The document discusses serverless computing and AWS Lambda. It begins with an introduction to serverless computing and AWS Lambda. It then covers topics like event-driven execution models, Lambda function anatomy, common use cases, and advantages of serverless like scalability, availability, and pay-per-use pricing. The document provides examples of serverless applications for areas like apps and services, data streams and analytics, development and deployment, and automation. It concludes with a discussion of GraphQL as it relates to serverless.

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@steven_bryen
sbryen@amazon.com
Introduction to Serverless
Steven Bryen, Senior Technical Evangelist

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What is Serverless?

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It Started with AWS Lambda
Serverless Computing

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Events
Services (anything)
Lambda functions
Event driven

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Event-driven platform
S3 event
notifications
DynamoDB
Streams
Kinesis
events
Cognito
events
SNS
events
Custom
events
CloudTrail
events
Lam bdaDynam oDB
Kinesis S3
Any custom
Invoked in response to events
- Changes in data
- Changes in state
Redshift
SNS
Access any service,
including your own
Such as…
CloudWatch
events
Lambda functions

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Function – first class citizen
def handler(event, context):
return {
"message": ”Hello World!",
"event": event
}
• Focus on solving business problems
• Reusable components
• Easier to isolate failure

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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, etc.)
def handler(event, context):
return {
"message": ”Hello World!",
"event": event
}

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Lambda execution model
Synchronous (push) Asynchronous (event) Stream-based
Amazon
API Gateway
AWSLambda
function
Amazon
DynamoDB
Amazon
SNS
/api/hello
AWSLambda
function
Amazon
S3
reqs
Amazon
Kinesis
changes
AWSLambda
service
function

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You can achieve massive scales with Lambda
processes 4,000 requests
per second
processes half a trillion
validations of stock
trades daily
reduced the time to
ingest and process data
for its analytics pipeline
by 97%
can handle spikes
of 80x normal traffic
triggers 1.2 billion
Lambda requests
each month

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Serverless is more than just functions.

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Serverless
Scalability without server management.

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Run less software.

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Why Serverless?
No servers to manage

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Why Serverless?
Availability and Fault Tolerance Built In

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AZ1 AZ2 AZ3
Serverless Services

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Why Serverless?
Scale with usage

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Serverless distributed computing: PyWren
PyWren prototype developed at University of California, Berkeley
Uses Python with AWS Lambda stateless functions for large-scale data
analytics
Achieved @ 30-40 MB/s write and read performance per core to Amazon S3
object store
Scaled to 60-80 GB/s across 2,800 simultaneous functions

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Why Serverless?
Pay-per request

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• Buy compute time in
100 ms increments
• No hourly, daily, or
monthly minimums
• No per-device fees
Free Tier
1 million requests and 400,000 GBs of compute
every month, every customer

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Bustle.com
Bustle.com is a news, entertainment, lifestyle, and
fashion website:
• 52 million monthly users
• 100 million events daily
• 84% cost savings
• 0 servers
• 0 operating system patches
• Automatic scaling
https://aws.amazon.com/solutions/case-studies/bustle/

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Why Lambda?
>95% decrease in overall deployment and operational costs
Scales up or down for variation in customer request volume
Migrating tasks to Lambda took only a few weeks
95% Reduction in Computation Cost
AWS Lambda enables the FICO Decision Management Suite (DMS)
to perform computations on machine learning models quickly, cheaply,
and efficiently
AWS Lambda

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Common Lambda use cases
Web
Applications
• Static
websites
• Complex web
apps
• Packages for
Flask and
Express
Data
Processing
• Real time
• MapReduce
• Batch
Chatbots
• Powering
chatbot logic
Backends
• Apps &
services
• Mobile
• IoT
</></>
Amazon
Alexa
• Powering
voice-enabled
apps
• Alexa Skills
Kit
IT
Automation
• Policy engines
• Extending
AWS services
• Infrastructure
management

