| name | lambda |
| description | AWS Lambda serverless functions for event-driven compute. Use when creating functions, configuring triggers, debugging invocations, optimizing cold starts, setting up event source mappings, or managing layers. |
| last_updated | 2026-01-07 |
| doc_source | https://docs.aws.amazon.com/lambda/latest/dg/ |
AWS Lambda
AWS Lambda runs code without provisioning servers. You pay only for compute time consumed. Lambda automatically scales from a few requests per day to thousands per second.
Table of Contents
Core Concepts
Function
Your code packaged with configuration. Includes runtime, handler, memory, timeout, and IAM role.
Invocation Types
| Type | Description | Use Case |
|---|---|---|
| Synchronous | Caller waits for response | API Gateway, direct invoke |
| Asynchronous | Fire and forget | S3, SNS, EventBridge |
| Poll-based | Lambda polls source | SQS, Kinesis, DynamoDB Streams |
Execution Environment
Lambda creates execution environments to run your function. Components:
- Cold start: New environment initialization
- Warm start: Reusing existing environment
- Handler: Entry point function
- Context: Runtime information
Layers
Reusable packages of libraries, dependencies, or custom runtimes (up to 5 per function).
Common Patterns
Create a Python Function
AWS CLI:
# Create deployment package
zip function.zip lambda_function.py
# Create function
aws lambda create-function \
--function-name MyFunction \
--runtime python3.12 \
--role arn:aws:iam::123456789012:role/lambda-role \
--handler lambda_function.handler \
--zip-file fileb://function.zip \
--timeout 30 \
--memory-size 256
# Update function code
aws lambda update-function-code \
--function-name MyFunction \
--zip-file fileb://function.zip
boto3:
import boto3
import zipfile
import io
lambda_client = boto3.client('lambda')
# Create zip in memory
zip_buffer = io.BytesIO()
with zipfile.ZipFile(zip_buffer, 'w') as zf:
zf.writestr('lambda_function.py', '''
def handler(event, context):
return {"statusCode": 200, "body": "Hello"}
''')
zip_buffer.seek(0)
# Create function
lambda_client.create_function(
FunctionName='MyFunction',
Runtime='python3.12',
Role='arn:aws:iam::123456789012:role/lambda-role',
Handler='lambda_function.handler',
Code={'ZipFile': zip_buffer.read()},
Timeout=30,
MemorySize=256
)
Add S3 Trigger
# Add permission for S3 to invoke Lambda
aws lambda add-permission \
--function-name MyFunction \
--statement-id s3-trigger \
--action lambda:InvokeFunction \
--principal s3.amazonaws.com \
--source-arn arn:aws:s3:::my-bucket \
--source-account 123456789012
# Configure S3 notification (see S3 skill)
Add SQS Event Source
aws lambda create-event-source-mapping \
--function-name MyFunction \
--event-source-arn arn:aws:sqs:us-east-1:123456789012:my-queue \
--batch-size 10 \
--maximum-batching-window-in-seconds 5
Environment Variables
aws lambda update-function-configuration \
--function-name MyFunction \
--environment "Variables={DB_HOST=mydb.cluster-xyz.us-east-1.rds.amazonaws.com,LOG_LEVEL=INFO}"
Create and Attach Layer
# Create layer
zip -r layer.zip python/
aws lambda publish-layer-version \
--layer-name my-dependencies \
--compatible-runtimes python3.12 \
--zip-file fileb://layer.zip
# Attach to function
aws lambda update-function-configuration \
--function-name MyFunction \
--layers arn:aws:lambda:us-east-1:123456789012:layer:my-dependencies:1
Invoke Function
# Synchronous invoke
aws lambda invoke \
--function-name MyFunction \
--payload '{"key": "value"}' \
response.json
# Asynchronous invoke
aws lambda invoke \
--function-name MyFunction \
--invocation-type Event \
--payload '{"key": "value"}' \
response.json
CLI Reference
Function Management
| Command | Description |
|---|---|
aws lambda create-function |
Create new function |
aws lambda update-function-code |
Update function code |
aws lambda update-function-configuration |
Update settings |
aws lambda delete-function |
Delete function |
aws lambda list-functions |
List all functions |
aws lambda get-function |
Get function details |
Invocation
| Command | Description |
|---|---|
aws lambda invoke |
Invoke function |
aws lambda invoke-async |
Async invoke (deprecated) |
Event Sources
| Command | Description |
|---|---|
aws lambda create-event-source-mapping |
Add event source |
aws lambda list-event-source-mappings |
List mappings |
aws lambda update-event-source-mapping |
Update mapping |
aws lambda delete-event-source-mapping |
Remove mapping |
Permissions
| Command | Description |
|---|---|
aws lambda add-permission |
Add resource-based policy |
aws lambda remove-permission |
Remove permission |
aws lambda get-policy |
View resource policy |
Best Practices
Performance
- Right-size memory: More memory = more CPU = faster execution
- Minimize cold starts: Keep functions warm, use Provisioned Concurrency
- Optimize package size: Smaller packages deploy faster
- Use layers for shared dependencies
- Initialize outside handler: Reuse connections across invocations
# GOOD: Initialize outside handler
import boto3
dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('MyTable')
def handler(event, context):
# Reuses existing connection
return table.get_item(Key={'id': event['id']})
Security
- Least privilege IAM roles — only grant needed permissions
- Use Secrets Manager for sensitive data
- Enable VPC only if needed (adds latency)
- Encrypt environment variables with KMS
Cost Optimization
- Set appropriate timeout — don't use max 15 minutes unnecessarily
- Use ARM architecture (Graviton2) for 34% better price/performance
- Batch process where possible
- Use Reserved Concurrency to limit costs
Reliability
- Configure DLQ for async invocations
- Handle retries — async events retry twice
- Make handlers idempotent
- Use structured logging
Troubleshooting
Timeout Errors
Symptom: Task timed out after X seconds
Causes:
- Function takes longer than timeout
- Network call to unreachable resource
- VPC configuration issues
Debug:
# Check function configuration
aws lambda get-function-configuration \
--function-name MyFunction \
--query "Timeout"
# Increase timeout
aws lambda update-function-configuration \
--function-name MyFunction \
--timeout 60
Out of Memory
Symptom: Function crashes with memory error
Fix:
aws lambda update-function-configuration \
--function-name MyFunction \
--memory-size 512
Cold Start Latency
Causes:
- Large deployment package
- VPC configuration
- Many dependencies to load
Solutions:
- Use Provisioned Concurrency
- Reduce package size
- Use layers for dependencies
- Consider Graviton2 (ARM)
# Enable Provisioned Concurrency
aws lambda put-provisioned-concurrency-config \
--function-name MyFunction \
--qualifier LIVE \
--provisioned-concurrent-executions 5
Permission Denied
Symptom: AccessDeniedException
Debug:
# Check execution role
aws lambda get-function-configuration \
--function-name MyFunction \
--query "Role"
# Check role policies
aws iam list-attached-role-policies \
--role-name lambda-role
VPC Connectivity Issues
Symptom: Cannot reach internet or AWS services
Causes:
- No NAT Gateway for internet access
- Missing VPC endpoint for AWS services
- Security group blocking outbound
Solutions:
- Add NAT Gateway for internet
- Add VPC endpoints for AWS services
- Check security group rules