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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Google Cloud Pub/Sub
Score 9.0 out of 10
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Google offers Cloud Pub/Sub, a managed message oriented middleware supporting many-to-many asynchronous messaging between applications.
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).
Using Google Cloud Pub/Sub will mainly depend on the cloud platform used. Our client didn't choose GCP for Google Cloud Pub/Sub, if we went with AWS we would be using SNS/SQS (obviously). However, Google Cloud Pub/Sub is a better solution in the GCP services compared to self-managed solutions such as RabbitMQ for instance (it is managed by GCP and integrates with GCP solutions).
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.
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.
It is limited to work with the same platform but with different datasets at the same time, you must request a prior security authentication.
It can sometimes lead to unexpected charges, as Pub/Sub will automatically keep on retrying messages continuously, even if failures are due to permanent code-level issues.
Message re-deliveries don't apply for ingested services like with Python based client. Push messages tried to be delivered immediately and if your service is busy dealing with some other task, it won't be done OR goes into a queue
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.
It serves all of our purposes in the most transparent way I can imagine, after seeing other message queueing providers, I can only attest to its quality.
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
It has many libraries in many languages, google provides either good guides or they're AI generated code libraries that are easy to understand. It has very good observability too.
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.
They have decent documentation, but you need to pay for support. We weren't able to answer all our questions with the documentation and didn't have time to setup support before we needed it so I can't give it a higher rating but I think it tends to be a bit slow unless you're a GCP enterprise support customer.
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.
You can just plug in consumers at will and it will respond, there's no need for further configuration or introducing new concepts. You have a queue, if it's slow, you plug in more consumers to process more messages: simple as that.
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.