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Let’s talk about Serverless for…
• Apps & Services
• Data Streams & Analytics
• Development & Deployment
• Automation

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Serverless for…
• Apps & Services

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3-Tier web application
Data stored in
Amazon
DynamoDB
Dynamic content
in AWS Lambda
Amazon API
Gateway
Browser
Amazon
CloudFront
Amazon
S3

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Amazon API
Gateway AWS
Lambda
Amazon
DynamoDB
Amazon
S3
Amazon
CloudFront
• Bucket Policies
• ACLs
• OAI
• Geo-Restriction
• Signed Cookies
• Signed URLs
• DDOS
AuthZ
Serverless web app security
• Throttling
• Caching
• Usage Plans
Static Content
Browser
Amazon
CloudFront
• HTTPS
• Disable Host
Header Forwarding
AWSWAF
IAM

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Amazon API
Gateway
AWS
Lambda
Amazon
DynamoDB
Amazon
S3
Amazon
CloudFront
• Access Logs in S3
Bucket• Access Logs in S3 Bucket
• CloudWatch Metrics-
https://aws.amazon.com/cl
oudfront/reporting/
Serverless web app monitoring
AWS WAF
• WebACL Testing
• Total Requests
• Allowed/Blocked
Requests by ACL
logslogs
• Invocations
• Invocation Errors
• Duration
• Throttled
Invocations
• Latency
• Throughput
• Throttled Reqs
• Returned Bytes
• Latency
• Count
• Cache Hit/Miss
• 4XX/5XX Errors
Streams
AWS
CloudTrail
Static Content
Browser
Custom CloudWatch
Metrics & Alarms

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AWS Step Functions:
Orchestrate a Serverless processing
workflow using AWS Lambda

33. https://github.com/awslabs/lambda-refarch-imagerecognition

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AWS SAM Demo

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Serverless for…
✅ Apps & Services
• Data Streams & Analytics

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Amazon Kinesis makes it easy to work with real-
time streaming data
Amazon Kinesis
Streams
• For Technical Developers
• Collect and stream data
for ordered, replay-able,
real-time processing
Amazon Kinesis
Firehose
• For all developers, data
scientists
• Easily load massive
volumes of streaming data
into Amazon S3, Redshift,
ElasticSearch
Amazon Kinesis
Analytics
• For all developers, data
scientists
• Easily analyze data
streams using standard
SQL queries

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“Amazon Kinesis does a lot of the
heavy lifting, and lets Supercell
focus on delivering games that
delight players worldwide.”
Sami Yliharju
Services Lead, Supercell Games
https://aws.amazon.com/solutions/case-studies/supercell/

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Real-time analytics for mobile web app
Amazon
Kinesis
Stream
Amazon
Kinesis
Analytics
Amazon
Cognito
Amazon
Kinesis
Stream
Amazon
DynamoDB
Amazon
Lambda
AmazonS3
JavaScript SDK

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AWS Lambda:
Splitter
Amazon S3
Object
Amazon DynamoDB:
Mapper Results
AWS Lambda:
Mappers
….
….
AWS Lambda:
Reducer
Amazon S3
Results
Serverless batch processing
Lambda MapReduce https://github.com/awslabs/lambda-refarch-mapreduce

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Serverless for…
✅ Apps & Services
✅ Data Streams & Analytics
• Development & Deployment

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Typical development steps
• Integration testing
• Load testing
• UI tests
• Security testing
• Commit
• Code review
• Compile code
• Unit tests
• Style / linting
• Container image
• Deploy to
production
environment
Source Build Test Production

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DevOps – What is it?
Faster development → More experimentation & innovation → Customer happiness
developers customers
releasetestbuild
plan monitor
Distribution pipeline
Feedback mechanism
Automation for agile software development cycles

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OK! Sounds good. What do I use?

