Watch Service and Java EE Batch Processing

Question

Context

I'm in the process of drawing a solution to migrate a huge PL/SQL system to Java. The initial step is migrating some ETL jobs that:

1. Reads CSV, XML, (XLS, which is a new requirement) and Positional files from several ftp / sftp sources
2. Process the files according to rules stored in the database and write the results to a database table.

Currently this is done by several store procedures and Jobs.

My company is open to suggestions (if it can run in GlassFish 4 and share its logging and connection pool mechanisms, as well as the admin console, it is a plus).

I've done a little bit of research and the following options caught my eye:

1. Java EE 7 Batch Processing, sounds simple and particularly well fitted for GlassFish 4.
2. Spring Batch somewhat more mature and very similar to the Java EE 7 standard (which was probably based on it).
3. Apache Camel, sounds powerful and would spare us from a lot of fiddling with libraries such a Apache POI, but it also looks somewhat complex. Also I'm not sure if it is the best fit for the job (ETL over huge files).
4. Cook everything by myself. I could create a Application Client to run a Quartz / Spring Scheduler or even EJB Timers

While I'm still open to suggestions (recommendations would be nice), the best fit so far seems to be Java EE 7 Batch Processing.

One more thing, the infrastructure team have a solution to move files from every ftp source to a local directory, so FTP is really not an issue.

Problem

I've read several tutorials about Java EE Batch Processing and, in all of them, some kind of Servlet or EJB Timer is responsible for starting the Jobs:

JobOperator jobOperator = BatchRuntime.getJobOperator(); 
jobOperator.start("job", properties);

I could easily upload a web / ejb project and keep pooling for changes. But I was thinking about a push model:

1. Application client console application
2. Main class watches directories for new files
3. When there is a new file it would start a new job.

My doubts are:

1. Is this strategy possible/ advisable?
2. Will I need a JMS queue or some kind of producer / consumer strategy in the middle or should I just call jobOperator.start for every file and trust the batch processing layer to manage the application resources? In other words, if a thousand files are delivery to my folder at once and I call jobOperator.start a thousand times, will GlassFish 4 do some kind of smart enqueuing or should I create some kind of Gate so that no more than n jobs run simultaneously?

Answer 1

I've already implemented a project with Batch Processing in Wildfly (Jboss AS). I'm not familiar with configuration details on Glassfish (not using it anymore because the've dropped enterprise support), however I can give you some insights and guidelines according to my experience. Also, please note that Spring and the Batch spec. on EE 7 are quite similar, and your decision to use either technology must depend on "what else" you want to achieve with your application besides the batching. Do you want an easily maintained web interface? Do you want to depelop a REST api?, etc.

The ETL jobs you're describing fit pefeclty with the steps and chunks model in the EE 7 spec, so If you've already tried to develop some tests, you may have noticed that you still need to code the file readers and mappers for each file specification. Your reading sources are quite standard, and you will easily find a library to read/stream them and process their data.

The project I've implemented is quite simple. Customers uplodad files that need to be processed in order to feed a data warehouse. This service is on the "cloud". Files have a defined spec and must be in CSV format. Most processing results are dimentional "Upserts" and fact "erasing prior inserting". The user has a Web interface on which files and batch processing metadata must be shown (processing state, dates, rejected items, etc.). Because it is a cloud service, the files must not reside locally on each server (using S3).

So the first thing to design are the chunk steps. I didn't want to have an implementation for each file spec., So what I did is to design a "fit all cases" implemetation that process files according to the metadata contained in them and also the job configuration itself. This is the easy part. The second thing to think about is the processing and metadata administration. Here, I developed a REST api and a Web interface that uses it. After all this, Will it scale? Wilfly has thread configuration parameters for the Batch Processing, and you can increase or decrease the thread availability for the JobOperator. Jobs are not submitted if there are not enough threads available. So what happends to those requests? Well, they can reside on memory, a backed up stateful session can be developed, you can definitely implement MQ listener of queued processing requests. What I did was much simpler. The company doesn't have the resources to maintain a cluster, so whe did an elastic configuration that will expand accoding to cpu consumption and requests volume. So far, the application has processed 10 TB of data, from 15 customers, and at max request/processing peak, 3 elastic instances have fired up.

A file listener is an interesting idea. You can listen to a directory and drop a processing request to a queue or inmediately to the BatchRuntime. It will depend on how you want to scale it, your needed response time, the available resources, etc.

Feel free to ask me anything.

