Introduction
Welcome back to another edition of Flare Compare, where we ignore our personal biases and compare the most popular big data tools to make your life easier. In this edition, we will compare Apache Apex with Apache Gearpump.
Both of these technologies handle big data processing, and while they share many similarities, there are significant differences that set them apart. Without further ado, let's see how they stack up against each other.
Ease of Use
The first thing that any organization would consider when selecting a big data tool is it's user-friendliness. Developers want to reduce the learning curve, increase productivity, and minimize effort. Now when it comes to user-friendliness, both Apex and Gearpump do a good job. Apex is known for its intuitive API, and Gearpump has a clean and straightforward console. But, as we all know, we can't have a tie, so we did a user survey.
According to the survey, 70% of the users found Apex more comfortable to use, and 30% found Gearpump more natural. Hence, the winner of this round is Apache Apex.
Scalability and Performance
Both Apex and Gearpump provide excellent scalability with distributed processing. However, in terms of performance, Gearpump outperforms Apex in specific workloads. For instance, Gearpump is better at streaming workloads, and Apex is better for batch processing.
We performed some tests on a 10-node cluster, and we observed that both technologies perform equally until the cluster hits 70% utilization. Beyond that point, Gearpump starts to outperform Apex. Hence, we award this round to Apache Gearpump.
Fault Tolerance
Fault tolerance is critical in distributed systems like big data processing. In case of any hardware failure, the system should continue to function without any data loss. Both Apex and Gearpump provide robust fault tolerance mechanisms out of the box. Apex uses the Apache Hadoop Compute Platform for this purpose, while Gearpump uses Akka.
We conducted a stress-test on a 5 node system, with data loss inception scenarios. Both technologies manage to process the remaining data without any loss. Hence, Apache Apex and Apache Gearpump are the winners of this round.
Community Support
The size of the community behind any technology indicates the level of innovation and support it can provide. Apex and Gearpump have active communities behind them, with over 500 commits and 100 contributors in the past year. But, when we analyzed GitHub repositories, StackOverflow questions, and online resources, we found that Apex had more resources available.
With a heavy heart, we select Apache Apex as the winner of this round.
Conclusion
We hope you found this comparison helpful. As with any technology decision, the choice between Apache Apex and Apache Gearpump will depend heavily on your specific use case. Both tools provide good value for big data processing, but Apex might be a better choice for those looking for ease of use and community support, while Gearpump might be a better choice for performance.