Getting Started with Serverless Architectures
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Serverless architectures let you build and deploy applications and services with infrastructure resources that require zero administration. In the past, you had to provision and scale servers to run your application code, install and operate distributed databases, and build and run custom software to handle API requests. Now, AWS provides a stack of scalable, fully-managed services that eliminates these operational complexities. In this session, you will learn about serverless architectures, their benefits, and the basics of the AWS’s serverless stack (e.g., AWS Lambda, Amazon API Gateway, and AWS Step Functions). You will also get practical tips and tricks, best practices, and architecture patterns that you can take back and implement immediately.
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Getting Started with Serverless Architectures
- 1. P U B L I C S E C T O R S U M M I T B rus s els
- 2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T GettingStarted withServerless Architectures Prakash Palanisamy Solutions Architect Amazon Web Services
- 3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Agenda • Serverless Foundations • Serverless Architecture Patterns • Web Application • Automation • Stream Processing • Batch Processing
- 4. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T
- 5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Whatis serverless? No infrastructure provisioning, no management Automatic scaling Pay for value Highly available and secure
- 6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T AWS Lambda AWS Fargate Amazon API Gateway Amazon SNS Amazon SQS AWS Step Functions COMPUTE DATA STORES INTEGRATION Amazon Aurora Serverless Amazon S3 Amazon DynamoDB AWS AppSync
- 7. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Customers love serverless
- 8. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Serverless compute engine for containers Long-running Bring existing code Fully-managed orchestration AWS Fargate Let’s focus on compute for now Serverless event-driven code execution Short-lived All language runtimes Data source integrations AWS Lambda
- 9. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T SERVICES (ANYTHING) Changes in data state Requests to endpoints Changes in resource state EVENT SOURCE FUNCTION Node.js Python Java C# Go Ruby Serverless applications
- 10. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Comparison of operational responsibility AWS Lambda Serverless functions AWS Fargate Serverless containers ECS/EKS Container-management as a service EC2 Infrastructure-as-a-Service More opinionated Less opinionated AWS manages Customer manages • Data source integrations • Physical hardware, software, networking, and facilities • Provisioning • Application code • Container orchestration, provisioning • Cluster scaling • Physical hardware, host OS/kernel, networking, and facilities • Application code • Data source integrations • Security config and updates, network config, management tasks • Container orchestration control plane • Physical hardware software, networking, and facilities • Application code • Data source integrations • Work clusters • Security config and updates, network config, firewall, management tasks • Physical hardware software, networking, and facilities • Application code • Data source integrations • Scaling • Security config and updates, network config, management tasks • Provisioning, managing scaling and patching of servers
- 11. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Using AWS Lambda Bring your own code • Node.js, Java, Python, C#, Go, Ruby • Bring your own libraries (even native ones) Simple resource model • Select power rating from 128 MB to 3 GB • CPU and network allocated proportionately Authoring functions • WYSIWYG editor or upload packaged .zip • Third-party plugins (Eclipse, Visual Studio)
- 12. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Amazon S3 Amazon SNSAmazon SES Cron events DATA STORES ENDPOINTS DEVELOPMENT AND MANAGEMENT TOOLS EVENT/MESSAGE SERVICES Event sources that trigger AWS Lambda …and more! Amazon API Gateway Amazon Alexa AWS IoT AWS Step Functions Amazon DynamoDB Amazon Kinesis Amazon Cognito AWS CloudFormation AWS CloudTrail AWS CodeCommit Amazon CloudWatch
- 13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Lambda Execution Model Synchronous (push) Asynchronous (event) Stream-based Amazon API Gateway AWS Lambda function Amazon DynamoDBAmazon SNS /order AWS Lambda function Amazon S3 reqs Amazon Kinesis changes AWS Lambda service function
