AWS Batch enables you to run batch computing workloads on the AWS Cloud. This service can automatically provision compute resources and optimizes the workload distribution based on the quantity and scale of the workloads.
Jobs: A unit of work (such as a shell script, a Linux executable, or a Docker container image) that you submit to AWS Batch. It runs as a containerized application on an Amazon EC2 instance in your computing environment, using parameters that you specify in a job definition. Container images are stored in and pulled from container registries.
Job Definitions: specifies how jobs are to be run e.g. specifying memory and CPU requirements.
Job Queues: When you submit an AWS Batch job, you submit it to a particular job queue, where it resides until it is scheduled onto a computing environment.
Compute Environment: A set of managed or unmanaged compute resources that are used to run jobs, can be mapped to one or many job queues.
- In a managed compute environment, AWS Batch manages the capacity and instance types of computing resources within the environment, based on the compute resource specification that you define when you create the compute environment. You can choose to use Amazon EC2 On-Demand Instances or EC2 Spot Instances in your managed compute environment.
- In an unmanaged compute environment, you manage your own compute resources.
There’s no charge for the use of AWS Batch; you pay only for the underlying AWS resources that you consume.
AWS Lambda VS. AWS Batch Jobs
The purpose of Lambda, as compared to AWS EC2, is to simplify building smaller, on-demand applications that are responsive to events and new information.
If your batch job is running within the limits of AWS Lambda, then you can go with a lambda function, which is cheaper.
You have a batch job => You can accept lambda limits => go with AWS Lambda.
You have a batch job => You can NOT accept lambda limits => Use AWS Batch.
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