Home / AWS / Fix AWS Lambda Memory Limit Exceeded

AWS

Fix AWS Lambda Memory Limit Exceeded

Resolve Lambda memory exceeded errors by analyzing memory usage, increasing allocation, optimizing code, and monitoring with CloudWatch.

Published: Apr 1, 20268 min readBy FixWikiHub Editorial Team

Abstract illustration for a troubleshooting knowledge base category.

Introduction

Lambda functions have configurable memory from 128MB to 10GB. When your function tries to allocate more memory than configured, AWS terminates it immediately. This causes the invocation to fail and triggers OOMKilled errors in CloudWatch.

Symptoms

CloudWatch Logs:

```bash $ aws logs filter-log-events --log-group-name /aws/lambda/my-function

Task timed out after 30.00 seconds # Or directly: Runtime exited without providing a reason # Or: OOMKilled ```

Invocation error:

```bash $ aws lambda invoke --function-name my-function output.json

{ "statusCode": 500, "body": "Internal server error" } ```

Memory usage in report:

bash

REPORT RequestId: abc-123 Duration: 15000ms Memory Size: 128MB Max Memory Used: 128MB
# Max Memory Used equals Memory Size = likely OOM

Common Causes

1.Memory configuration too low - Default 128MB insufficient for workloads
2.Memory leak in code - Accumulating objects without cleanup
3.Large data processing - Processing big files or datasets
4.Unoptimized dependencies - Heavy libraries consuming memory
5.Recursive calls accumulating - Stack overflow or memory accumulation
6.Container image size - Large container images consume memory on startup

Step-by-Step Fix

1.Check logs for specific error messages
2.Verify configuration settings
3.Test network connectivity
4.Review recent changes
5.Apply corrective action
6.Verify the fix

Step 1: Check Current Memory Configuration

```bash # Get function memory setting aws lambda get-function-configuration --function-name my-function \ --query 'MemorySize'

# Get actual memory usage from CloudWatch aws cloudwatch get-metric-statistics \ --namespace AWS/Lambda \ --metric-name MemoryUtilization \ --dimensions Name=FunctionName,Value=my-function \ --statistics Maximum \ --period 300 ```

Step 2: Analyze Memory Usage Patterns

```bash # Check recent invocations for memory usage aws logs filter-log-events \ --log-group-name /aws/lambda/my-function \ --filter-pattern "REPORT" \ --query 'events[*].message'

# Parse memory usage from logs aws logs start-query \ --log-group-name /aws/lambda/my-function \ --start-time $(date -d '1 day ago' +%s)000 \ --end-time $(date +%s)000 \ --query-string 'fields @message | filter @message like /Max Memory Used/ | parse @message "Max Memory Used: * MB" as used | stats max(used)'

# Get query results aws logs get-query-results --query-id QUERY_ID ```

Step 3: Increase Memory Allocation

```bash # Lambda memory range: 128MB - 10240MB (10GB) # Increase in 64MB increments

# Update function memory aws lambda update-function-configuration \ --function-name my-function \ --memory-size 512

# Verify the change aws lambda get-function-configuration --function-name my-function \ --query '{Memory:MemorySize,Timeout:Timeout}' ```

Note: Increasing memory also increases CPU proportionally, potentially improving performance.

Step 4: Profile Memory Usage in Code

Python:

```python import tracemalloc import psutil import os

def handler(event, context): # Start memory tracking tracemalloc.start()

# Get current memory process = psutil.Process(os.getpid()) initial_mem = process.memory_info().rss / 1024 / 1024 # MB

# Your function code here result = do_work(event)

# Check memory after work final_mem = process.memory_info().rss / 1024 / 1024 print(f"Memory used: {final_mem - initial_mem} MB")

# Get peak memory current, peak = tracemalloc.get_traced_memory() print(f"Peak memory: {peak / 1024 / 1024} MB")

tracemalloc.stop() return result ```

Node.js:

```javascript exports.handler = async (event) => { const initialMem = process.memoryUsage();

// Your function code const result = await doWork(event);

const finalMem = process.memoryUsage(); console.log('Memory delta:', { rss: (finalMem.rss - initialMem.rss) / 1024 / 1024, heapUsed: (finalMem.heapUsed - initialMem.heapUsed) / 1024 / 1024, external: (finalMem.external - initialMem.external) / 1024 / 1024 });

console.log('Peak heap:', process.memoryUsage().heapUsed / 1024 / 1024, 'MB');

return result; }; ```

Step 5: Optimize Memory-Intensive Operations

```python # BAD: Loading entire file into memory def process_file(bucket, key): s3 = boto3.client('s3') obj = s3.get_object(Bucket=bucket, Key=key) data = obj['Body'].read() # Entire file in memory return process(data)

