How to use AWS Batch array jobs for embarrassingly parallel work
| Service | AWS Batch |
|---|---|
| Cloud | Amazon Web Services (AWS) |
| Guide type | Procedure |
| Skill level | Intermediate to advanced |
| Time | 15 - 60 minutes depending on account size |
When How to use AWS Batch array jobs for embarrassingly parallel work bites you on AWS Batch, the first instinct is to open a ticket. Most of the time you do not have to. The steps below are the ones AWS Support would walk you through on the call.
What how to use aws batch array jobs for embarrassingly parallel work actually involves on AWS Batch
This task on Batch is one of the more searched operational topics on AWS in the last 12 months. The procedure below is the path that works in a current AWS account with default IAM and standard VPC config.
The rest of this page is the structured fix path. Start with diagnose, then remediation, then the automation options so you do not have to do this by hand the next time it surfaces. Verify and safety sections at the end are the discipline that keeps the fix from regressing in production.
Diagnose first, fix second
Look at the CloudTrail event for the failed call, even if you are not enrolled in CloudTrail Lake. The basic 90-day event history works for most diagnostic purposes and lives in the console under CloudTrail > Event history. Filter by event name (the API action) and time range; the event JSON shows the exact user identity, source IP, request parameters, and error code.
Diff against last known good. The last config change you made is the cause about three quarters of the time, even when the change should not have mattered. Use AWS Config history (or your Terraform / CloudFormation drift report) to see the actual delta between the resource state when it worked and when it broke. The change you remember is often not the only change that happened.
Pull the AWS request ID from the response headers: x-amz-request-id for most services, x-amzn-RequestId for API Gateway, both x-amz-request-id and x-amz-id-2 for S3. AWS Support needs these IDs to look up your call in their internal logs - without them, the first reply on a ticket will ask you to reproduce the call and capture them. Save them with a timestamp; AWS Support cannot retrieve calls older than 90 days for most services.
Solution-focused remediation path
When the fix involves a destructive operation (delete VPC endpoint, swap KMS key, rotate root credential), do it during a maintenance window with at least one teammate watching. Several AWS Batch operations have implicit dependencies that only show up when traffic starts flowing again. Document the rollback path before you start, not during the incident.
If networking is suspect, use VPC Reachability Analyzer. It is the only tool that simulates the full ENI-to-ENI path including security groups, NACLs, route tables, and VPC endpoint policies in one call. Manual trace is slower and misses transitive issues. The analyzer charges $0.10 per analysis - cheaper than a 30-minute call with your network team.
If you cannot reproduce the failure consistently, the cause is probably a race condition or a session-cache issue. Run the call with --profile set to a fresh STS session, in a different region you control, with a single concurrent request. If it works there but fails in your normal setup, the difference is the bug.
Automate this fix so you do not do it twice
Add a Systems Manager Automation runbook
For multi-step fixes that include a manual approval, use SSM Automation. Document the fix as a runbook with aws:approve steps where a human signs off and aws:executeAwsApi steps where the runbook calls the AWS API. Approvers are notified by SNS; the runbook execution shows up in CloudTrail with the approver's identity attached. This makes audit trails easy and stops production fixes from being one-person operations.
Automate the fix with the AWS CLI
The CLI one-liner pattern for AWS Batch operations is roughly: aws batch describe-... --query ... to read state, aws batch modify-... --no-dry-run to apply the change, and aws batch describe-... --query ... again to verify. Wrap it in a shell script that sets a region variable at the top and exits on first error with set -euo pipefail so a partial run does not leave the account in a half-fixed state.
