or a log aggregator making it easy to maintain as one system feeding multiple applications.
MOD_CLUSTER integration. JBoss EAP integrates pretty well with mod_cluster. This is an intelligent load balancer especially useful in highly clustered environments.
Supports enterprise-grade features such as high availability clustering, distributed caching, messaging etc.
Supports deployment in on-premise, virtual and hybrid cloud environments.
The Kafka Tool is a community-made Java application that looks and feels from the past century.
Logging can be confusing. This certainly shows when we have to do troubleshooting.
Hybrid scenarios - pub/sub, but there are services in and outside a Kubernetes cluster. Then there are a ~3 options, but only 2 (the harder ones) are production-safe.
Jboss CLI is a great tool but we had trouble using it to get values that are displayed on Jboss GUI. It also has limitations parsing the applications.xml files and we had to use a mix of jboss-cli and linux bash commands to automate certain application administrative tasks.
JBoss doesn't really provides performance tuning recommendations. It would have been nice if it could learn from the current demand vs current settings for things like connection pool, server configurations, garbage collection etc.
Kafka has suited our use case very well so far. Going forward we are planning to expand our platform manifold so the load on Kafka and our reliance on Kafka is going to increase only.
Apache Kafka is highly recommended to develop loosely coupled, real-time processing applications. Also, Apache Kafka provides property based configuration. Producer, Consumer and broker contain their own separate property file
Support for Apache Kafka (if willing to pay) is available from Confluent that includes the same time that created Kafka at Linkedin so they know this software in and out. Moreover, Apache Kafka is well known and best practices documents and deployment scenarios are easily available for download. For example, from eBay, Linkedin, Uber, and NYTimes.
Apache Kafka is built for scale. From high throughput and real-time data streaming, it has a strong advantage over RabbitMQ with its low latency. This put Apache Kafka at the forefront as the platform of choice for large datasets messaging and ensuring scalability when data scale up tremendously. RabbitMQ however has its strengths in traditional messaging. Routing and message delivery reliability are the bedrock of RabbitMQ and this is where RabbitMQ excels. In my previous workplace, RabbitMQ was of choice as reliability matters more than scale. In two words. Apache Kafka for scale, RabbitMQ for reliability. And for cloud deployment and large dataset messaging in what I am doing now, Apache Kafka is the default choice.
WebSphere Application Server is propriety and increases project cost. It is slightly complicated to learn when compared to Jboss EAP. These were the two main reasons why we chose Red Hat JBoss EAP over WebSphere Application Server. Also, JBoss EAP is light weight and requires less server resource
Positive: bursts of traffic on special holidays are easy to handle because Kafka can absorb and buffer all the messages we need to process long enough to let an understaffed set of back-end services catch up on processing. Hard to put a number to it but we probably save $5k a month having fewer machines running.
Positive: makes decoupling the web and API services from the deeper back-end services easier by providing topics as an interface. This allowed us to split up our teams and have them develop independently of each other, speeding up software development.
Negative: our engineers have made mistakes such as accidentally dropping a few thousand messages due to the CLI being confusing to use, and as a result a customer lost some of their precious data. I'd say that was more our fault than Kafka's though.
Jboss EAP is easy to deploy and configure. This lead to lower cost and faster delivery.
Even though we have large number of machines running JBoss, we have only two Jboss Administrators. It doesn't requires too much administration and maintenance on daily basis and reduces number of administrators required for large implementations.
