Kafka vs PubNub: What are the differences?
Developers describe Kafka as "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. On the other hand, PubNub is detailed as "Build real-time apps quickly and scale them globally". PubNub makes it easy for you to add real-time capabilities to your apps, without worrying about the infrastructure. Build apps that allow your users to engage in real-time across mobile, browser, desktop and server.
Kafka and PubNub are primarily classified as "Message Queue" and "Realtime Backend / API" tools respectively.
Some of the features offered by Kafka are:
- Written at LinkedIn in Scala
- Used by LinkedIn to offload processing of all page and other views
- Defaults to using persistence, uses OS disk cache for hot data (has higher throughput then any of the above having persistence enabled)
On the other hand, PubNub provides the following key features:
- Data Push - Establish and maintain persistent socket connections to any device (mobile, browser, desktop and server) and push data to global audiences in less than ¼ of a second
- Presence - Automatically detect when users enter or leave your app and whether machines are online
"High-throughput" is the top reason why over 95 developers like Kafka, while over 31 developers mention "Massively scalable & easy to use" as the leading cause for choosing PubNub.
Kafka is an open source tool with 12.7K GitHub stars and 6.81K GitHub forks. Here's a link to Kafka's open source repository on GitHub.
Uber Technologies, Spotify, and Slack are some of the popular companies that use Kafka, whereas PubNub is used by OpenGov, Hinge, and Blossom. Kafka has a broader approval, being mentioned in 509 company stacks & 470 developers stacks; compared to PubNub, which is listed in 43 company stacks and 12 developer stacks.
What is Kafka?
What is PubNub?
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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.
I recently used PubNub to implement a realtime leaderboard into my Unity game. It was easy and straight forward to get up and running. I choose Pubnub because they had a Unity SDK and an example project which showed me how to use it. I downloaded the example project and tweaked it to fit my game.
In their documentation I found good information about what I needed to know. Also, their support is very helpful when you have questions.
I used PubNub because we needed a real time server for our product. Even though we selected a fixed amount paid version, the amount they charged was incredibly high, 8x of the fixed amount. We could not understand how did they do the calculation and there is no easy way of understanding it. Regarding to the analytics they have, we spent millions of messages with few hundred active users.
Then for the second month we tried to change our architecture to decrease the messages but nothing changed. Then we wanted to get some support from them. We arranged a meeting and the person who joined the meeting didn't even have permissions to see our usage, so it was waste of time. Then they arranged another meeting WEEKS after which didn't solve anything either. As a result, we stopped using it and implemented our own simple pub-sub server.
I was building up an Alexa Skill in which user orders a meal and its notification gets to the system of the restaurant and gets displayed which was implemented using PubNub which helped me a lot in getting real time updates and notifications. I used simple publish and subscribe module to implement it which resolved a major challenge. The 5 to 6 lines of python code get my every single chain working with notifications. Thanking PubNub for this major support for solving the challenge.
I use PubNub because is simple and easy I use on Chat app this service has stable, cheap and userfriendly. The admin interface is very easy to understand and you will find a lot of tutorials and learning at their website. Pubnub comes with my highest recommendation for anyone working with apps with chat, push notifications and other realtime data.
PubNub is my goto solution for when I want to use sockets at scale. They have redundant data centers in 14 regions with 99.999% SLA allowing me to build realtime apps for any platform. Also Stephen (PubNub CTO) is Awesome!
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.
Using websockets is something, scaling a message pipeline to thousands of concurrent connections is something else.
I trust PubNub with what it does best, and use it as zerotoherojs.com ’s messaging pipeline.
With PubNub, I have one less problem to think about.
Building out real-time streaming server to present data insights to Coolfront Mobile customers and internal sales and marketing teams.
Flutter uses PubNub for sending push notifications when the users add new posts to the Flutter Community or leave comments.