Apache Kafka vs. Apache Pulsar vs. BlazeMeter

Apache Kafka is well-suited for most data-streaming use cases. Amazon Kinesis and Azure EventHubs, unless you have a specific use case where using those cloud PaAS for your data lakes, once set up well, Apache Kafka will take care of everything else in the background. Azure EventHubs, is good for cross-cloud use cases, and Amazon Kinesis - I have no real-world experience. But I believe it is the same.

It is well suited for applications that are mission-critical or applications that can receive high traffic/transactions at unscheduled time periods. Using the load testing feature of BlazeMeter, we can test and ascertain the capacity of the application without the drawbacks of the usual Apache JMeter load testing which depends heavily on the host system from where the load testing is performed.

Incentivized

• Really easy to configure. I've used other message brokers such as RabbitMQ and compared to them, Kafka's configurations are very easy to understand and tweak.
• Very scalable: easily configured to run on multiple nodes allowing for ease of parallelism (assuming your queues/topics don't have to be consumed in the exact same order the messages were delivered)
• Not exactly a feature, but I trust Kafka will be around for at least another decade because active development has continued to be strong and there's a lot of financial backing from Confluent and LinkedIn, and probably many other companies who are using it (which, anecdotally, is many).

Incentivized

• BlazeMeter takes the .jmx script that teams had written for JMeter. The transition from JMeter to BlazeMeter was seamless.
• BlazeMeter offers its own cloud and hence we did not have to set it up on-premise.
• BlazeMeter hits our application from various geographic locations that simulates real life users.

Incentivized

• Sometimes it becomes difficult to monitor our Kafka deployments. We've been able to overcome it largely using AWS MSK, a managed service for Apache Kafka, but a separate monitoring dashboard would have been great.
• Simplify the process for local deployment of Kafka and provide a user interface to get visibility into the different topics and the messages being processed.
• Learning curve around creation of broker and topics could be simplified

• Blazemeter reporting is very basic and shallow. There is no way to drill down or correlate. I can get better reports by using JMeter for free.
• Blazemeter is very costly. Testing with volumes of more than 1K cu is expensive, and can be done for much cheaper if a company/team is willing to invest a bit of time to figure out how to use cloud instances and jmeter slaves, and to write a basic script to collect resulting xml output.

Incentivized

Kafka is quickly becoming core product of the organization, indeed it is replacing older messaging systems. No better alternatives found yet

Incentivized

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

Incentivized

Easy to setup to do API Performance Testing. Dashboard to showcase results Capability to showcase runtime results

Incentivized

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.

Incentivized

I used other messaging/queue solutions that are a lot more basic than Confluent Kafka, as well as another solution that is no longer in the market called Xively, which was bought and "buried" by Google. In comparison, these solutions offer way fewer functionalities and respond to other needs.

Incentivized

In comparison with Blazemeter the closes competitor is JMeter but it has disadvantages like it is not a tool that can be use as a collaborative tool and works locally in a computer, Blazemeter is in the web so different people can access and run tests or collaborate do add, edit or delete the existent scripts.

Incentivized

• Positive: Get a quick and reliable pub/sub model implemented - data across components flows easily.
• Positive: it's scalable so we can develop small and scale for real-world scenarios
• Negative: it's easy to get into a confusing situation if you are not experienced yet or something strange has happened (rare, but it does). Troubleshooting such situations can take time and effort.

Incentivized

• It helped positively by helping to identify the maximum capacity needed for high traffic periods
• Saved revenue by eliminating unwanted duplication of systems

Incentivized