- 14. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Making development easierwithAWS Lambda Trillions of executions every month for hundreds of thousands of active customers Accessible for all developers Enable new application patterns Greater productivity Support for all runtimes with Lambda Layers and Runtime API ISO, PCI, HIPAA, SOC, GDPR, and FedRamp compliances 15 minute functions SQS for Lambda Automatic Load Balancing for Lambda Support for Kinesis Data Streams Enhanced Fan-Out and HTTP/2 Toolkits for popular IDEs: VSCode, IntelliJ, and PyCharm Simplified deployment with nested apps
- 15. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T AWS Lambda permissions model Fine-grained security controls for both invocation and execution Function policies: • What can invoke the Lambda function Execution role: • What the Lambda function can access
- 16. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Basic Function Code 'use strict' exports.handler = function (event, context, callback) { console.log(“Event:”, JSON.stringify(event)) callback(null, ”Hello World!") } { “Records”:[ “s3”: { “object”:” { … } } ] } JSON Context
- 17. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Amazon API Gateway Create a unified API frontend for multiple microservices Authenticate and authorize requests to a backend DDoS protection and throttling for your backend Throttle, meter, and monetize API usage by third- party developers
- 18. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T APIGatewayIntegrations Internet Mobile apps Websites Services AWS Lambda functions AWS API Gateway Cache Endpoints on Amazon EC2 All publicly accessible endpoints Amazon CloudFront Any other AWS service Your VPC Endpoints in your VPC Regional API endpoints Amazon CloudWatch Monitoring
- 19. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T AWS Step Functions “Serverless” workflow management with zero administration: • Coordinates distributed applications using visual workflows • Automatically triggers, tracks, and logs each step
- 20. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Security and Identity • User pools for secure, managed user directories • Identity pools for federation and role-based access control Amazon Cognito • Fine-grained access control to AWS resources AWS Identity and Access Management
- 21. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Logging and Monitoring Amazon CloudWatch AWS X-Ray
- 22. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T AWS Serverless Application Model (SAM) • Extension of AWS CloudFormation • Automate deployment via AWS CodePipeline • Build, test, and debug locally via SAM CLI • Search and deploy serverless apps on the AWS Serverless Application Repository
- 23. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T
- 24. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Web applications • Static websites • Complex web apps • Packages for Flask and Express Data processing • Real-time • MapReduce • Batch Chatbots • Powering chatbot logic Backends • Apps and services • Mobile • IoT </></> Amazon Alexa • Powering voice-enabled apps • Alexa Skills Kit IT automation • Policy engines • Extending AWS services • Infrastructure management Common serverless use cases
- 25. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Web application characteristics 1. Static and Dynamic Content 2. Microservices 3. Scalable 4. Authenticate and Authorize 5. Globally available
- 26. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Web application Browser Amazon CloudFront Microservices Amazon DynamoDB AWS LambdaAmazon API Gateway Amazon Cognito Amazon S3
- 27. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Bustle Achieves 84% Cost Savings with AWS Lambda Bustle is a news, entertainment, lifestyle, and fashion website targeted towards women. With AWS Lambda, we eliminate the need to worry about operations Tyler Love CTO, Bustle ” “ • Bustle had trouble scaling and maintaining high availability for its website without heavy management • Moved to serverless architecture using AWS Lambda and Amazon API Gateway • Experienced approximately 84% in cost savings • Engineers are now focused on innovation
- 28. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Multi-Region with API Gateway eu-central-1 eu-west-1 Client Amazon Route 53 Regional API Endpoint Regional API Endpoint Custom Domain Name Custom Domain Name API Gateway API Gateway Lambda Lambda
- 29. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Automation characteristics • Periodic jobs • Event triggered workflows • Enforce security policies • Audit and notification • Respond to alarms • Extend AWS functionality
- 30. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Enforce security policies RDP from 0.0.0.0/0 RDP from 0.0.0.0/0 CloudWatch Event Bus in another AWS Account New Security Group ingress rule Amazon CloudWatch Events: Rule AWS Lambda: Remediate and alert AWS SNS: Email alert Ingress rule deleted
