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© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS Lambda function performance tuning Alex Casalboni S V S 2 2 4 - R Technical Evangelist Amazon Web Services

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Agenda Fundamentals & news Optimization best practices AWS Lambda Power Tuning Real-world examples Whiteboard discussion

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© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. “So what does the future look like? All the code you ever write is business logic.” Dr. Werner Vogels CTO of Amazon.com

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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

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© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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Provisioned Concurrency for AWS Lambda Simple config to avoid cold starts No more manual pre-warming Predictable performance during spikes Good fit for latency-sensitive apps No code changes required Bound to version or alias AWS CloudFormation support Scheduling with AWS Application Auto Scaling From 1 to account concurrency limit Two new metrics Ramp-up time (500 per minute) Initialization code is executed automatically

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Provisioned Concurrency for AWS Lambda

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Provisioned Concurrency for AWS Lambda

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Monitoring Deployment frameworks

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© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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Cost & performance optimization best practices Avoid «monolithic» functions Optimize dependencies (and imports) Minify/uglify production code Lazy initialization of shared libs/objs Externalize orchestration Fine-tune resources allocation Lambda Destinations Discard uninteresting events asap Keep in mind retry policies Understand currency controls

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Cost & performance optimization best practices Avoid «monolithic» functions Optimize dependencies (and imports) Minify/uglify production code Lazy initialization of shared libs/objs Externalize orchestration Fine-tune resources allocation Lambda Destinations Discard uninteresting events asap Keep in mind retry policies Understand currency controls

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Resources allocation Memory Power

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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

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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

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CPU-bound example

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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

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© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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“AWS Lambda Power Tuning” Data-driven cost & performance optimization for AWS Lambda Available as a SAR app Easy to integrate with CI/CD Don’t guesstimate! v1 github.com/alexcasalboni/aws-lambda-power-tuning

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

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

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

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

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

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

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

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

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

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

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

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© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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No-Op (trivial data manipulation <100ms) github.com/alexcasalboni/aws-lambda-power-tuning

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CPU-bound (numpy: inverting 1500x1500 matrix) github.com/alexcasalboni/aws-lambda-power-tuning

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CPU-bound (prime numbers) github.com/alexcasalboni/aws-lambda-power-tuning

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CPU-bound (prime numbers – more granularity) github.com/alexcasalboni/aws-lambda-power-tuning

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Network-bound (third-party API call) github.com/alexcasalboni/aws-lambda-power-tuning

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Network-bound (3x DDB queries) github.com/alexcasalboni/aws-lambda-power-tuning

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Network-bound (S3 download – 150MB) github.com/alexcasalboni/aws-lambda-power-tuning

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Network-bound (S3 download multithread – 150MB) github.com/alexcasalboni/aws-lambda-power-tuning

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

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Takeaways Memory Power Avoid cold starts with Provisioned Concurrency 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

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© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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Thank you! © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Alex Casalboni [email protected] @alex_casalboni