Spring Boot Batch VS AWS Batch

Spring Batch and AWS Batch are often compared as alternatives. They are better understood as operating at different layers: Spring Batch is a Java framework for structuring the inside of a batch job — reading, processing, writing, restarting — while AWS Batch is a service for scheduling jobs and provisioning the capacity to run them, whatever those jobs are written in. A Spring Batch application packaged as a container is a perfectly reasonable AWS Batch job.

This page compares them on architecture, features, scaling, cost and operational model, and ends with where each one belongs.

Architecture and infrastructure

Spring Batch runs anywhere a JVM does, on-premises or in the cloud. It is part of the Spring ecosystem, so it inherits Spring’s dependency injection, transaction management and data access. By default a job runs in a single JVM process, and the servers it runs on are yours to provision and operate.

AWS Batch is a fully managed service. It provisions and scales compute automatically across EC2, Fargate, Amazon ECS Managed Instances or Amazon EKS, integrates with the rest of AWS, and distributes work across containers and instances. See AWS Batch.

Feature comparison

Feature	Spring Batch	AWS Batch
Job control	Job repository, job launcher, job operator	Job definitions, job queues, priorities and dependencies
Scalability	Manual, through clustered deployment or partitioning	Automatic, through compute environments
Processing model	Chunk-oriented, single or multi-threaded within a JVM	Distributed across containers and instances
State	In the job repository, usually a relational database	Managed by the service
Restart	Built-in restart, skip and retry semantics per step and per item	Retry strategy per job attempt
Language	Java	Any — jobs are containers
Cost	Infrastructure you provision, whether busy or idle	Per resource consumed, scaling to zero

The restart row is where the difference is sharpest. Spring Batch tracks progress at the item and chunk level and can resume a failed job from the chunk it stopped on, skipping records that were already committed. AWS Batch retries the whole job attempt — restart granularity inside the job is the job’s own problem.

Code shape

A Spring Batch job defines readers, processors and writers, and the framework drives the chunk loop:

@Configuration
public class BatchConfig {

    @Bean
    public Job importUserJob(JobRepository jobRepository, Step step1) {
        return new JobBuilder("importUserJob", jobRepository)
            .incrementer(new RunIdIncrementer())
            .start(step1)
            .build();
    }

    @Bean
    public Step step1(JobRepository jobRepository,
                      PlatformTransactionManager txManager,
                      ItemReader<User> reader,
                      ItemProcessor<User, User> processor,
                      ItemWriter<User> writer) {
        return new StepBuilder("step1", jobRepository)
            .<User, User>chunk(10, txManager)
            .reader(reader)
            .processor(processor)
            .writer(writer)
            .build();
    }
}

An AWS Batch job definition describes a container and the resources it needs:

{
    "jobDefinitionName": "ProcessUserData",
    "type": "container",
    "containerProperties": {
        "image": "user-processor:latest",
        "resourceRequirements": [
            { "type": "VCPU",   "value": "2" },
            { "type": "MEMORY", "value": "2048" }
        ],
        "command": ["python", "process_users.py", "Ref::inputFile"],
        "jobRoleArn": "arn:aws:iam::account:role/BatchJobRole"
    },
    "retryStrategy": { "attempts": 3 },
    "timeout": { "attemptDurationSeconds": 3600 }
}

Integration

Spring Batch ships readers and writers for the sources enterprise jobs usually face — JDBC cursors and paging, flat files, XML, JMS — and integrates with whatever else Spring can reach:

@Bean
public JdbcCursorItemReader<User> reader(DataSource dataSource) {
    return new JdbcCursorItemReaderBuilder<User>()
        .dataSource(dataSource)
        .name("userReader")
        .sql("SELECT id, name, email FROM users")
        .rowMapper(new UserRowMapper())
        .build();
}

AWS Batch is wired up as infrastructure rather than in code:

Resources:
  BatchJobQueue:
    Type: AWS::Batch::JobQueue
    Properties:
      Priority: 1
      ComputeEnvironmentOrder:
        - ComputeEnvironment: !Ref BatchComputeEnvironment
          Order: 1

  BatchComputeEnvironment:
    Type: AWS::Batch::ComputeEnvironment
    Properties:
      Type: MANAGED
      ComputeResources:
        Type: FARGATE
        MaxvCpus: 4
        SecurityGroupIds:
          - !Ref SecurityGroup
        Subnets:
          - !Ref Subnet

Scaling

Spring Batch scales vertically within a JVM by default. Beyond that it offers multi-threaded steps, partitioned steps and remote chunking, all of which have to be designed and operated. The usual ceiling in practice is the database behind the job repository and the data source.

AWS Batch scales horizontally by launching more containers and instances, driven by queue depth. It supports Spot capacity, and different queues can target different compute environments.

Cost and operations

Spring Batch has no licence cost and no service cost; you pay for the infrastructure it runs on, idle or not, and for the engineering time to build and operate the scheduling, monitoring and scaling around it.

AWS Batch charges nothing for the service and everything for the resources it provisions, which scale to zero between runs. Spot capacity typically takes a large fraction off that.

Monitoring differs accordingly. Spring Batch exposes job and step execution status through its job repository and listeners:

@Bean
public JobExecutionListener listener() {
    return new JobExecutionListener() {
        @Override
        public void afterJob(JobExecution jobExecution) {
            logger.info("Job {} finished with status {}",
                jobExecution.getJobId(), jobExecution.getStatus());
        }
    };
}

AWS Batch reports through its own API and CloudWatch:

aws batch describe-jobs --jobs "$jobId"
aws batch list-jobs --job-queue "$queueName" --job-status RUNNING

Where each belongs

Spring Batch suits jobs whose difficulty is in the processing: complex transformations, per-item skip and retry rules, fine-grained transaction control, restart from the exact point of failure, and tight integration with an existing Spring application and its data model.

AWS Batch suits jobs whose difficulty is in the capacity: large volumes, compute-intensive work, GPU requirements, variable arrival patterns, and anything that would otherwise need a scheduler and a warm cluster.

They are not mutually exclusive, and the combination is often the right answer: write the job with Spring Batch for its restart and chunking semantics, package it as a container, and let AWS Batch decide when and where to run it.