- 31. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Autodesk - Tailor Serverless AWS Account Provisioning and Management Service: • Automates AWS Account creation, • Configures AWS IAM, AWS CloudTrail, AWS Config, AWS Direct Connect, and Amazon VPC • Enforces corporate standards • Audit for compliance Provisions new Accounts in 10 minutes vs 10 hours in earlier manual process Open source and extensible: https://github.com/alanwill/aws-tailor
- 32. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T AWS Ops Automator Amazon CloudWatch: Time-based events AWS Lambda: Event handler AWS Lambda: Task executors AWS SNS: Error and warning notifications Resources in multiple AWS Regions and Accounts Amazon EC2 Tags OpsAutomatorTaskList CreateSnapshotAmazon DynamoDB: Task configuration & tracking Amazon CloudWatch: Logs Amazon Redshift https://aws.amazon.com/answers/infrastructure-management/ops-automator/
- 33. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Image recognition and processing Web App Amazon Cognito: User authentication Amazon S3: Image uploads Start state machine execution Extract image meta-data Amazon Rekognition: Object detection Invoke Amazon Rekognition Generate image thumbnail Amazon DynamoDB: Image meta-data & tags Store meta-data and tags https://github.com/awslabs/lambda-refarch-imagerecognition AWS Step Functions: Workflow orchestration Image recognition and processing
- 34. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Stream processing characteristics • High ingest rate • Near real-time processing (low latency from ingest to process) • Spiky traffic (lots of devices with intermittent network connections) • Message durability • Message ordering
- 35. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Amazon Kinesis AWS Lambda 1 Amazon CloudWatch Amazon DynamoDB AWS Lambda 2 Amazon S3 Real-time data processing: 1. Real-time event data sent to Amazon Kinesis allows multiple AWS Lambda functions to process the same events 2. In AWS Lambda, Function 1 processes and aggregates data from incoming events, then stores result data in Amazon DynamoDB 3. Lambda Function 1 also sends values to Amazon CloudWatch for simple monitoring of metrics 4. In AWS Lambda function, Function 2 does data manipulation of incoming events and stores results in Amazon S3 https://s3.amazonaws.com/awslambda-reference-architectures/stream-processing/lambda-refarch-streamprocessing.pdf AWS Lambda + Amazon Kinesis
- 36. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Sensor datacollection IoT rules IoT actions MQTT Amazon S3: Raw records Amazon Kinesis Firehose: Delivery stream Amazon S3: Batched records Amazon Kinesis Streams: Real-time stream AWS IoT: Data collection IoT Sensors Real-time analytics applications
- 37. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Batchprocessing AWS Lambda: Splitter Amazon S3 Object Amazon DynamoDB: Mapper Results AWS Lambda: Mappers …. …. AWS Lambda: Reducer Amazon S3 Results Analytics & Processing Amazon SQS
- 38. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. P U B L I C S E C TO R S U M M I T Fannie MaeServerlessFinancialModeling Financial Modeling is a Monte-Carlo simulation process to project future cash flows, which is used for managing the mortgage risk on daily basis: • Underwriting and valuation • Risk management • Financial reporting • Loss mitigation and loan removal • ~10 quadrillion (10𝑥1015 ) of cash flow projections each month in hundreds of economic scenarios. • One simulation run of ~ 20 million mortgages takes 1.4 hours, >4 times faster than the existing process.
- 39. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T
- 40. Thank you! © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T Prakash Palanisamy pprakash
- 41. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.P U B L I C S E C TO R S U M M I T
Hinweis der Redaktion
- So what is serverless? When we say serverless, we mean it’s the removal of the undifferentiated heavy lifting that is server operations. This is an important distinction for customers because it allows customers to focus on the building of the application rather than the management and scaling of the infrastructure to support the application. This means not thinking about infrastructure or scaling. It means you only pay for what you value, and it means availability and security are built in. --- These are the tenets that define serverless as an operational model (unless you know better ones): No infrastructure to provision or manage (no servers to provision, operate, patch, etc.) Automatically scales by unit of consumption (scales by unit of work/consumption rather than by server unit) Pay for value billing model (if you value consistent throughput or execution duration you only pay for that unit rather than by server unit) Built-in availability and fault tolerance (no need to architect for availability because it is built into the service)
- AT AWS this spans many many different services. Today we’re going to talk about compute.
