Alex Casalboni Sr. Developer Advocate, AWS @alex_casalboni Data-driven & cost-aware performance tuning for AWS Lambda @ 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved

About me • Software Engineer & Web Developer • Data science background • Worked in a US startup for 4.5 years • ServerlessDays Milan co-organizer • AWS customer since 2013

Fundamentals (very quickly!) Optimization best practices Idea: data-driven approach Real-world examples & patterns Agenda

Fundamentals

No content

AWS operational responsibility models On-premises Cloud Less More Compute Virtual machine Amazon EC2 AWS Elastic Beanstalk AWS Lambda AWS Fargate Databases MySQL MySQL on EC2 Amazon RDS MySQL Amazon Aurora Aurora Serverless Amazon QLDB/DynamoDB Storage Storage Amazon S3 Messaging ESBs Amazon MQ Amazon Kinesis Amazon EventBridge/SNS/SQS Analytics Hadoop Hadoop on EC2 Amazon EMR Amazon Elasticsearch Service Amazon Athena

Anatomy of a Lambda function Handler() function Function to be executed upon invocation Event object Data sent during Lambda function Invocation Context object Methods available to interact with runtime information (request ID, log group, more) import json def lambda_handler(event, context): # TODO implement return { 'statusCode': 200, 'body': json.dumps('Hello World!') }

The request lifecycle Bootstrap the runtime Start your code Cold start Warm start Download your code Start new micro vm AWS optimization Your optimization

Lambda + VPC = ß Before: 14.8 sec duration

Lambda + VPC = ß Before: 14.8 sec duration After: 933ms duration à

Optimization best practices

Avoid «monolithic» functions Reduce deployment package size Micro/Nano services Optimize dependencies (and imports) E.g. up to 120ms faster with Node.js SDK Optimization best practices (cold start) Minify/uglify production code Browserify/Webpack Lazy initialization of shared libs/objs Helps if multiple functions per file

Provisioned Concurrency for AWS Lambda New! Managed solution to avoid “cold starts” You set provisioned concurrency for individual functions No code changes required Integrated with AWS Auto Scaling Provisioning rampup (500/min) Wide support (CloudFormation, Terraform, Serverless Framework, Datadog, Epsagon, Lumigo, Thundra, etc.)

Externalize orchestration Avoid idle/sleep – delegate to Step Functions Transform, not Transport Minimize data transfer (S3 Select, advanced filtering, etc.) Discard uninteresting events asap Trigger config (S3 prefix, SNS filter, etc.) Fine-tune resources allocation Don’t guesstimate function memory Lambda Destinations Simplified chaining (async) and DLQ Keep in mind retry policies Very powerful (and configurable), but not free Optimization best practices (performance/cost)

Externalize orchestration Avoid idle/sleep – delegate to Step Functions Transform, not Transport Minimize data transfer (S3 Select, advanced filtering, etc.) Discard uninteresting events asap Trigger config (S3 prefix, SNS filter, etc.) Fine-tune resources allocation Don’t guesstimate function memory Lambda Destinations Simplified chaining (async) and DLQ Keep in mind retry policies Very powerful (and configurable), but not free Optimization best practices (performance/cost)

Resources allocation Memory Power

CPU-bound example “Compute 1,000 times all prime numbers <= 1M” 128 MB 11.722 sec $0.024628 256 MB 6.678 sec $0.028035 512 MB 3.194 sec $0.026830 1024 MB 1.465 sec $0.024638

CPU-bound example “Compute 1,000 times all prime numbers <= 1M” 128 MB 11.722 sec $0.024628 256 MB 6.678 sec $0.028035 512 MB 3.194 sec $0.026830 1024 MB 1.465 sec $0.024638

CPU-bound example

Cost-aware performance optimization 310ms 400ms 294ms 300ms 5% performance optimization 25% cost optimization 480ms 500ms 408ms 500ms 15% performance optimization 0% cost optimization A B

Idea: data-driven approach

Data-driven cost & performance optimization for AWS Lambda Available as a SAR app Easy to integrate with CI/CD Meet AWS Lambda Power Tuning github.com/alexcasalboni/aws-lambda-power-tuning

github.com/alexcasalboni/aws-lambda-power-tuning v1 v2 v3

AWS Lambda Power Tuning (input) github.com/alexcasalboni/aws-lambda-power-tuning

AWS Lambda Power Tuning (output) github.com/alexcasalboni/aws-lambda-power-tuning

AWS Lambda Power Tuning (visualization) github.com/alexcasalboni/aws-lambda-power-tuning

Real-world examples and patterns

No-Op (trivial data manipulation <100ms)

CPU-bound (numpy: inverting 1500x1500 matrix)

CPU-bound (prime numbers)

CPU-bound (prime numbers – more granularity)

Network-bound (third-party API call)

Network-bound (3x DDB queries)

Network-bound (S3 download – 150MB)

Network-bound (S3 download multithread – 150MB)

Cost/Performance patterns github.com/alexcasalboni/aws-lambda-power-tuning

Takeaways

Takeaways Optimization best practices (what are you trying to optimize?) Avoid cold starts with Provisioned Concurrency Memory Power Optimal resources allocation can be automated (CI/CD) Think in terms of workload categories and cost/performance patterns Visualize optimal trade-offs with /alexcasalboni/aws-lambda-power-tuning

Go Build! Here to help you build

Alex Casalboni Sr. Developer Advocate, AWS @alex_casalboni @ 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved Thank you!