I would like to read directory with specific file path and get the file contents with apache camel and spring boot. I have router and processor class in Java. There are not many resources on the Internet, but only on the official website of apache camel. Thank you in advance.
One option is to use the Apache Camel File component to consume files. One thing to keep in mind however is that if you're deploying in a clustered environment, extra precautions need to be taken to avoid competing consumer issue. From the documentation:
Warning: most of the read lock strategies are not suitable for use in clustered mode. That is, you cannot have multiple consumers attempting to read the same file in the same directory. In this case, the read locks will not function reliably. The idempotent read lock supports clustered reliably if you use a cluster aware idempotent repository implementation such as from Hazelcast Component or Infinispan.
Because of this and other complexities, I typically avoid using the Camel file component for consuming files and just use java.nio.file.Files API in a bean/processor as it is more straightforward and provides easier mechanisms for dealing with this and other limitations.
Related
I have an application with file-supplier Srping Cloud module included. My workflow is like track file creating/modifying in particular directory->push events to Kafka topic if there are such events. FileSupplierConfiguration is used to configure this file supplier. But now I have to track one more directory and push events to another relevant Kafka topic. So, there is an issue, because there is no possibility to include multiple FileSupplierConfiguration in project for configuration another file supplier. I remember that one of the main principles of microservices for which spring-cloud-stream was designed for is you do one thing and do it well without affecting others, but it still the same microservice with same tracking/pushing functionality but for another directory and topic. Is there any possibility to add one more file supplier with relevant configuration with file-supplier module? Or is the best solution for this issue to run one more application instance with another configuration?
Yes, the best way is to have another instance of this application, but with its own specific configuration properties. This is really how these functions have been designed: microservice based on the convention on configuration. What you are asking really contradicts with Spring Boot expectations. Imaging you'd need to connect to several data bases. So, you only can have a single JdbcTemplate auto-configured. The rest is only possible manually. But better to have the same code base which relies on the auto-configuration and may apply different props.
Let me preface this with the fact that I am still very new to Apache Camel. I'm still trying to understand how it all works, and what needs to be done (and HOW to do it) to achieve a particular effect.
I am trying to develop a Spring Boot application that will use Apache Camel to handle the transmission (and possibly also receipt) of data to/from a number of possible sources and destinations. The purpose of the application is to provide a means to produce/generate network traffic, at the network application level, that will be fed into another Spring Boot application - let's call this the target. We are trying to observe and measure the effects various network loads have on the target.
We would like to be able to transmit data via a number of protocols, including: ftp, http/s, file systems (nfs), various mail protocols (smtp, pop) and data streaming protocols for voice and video. There may be other protocols added at a later time. The data itself is irrelevant, we just need to be able to transmit data via various protocols with various loads.
These applications/services will be running in a containerized environment (Docker) that will be run within our local development and test environment, as well as possibly in a cloud environment, such as AWS. We have used Docker, Ansible, Terraform and are currently working towards using Kubernetes and Istio to manage the configuration, deployment, and operation of these applications.
We need to be able to provide specific configurations of Camel routes for particular deployments.
It would appear that the preferred method to configure Camel routes is via Java DSL, rather than XML DSL. The Camel documentation and nearly every other source of information I've found have a strong bias towards using Java DSL. Examples of XML DSL route configuration are far and few.
My initial impression is that going the Java DSL route (excuse the pun), would not work well with our need to be able to deploy a Camel application with a specific route configuration. It seems like you are required to have Java DSL defined route configurations hardwired into the code.
We think that it will be easier to provide a specific route configuration via an XML file that can be included in a deployment, hence why I've been trying to investigate and experiment with XML DSL. Perhaps we are mistaken in this regard.
My question to the community is: Considering what I've described above, can the Java DSL approach be used to meet the requirements as I've described them? Can we use Java DSL in a way that allows for dynamic route configuration? Keep in mind we would not be attempting to change configuration during operation, just in the course of performing a deployment.
If Java DSL could be used for this purpose, it would be very much appreciated if pointers to documentation, examples, etc. could be provided.
For your use cases you could use XML DSL also. Anyhow below book covers most aspects Camel development with examples. In this book authors describes XML DSL use for most of java DSL examples.
https://www.manning.com/books/camel-in-action-second-edition
In below github repository you can find the source code for all the examples listed in above book.
https://github.com/camelinaction/camelinaction2
Simple tutorial and github repository for Apache Camel using Spring boot.
https://www.baeldung.com/apache-camel-spring-boot
https://github.com/eugenp/tutorials/tree/master/spring-boot-modules/spring-boot-camel
Maven Plugin for build and deployment of spring boot container application into Kubernetes cluster
https://maven.fabric8.io/
In case if your company can afford some funding for your effort look at below link which provides commercial offerings around Camel.
https://camel.apache.org/manual/latest/commercial-camel-offerings.html
Thanks
Madhu Gupta
Our team has a few projects which use the Java DSL for building routes. In order to make them dynamic, there are control structures for iterating and setting endpoints based off configurations. That works for us because the routes are basically all the same, just with different sources and sinks.
If you could dynamically add/change the XML DSL files in a way that doesn't involve redeploying your application, that might be a viable route to follow. One might, for example, change the camel.springboot.xml-routes property to point to a folder which changes as needed.
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 would like to use the Apache Camel JDBC component to read an Oracle table. I want Camel to run in a distributed environment to meet availability concerns. However, the table I am reading is similar to a queue, so I only want to have a single reader at any given time so I can avoid locking issues (messy in Oracle).
If the reader goes down, I want another reader to take over.
How would you accomplish this using the out-of-the-box Camel components? Is it possible?
It depends on your deployment architecture. For example, if you deploy your Camel apps on Servicemix (or ActiveMQ) in a master/slave configuration (for HA), then only one consumer will be active at a given time...
But, if you need multiple running (clustered for scalability), then (by default) they will compete/duplicate reads from the table unless you write your own locking logic.
This is easy using Hazelcast Distributed Locking. There is a camel-hazelcast component, but it doesn't support the lock API. Once you configure your apps to participate in a Hazelcast cluster, then just just the lock API around any code that you need to synchronize for a given object...
import com.hazelcast.core.Hazelcast;
import java.util.concurrent.locks.Lock;
Lock lock = Hazelcast.getLock(myLockedObject);
lock.lock();
try {
// do something here
} finally {
lock.unlock();
}