Slide 1

Slide 1 text

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Dr. Frank Munz Technical Evangelist, AWS @frankmunz 2019 Update Serverless Beyond AWS Lambda

Slide 2

Slide 2 text

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. About me • Software Architect / DevOps Engineer • Technical Evangelist @ AWS • Published an AWS book a while ago • Containers, serverless and a sprinkle of ML & big / fast data @frankmunz

Slide 3

Slide 3 text

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Ok, so serverless …

Slide 4

Slide 4 text

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

Slide 5

Slide 5 text

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. The Serverless Operational Model No provisioning, no management Pay for value Automatic scaling Highly available and secure

Slide 6

Slide 6 text

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AWS Serverless Portfolio COMPUTE AND DATASTORES AWS Lambda AWS Fargate Amazon API Gateway Amazon SNS Amazon MQ Amazon SQS AWS Step Functions APPLICATION INTEGRATION DEVELOPER TOOLS SECURITY AND ADMINISTRATION Amazon Aurora Serverless Amazon S3 Amazon DynamoDB AWS AppSync AWS IAM Amazon Cognito Amazon Inspector Amazon VPC Amazon GuardDuty AWS CloudFormation AWS Cloud9 AWS CloudTrail Amazon CloudWatch AWS X-Ray AWS CodePipeline AWS Config AWS SSO AWS Shield AWS WAF Amazon Kinesis AWS Serverless Application Repository

Slide 7

Slide 7 text

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AWS Lambda

Slide 8

Slide 8 text

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T Custom Runtimes Rule Stack Support for Ruby + AWS OPEN SOURCE o f f e r e d b y o f f e r e d b y o f f e r e d b y o f f e r e d b y PARTNER SUPPORTED

Slide 9

Slide 9 text

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T Layers Upload layer once, reference up to 5 layers within any function (one can be custom runtime) Layers are immutable, versioned and can overwrite each other Promote separation of responsibilities Secure sharing by ecosystem https://aws.amazon.com/blogs/aws/new-for- aws-lambda-use-any-programming-language-and- share-common-components/

Slide 10

Slide 10 text

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T Custom Runtimes Bring any Linux compatible language runtime Powered by new Runtime API Custom runtimes distributed as “layers” Rule Stack

Slide 11

Slide 11 text

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. References https://www.youtube.com/watch?v =QdzV04T_kec https://aws.amazon.com/blogs/a ws/new-for-aws-lambda-use-any- programming-language-and- share-common-components/ https://docs.aws.amazon.com/lambda/latest/dg /runtimes-custom.html

Slide 12

Slide 12 text

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Containers

Slide 13

Slide 13 text

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T AWS Fargate No cluster or infrastructure to manage or scale Everything is handled at the container level Scale seamlessly on demand Time and event-based scheduling, network integration, individually metered, and billed. Native service discovery. Containers as first-class primitive

Slide 14

Slide 14 text

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T Recent launches - Fargate Cost reduction of up to 50% Tagging & Cost Allocation Cloud Map Integration AWS App Mesh Secrets management Private Link Support Rule Stack

Slide 15

Slide 15 text

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T App Mesh works across compute services Amazon ECS AWS Fargate Amazon EKS Amazon EC2 Kubernetes on EC2

Slide 16

Slide 16 text

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T Based on Envoy proxy Start App Mesh from the AWS CLI, console or SDK There is no additional charge for using AWS App Mesh Supports any third-party tool that works with Envoy App Mesh

Slide 17

Slide 17 text

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T References https://www.slideshare.net/Amaz onWebServices/introducing-aws- app-mesh-mad303-santa-clara- aws-summit https://www.youtube.com/watch?v=f DmJf9kWFws https://aws.amazon.com/containers/new/

Slide 18

Slide 18 text

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Firecracker

Slide 19

Slide 19 text

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T

Slide 20

Slide 20 text

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T Firecracker • microVM, developed in Rust • Boots in 125 msec. • < 5MB of system overhead • Linux host and guest 4.14+ • Minimal device model -> small attack surface • Apache License, version 2.0 • Used for AWS Fargate and Lambda https://github.com/firecracker-microvm/firecracker https://aws.amazon.com/blogs/aws/firecracker-lightweight-virtualization- for-serverless-computing/

Slide 21

Slide 21 text

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T Firecracker Architecture and Benefits • Same security as EC2 • Designed for low overhead, high density, and fast start times

