I am reading Spring user guide. I came across below statement. I confused by statement "let the framework take care of infrastructure". I mean infrastructure means any Hardware..Nw in Spring Batch is framework, where does infrastructure came in picture
Batch developers use the Spring programming model: concentrate on business logic; let the
framework take care of infrastructure
Please help me in understanding/
If you will read the complete documentation, you will get:
Figure: Spring Batch Layered Architecture
This layered architecture highlights three major high level
components: Application, Core, and Infrastructure. The application
contains all batch jobs and custom code written by developers using
Spring Batch. The Batch Core contains the core runtime classes
necessary to launch and control a batch job. It includes things such
as a JobLauncher, Job, and Step implementations. Both Application and
Core are built on top of a common infrastructure. This infrastructure
contains common readers and writers, and services such as the
RetryTemplate, which are used both by application
developers(ItemReader and ItemWriter) and the core framework itself.
(retry)
spring-batch reference
The Spring Batch framework is designed to cater to batch applications that run on a daily basis in enterprise organizations. It helps to leverage the benefits of the Spring framework along with the advance services. Spring Batch is mainly used to process huge volume of data. It offers better performance and is highly scalable using different optimization and partition techniques. It also provides advantage over logging/tracing, transaction management, job processing statistics, job restart, steps, and resource management. By using the Spring programming model, I can write the business logic and let the framework take care of infrastructure.
Spring Batch includes three components: batch application, batch execution environment and batch infrastructure.
The Application component contains all the batch jobs and custom code written using Spring Batch.
The Core component contains the core runtime classes necessary to launch and control a batch job. It includes things such as a JobLauncher, Job, and Step implementations. Both Application and Core are built on top of a common infrastructure.
The Infrastructure contains readers, writers and services which are used both by application and the core framework itself. They include things like ItemReader, ItemWriter and MongoTemplate. To use the Spring Batch framework, you need only to configure and customize the XML files. All existing core services should be easy to replace or extend, without any impact to the infrastructure layer.
-from Devx
I hope this would help you understand how it works.
Related
I have been working with Apache Spark + Scala for over 5 years now (Academic and Professional experiences). I always found Spark/Scala to be one of the robust combos for building any kind of Batch or Streaming ETL/ ELT applications.
But lately, my client decided to use Java Spring Batch for 2 of our major pipelines :
Read from MongoDB --> Business Logic --> Write to JSON File (~ 2GB | 600k Rows)
Read from Cassandra --> Business Logic --> Write JSON File (~ 4GB | 2M Rows)
I was pretty baffled by this enterprise-level decision. I agree there are greater minds than mine in the industry but I was unable to comprehend the need of making this move.
My Questions here are:
Has anybody compared the performances between Apache Spark and Java Spring Batch?
What could be the advantages of using Spring Batch over Spark?
Is Spring Batch "truly distributed" when compared to Apache Spark? I came across methods like chunk(), partition etc in offcial docs but I was not convinced of its true distributedness. After all Spring Batch is running on a single JVM instance. Isn't it ???
I'm unable to wrap my head around these. So, I want to use this platform for an open discussion between Spring Batch and Apache Spark.
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
I'm dipping my toes into the microservices, is spring boot batch applicable to the following requirements?
Files of one or multiple are read from a specific directory in Linux.
Several operations like regex, build new files, write the file and ftp to a location
Send email during a process fail
Using spring boot is confirmed, now the question is
Should I use spring batch or just core spring framework?
I need to integrate with Control-M to trigger the job. Can the Control-M be completely removed by using Spring batch library? As we don't know when to expect the files in the directory.
I've not seen a POC with these requirements. Would someone provide an example POC or an affirmation this could be achieved with Spring batch?
I would use Spring Batch for that use case. Not only does it provide out of the box components for reading, processing, and writing files, it adds a lot more for error handling, scalability, etc. All of those things you'd probably end up wiring up by yourself if you go without Spring Batch.
As for being launched via Control-M, yes MANY large customers use Control-M to launch their jobs. Unfortunately, I've never done it myself so I cannot provide any details on the mechanics, but if Control-M can either launch a script or call a REST API, you can launch a job with it.
I would suggest you, go for spring batch as it has much-inbuilt functionality which will be provided to you for file reading and writing to your required location. Even you will be able to handle record skipping requirement. Your mail triggering requirement will be handled by Control M. You just need to decide one exit code for your handled exception and on the basis of that exit code you can trigger the mail to respective members. And there are many other features which will be helpful if you go for spring batch.
I'm willing to build a synchronization service in Java. The use case is, that i'm fetching data from an exchange-service (via Exchange Web Services), normalize the data a bit (process probably) and then write it to a backend via GraphQL. I already had a look around the spring modules, but am not quite sure what modules to use. I found spring batch and spring quartz.
The synchronization will have to trigger all X seconds, fetch information from the Exchange, look what's in the backend already and update what's needed.
Do you guys have any suggestions? I started implementing this whole thing in nodejs before, but as it has to run on both, Windows Servers and Docker/Linux, it has been a real pain to keep it running smooth (mostly because bundling nodejs to an application for Windows is pain).
Difference between Spring Batch & Quartz:
Spring Batch and Quartz have different goals. Spring Batch provides functionality for processing large volumes of data and Quartz provides functionality for scheduling tasks.