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AWS CodeStar

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Serverless for…
✅ Apps & Services
✅ Data Streams & Analytics
✅ Development & Deployment
• Automation

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Automation characteristics
• Respond to alarms or events
• Periodic jobs
• Auditing and Notification
• Extend AWS functionality
• Highly Available and scalable

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AWS Lambda:
Update Route53
Amazon CloudWatch Events:
Rule Triggered
Amazon EC2 Instance
State Changes
Amazon DynamoDB:
EC2 Instance Properties
Amazon Route53:
Private Hosted Zone
Tag:
CNAME = ‘xyz.example.com’
xyz.example.com A 10.2.0.134
Automation: dynamic DNS for EC2 instances

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AWS Lambda:
Resize Images
Users upload photos
S3:
Source Bucket
S3:
Destination Bucket
Triggered on
PUTs
Automation: image thumbnail creation from S3

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Serverless for…
✅ Apps & Services
✅ Data Streams & Analytics
✅ Development & Deployment
✅ Automation

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What about GraphQL?

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GraphQL
Open, declarative data-fetching specification
!= Graph database
Use NoSQL, Relational, HTTP, etc.
Traditional data-fetching GraphQL
/posts
/postInfo
/postJustTitle
/postsByAuthor
/postNameStartsWithX
/commentsOnPost

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How does GraphQL work?
{
"id": "1",
"name": "Get Milk",
“priority": "1"
},
{
"id": “2",
"name": “Go to gym",
“priority": “5"
},…
type Query {
getTodos: [Todo]
}
type Todo {
id: ID!
name: String
description: String
priority: Int
duedate: String
}
query {
getTodos {
id
name
priority
}
}
Model data with
application schema
Client requests what it
needs
Only that data is
returned

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GraphQL
Schema
type Event {
id: ID!
name: String
where: String
when: String
description: String
comments: [Comment]
}
type Comment {
commentId: String!
eventId: ID!
content: String!
createdAt: String!
}

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GraphQL
Schema
Mutation
type Mutation {
createEvent(
name: String!,
when: String!,
where: String!,
description: String!
): Event
deleteEvent(id: ID!): Event
commentOnEvent(
eventId: ID!,
content: String!,
createdAt: String!
): Comment
}

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GraphQL
Schema
Mutation
Query
type Query {
getEvent(id: ID!): Event
listEvents(
limit: Int,
nextToken: String
): EventConnection
}

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GraphQL
Schema
Mutation
Query
Subscription
type Subscription {
subscribeToEventComments(eventId: String!): Comment
@aws_subscribe(mutations: ["commentOnEvent"])
}

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GraphQL
Schema
Mutation
Query
Subscription
Realtime? YES
Batching? YES
Pagination? YES
Relations? YES
Aggregations? YES
Search? YES
Offline? YES

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AWS AppSync
DynamoDB
Table
Lambda
Function Elasticsearch
Service
GraphQL
Schema
Upload
Schema
GraphQL
Query
Mutation
Subscription
Real-time
Offline
AppSync
API
Cognito
User Pool
Legacy
Application

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No servers to provision
or manage
Scales with usage
Never pay for idle Availability and fault
tolerance built in
Serverless means…

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AW S
Lam bda
Am azon
S3
Am azon
Dynam oDB
AW S
CodeDeploy
AW S
CodePipeline
AW S
CodeStar
Am azon
Kinesis
Am azon
Athena
Am azon
Lex
Am azon
Polly
Am azon
Rekognition
Am azon
Pinpoint
Am azon
Cognito
AW S Step
Functions
Am azon
SQ S
Am azon
SNS
Am azon
SES
Am azon
Aurora
Serverless
Functions
DevOps
Queues & Notifications
Storage
Identity & App Analytics
AI Application Services Streams & Data Analytics
All of this is
“serverless.”

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An expanding Serverless ecosystem
Build and CI/CD
Logging and MonitoringApplications and Deployment
ChaliceFramework Serverless JavaContainer
©2017, AmazonWebServices, Inc. or itsAffiliates.All rightsreserved.

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Thank you!
Steven Bryen, Senior Technical Evangelist
@steven_bryen
sbryen@amazon.com