Slide 22

Slide 22 text

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T „The great thing about Lambda internals and Firecracker is you can leave this room and forget about all this. It was just for your entertainement. You can build your business logic and deliver value to your customers.“ Mark Brooker

Slide 23

Slide 23 text

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. IDEs

Slide 24

Slide 24 text

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T Author and debug Lambda applications Python, Node New Python Developer Preview Java, Python Developer Preview .NET, Node

Slide 25

Slide 25 text

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Databases

Slide 26

Slide 26 text

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Dynamo DB • Multi-region • Multi-master • Encryption at rest • Time to live • Backup & Restore • Point-in-time recovery • In-memory caching • Single digit ms perf, 99.99% uptime SLA • You define rcu and wcu Fully managed key-value and document DB

Slide 27

Slide 27 text

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Autoscaling DynamoDB (J. Barr Blog 2017) https://aws.amazon.com/blogs/aws/new-auto-scaling-for-amazon-dynamodb/

Slide 28

Slide 28 text

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. DynamoDB: On Demand • Thousands of request / sec • Pay per request • For new or existing tables • Also for indexes Good use cases for on demand: - Serverless stacks - Unpredictable, spiky load https://aws.amazon.com/blogs/aws/amazon-dynamodb-on-demand-no-capacity-planning-and-pay- per-request-pricing/

Slide 29

Slide 29 text

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Dynamo DB https://aws.amazon.com/blogs/aws/amazon-dynamodb-on-demand-no-capacity- planning-and-pay-per-request-pricing/ https://www.youtube.com/watch ?v=1CN0y2bfHac https://www.youtube.com/ watch?v=eTbBdXJq8ss

Slide 30

Slide 30 text

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Watch This! (if you care about DBs on AWS)

Slide 31

Slide 31 text

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T Aurora Aurora Provisioned: Specifiy instance size and #read replicas Aurora Serverless • Endpoint without instance size • Min and max Aurora Capacity Units (ACUs) = processing and memory capacity • Auto rules based on CPU/mem/#connections • Can scale to zero, no cool-down for scale up • MySQL (Postgres in preview) Cloud native DB - compatible with MySQL and PostgreSQL

Slide 32

Slide 32 text

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Aurora Serverless Autoscaling Scale compute to zero after period of inactivity

Slide 33

Slide 33 text

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. The Computer Science behind Aurora https://www.allthingsdistri buted.com/files/p1041- verbitski.pdf

Slide 34

Slide 34 text

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Aurora https://www.slides hare.net/AmazonWeb Services/amazon- aurora-storage- demystified-how- it-all-works- dat363-aws- reinvent-2018 https://www.youtube.com/wat ch?v=2WG01wJIGSQ https://www.youtube.co m/watch?v=4DqNk7ZTYjA https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUs erGuide/aurora-serverless.how-it-works.html https://www.allthingsdistributed.com/files/p1041- verbitski.pdf

Slide 35

Slide 35 text

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Analytics

Slide 36

Slide 36 text

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Data Analytics: AWS Lambda Lambda polls each shard once per second Lambda’s maximum execution time is 15 minutes data producer Kinesis Data Streams Amazon SNS Continuously stream data Lambda service Lambda function A Lambda function B Continuously polls for new data, 1 poll per second Automatically invokes your function(s) when data found

Slide 37

Slide 37 text

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T AWS Lambda supports Kinesis Data Streams Enhanced Fan-Out and HTTP/2 for faster streaming Enhanced fan-out allows customers to scale the number of functions reading from a stream in parallel while maintaining performance. HTTP/2 data retrieval API improves data delivery speed between data producers and Lambda functions by more than 65% Amazon Kinesis Data Streams https://aws.amazon.com/blogs/aws/kds-enhanced-fanout/

Slide 38

Slide 38 text

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T Kinesis Streaming Data Analytics / SQL

Slide 39

Slide 39 text

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T Kinesis Streaming Data Analytics / Apache Flink Framework and engine for stateful processing of data streams. Simple programming High performance Stateful Processing Strong data integrity Easy to use and flexible APIs make building apps fast In-memory computing provides low latency & high throughput Durable application state saves Exactly-once processing and consistent state

Slide 40

Slide 40 text

© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. S U M M I T Conclusion Serverless is more than just AWS Lambda (FaaS). AWS Lambda integration to other AWS services bring customer value

Slide 41

Slide 41 text

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. frankmunz @frankmunz https://medium.com/@frank.munz (Blog) https://speakerdeck.com/fmunz (Slides) Thank You!