Hibernate vs Kafka: What are the differences?
Hibernate: Idiomatic persistence for Java and relational databases. Hibernate is a suite of open source projects around domain models. The flagship project is Hibernate ORM, the Object Relational Mapper; Kafka: Distributed, fault tolerant, high throughput pub-sub messaging system. Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
Hibernate and Kafka are primarily classified as "Object Relational Mapper (ORM)" and "Message Queue" tools respectively.
"Easy ORM" is the primary reason why developers consider Hibernate over the competitors, whereas "High-throughput" was stated as the key factor in picking Kafka.
Kafka is an open source tool with 12.5K GitHub stars and 6.7K GitHub forks. Here's a link to Kafka's open source repository on GitHub.
Slack, Shopify, and SendGrid are some of the popular companies that use Kafka, whereas Hibernate is used by Bodybuilding.com, StyleShare Inc., and Peewah. Kafka has a broader approval, being mentioned in 501 company stacks & 451 developers stacks; compared to Hibernate, which is listed in 85 company stacks and 72 developer stacks.
What is Hibernate?
What is Kafka?
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Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :
Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:
(Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )
I use Kafka because it has almost infinite scaleability in terms of processing events (could be scaled to process hundreds of thousands of events), great monitoring (all sorts of metrics are exposed via JMX).
Downsides of using Kafka are: - you have to deal with Zookeeper - you have to implement advanced routing yourself (compared to RabbitMQ it has no advanced routing)
The question for which Message Queue to use mentioned "availability, distributed, scalability, and monitoring". I don't think that this excludes many options already. I does not sound like you would take advantage of Kafka's strengths (replayability, based on an even sourcing architecture). You could pick one of the AMQP options.
I would recommend the RabbitMQ message broker, which not only implements the AMQP standard 0.9.1 (it can support 1.x or other protocols as well) but has also several very useful extensions built in. It ticks the boxes you mentioned and on top you will get a very flexible system, that allows you to build the architecture, pick the options and trade-offs that suite your case best.
For more information about RabbitMQ, please have a look at the linked markdown I assembled. The second half explains many configuration options. It also contains links to managed hosting and to libraries (though it is missing Python's - which should be Puka, I assume).
I used Kafka originally because it was mandated as part of the top-level IT requirements at a Fortune 500 client. What I found was that it was orders of magnitude more complex ...and powerful than my daily Beanstalkd , and far more flexible, resilient, and manageable than RabbitMQ.
So for any case where utmost flexibility and resilience are part of the deal, I would use Kafka again. But due to the complexities involved, for any time where this level of scalability is not required, I would probably just use Beanstalkd for its simplicity.
I tend to find RabbitMQ to be in an uncomfortable middle place between these two extremities.
Front-end messages are logged to Kafka by our API and application servers. We have batch processing (on the middle-left) and real-time processing (on the middle-right) pipelines to process the experiment data. For batch processing, after daily raw log get to s3, we start our nightly experiment workflow to figure out experiment users groups and experiment metrics. We use our in-house workflow management system Pinball to manage the dependencies of all these MapReduce jobs.
Mybatis 로 쿼리를 만들고 조건분 분기식 for 문을 쿼리에 달아 더이상 쿼리를 알아 볼 수 없게 되었을때 이게 의마가 있나 싶었다. 그 때 한번 orm 을 써보면 어떨까 싶어 최근에 배우기 시작한 orm 이다. 정말 편하게 개발을 할 수 있는데 일조하고 있다. 다만 결국에 쿼리를 날려 맵핑을 하는데, 쿼리를 잘 모르거나 그에 대한 지식 없이 쓰다가는 망하겠구나 하는 생각이 많이 들었다.
We use a Clojure-powered wrapper around Hibernate to provide an ORM access to our data store for applications, as well as offering SSO integration and HIPAA logging functionality.
Building out real-time streaming server to present data insights to Coolfront Mobile customers and internal sales and marketing teams.
Can't escape from when you're on the Java stack and deal with relational db.
Strut や Spring など Java web app flame work での Object Relation Mapperとして