I'm trying to utilize Spring Batch in one of the projects that I have, as there is another project that is based on Spring Batch.
However the more I read the more I realize that Spring batch is nothing like ApacheBeam or MapReduce, it is only used for transferring the SAME data from one place to another with some type mapping like varchar -> string.
However, the task in hand requires some processing, not only types mapping and converting but also aggregations and data structure.
Can Spring batch used for data processing or it is only an ETL tool ?
well, i disagree on this point that spring batch - is only used for transferring the SAME data from one place to another with some type mapping like varchar -> string.
Worked in 4 years in this technology and have witnessed this framework grow a lot.
Spring batch is well capable of processing data, mapping, required conversion and data aggregations - spring batch can definitely be used for data processing .
being open source technology - you will get lot of material to read about, and the forums like stackoverflow have ton of FAQs around it.
For scaling and paralleling there are various architectures in spring batch, which will help in enhancing your performance.
Further details you can find here
SPRING_BATCH_SCALING_AND_PARALLELING
If you want to monitor your jobs then you cas use - Spring cloud date flow.
Monitoring can also be done - with AppDynamics.
Referrer this blog -
MONITOR_SPRING_BATCH_JOB_WITH_APP_DYNAMICS
Another advantage of using spring batch is you have lot of standerd predefined reader , processor and writer types - which support sources like file , DB , stream etc..
On top of this - as it is a java based framework you can do all stuff that can be done with java.
I hope this helps.
Your below write up is incorrect because its comparing apples to oranges,
However the more I read the more I realize that Spring batch is
nothing like ApacheBeam or MapReduce, it is only used for transferring
the SAME data from one place to another with some type mapping like
varchar -> string.
Unlike ApacheBeam or MapReduce, Spring Batch is not an engine but a programming framework. A programming framework usually consists of two major components - Code Structure Guidelines + APIs
So only restriction on a Java developer is to follow Spring Batch program structure guidelines and usage of Spring Batch APIs is optional.Though the modeling is Read -> Process -> Write, but a Java developer is free to write any kind of logic that he or she wishes to write in these components - only thoughts can limit as what a Java developer could write in these components.Further on, one artifact can be integrated with another artifact.
So I reiterate again that Spring Batch is a programming framework & not an engine or pre configured software like Hadoop so that comparison is like apple to oranges.
See this - Spring Tips: Spring Batch and Apache Kafka
As I have already said, a Java developer can develop any kind of program by being only in program structure limitations but logic being written has no bounds!
Saying one more time - Spring Batch is not an ETL tool like Informatica or Pentaho but a programming framework using Java and Spring. A developer can be as creative as he or she wants to be.
I had developed a real time data matching job that needed free text search capabilities using Apache Lucene by fitting in my programming into Spring Batch model.
Spring Batch (SB) gives us all three - E, T and L.
However, we have to decide whether or not use SB. Its again a quantitative decision whether if an individual/team really needs to learn it, if they dont know it. Need to evaluate ROI (Return on Investment). If its just E or T or L only, there might be another simpler solutions.
If we talk about Java only, AND either of these three, SB is not required. But again, when it comes to simplicity (if you know SB), scalability, monitoring, Transaction Managed Parallel Processing - all these come hand-in-hand with SB out of the box.
Related
We are beginning the process of re-architecting the systems within our company.
One of the key components of the work is a new data model which better meets our requirements.
A major part of the initial phase of the work is to design and build a data migration tool.
This will take data from one or more existing systems and migrate it to the new model.
Some requirements:
Transformation of data to the new model
Enrichment of data, with default values or according to business rules
Integration with existing systems to pull data
Integration with Salesforce CRM which is being introduced into the company.
Logging and notification about failures
Within the Spring world, which is the best Spring project to use as the underlying framework for such a data migration tool?
My initial thoughts are to look at implementing the tool using Spring Integration.
This would:
Through the XML or DSL, allow for the high level data flow to be seen, understood, and edited (possibly using a visual tool such as a STS plugin). Being able to view the high level flow in such a way is a big advantage.
Connectors to work with different data sources.
Transformers components to be built to migrate data formats.
Routers to route the data in the new model to endpoints which connect with systems.
However, are there other Spring projects, such as Spring Data or Spring Batch, which are a better match for the requirements?
Very much appreciate feedback and ideas.
I would certainly start with spring-integration which exposes bare bones implementation for Enterprise Integration Patterns which are at the core of most/all of your requirements listed.
It is also an exceptionally great problem modelling tool which helps you better understand the problem and then envision its implementation in one cohesive integration flow
Later on, once you have a clear understanding of how things are working it would be extremely simple to take it to the next level by introducing the "other frameworks" you mentioned/tagged adding #spring-cloud-data-flow and #spring-cloud-stream.
Overall this question is rather broad, so consider following the above pointers and get started and raise more concrete questions.
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 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.
Closed. This question is opinion-based. It is not currently accepting answers.
Want to improve this question? Update the question so it can be answered with facts and citations by editing this post.
Closed 8 years ago.
Improve this question
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.
Does anybody has an experience with Spring Integration project as embedded ESB?
I'm highly interesting in such use cases as:
Reading files from directory on schedule basis
Getting data from JDBC data source
Modularity and possibility to start/stop/redeploy module on the fly (e.g. one module can scan directory on schedule basis, another call query from jdbc data source etc.)
repeat/retry policy
UPDATE:
I found answers on all my questions except "Getting data from JDBC data source". Is it technically possible?
Remember, "ESB" is just a marketing term designed to sell more expensive software, it's not a magic bullet. You need to consider the specific jobs you need your software to do, and pick accordingly. If Spring Integration seems to fit the bill, I wouldn't be too concerned if it doesn't look much like an uber-expensive server installation.
The Spring Integration JDBC adapters are available in 2.0, and we just released GA last week. Here's the relevant section from the reference manual: http://static.springsource.org/spring-integration/docs/latest-ga/reference/htmlsingle/#jdbc
This link describes the FileSucker with Spring Integration. Read up on your Enterprise Integration patterns for more info I think.
I kinda think you need to do a bit more investigation your self, or do a couple of tries on some of your usecases. Then we can discuss whats good and bad
JDBC Adapters appear to be a work in progress.
Even if there is no specific adapter available, remember that Spring Integration is a thin wrapper around POJOs. You'll be able to access JDBC in any component e.g. your service activators.
See here for a solution based on a polling inbound channel adapter too.