Data-driven & cost-aware performance tuning for AWS Lambda

How can we combine cost optimization and performance tuning into a single data-driven process, and even integrate it into our CI/CD pipelines? This talk will showcase an open-source tool to optimize Lambda functions resource allocation without requiring complex refactors or architectural changes, while showcasing charts and cost/performance patterns of real-world use cases.

Transcript

1. 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
2. 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
3. Fundamentals (very quickly!) Optimization best practices Idea: data-driven approach Real-world examples & patterns Agenda
4. Fundamentals
6. 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
7. 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!') }
8. The request lifecycle Bootstrap the runtime Start your code Cold start Warm start Download your code Start new micro vm AWS optimization Your optimization
9. Lambda + VPC = ß Before: 14.8 sec duration
10. Lambda + VPC = ß Before: 14.8 sec duration After: 933ms duration à
11. Optimization best practices
12. 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
13. 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.)
14. 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)
15. 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)
16. Resources allocation Memory Power
17. 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
18. 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
19. CPU-bound example
20. 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
21. Idea: data-driven approach
22. 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
23. github.com/alexcasalboni/aws-lambda-power-tuning v1 v2 v3
24. AWS Lambda Power Tuning (input) github.com/alexcasalboni/aws-lambda-power-tuning
25. AWS Lambda Power Tuning (output) github.com/alexcasalboni/aws-lambda-power-tuning
26. AWS Lambda Power Tuning (visualization) github.com/alexcasalboni/aws-lambda-power-tuning
27. Real-world examples and patterns
28. No-Op (trivial data manipulation <100ms)
29. CPU-bound (numpy: inverting 1500x1500 matrix)
30. CPU-bound (prime numbers)
31. CPU-bound (prime numbers – more granularity)
32. Network-bound (third-party API call)
33. Network-bound (3x DDB queries)
34. Network-bound (S3 download – 150MB)
35. Network-bound (S3 download multithread – 150MB)
36. Cost/Performance patterns github.com/alexcasalboni/aws-lambda-power-tuning
37. Takeaways
38. 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
39. Go Build! Here to help you build
40. Alex Casalboni Sr. Developer Advocate, AWS @alex_casalboni @ 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved Thank you!