I am running the spring batch job in three machines. For example the database has 30 records, the batch job in each machine has to pick up unique 10 records and process it.
I read partitioning and Parallel processing and bit confused, which one is suitable?
Appreciate your help.
What you are describing is partitioning. Partitioning is when the input is broken up into partitions and each partition is processed in parallel. Spring Batch offers two different ways to execute partitioning, one is local using threads (via the TaskExecutorPartitionHandler). The other one is distributing the partitions via messages so they can be executed either locally or remotely via the MessageChannelPartitionHandler found in Spring Batch Admin's spring-batch-integration project. You can learn more about remote partitioning via my talk on multi-jvm batch processing here: http://www.youtube.com/watch?v=CYTj5YT7CZU
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I have a kafka topic "mytopic" with 10 partitions and want to use S3 sink connector to sink records to an S3 bucket. For scaling purposes it should be running on multiple nodes to write partitions data in parallel to the same S3 bucket.
In Kafka connect user guide and actually many other blogs/tutorials it's recommended to run workers in distributed mode instead of standalone to achieve better scalability and fault tolerance:
... distributed mode is more flexible in terms of scalability and offers the added advantage of a highly available service to minimize downtime.
I want to figure out which mode to choose for my use case: having one logical connector running on multiple nodes in parallel. My understanding is following:
If I run in distributed mode, I will end up having only 1 worker processing all the partitions, since it's considered one connector task.
Instead I should run in standalone mode in multiple nodes. In that case I will have a consumer group and achieve parallel processing of partitions.
In above described standalone scenario I will actually have fault tolerance: if one instance dies, the consumer group will rebalance and other standalone workers will handle the freed partitions.
Is my understaning correct or am I missing something?
Unfortunately I couldn't find much information on this topic other than this google groups discussion, where the author came to the same conclusion as I did.
In theory, that might work, but you'll end up ssh-ing to multiple machines, having basically the same config files, and just not using the connect-distributed command instead of connect-standalone.
You're missing the part about Connect server task rebalancing, though, which communicates over the Connect server REST ports
The underlying task code is all the same, only the entrypoint and offset storage are different. So, why not just use distributed if you have multiple machines?
You don't need to run, multiple instances of standalone processes, the Kafka workers are taking care of distributing the tasks, rebalancing, offset management under the distributed mode, you need to specify the same group id ...
I am currently working on Spring Boot and Spring Batch application to read 200,000 records from Database, process it and generate XML output.
I wrote single threaded Spring Batch program which uses JDBCPagingItemReader to read batch of 10K records from Database and StaxEventItemReader to generate this output. Total process is taking 30 minutes. I am wanting to enhance this program by using Spring Batch local Partitioning. Could anyone share Java configuration code to do this task of Spring Batch partitioning which will split processing into multi thread + multi files.. I tried to multi thread java configuration but StaxEventItemReader is single thread so it didn't work. Only way I see is Partition.
Appreciate help.
You are correct that partitioning is the way to approach this problem. I don't have an example of JDBC to XML of how to configure a partitioned batch job, but I do have one that is CSV to JDBC in which you should be able to just replace the ItemReader and ItemWriter with the ones you need (JdbcPagingItemReader and StaxEventItemWriter respectively). This example actually uses Spring Cloud Task to launch the workers as remote processes, but if you replace the partitionHandler with the TaskExecutorPartitionHandler (instead of the DeployerPartitionHandler as configured), that would execute the partitions internally as threads.
https://github.com/mminella/S3JDBC
Need to design multi threading with Spring batch. Spring batch partition or using java multi threading, Which one is a better choice? We have many processes, each process holds jobs and sub jobs. these sub jobs needs to be executed in parallel.How can I do the retry mechanism in partition??
Go for the partition with master-slave concept. I have tried this and it boots the performance in good amount.
Restart Scenario :
Once your partitioner starts and your items are divided to the slaves.
Lets say you have 3 slaves and each slave holds 1 file to process.
Manually delete some items in the file which is assigned to the Slave2 so that it should get failed(Either in reader or writer of your slave step).
Then restart the job. Now it should start reading from the file which was assigned to the Slave2.
In the job I read from a file and store something in a database.
I would like to have many running jars of the batch job in different processes and partition the data from the file among the running instances.
I would also like to be able to keep adding files to be processed and also distribute the reads from those.
I read spring xd might be a good fit, but can't find good tutorials on it.
YES I am also a noob of spring batch and xd.
The first thing to understand is how to remotely partition batch jobs. See the batch documentation for Spring Batch Integration and its support for remote partitioning, based on basic batch partitioning.
Spring XD provides out-of-the-box support for single-step partitioned work-loads.
You just have to import singlestep-partition-support.xml and provide partitioner and tasklet beans. See the XD Documentation for an example.
We have partitioned a large number of our jobs to improve the overall performance of our application. We are now investigating running several of these partitioned jobs in parallel (kicked off by an external scheduler). The jobs are all configured to use the same fixes reply queue. As a test, I generated a batch job that has a parallel flow where partitioned jobs are executed in parallel. On a single server (local testing) it works fine. When I try on a multiple server cluster I see the remote steps complete but the parent step does not ever finish. I see the messages in the reply queue but they are never read.
Is this an expected problem with out approach or can you suggest how we can try to resolve the problem?