- FINRA is a great example of how serverless can enable rapid innovation. FINRA protects investors, and ensures market integrity through the regulation of broker-dealers. FINRA audits 99% of equities trades and 65% of options trades in the US, and must validate stock data daily as soon as it arrives. FINRA wanted to move an on-premises Hadoop cluster to AWS, because it was hard to maintain, only ran in batches and couldn’t scale easily. And, it was idle 50% of time. <!> Using serverless building blocks like AWS Lambda, Amazon S3 and Amazon SQS, FINRA developed a serverless stock trade validation system in only 3 months. They increased cost efficiency by a factor of two vs their previous on-premises system. They also reduced their processing times and can now easily scale to handle up to half a trillion validations every day.
- Let’s quickly look at the two serverless computing options from AWS. First, we have serverless functions in the form of AWS Lambda. Lambda can offer great agility and economic benefits. With Lambda, developers write code in units called functions, and define a trigger to run the code. Lambda starts, stops and scales automatically based on the usage load it receives. For another class of applications, you may want a little more control over networking, or how services communicate with each other, or what your packaging should look like. Here your unit of work goes from being a piece of code to a set of container image stored in a registry But you don’t want to have to think about how you take that bundle and where in some cluster you will launch it or what that will look like. You can just define it, and deploy it. This is what Fargate does. ---- Offload operations: You never have to worry about provisioning, scaling, patching, or managing the underlying servers. AWS takes care of managing the compute fleet, including cycling, OS and security patches and updates, scaling horizontally and starting and stopping the underlying instance to fit the exact needs of the workload, and deploying your application. The only configuration choice that a developer needs to make is the choice of language & runtime. Availability: Lambda automatically deploys to multiple availability zones, so you don’t need to architect for high availability or fault tolerance. Security: Lambda also enables you to secure your modern applications with very little administration. AWS secures the infrastructure and takes care of security patching servers multiple times a day. You secure the software stack. Cost: You pay only for the compute time you consume – there is no charge when the code is not running. Use cases: Lambda is a great choice for applications that don’t have specific infrastructure requirements. Because of its event-driven nature, AWS Lambda is great for data-driven applications that need to respond in real-time to changes in data, shifts in system state, or actions by users for use cases such as batch processing, stream analytics, and machine learning inference. Lambda can actually react to events from over 18 AWS services Fargate With Fargate, you no longer have to manage or patch the hosts your clusters run on. However, you are responsible for updating and patching the container. First implementation of serverless containers on the market Runs containers launched by Amazon ECS or Amazon EKS Fargate is a great choice for building long-lived processes such as backends for web and mobile and PaaS platforms. With Fargate, customers get the control of containers and the flexibility to choose when they run without worrying about provisioning or scaling servers. Fargate offers full control of networking, security, and service to service communication and is natively integrated with AWS services for security, networking, access control, developer tooling, monitoring, and logging. For customers that want control of their underlying infrastructure (including the OS, runtime, networking, or how their application scales) and for customers who use container-based build and deploy technologies and pipelines (such as Docker as a build/deploy technology, ECS or Kubernetes as an orchestration/deploy technology, or service-mesh technologies such as Envoy), Fargate is a great choice. Fargate is a technology built on ECS and it is also a container-native compute engine for ECS and EKS, so it requires no change to your existing development practices if you have already been working with containers and are committed to Docker as a build/deploy technology, or if your application needs service-mesh technologies such as Envoy, or if you want to use a container orchestrator and deployment technology such as ECS or Kubernetes. If you have an application where you want the ability to configure and tune the network/OS settings, or even where you need to tune application settings to provide more CPU to apps that need higher throughput, and reduced CPU for lower throughput apps, Fargate can be a great choice. With Fargate, you can choose to configure the networking, how your application scales (such as setting up the scaling characteristics for the containers using Auto Scaling), and your runtime environment (such as choosing combinations of OS distribution and programming language). With Fargate, we extended serverless to containers so developers can recognize the benefits of agility, flexible scaling, and high availability for their container-based applications, all without needing to manage infrastructure Fargate is a great choice for building long-running processes such as microservices backends for web and mobile and PaaS platforms Customers simply define how they want their containers to run and the containers automatically deploy and scale across multiple availability zones When to use each Every application has its own requirements and needs Containers and functions work harmoniously together A Lambda-based or Fargate-based approach can be applied to virtually any use case. Choose the architectural approach that best fits with yourc use case or technical requirements Our customers often choose to use a powerful combination of both. For more detail on how to select the right compute option for your application, see <SESSION>. These are unique, game changers etc. these are core to that transformation While EC2 is the largest compute service in terms of sheer volume of utilization, we have developed a breadth of options that are best of breed in all three modalities of instances, containers and serverless. In fact, no other cloud treats all three as equals. And while it is common for customers to use a combination of all three across their application portfolio, picking the best approach based on their business and technical requirements, I would like to walk you through the core strengths of each category and show you how customers are using them to solve for specific challenges. Fargate is so important because it addresses long running steady capacity workloads that are stateful.