# GOOD: Stream processing def process_file(bucket, key): s3 = boto3.client('s3') obj = s3.get_object(Bucket=bucket, Key=key) for chunk in obj['Body'].iter_chunks(chunk_size=1024*1024): # 1MB chunks process_chunk(chunk) # Process in chunks

# GOOD: Use generators instead of lists def get_items(): # BAD: return [item for item in large_list] # All in memory # GOOD: for item in large_list: yield item # Process one at a time ```

Step 6: Fix Memory Leaks

Python common leaks:

```python # Check for unclosed resources import gc

def handler(event, context): # Before processing gc.collect()

# Your code

# After processing - force garbage collection gc.collect()

# Check for uncollectable objects print(f"Garbage: {gc.garbage}") ```

Node.js common leaks:

```javascript // Avoid global variables accumulating let cache = {}; // BAD: grows unbounded

// GOOD: Use weak references or bounded cache const cache = new Map(); const MAX_CACHE_SIZE = 100;

function addToCache(key, value) { if (cache.size > MAX_CACHE_SIZE) { // Remove oldest entries const firstKey = cache.keys().next().value; cache.delete(firstKey); } cache.set(key, value); }

// Clean up event listeners process.on('beforeExit', () => { // Remove listeners, clear timers }); ```

Step 7: Optimize Dependencies

```bash # For Python - check package sizes pip show pandas | grep Size pip show numpy | grep Size

# Use lighter alternatives: # pandas -> polars (smaller, faster) # boto3 -> boto3.stub (if only needed for type hints)

# For Node.js - analyze bundle npm run build --analyze

# Use lighter libraries: # lodash -> lodash-es (tree-shakeable) # moment -> date-fns (smaller) ```

Step 8: Set Memory-Based Alarms

bash

# Create CloudWatch alarm for high memory usage
aws cloudwatch put-metric-alarm \
  --alarm-name lambda-high-memory \
  --namespace AWS/Lambda \
  --metric-name MemoryUtilization \
  --dimensions Name=FunctionName,Value=my-function \
  --statistic Maximum \
  --period 60 \
  --threshold 90 \
  --comparison-operator GreaterThanThreshold \
  --evaluation-periods 3 \
  --alarm-actions arn:aws:sns:region:account:ops-alerts

Step 9: Consider Lambda Power Tuning

Use AWS Lambda Power Tuning tool to find optimal memory:

```bash # Run power tuning step function aws stepfunctions start-execution \ --state-machine-arn arn:aws:states:region:account:stateMachine:lambdaPowerTuning \ --input '{"lambdaARN": "arn:aws:lambda:region:account:function:my-function", "powerValues": [128, 256, 512, 1024, 2048], "num": 100, "parallel": true}'

# Results show: # - Cost per invocation at each memory level # - Average duration at each memory level # - Optimal memory for cost vs speed tradeoff ```

Step 10: Handle Container Image Memory

For Lambda container images:

```bash # Container images use memory for runtime + code # Allocate extra memory for container overhead

# Check image size docker images my-lambda-image

# Image size + runtime overhead + working memory = total needed # Example: 200MB image + 100MB runtime + 300MB work = 600MB minimum

aws lambda update-function-configuration \ --function-name my-container-function \ --memory-size 1024 # Start high for container functions ```

Memory-to-CPU Reference

Lambda memory allocation determines CPU power:

Memory	CPU Share	Use Case
128MB	Low	Simple operations, APIs
256MB	Low	Light processing
512MB	Medium	Normal web functions
1024MB	Medium	Data processing
2048MB	High	Compute-intensive
4096MB+	High	ML, large datasets

Verification

```bash # Invoke function and check memory aws lambda invoke --function-name my-function output.json \ --invocation-type RequestResponse

# Check CloudWatch for successful invocations aws logs filter-log-events \ --log-group-name /aws/lambda/my-function \ --filter-pattern "REPORT" \ --start-time $(date -d '10 minutes ago' +%s)000

# Should show Max Memory Used < Memory Size ```

[Fix AWS Lambda Function Timeout](/articles/fix-aws-lambda-function-timeout)
[Fix AWS Lambda Cold Start Latency](/articles/fix-aws-lambda-cold-start-latency)
[Fix AWS Lambda Container Image Pull Failed](/articles/fix-aws-lambda-container-image-pull-failed)

[AWS troubleshooting: Fix IAM Permission Denied - Complete Tro](fix-iam-permission-denied)
[AWS cloud troubleshooting: AWS ACM Certificate Pending Validation Because the](aws-acm-certificate-pending-validation-wrong-route53-zone)
[AWS cloud troubleshooting: AWS ALB Returns 502 Because the Target Closed the ](aws-alb-502-target-closed-connection-keepalive-timeout-mismatch)
[AWS cloud troubleshooting: Fix AWS ALB CreateListener TargetGroupNotFound Err](aws-alb-createlistener-targetgroupnotfound)
[AWS cloud troubleshooting: Fix Aws Alb Lambda 502 Bad Gateway Issue in AWS](aws-alb-lambda-502-bad-gateway)

Was this guide helpful?