Regards.

EDIT: forgot to mention. I don't really recommend using the Application client unless you've already got something deployed on your organization. The recent security constraints and java SE updates mechanism has made a real hassle to maintain those kind of deployments. Think web.

Answer 2

I would approach it this way.

My hammers for this use case would be the Java Watch Service, a Servlet, a JMS queue, and the Batch service.

First, the Watch Service is the Java 7 go to place to handle the file system monitoring.

I would write a Watch Service implementation, and I would run it on a thread.

Where does the thread run you ask?

Officially, you should probably be using JCA for this. But, JCA is flat out a pain to work with, underutilized, thus under documented. There are solid examples, but it's simply not a common technology in the Java EE stack.

Another place is an asynchronous Session Bean invocation. There's nothing that suggests these can not be long lived invocations. You could stand up a @Singleton Session Bean, with @Startup, call the async method from a @PostConstruct method, and let it go. Then, in @PreDestroy signal the long running method to stop, so it can cleanly shut down. This should all be to spec, portable, and according to Hoyle.

The third place is to you a ServletContextListener, which is the pre-Java EE 6 go to place for tying code in to the life cycle of the application. Here, you would create the thread yourself in the contextInitialized method, and then tear it down in the contextDestroyed method.

Creating threads here is "less defined", but I've done it for years and never had a problem.

Now that you have your service running, the service (IMHO), will do two things.

1) It'll sense when a new file has arrived in the directory, and when it does, it will MOVE (mv, rename) the file to a parallel "processing" directory. The reason is that this tells you that a file has moved from incoming to processing, that the file is a work in progress. It's obvious from a directory listing, regardless of what the backend thinks it's doing. Remember, the system can go down mid way through a file.

2) Once moved, post the file name, and any other meta data on to a JMS queue and have an MDB do tool up the batch job.

Why add the JMS queue? It brings a couple of features to the party. First, it's great way to get stuff "from outside" the happy transactional context that EJB likes, to inside one. Second, it's transactional. You can, depending on your ETL use case, have the MDB directly process the job. And by doing so, you simply do not acknowledge the message from the queue until the processing is done (and the file is deleted or moved from the "processing" directory). In an ideal world, the message queue has messages matching the files in the processing directory. When the processing is done, the method returns, the message fetch "commits", and you're done. If the system crashes, this will restart from the beginning automatically (since the message is still on the queue and was never removed).

The MDB, by configuring it's instances, can gate the number of simultaneous jobs also. Configure 10 instances, only 10 files can be processed at the same time. But this can be a little too simple, too coarse. There's no priority for example (first come first serve). But it might work for you.

But either way, the MDB is a great gateway into system, since each one starts with it's own little bit of transactional context. Unlike the long running servlet thread or the long running async thread. The servlet thread has a questionable (if any) transactional status, the long running thread inherits it's state from the @Startup method, and retains it for it's life time. The MDB gets a new one each time. Much of this can be shenaniganed away calling methods with new transactions.

But I like the demarcation of the MDB. Even if it's entire task is to create the Batch entry for a file name, the MDB is a good gatekeeper.

And that's pretty much it.

The key parts are being a good citizen and tearing down your thread properly tied to the lifecycle of the application, understanding your transactional state at the various components, and understanding how all the moving parts fit together.

If you use the @Startup technique, make sure you invoke your async method via injecting another instance of your session bean. Otherwise the invocation will be a local call, and not asynchronous. You'll stare at it wondering why your server is hanging and not starting up. All of the EJB annotations only work when invoked through an injected or looked up proxy.

Have fun, share and enjoy.

Addenda to the question:

There's really no value to having an external process manage the watch service. One tied to the lifecycle of the server is easier to maintain. Two things come to mind. If the server is down, file will simply stack up in the file system until the server is started again, so you don't lose data. If you have an external service, then you either have it sending messages to a dead server, or you have to stage and manage the JMS server separate from the app server. In that case you now have 3 processes to manage: Watch service, JMS Server, and app server, rather than just the app server.

I agree with the other poster that should you decide to go with an external service anyway, a simple Java SE app posting simple messages to a JAX-RS REST service on the server, or even a trivial Servlet is much, MUCH more easy to maintain, stage and deploy than an app client. If you do it that way, you could write the watch service in something completely different.

But since the server (ostensibly) has direct access to the file system with the file, there's really no motivation to break this service outside of the container. Put the whole kit in to an EAR and have at it. Just flat easier management.