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Building Scalable Serverless ApplicationsBuilding Scalable Serverless ApplicationsBuilding Scalable Serverless Applicationson AWSon AWSon AWSBy: Kevin S LinBy: Kevin S LinBy: Kevin S Lin

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Agenda

Hello, my name is...

Serverless: What is it?

Serverless: Why Care

Serverless: Offerings & Applications

Serverless: Scaling

Takeaways

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founder of cloud.thence, consulting firm that helps companies scale and prevail onAWS

5+ years as developer at AWS with focus on scaling and monitoring

run FolkStories podcast where I have longform conversation with awesome people

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Serverless: What is it?Serverless: What is it?Serverless: What is it?

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What Its Not

Courtesy of giphy

having no serversmanaged serverscontainers

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Serverless as a Spectrum

ephermal compute, nothing allocated with no utilization

pay for use

higher abstraction knobs

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Serverless: Why care?Serverless: Why care?Serverless: Why care?

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Serverless in Context

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Serverless Benefits

lower costs for most use cases, no $ when no utilization

∞* scale

no provisioning or management

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Serverless OfferingsServerless OfferingsServerless Offerings

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AWS Lambda: Serverless Compute

Run code without servers

Automatic scaling

Pay only for compute you consume (charged by every 100ms)$0.20 per 1 million requests$0.00001667 for every GB-second of compute1 million requests running 200ms = $0.62 per month

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API Gateway: Serverless REST Endpoint

Create API endpoint without servers

Automatic scaling

Builtin Versioning, Security Controls, and Monitoring

Pay only for API calls made (num requests)$3.5 per million API calls$1 per million websocket messages1 million API calls = $3.5

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DynamoDB Serverless: Serverless NoSQL DB

Create Databases without servers

Automatic scaling

Pay only for database resource consumed (per second basis)$1.25 per million write request units$0.25 per million read request units$0.25 per GB-month (first 25GB free)1 million read & write + 50GB storage = $20.25

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Serverless ApplicationsServerless ApplicationsServerless Applications

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Web AppWeather Application

Courtesy of aws.amazon.com/serverless

Reference Architecture: https://github.com/aws-samples/lambda-refarch-webapp

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Mobile AppMobile Backend for Social Media App

Courtesy of aws.amazon.com/serverless

Reference Architecture: https://github.com/awslabs/lambda-refarch-mobilebackend/

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Data ProcessingImage Thumbnail Creation

Courtesy of aws.amazon.com/serverless

Reference Architecture: https://github.com/awslabs/lambda-refarch-fileprocessing

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Serverless: ScalingServerless: ScalingServerless: Scaling

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Challenges

platform

performance

architecture

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Platform LimitationsThey exist

max execution time: 15min

max memory: 3GB

payload: 6MB

concurrent executions: 1000

state persistence: false

non-persistent scratch area: 512 MB

deployment package size (zipped): 50MB

deployment package size (unzipped): 250MB

file descriptors: 1024

execution processors/threads: 1024

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Platform LimitationsIts not me its you

consider if your micro service is doing too much

persistent state not a good assumption in cloud based world

guardrails can be nice

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Platform LimitationsIts Getting Better

execution time: 5min -> 15min

memory: 1GB -> 3GB

deployment package size: Lambda Layers

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Platform LimitationsWorkarounds

concurrent executions: ask for limit increase

max executiont time: API + Workflow pattern

payload: use S3 signed urls (limit of 5TB)

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PerformanceCold Starts

first start penalty

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PerformanceOptimizations

use the right language

don't use VPC

cache expensive things outside of function handler

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PerformanceRight Language

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PerformanceAvoid VPC

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PerformanceAvoid VPC

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PerformanceLambda Execution Contextletlet dbletlet initialized = falseletlet var1, var2, var3

functionfunction initinit() { ifif (initialized) { returnreturn } db = mysql.create(...) var1 = initVar1 // ... initialized = true returnreturn}

module.exports.handler = (event, context, cb) => {

init() // ... other function logic cb(null, results)}

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ArchitectureMonolith vs MicroServices

Courtesy of https://www.weave.works

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ArchitectureInstrumentation/Tracing

AWS X-Ray

trace code through multiple subsystemsautomatically capture http[s] calls, aws sdk calls, etc

traces, segments and annotations

sampling

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ArchitectureInstrumentation/Tracing

Courtesy of AWS X-ray docs 32 / 40

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ArchitectureInstrumentation/Tracing

Courtesy of AWS X-ray docs33 / 40

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ArchitectureInstrumentation/Logging

request idx-amzn-requestid: api gateway requestxray-trace-id

correlation idgenerate in initial request(eg. api gateway)add to all outgoing requestpass along in all services

DEBUGif an issue occurs, you might need to switch to debug logsconsider having special header to enable DEBUG on all downstream serviceshave small percentage of logs always be DEBUG

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ArchitectureEtc

all the servicesone server vs 100 microservices

pricingfixed hourly cost vs cost by use

best practicesdeployment, languages, libraries, frameworks

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Getting Started

strangler pattern

longstanding workloads, still use servers (for now)

use toolsbuiltin: XRay, Cloudwatch Logsframeworks: serverless, SAMservices: IOPipe, Stackery.io

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Mental Model

Compute ModelOnPrem Pets: need to be taken care ofCloud Cattle: swap out when neededServerless Bug: should not exist

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Takeaways

serverless is a spectrum, "serverless" is at the end of the spectrum

serverless is higher abstractions, pay-by-use, and "infinite" scale

serverless requires a deep understanding of platform and performance limitations

serverless is coming

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Everything is Broken on April 3rd where I'm giving a talk focused exclusively on scaling Lambda

kevin@cloud.thence.io if you have quesitons or want to talk further

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