Scaling Spring Batch application with 30 Batch jobs

[abhinavsingh-ccm]

I’m working with a Spring Batch app that has around 30 jobs. Some jobs are dependent on others (e.g., Job B only runs after Job A completes), and all jobs run sequentially for ~500 accounts. To optimize, we’ve set this up as a StatefulSet and assigned accounts to specific pods (although the distribution isn’t perfect).

Eg:
Pod0 -> 50 accounts

Pod1 -> 50 accounts
...

Each Job has to be executed for all the accounts. That means, JobA has to be executed for all the 50 accounts on Pod0 and similarly in each pod.

        accountShardService.getAccountIds().forEach(account
        -> jobs.stream()
               .forEach(job -> runJob(account, job)));

Challenges:

Some accounts have a ton of data, slowing down other jobs. Long-running jobs consume most resources, causing delays in subsequent scheduled jobs. for eg: Pod0 has to execute a job name Job-A for 50 accounts. If one of the account has huge data size to process, it simply takes most of the time and delays the execution of the remaining 49 accounts.
What could be the best way to optimise this?

Also, we are thinking to go stateless, so any pod can pick up any job to improve flexibility. But I’m unsure how to set up HPA effectively—especially around what metrics to use to scale up/down based on job load. Because any pod's CPU and Memory will not be high if one account's job takes longer to process but it will delay the execution jobs for other accounts.

I’d love any advice on:

Good metrics for HPA in this setup
Ways to dynamically assign accounts across pods without impacting job dependencies

Note:
We are using external Postgres metadata job repository.

[fmbenhassine]

Please do not post the same question in multiple channels. I have already answered this on SO and LinkedIn:

https://stackoverflow.com/questions/79157985/scaling-spring-batch-application-with-30-batch-jobs