So Quartz could complement Spring Batch, A common combination would be to use Quartz as a trigger for a Spring Batch job using a Cron expression.
Conclusion : So basically Spring Batch defines what should be done, Quartz defines when it should be done.
Quartz is a scheduling framework. Like "execute something every hour or every last friday of the month"
Spring Batch is a framework that defines that "something" that will be executed.
You can define a job, that consists of steps. Usually a step is something that consists of item reader, optional item processor and item writer, but you can define a custom stem. You can also tell Spring batch to commit on every 10 items and a lot of other stuff.
You can use Quartz to start Spring Batch jobs.
Recommended for your use case :
Quartz scheduling as you want trigger after specific interval.
Reference :https://projects.spring.io/spring-batch/faq.html
I have got a use case to implement. It's basically a workflow kind of use case. Below is the requirements
Extract and import data from an external db to an internal db
Make this imported data into different formats and supply it to multiple external systems and invoke some script there. The external interfaces are SFTP, SOAP, JDBC, Python over CORBA. There are around 14 external systems with one of these interfaces.
Interface transactions are executed in around 15 steps, with the ability to run some steps in parallel
These steps should be configurable. ie, a particular flow may execute 10 of these 15 steps and another flow executes 15 of 15 steps
Should have the ability to restart each step individually or restart from a particular step
There are some steps that are manual and completion of manual step should trigger next step
Volume of data is not that large. Total data size is around 400k records. But this process is executing for around 30k records at a time. Time for development is less and we are looking for some light weight easy to learn and implement solution.
We are looking for Spring based or Spring integratable solutions.
The solutions we considered are
For workflow:
Activiti, Spring Batch
For interfaces:
Spring Integration
My question is
Can Spring batch considered for managing a work flow kind of use case? I don't think it's a best fit use case for Spring Batch but as its simple and easy to implement looked for its scope. We considered doing the interfaces interaction as each step in a batch job and inside the tasklet do the Spring Integration for external interfaces, with few issues as far as I understand are
a) Dynamic step configuration can be done with Java configuration, but how flexible it is and is it recommended?
b) Manual step processing is not possible in Spring Batch
Is there any work around for this? Is there any other issues or performance impacts on doing this?
Activiti seems to a solution. Can you please provide some feedback on Activiti with Spring and Spring integration for this use case and ease of implementing it? And support for Activiti
Can Activiti workflows restarted from a particular task? Is a task can be rollbacked?
Welcoming any suggestions !!
1) For managing workflows, Activiti would be a great choice. They have created a really good process engine which should comply your needs for delegating your tasks as well as calling your custom logic. Moreover, it is based entirely on Spring Framework so Integration with your logic would be easy.
2) i've provided the same in first answer.
3) No, you will have to create a new workflow for that and Yes!, a task can be rolled back.
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I understood that spring batch framework processes data in chunks. However, I was thinking that when the same chunking functionality can be acheived through java why do we need to go for batch framework.
Could any one please let me know if there are more reasons for going to spring batch framework?
Let me rephrase your question a bit and see if this addresses it.
What does Spring Batch provide that I'd have to handle myself when building a batch application?
Spring Batch served as the basis for JSR-352 (the java batch specification) and since that specification has come out, there is a lot of Spring Batch now available within the java space. That being said, there is still a lot that Spring Batch provides outside of the scope of what basic Java does:
Within a "basic" batch job
Within the scope of a simple batch job, Spring Batch provides a collection of utilities and implementations that have been battle tested in all enterprise verticals. Some examples are:
Over 17 ItemReader and 15 ItemWriter implementations covering vast options for input and output (File, JDBC, NoSQL, JMS, etc). All of these provide declarative I/O options so that you don't have to write and test code for stateful readers and writers.
A collection of Tasklet (Spring Batch's equivalent to JSR-352's Batchlet) implementations including ones for executing shell commands and interfacing with Hadoop.
The ability to stop/start/restart jobs and maintain state between executions.
The ability to skip and retry records as they are being processed.
Transaction management. Spring Batch handles transactions for you.
The ability to notify other systems when errors occur via messaging by integrating Spring Integration.
Java or XML based configuration.
All the Spring features like DI, AOP, testability, etc.
Vendor independence - By using Spring Batch, you get to use a framework that open source and not tied to any one vendor.
Additional advantages
Beyond the above examples of what Spring Batch brings to the table, it goes much further:
Scalability options - Spring Batch provides a number of scalability options that range from within a single JVM via threads (multithreaded step, local partitioning, and splits) to multi-JVM scalability (remote partitioning and remote chunking).
Integration with Spring Integration - Spring Integration provides a number of useful elements that allow you to build robust batch applications to handle things like error messages, poling directories for files, automatically FTPing files, etc.
Big data support - Through the Spring for Apache Hadoop project, there are a number of extensions to Spring Batch that allow it to work well with Hadoop. You can run Spring Batch jobs on YARN, you can execute Pig, Hive, MapReduce, etc jobs.
Integration with Spring XD - Spring XD provides a distributed runtime for the deployment, management, and execution of batch jobs.
I personally view batch processing as the "set it and forget it" model of programming. While it isn't sexy, batch processing is a very useful model of processing and is more useful in places than most people realize. Spring Batch provides an environment that makes developing robust batch jobs as easily as possible.