AWS Batch: Introduction and Step-by-Step Guide to Automate an AWS Batch Job

January 21, 2022 | Comments(2) |

AWS Batch is a beneficial service for batch computing heavy workloads. It removes the complexity of having a complete infrastructure setup and maintaining it. Also, it decreases the cost of the environment and is open to several types of automation to run the batch job.

AWS Batch is a very effective service introduced by the AWS Team. It helps to run batch computing workloads on the AWS Cloud. We can also say that it is a service that helps us use aws resources more effectively and efficiently, making the aws cloud more convenient to its users. This service also provisions the underlying resources efficiently once job is submitted. It helps to eliminate capacity constraints, reduce compute costs, and deliver results quickly.

What is Batch Processing or computing?

It is used for running high-volume, repetitive data jobs at our ease. This method allows its users to process data when all the computing resources are available and minimize user Interaction.

AWS Batch makes all the required resources available only when the user needs to process any data, or we can say to run any computing job. Once the job completes, it automatically releases the resources saving some money for the user.

Automating an AWS Batch Job

To run a Batch process, we need to create and run a job; a job can be created and run directly from the console. But in most use cases, users need to automate this process so that whenever they want to run any batch job, they don’t need to create and run the batch job manually.

The two most effective ways for automating this process are using CloudWatch Events and AWS Lambda. We can easily trigger these services to create and run a job on the AWS batch.

Components of AWS Batch with Simple steps of Batch creation

AWS Batch Job

  1. Compute Environment: It is a set of managed or unmanaged compute resources that are used to run jobs. Using managed to compute environments, users can specify desired compute type (Fargate or EC2) at several levels of detail. Users can easily set up compute environments that use a particular EC2 instance, any specific model such as c5.2xlarge or m5.10xlarge. Users can also choose to specify that they want to use the newest instance types. They can also set the minimum, desired, and maximum number of vCPUs for the environment and the amount they are willing to pay for a Spot Instance as a percentage of the On-Demand Instance price a target set of VPC subnets. AWS Batch efficiently launches, manages, and terminates compute types as needed. Users can also manage their computing environments. As such, they’re responsible for setting up and scaling the instances in an Amazon ECS cluster that AWS Batch creates for them.
    AWS Batch Job
  2. Job Queues: Any Batch is submitted to a particular job queue, where the job resides until it is scheduled onto a compute environment. Users can easily associate one or more compute environments with a single job queue. It also gives us leverage to assign priority values for these compute environments and even across job queues themselves. For example, user can easily give high priority to any time-sensitive job and a low priority to jobs that can be run anytime when compute resources are cheaper.
    AWS Batch Job
  3. Job Definitions: If is basically a blueprint for the resources in your job that specifies how jobs are to be run. You need to provide a suitable IAM role to the job to access the underlying resources easily. It also allows you to specify both memory and CPU requirements. It can also easily control container properties, environment variables, and mount points for persistent storage. You can also provide any commands we want to execute when your job is run.
    Note: – Many of the specifications provided in the Job Definition can be overridden by specifying new values when submitting individual Jobs.
    AWS Batch Job
  4. Creating a Job: A job is created to execute the batch process. Once you submit a job from the AWS console or any automation like lambda function, it starts doing the processing job. Jobs can reference other jobs by name or by ID and depend on the successful completion of different jobs. You can also pass commands, parameters, and environment variables (in additional configuration) at the time of job creation.
    AWS Batch Job
    AWS Batch Job
    AWS Batch Job

Conclusion:

AWS Batch multi-node parallel jobs let users run a single job on multiple servers. Multi-node parallel job nodes are single tenants, which means that only a single job container is run on each Amazon EC2 instance. To know more about AWS Batch, drop a query in the below comments section, and I will get back to you quickly.

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2 Responses to “AWS Batch: Introduction and Step-by-Step Guide to Automate an AWS Batch Job”

  1. Panchanathan

    Hi, I am new to AWS and your AWS Batch guide looks really good and easy to understand. However is it possible to give overall flow diagram for the AWS batch (read file from S3 and insert into RDS SB) to have better understanding before i start AWS batch solution

    Reply
    • Nishant Ranjan Nishant Ranjan

      Please contact the CloudThat team for support. we will be happy to help you.

      Reply

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