How to Fix AWS Glue “Command Failed with Exit Code 1”

If you’ve been working with AWS Glue for ETL pipelines and data integration, there’s a high chance you’ve run into the dreaded error:

Command failed with exit code 1

It usually appears when running a Glue job that suddenly stops without a helpful explanation — especially frustrating when everything worked fine yesterday. Most Indian SMEs and data engineering teams using cloud solutions for data transformation face this issue at some point, whether they’re processing CSV files from S3, running PySpark transformations, or loading data into RDS / Redshift.

The good news?
This error is almost always caused by memory misconfiguration or IAM permissions — and once you know where to look, fixing it becomes easy.

In this blog, we’ll break it down step-by-step in a simple, practical, and highly actionable way.

👀 What Does “Command Failed with Exit Code 1” Actually Mean?

This error means your AWS Glue job terminated unexpectedly before finishing, but AWS couldn’t provide a detailed error message. That typically happens when:

The job runs out of memory
The script doesn’t have permission for a resource
The job fails to communicate with input/output services

Most cases fall into two categories:

Memory / resource capacity issues
Missing or incorrect IAM roles & permissions

Let’s look at how to diagnose and fix each one.

🧠 Fix 1: Memory Issues in AWS Glue

If your Glue job stops abruptly while processing large datasets or complex transformations, you’re likely hitting a memory limit.

Symptoms

Job ends after long processing time
No detailed error logs in CloudWatch
Spark tasks suddenly stop executing

How to Fix

1. Increase DPU Allocation

Go to your Glue Job → Job Details

Increase DPUs from G.1X to G.2X or higher
Example: From 2 DPUs to 8 DPUs for large datasets

2. Enable Job Bookmarking

Avoids reprocessing entire data when not needed.

3. Use Spark DataFrame Optimizations

df = df.repartition(10)  # Prevents skew & memory overload

4. Use Pushdown Predicates

Reduce load by filtering early:

datasource = glueContext.create_dynamic_frame.from_options(
    connection_type="s3",
    connection_options={"paths": ["s3://bucket/data"], "recurse": True, "groupFiles": "inPartition"},
    format="parquet"
)

Quick Checklist

Issue	Fix
Large dataset fails	Increase DPUs
Slow Spark transformations	Repartition & cache
Reprocessing full dataset	Use bookmarks
Parquet/Columnar formats	Lower memory footprint

🔐 Fix 2: IAM Role & Permission Issues

If your Glue job fails instantly or after the first read/write operation, the problem is likely permissions.

Common IAM Causes

No access to S3 bucket
Denied access to Redshift / RDS / Glue Catalog
Missing KMS key access
Restricted logs write permissions

How to Fix IAM Issues

1. Update IAM Role

Make sure the job role contains policies like:

{
  "Effect": "Allow",
  "Action": ["s3:*", "logs:*", "glue:*"],
  "Resource": "*"
}

2. Enable CloudWatch Logs

It reveals exact missing permissions.

3. Check KMS Encryption

If using encrypted S3 or RDS:
Add KMS key permissions:

kms:Decrypt
kms:Encrypt

4. Review Job Role Trust Policy

Ensure role is allowed to use Glue service.

🧪 How to Troubleshoot Step-by-Step

Follow this path whenever you see Exit Code 1:

Check CloudWatch logs for memory or permission messages
Verify IAM role access to S3, Glue, Redshift, CloudWatch
Increase DPUs
Optimize Spark and reduce workload size
Test the job in small dataset mode
Check connection configs & network access (VPC settings)

📍 Real Example

A retail SME running nightly ETL to build sales dashboards faced random job failures. The job processed 12GB incremental CSV files, and Glue crashed with Exit Code 1 repeatedly.

Fix Applied

Increased from 3 DPUs to 6 DPUs
Enabled partitioning on date column
Allowed Glue IAM role full S3 & KMS access

Outcome

Job runtime reduced from 46 min → 18 min
Zero failures in 30 days
Faster insights for business decisions

This shows how improving cloud efficiency directly impacts growth — proving again why digital transformation for SMEs India is essential.

💸 Cost Perspective: Memory vs Budget

Increasing DPUs costs money — but failing jobs cost more time and productivity.

For sustainable savings, implement:

Cloud cost optimisation India Best Practices

Autoscaling Glue jobs
Intelligent resource allocation
Reserved capacity pricing
Effectively tuning ETL loads

Optimizing efficiently is the key to reduce operational costs with cloud.

🤝 Should You Use an AWS Partner for ETL Optimization?

If your workloads are scaling or mission-critical, working with an AWS partner India can:

Improve performance reliability
Cut ETL execution costs 30–60%
Help in debugging Glue, Athena, Redshift & S3 optimally
Support AWS cloud migration India with zero risk

🔧 AWS Glue Fix Summary Checklist

Problem	Solution
Job stops mid-run	Increase memory (DPUs)
Fails immediately	Fix IAM permissions
Slow performance	Use partitioning & Pushdown
Bad logs visibility	Enable CloudWatch
Costs rising	Apply AWS cost optimisation

🚀 Final Thoughts

Command Failed with Exit Code 1 is annoying — but very fixable.
It’s usually about:

🧠 Memory
🔐 Permissions

Once identified correctly, your ETL pipelines will run smoothly and efficiently, helping your business scale faster with cloud solutions for Indian SMEs.

How to Fix AWS Glue “Command Failed with Exit Code 1” (Memory vs IAM Issues Explained)