As the lead of the Spring Batch project, I’m sure you’ll understand I have a specific perspective. However, before beginning, I should call out that the frameworks we are talking about were designed for two very different use cases. Spring Batch was designed to handle traditional, enterprise batch processing on the JVM. It was designed to apply well understood patterns that are common place in enterprise batch processing and make them convenient in a framework for the JVM. Spark, on the other hand, was designed for big data and machine learning use cases. Those use cases have different patterns, challenges, and goals than a traditional enterprise batch system, and that is reflected in the design of the framework. That being said, here are my answers to your specific questions.
Has anybody compared the performances between Apache Spark and Java Spring Batch?
No one can really answer this question for you. Performance benchmarks are a very specific thing. Use cases matter. Hardware matters. I encourage you to do your own benchmarks and performance profiling to determine what works best for your use cases in your deployment topologies.
What could be the advantages of using Spring Batch over Spark?
Programming model similar to other enterprise workloads
Enterprises need to be aware of the resources they have on hand when making architectural decisions. Is using new technology X worth the retraining or hiring overhead of technology Y? In the case of Spark vs Spring Batch, the ramp up for an existing Spring developer on Spring Batch is very minimal. I can take any developer that is comfortable with Spring and make them fully productive with Spring Batch very quickly. Spark has a steeper learning curve for the average enterprise developer, not only because of the overhead of learning the Spark framework but all the related technologies to prodictionalize a Spark job in that ecosystem (HDFS, Oozie, etc).
No dedicated infrastructure required
When running in a distributed environment, you need to configure a cluster using YARN, Mesos, or Spark’s own clustering installation (there is an experimental Kubernetes option available at the time of this writing, but, as noted, it is labeled as experimental). This requires dedicated infrastructure for specific use cases. Spring Batch can be deployed on any infrastructure. You can execute it via Spring Boot with executable JAR files, you can deploy it into servlet containers or application servers, and you can run Spring Batch jobs via YARN or any cloud provider. Moreover, if you use Spring Boot’s executable JAR concept, there is nothing to setup in advance, even if running a distributed application on the same cloud-based infrastructure you run your other workloads on.
More out of the box readers/writers simplify job creation
The Spark ecosystem is focused around big data use cases. Because of that, the components it provides out of the box for reading and writing are focused on those use cases. Things like different serialization options for reading files commonly used in big data use cases are handled natively. However, processing things like chunks of records within a transaction are not.
Spring Batch, on the other hand, provides a complete suite of components for declarative input and output. Reading and writing flat files, XML files, from databases, from NoSQL stores, from messaging queues, writing emails...the list goes on. Spring Batch provices all of those out of the box.
Spark was built for big data...not all use cases are big data use cases
In short, Spark’s features are specific for the domain it was built for: big data and machine learning. Things like transaction management (or transactions at all) do not exist in Spark. The idea of rolling back when an error occurs doesn’t exist (to my knowledge) without custom code. More robust error handling use cases like skip/retry are not provided at the level of the framework. State management for things like restarting is much heavier in Spark than Spring Batch (persisting the entire RDD vs storing trivial state for specific components). All of these features are native features of Spring Batch.
Is Spring Batch “truly distributed”
One of the advantages of Spring Batch is the ability to evolve a batch process from a simple sequentially executed, single JVM process to a fully distributed, clustered solution with minimal changes. Spring Batch supports two main distributed modes:
- Remote Partitioning - Here Spring Batch runs in a master/worker configuration. The masters delegate work to workers based on the mechanism of orchestration (many options here). Full restartability, error handling, etc. is all available for this approach with minimal network overhead (transmission of metadata describing each partition only) to the remote JVMs. Spring Cloud Task also provides extensions to Spring Batch that allow for cloud native mechanisms to dynamically deploying the workers.
- Remote Chunking - Remote chunking delegates only the processing and writing phases of a step to a remote JVM. Still using a master/worker configuration, the master is responsible for providing the data to the workers for processing and writing. In this topology, the data travels over the wire, causing a heavier network load. It is typically used only when the processing advantages can surpass the overhead of the added network traffic.
There are other Stackoverflow answers that discuss these features in further detail (as does as the documentation):
Advantages of spring batch
Difference between spring batch remote chunking and remote partitioning
Spring Batch Documentation
