AWS Batch Specialized2017年〜
A managed service for efficiently scheduling and running batch computing jobs
What It Does
AWS Batch is a fully managed service that efficiently schedules large volumes of batch processing jobs and automatically runs them on optimal compute resources (EC2 instances or Fargate). You can define job dependencies to control execution order, and compute resources automatically scale based on job volume. AWS handles all management of job queues, the scheduler, and compute environments.
Use Cases
Used for machine learning model training, large-scale data transformation and ETL processing, video encoding and transcoding, scientific computing and simulations, and financial risk analysis batch processing. It is particularly well suited for large-scale batch workloads that run hundreds to tens of thousands of jobs in parallel.
Everyday Analogy
Think of it like a factory production line manager. When a large volume of orders (jobs) comes in, the manager (Batch) checks the priority and dependencies of each order, sets up the required number of workstations (compute resources), and efficiently distributes the work. When orders decrease, workstations are put away to minimize costs.
What Is AWS Batch?
AWS Batch is a service that automates job management and resource management for batch processing. Batch processing refers to processing large volumes of data or tasks in bulk. Traditionally, running batch jobs required building a job scheduler, managing servers, and estimating capacity. AWS Batch automates all of this, letting developers focus on defining their jobs.
Defining and Running Jobs
AWS Batch jobs are defined as Docker containers. In a job definition, you specify the container image, CPU and memory requirements, environment variables, and more. When you submit a job to a job queue, the Batch scheduler determines the execution order based on priority and dependencies. By setting up job dependencies, you can build workflows where one job runs only after the previous one completes.
Compute Environments
AWS Batch automatically manages the compute resources needed to run your jobs. With managed compute environments, EC2 instances or Fargate tasks automatically scale based on job volume. By leveraging Spot Instances, you can run jobs at up to 90% off on-demand pricing, dramatically reducing costs for large-scale batch processing. For case studies and best practices on compute environments, specialized books on Amazon are a helpful reference.
Getting Started
In the Batch console, create a compute environment and choose Fargate or EC2. Create a job queue and link it to the compute environment, then create a job definition specifying the container image and required resources. Submit a job from the Submit Job page, and resources are automatically provisioned to run it. Starting with a small Fargate-type job is recommended for beginners.
Things to Watch Out For
- AWS Batch itself is free - you only pay for the EC2 instances or Fargate tasks that actually run your jobs
- When using Spot Instances, jobs may be interrupted, so implementing checkpointing in your jobs is recommended for safety
- The Fargate type is simpler to manage but offers fewer GPU instance options and less customization compared to the EC2 type
