Sequencing machine output
Cloud / Knowledge library
AWS Batch
Plans and runs containerized batch workloads using managed compute environments and queues
Why learn AWS Batch?
Teams use AWS Batch to adopt batch computing without building the complete underlying platform themselves.
The problem it solves
Schedules large numbers of noninteractive compute jobs
Where it came from
AWS Batch is Amazon Web Services's offering for batch computing. Its closest catalog counterparts are Azure Batch and Google Cloud Batch; they address the same broad need but are not feature-for-feature identical.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Explain the main responsibility of AWS Batch.
- Choose a workload that fits AWS Batch.
- Identify one operational tradeoff of AWS Batch.
- Compare AWS Batch with Azure Batch.
- Compare AWS Batch with Google Cloud Batch.
Documentation and sources
- AWS cloud services catalog (aws.amazon.com)
Connect the ideas
Alternatives
Real World Example
Genomic Sequence Processing Pipeline
A bioinformatics research firm performing high-throughput DNA sequencing analysis.
The business problem
The firm faces unpredictable spikes in data volume, leading to idle compute resources during off-peak hours and massive processing delays when sequencing machines output thousands of files simultaneously.
How this topic solves it
AWS Batch automates the execution of containerized batch workloads by dynamically provisioning compute resources based on the volume of pending jobs in a queue, ensuring that infrastructure scales up to meet demand and scales down to zero when idle.
- Define a Job Definition specifying the Docker image containing the sequencing analysis tools and required resource constraints.
- Create a Compute Environment that specifies the instance types and the maximum vCPU capacity for the cluster.
- Configure a Job Queue that maps to the Compute Environment, acting as the landing zone for incoming analysis requests.
- Submit jobs to the queue, allowing the service to orchestrate the scheduling and placement of containers on managed instances.
This diagram illustrates how sequencing data triggers containerized batch jobs.
Holds pending analysis tasks
Managed EC2 instances
Analysis application
- Data Source → Submits job → Job Queue
- Job Queue → Triggers scaling → Compute Environment
- Compute Environment → Executes task → Container
Expected business outcome
The firm achieves a cost-efficient architecture where compute capacity perfectly aligns with the arrival rate of sequencing data, eliminating manual cluster management.
Limitations and checks
AWS Batch introduces a delay in job startup time because the service must provision new compute resources if the current capacity is exhausted. This can be checked by monitoring the time difference between job submission and the transition to a RUNNING state in the job logs.