Batch

🧠 What Is AWS Batch?

AWS Batch is a fully managed batch computing service that efficiently runs hundreds to thousands of batch processing jobs on AWS infrastructure, dynamically provisioning EC2 or Fargate compute resources.

✅ Think of it as an autoscaling job queue + compute manager for parallel processing tasks — like image/video rendering, data processing, machine learning jobs, and simulations.

---

🎯 Use Cases

Use Case	Example
🧬 Genomics/Bioinformatics	Sequence alignment, data pipelines
📊 Big Data Processing	ETL jobs, log analysis, aggregation
📷 Media Rendering	Video transcoding, image processing
🧠 Machine Learning	Parallel hyperparameter tuning
🔬 Simulations	Scientific and engineering simulations

---

🧱 Key Components

Component	Description
Job	A unit of work (e.g., a script, container task)
Job Definition	Metadata: container image, vCPUs, memory, env vars, retry policy, etc.
Job Queue	FIFO queue that holds jobs until compute is available
Compute Environment	Backend compute layer (EC2, Spot, or Fargate) with autoscaling

---

🔄 Architecture Overview

+-----------------+
| Submit Job      |
| (via CLI/SDK)   |
+--------+--------+
         |
         v
+--------+--------+           +---------------------------+
| Job Queue        |--------->| Compute Environment       |
+------------------+          | (EC2/Spot/Fargate Managed)|
                               +-------------+-------------+
                                             |
                                      Run Docker container

---

🛠️ Job Definition Example (JSON)

{
  "jobDefinitionName": "my-batch-job",
  "type": "container",
  "containerProperties": {
    "image": "amazonlinux",
    "vcpus": 2,
    "memory": 2048,
    "command": ["echo", "Hello from AWS Batch!"]
  }
}

---

🧪 AWS CLI Job Submission

aws batch submit-job \
  --job-name my-first-job \
  --job-queue my-job-queue \
  --job-definition my-batch-job:1

---

🧱 Compute Environment Types

Type	Description
Managed	AWS provisions and scales EC2/Spot/Fargate
Unmanaged	You manage compute instances yourself

---

☁️ EC2 vs Fargate in AWS Batch

Feature	EC2	Fargate (Serverless)
Scaling	Fully configurable	No infra mgmt, scales automatically
Pricing	Pay-per-second	Pay-per-task
Start latency	Slightly longer (EC2 boot time)	Faster
Best For	Complex, stateful jobs	Stateless, short jobs

---

⚙️ Retry Strategy Example

"retryStrategy": {
  "attempts": 3
}

• Automatically retries failed jobs

---

🔁 Job Dependencies

AWS Batch supports dependent jobs:

aws batch submit-job \
  --job-name child-job \
  --job-queue my-queue \
  --job-definition my-job \
  --depends-on jobId=abcd1234

---

🧠 Scheduling Options

Method	Use Case
Manual	Trigger via CLI/API
Scheduled (CloudWatch)	Run jobs at cron intervals (ETL, cleanup)
Event-based (Lambda)	Trigger based on S3 uploads, SNS, etc.

---

🧩 Integration with Other AWS Services

Service	Purpose
S3	Input/output data for jobs
CloudWatch Logs	Monitor job logs
EventBridge	Schedule jobs via cron
Lambda	Submit jobs programmatically
DynamoDB/Step Functions	Orchestrate multi-step workflows

---

🧪 Monitoring Jobs

You can track:

• Job status: SUBMITTED, RUNNABLE, STARTING, RUNNING, SUCCEEDED, FAILED

• Logs: via CloudWatch Logs

• Metrics: via CloudWatch Metrics (CPU, memory, count)

---

✅ Best Practices

Area	Best Practice
Scaling	Use Spot for large-scale cost-effective jobs
Job Size	Keep jobs granular and fault-tolerant
Logging	Enable CloudWatch Logs for debugging
Compute Isolation	Use separate compute environments for dev/prod
Security	Use least-privilege IAM roles for jobs

---

🧱 Example Terraform (AWS Batch Setup)

resource "aws_batch_compute_environment" "example" {
  compute_environment_name = "example"
  compute_resources {
    max_vcpus          = 16
    instance_types     = ["m5.large"]
    type               = "EC2"
    subnets            = [aws_subnet.my_subnet.id]
    security_group_ids = [aws_security_group.sg.id]
  }
  service_role = aws_iam_role.batch_service.arn
  type         = "MANAGED"
}

resource "aws_batch_job_queue" "example" {
  name                  = "example"
  state                 = "ENABLED"
  priority              = 1
  compute_environments  = [aws_batch_compute_environment.example.arn]
}

resource "aws_batch_job_definition" "example" {
  name = "example-job"
  type = "container"
  container_properties = jsonencode({
    image    = "amazonlinux"
    vcpus    = 1
    memory   = 1024
    command  = ["echo", "Hello from Terraform AWS Batch"]
  })
}

---

💰 Pricing

Component	Cost
AWS Batch Service	✅ Free (you pay for underlying compute)
EC2 Instances	Based on EC2 pricing (on-demand/spot)
Fargate Jobs	Based on task duration & vCPU/memory
Logs	Charged via CloudWatch logs

---

✅ TL;DR Summary

Feature	Value
Managed Service	✅ Yes, serverless compute management
Supported Compute	EC2 (on-demand/spot), Fargate
Job Scheduling	Manual, cron (EventBridge), event-triggered
Orchestration	Supports dependencies, retries
Container Support	✅ Docker, no orchestration required
Pricing	Free service; pay for compute

---