- This is essentially a recap of what we discussed earlier with databases. There is a spectrum of shared responsibility you have over your options for compute. With EC2, you can build and run things, but you manage integrations, scaling, security config, provisioning, patching etc. in addition to your code. Compare that to Lambda, where all you mange is your application code.
- We just talked about how we remove responsibility for managing infrastructure with Lambda. But you still have to write the code. And we want to make it easier to write application code with Lambda. Lambda serves trillions of executions each month, and so we’ve prioritized making it easier to build lambda functions quickly.
- Abstracts the implementation so that you can switch from Lambda to EC2 or Combine multiple backends. Similarly you can use mapping templates to unify different versions of your APIs Network protection is something we do very well and requires hyperscale, you won’t be able to auto-scale to meet an attack, let us do it Centralize authorization decisions in a policy and remove the concern from the code in your backend, fewer bugs
- The first thing we want to look at is the standard flow of an API call, including all components in the system First, a request comes in from a client, this could be a mobile device, a web application or a backend service The requests arrives at one of our CloudFront PoP locations, it’s accepted and routed through to the API Gateway in the customer’s region The API Gateway receives the request, then checks for records in the dedicated cache (if it is configured). If there are no cached records available then it will forward the request to the backend for processing The backend can be a Lambda function, a web service running on Amazon EC2, or any other publicly accessible web service Once the backend has processed the request the API call metrics are logged in Amazon CloudWatch and the content is returned to the client Two new API Gateway features: Access endpoints in your VPC: On the back end, API Gateway can now take advantage of the newly launched PrivateLink support to set up tunnels for an API to an integration point, like a service running on EC2, that resides in your own VPC. Regional API Endpoints: On the front end, we want regionalized APIs that we can call from Lambda that don’t route traffic out to CloudFront POPs and back in again.
- Web application pattern - this is one of the common usecase where customers are leveraging Serverless services. These are some of the key aspects of typical web application. And we want to build a reference architecture using Serverless services with these key characteristics in mind. There is static content, images, java scripts, css, etc being renedered in your website, your application users might be seeking dynamic content, and you might have created bunch of microservices! Ofcourse, You want to provide a way for your users to authn and authz. And all of this while being able to scale and globally present!
- Combing Lambda and Kinesis is a popular architecture model for streaming applications, there's a couple different components to Kinesis today you can use two streams Kinesis firehose can use analytics and actually lambda can interface with all of those but primarily what we're seeing customers do is ingesting data and then they need to do some sort of near real-time compute against that data and so in this case we have a Kenisha stream coming in that data is then being processed by lambda one of my lambda functions is going to take that data and put it in s3 the other ones gonna maybe process that data and pull out some metadata about something put into dynamo and maybe put some data into cloud watch logs maybe i'm processing an event stream from one of my own products and so there's a lot of different things that you can do in this space and this is again one of the larger use cases for service applications today. Bustle.com 52 million monthly users, and 100 million events daily.
- There's also a batch a serverless batch processing pattern that I want to showcase for your analytical processing needs this is more of a DIY approach where you have a lambda function that takes your source data and splits it up somehow maybe by lines or by by size and hands it off to a bunch of mapper functions that run in parallel to process your data and then write results to a persistent store and then we have a reducer function in this example collecting that data and storing the results in a durable store like S3.
- this is a pattern that's also well live well leveraged by customers Fannie Mae actually leverages a pattern very similar to this to process Monte Carlo simulations and cash flow projections for their mortgages they actually used to do this on more of a server based approach and by adopting this approach they were able to reduce the time it takes to run simulations by a factor of four.