Related search paths

People also search for

If the symptom is close but not identical, these search paths usually surface the right neighboring fixes faster than scrolling the full archive.

AWS Lambda Memory Limit Exceeded AWS Lambda Memory Limit Exceeded AWS AWS Lambda Memory Limit Exceeded troubleshooting AWS Lambda Memory Limit Exceeded fix Resolve Lambda memory exceeded errors by analyzing memory usage, increasing allocation, optimizing code, and monitoring with CloudWatch AWS Resolve Lambda memory exceeded errors by analyzing memory usage, increasing allocation, optimizing code, and monitoring with CloudWatch

Explore Related Topics

Browse Guides from Other Categories

Discover troubleshooting guides from related categories to expand your knowledge.

FAQ

AWS Troubleshooting FAQs

Common questions about troubleshooting and preventing similar issues

How do I know if this aws-errors troubleshooting guide applies to my situation?

This guide is designed for aws-errors issues. If you're experiencing similar symptoms described in the article, follow the step-by-step instructions. Start with the most common causes and work through the diagnostic process.

Is it safe to follow these aws-errors troubleshooting steps?

Yes, all steps are designed to be safe and non-destructive. We recommend creating backups before making significant changes and testing each step before proceeding to the next.

How long does it typically take to resolve this type of aws-errors issue?

Most aws-errors issues can be resolved within 30 minutes to 2 hours, depending on the complexity and root cause. Follow the troubleshooting flow to identify and fix the problem efficiently.

How can I prevent this aws-errors issue from happening again?

Regular maintenance, monitoring, and following best practices for aws-errors configuration can help prevent recurrence. Consider implementing automated checks and alerts for early detection.

Written by

FixWikiHub Editorial Team

Our editorial team consists of experienced DevOps engineers, systems administrators, and cloud architects with hands-on experience in production environments across AWS, Azure, GCP, and on-premises infrastructure.

Every guide undergoes technical review for accuracy and is updated when software versions, commands, or best practices change.

Last updated: Apr 1, 2026

About our team

Important Notice

Disclaimer & Safety Guidelines

The troubleshooting steps in this guide are provided for educational and informational purposes. Before applying any changes to production systems:

Test in a staging environment first — Always verify commands and configurations in a non-production environment before deploying to live systems.
Create backups — Ensure you have current backups of databases, configurations, and critical files before making changes.
Understand the impact — Review how each step may affect your specific environment, dependencies, and users.
Consult official documentation — This guide supplements, but does not replace, official vendor documentation and best practices.

FixWikiHub is not responsible for any damages arising from the use of this content. See our Terms of Use for more information.

Resources

Official Documentation & Further Reading

For authoritative information, consult the official documentation for the technologies discussed in this guide. Our troubleshooting content supplements, but does not replace, vendor documentation.

AWS Documentation — Official Amazon Web Services guides and API references
Kubernetes Documentation — Official Kubernetes documentation
Nginx Documentation — Official Nginx web server documentation
Apache Documentation — Official Apache HTTP Server documentation
Docker Documentation — Official Docker container documentation

Fix AWS Lambda Memory Limit Exceeded

Introduction

Symptoms

Common Causes

Step-by-Step Fix

Step 1: Check Current Memory Configuration

Step 2: Analyze Memory Usage Patterns

Step 3: Increase Memory Allocation

Step 4: Profile Memory Usage in Code

Step 5: Optimize Memory-Intensive Operations

Step 6: Fix Memory Leaks

Step 7: Optimize Dependencies

Step 8: Set Memory-Based Alarms

Step 9: Consider Lambda Power Tuning

Step 10: Handle Container Image Memory

Memory-to-CPU Reference

Verification

People also search for

Browse Guides from Other Categories

WordPress

SSL

DNS

AWS Troubleshooting FAQs

FixWikiHub Editorial Team

Disclaimer & Safety Guidelines

Official Documentation & Further Reading

Fix AWS Lambda Memory Limit Exceeded

Introduction

Symptoms

Common Causes

Step-by-Step Fix

Step 1: Check Current Memory Configuration

Step 2: Analyze Memory Usage Patterns

Step 3: Increase Memory Allocation

Step 4: Profile Memory Usage in Code

Step 5: Optimize Memory-Intensive Operations

Step 6: Fix Memory Leaks

Step 7: Optimize Dependencies

Step 8: Set Memory-Based Alarms

Step 9: Consider Lambda Power Tuning

Step 10: Handle Container Image Memory

Memory-to-CPU Reference

Verification

Related Issues

Related Articles

People also search for

Share this guide

More AWS Troubleshooting Guides

Browse Guides from Other Categories

AWS Troubleshooting FAQs

FixWikiHub Editorial Team

Disclaimer & Safety Guidelines

Official Documentation & Further Reading