We performed a comparison between Apache Spark and Spring MVC based on real PeerSpot user reviews.Find out in this report how the two Java Frameworks solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
"The solution has been very stable."
"It is useful for handling large amounts of data. It is very useful for scientific purposes."
"There's a lot of functionality."
"One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast."
"Apache Spark can do large volume interactive data analysis."
"Spark can handle small to huge data and is suitable for any size of company."
"One of the key features is that Apache Spark is a distributed computing framework. You can help multiple slaves and distribute the workload between them."
"The most valuable feature of Spring MVC is the configuration, such as WAF."
"When we shifted from our legacy frameworks to the Spring framework, we discovered that Spring definitely made our development easier. One good example is that there is a lot of boiler plate code available that you don't have to write from scratch, making the development of web applications a much simpler process."
"Spring gives you the opportunity to develop architecture in the simplest way possible. It comes with everything you would want in terms of security. If you want to access the database, you have the ability to do that."
"The solution is open-source and free to use."
"We appreciate that this product is really easy to integrate with third-party UI services."
"Its UI can be better. Maintaining the history server is a little cumbersome, and it should be improved. I had issues while looking at the historical tags, which sometimes created problems. You have to separately create a history server and run it. Such things can be made easier. Instead of separately installing the history server, it can be made a part of the whole setup so that whenever you set it up, it becomes available."
"When you are working with large, complex tasks, the garbage collection process is slow and affects performance."
"It's not easy to install."
"We are building our own queries on Spark, and it can be improved in terms of query handling."
"Spark could be improved by adding support for other open-source storage layers than Delta Lake."
"This solution currently cannot support or distribute neural network related models, or deep learning related algorithms. We would like this functionality to be developed."
"The logging for the observability platform could be better."
"The initial setup was not easy."
"We would like the deployment of this solution to be easier as, at present, it is quite complicated."
"The initial setup could be more straightforward."
"It can be difficult for a basic user to understand the concepts in this solution, such as inversion of control."
"The newer versions of Spring MVC have released a lot of features that we are not using right now because, in many cases, we are limited to running older versions. As such, it would be nice if Spring were to improve support for upgrading to newer versions, especially for legacy applications."
"Spring MVC could improve the integration with DevOps and other applications."
Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory
Spring MVC is a Java web framework built on the Servlet API and has been included in the Spring Framework from the very beginning. It handles web applications that use server-rendered HTML user interface, REST APIs, and much more. The documentation includes Spring MVC, View Technologies, CORS Support, and WebSocket Support.
For baseline information and compatibility with Servlet container and Java EE version ranges please visit the Spring Framework Wiki.
Apache Spark is ranked 2nd in Java Frameworks with 12 reviews while Spring MVC is ranked 4th in Java Frameworks with 5 reviews. Apache Spark is rated 8.0, while Spring MVC is rated 8.0. The top reviewer of Apache Spark writes "Reliable, able to expand, and handle large amounts of data well". On the other hand, the top reviewer of Spring MVC writes "Made our web app development easier with lots of boilerplate code and good community support". Apache Spark is most compared with Spring Boot, Azure Stream Analytics, AWS Batch, AWS Lambda and SAP HANA, whereas Spring MVC is most compared with Jakarta EE, Oracle Application Development Framework, Spring Boot, Vert.x and Open Liberty. See our Apache Spark vs. Spring MVC report.
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