# Template - replace placeholders with your account specifics
export AWS_REGION=us-east-1
export AWS_PROFILE=prod
aws batch describe-... --query 'Resources[?Status==`FAILED`].[Id,Reason]' --output table
aws batch modify-... --resource-id RESOURCE_ID --no-dry-run
aws batch describe-... --resource-id RESOURCE_ID --query 'Status'Automate the fix with Python and boto3
For anything you do more than twice, write a small Python script. The boto3 pattern below uses paginators (so it does not blow up on accounts with thousands of resources), explicit region binding, and a dry-run flag that defaults to True. Keep the script under 100 lines; if it grows beyond that, you are building a tool and should put it behind a Lambda with proper logging.
import boto3, sys
DRY_RUN = '--apply' not in sys.argv
client = boto3.client('batch', region_name='us-east-1')
paginator = client.get_paginator('describe_...')
for page in paginator.paginate(): for item in page.get('Items', []): if item.get('Status') == 'FAILED': if DRY_RUN: print(f'[dry-run] would fix {item["Id"]}') else: client.modify_...(ResourceId=item['Id']) print(f'fixed {item["Id"]}')
Common pitfalls and what to watch for
The pitfall most teams hit on AWS Batch is moving too fast and skipping the read-only validation step. Before any write, list the current state and save it. AWS APIs are eventually consistent for many resource types, so the validation snapshot is your only reliable reference if you need to undo. Save the output of the describe call to S3, not to your laptop.
Second pitfall: confusing IAM permission errors with networking errors. AccessDenied can be IAM (policy missing), networking (VPC endpoint policy blocking the call), or KMS (key policy missing). The error string looks identical for all three. Distinguish by looking at the CloudTrail event's errorCode and the encoded authorization message; do not assume IAM is the culprit just because the message says AccessDenied.
Verify the fix worked
- Reproduce the original symptom path. If it still surfaces in any account or region or IAM role, you have not fixed it.
- Watch for 24 to 48 hours. AWS metrics and policy systems can mask issues with cached health for 6 to 12 hours, especially CloudFront and Route 53.
- Run a smoke test under realistic load. Happy-path tests miss race conditions and IAM session-cache issues.
- Capture the new state in a runbook so the next person on call does not have to rediscover this. Push it to Confluence or your team wiki, not into Slack.
- If the fix involved a permission change, run IAM Access Analyzer one more time to confirm you did not open a separate hole while closing this one.
Safety, rollback, blast radius
- Test in a non-production account if your environment has Control Tower or AWS Organizations. The cost of one sandbox account is cheaper than one rollback meeting.
- Export the existing config before changing it. Most AWS Batch resources support describe + export to JSON via CLI - capture that to source control before you start.
- Know your rollback path. Some AWS Batch operations are one-way (region migration, account-level feature opt-in, KMS key deletion past pending window). Confirm reversibility on the AWS doc before you commit.
- Be aware of cross-service impact. IAM role changes ripple to every service trusting that role. KMS key changes break every workload depending on that key. VPC endpoint changes affect every VPC consumer of that endpoint.
- Maintenance window discipline: if the change touches DNS, certificate rotation, or anything that emits TLS handshakes, line up a window with stakeholder notification, not a heroic mid-day swap.
FAQ
aws batch describe-... first, then commit it before you change anything. A few operations are one-way (KMS key deletion past the pending window, region migration, account closure). Check the AWS doc for the specific API before you commit.aws CLI or SDK calls - those almost always still work.References
- docs.aws.amazon.com - official documentation for AWS Batch
- AWS re:Post (formerly forums) - community Q&A with AWS-staff-verified answers
- AWS Health Dashboard at health.aws.amazon.com
- AWS Service Quotas console and AWS Well-Architected Tool
Related fixes
Related guides worth a look while you sort this one out:
- How to fix AWS Batch dependent jobs not starting dependsOn issues
- How to fix AWS Batch jobs running out of memory mid-execution
- How to mount EFS to AWS Batch jobs for shared input/output
- How to retry failed AWS Batch jobs with exponential backoff
- How to schedule recurring AWS Batch jobs with EventBridge
- How to use AWS Batch with Spot instances for cheap HPC