As serverless architectures become more popular, customers need a framework of patterns to help them identify how they can leverage AWS to deploy their workloads without managing servers or operating systems. This session describes reusable serverless patterns while considering costs. For each pattern, we provide operational and security best practices and discuss potential pitfalls and nuances. We will also discuss considerations for moving an existing server-based workload to a serverless architecture. The patterns use services like AWS Lambda, Amazon API Gateway, Amazon Kinesis Streams, Amazon Kinesis Analytics, Amazon DynamoDB, Amazon S3, AWS Step Functions, AWS Config, AWS X-Ray, and Amazon Athena. This session can help you recognize candidates for serverless architectures in your own organization and understand areas of potential savings and increased agility.

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Serverless Architectural Patterns
Monica Lora
Johannes Verwijnen
Solutions Architect, AWS
CTO, Aino Health

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Agenda
• Serverless Foundations
• Serverless Architectural patterns:
o Web Application
o Data Lake
o Stream processing
o Automation
AWS
Lambda
Amazon
Kinesis
Amazon
S3
Amazon API
Gateway
Amazon
SQS
Amazon
DynamoDB
AWS IoT
Amazon
Cognito
Amazon
CloudWatch

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Serverless Means…
No servers to provision
or manage
Scales with usage
Never pay for idle Built-in availability and
fault tolerance

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Common Serverless Application Use Cases
Web applications
•Static websites
•Complex web apps
•Packages for Flask
and Express
Data processing
•Real time
•MapReduce
•Batch
•Machine learning
inference
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

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Pattern 1: Web App/Microservice/API

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Serverless Web Application
Data stored in
Amazon DynamoDB
Dynamic content in
AWS Lambda
Amazon API Gateway
Browser
Amazon
CloudFront
Amazon S3 Amazon Cognito

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Serverless Web Application - Security
Amazon API
Gateway
AWS
Lambda
Amazon
DynamoDB
Amazon
S3
Amazon
CloudFront
• Bucket Policies
• ACLs
• OAI
• Geo-Restriction
• Signed Cookies
• Signed URLs
• DDoS Protection
IAM
AuthZ
IAM
• Throttling
• Caching
• Usage Plans
• ACM
Static Content
Browser
Amazon Cognito

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Serverless Web Application - Security
Amazon API
Gateway
AWS
Lambda
Amazon
DynamoDB
Amazon
S3
Amazon
CloudFront
• Bucket Policies
• ACLs
• OAI
• Geo-Restriction
• Signed Cookies
• Signed URLs
• DDOS
IAM
AuthZ
IAM
• Throttling
• Caching
• Usage Plans
Static Content
Browser
Amazon
CloudFront
• HTTPS
• Disable Host
Header Forwarding
AWS WAF
Amazon Cognito

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Serverless Web Application - Monitoring
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/
cloudfront/reporting/
AWS WAF
• WebACL Testing
• Total Requests
• Allowed/Blocked
Requests by ACL
logs
logs
• Invocations
• Invocation Errors
• Duration
• Throttled Invocations
• Latency
• Throughput
• Throttled Reqs
• Returned Bytes
• Documentation
• Latency
• Count
• Cache Hit/Miss
• 4XX/5XX Errors
Streams
AWS
CloudTrail
Static Content
Browser
Custom CloudWatch
Metrics & Alarms

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Serverless Web Application Lifecycle Management
AWS SAM (Serverless Application Model)
AWS
Lambda
Amazon API
Gateway
AWS
CloudFormation
Amazon
S3
Amazon
DynamoDBPackage &
Deploy
Code/Packages/
Swagger
Serverless
Template
Serverless
Template
w/ CodeUri
package deploy
CI/CD Tools

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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

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Pattern 2: Data Lake

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Data Lake Characteristics
• Collect/Store/Process/Consume and Analyze all organizational data
• Structured/Semi-Structured/Unstructured data
• AI/ML and BI/Analytical use cases
• Fast automated ingestion
• Schema on Read
• Complementary to EDW
• Decoupled Compute and Storage

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AWS Serverless Data Lake
S3
Bucket(s)
Key
Management
Service
Amazon
Athena
AWS
CloudTrail
Amazon
Cognito
AWS
IAM
Amazon
Kinesis
Streams
Amazon
Kinesis
Firehose
Amazon ES
Amazon
QuickSight
AWS
Glue
Amazon
DynamoDB
Amazon
Macie
Amazon API
Gateway
AWS
IAM
Amazon
Redshift
Spectrum
AWS
Direct
Connect
Ingest
Catalog & Search
Security & Auditing
API/UI
Analytics & Processing
AWS
Glue
AWS
Lambda

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Fannie Mae Serverless Financial Modeling
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 (10#10$%) 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.

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Pattern 3: Stream Processing

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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

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Streaming Data Ingestion
Amazon CloudWatch:
Delivery metrics
Amazon S3:
Buffered files
Kinesis
Agent
Record
Producers Amazon Redshift:
Table loads
Amazon Elasticsearch Service:
Domain loads
Amazon S3:
Source record backup
AWS Lambda:
Transformations &
enrichment
Amazon DynamoDB:
Lookup tables
Raw records
Lookup
Transformed records
Transformed recordsRaw records
Amazon Kinesis Firehose:
Delivery stream

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Sensor data collection
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

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Real-time analytics
Amazon Kinesis Streams:
Ingest stream
Amazon Kinesis Analytics:
Time window aggregation
Amazon Kinesis Streams:
Aggregates stream
Amazon Kinesis Firehose:
Error stream
Amazon S3:
Error records
Record
Producers
AWS Lambda:
Alert function
Amazon DynamoDB:
Device thresholds
AWS SNS:
Notifications

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Thomson Reuters – Product Insight
Solution for usage analysis tracking:
Capture, analyze, and visualize analytics data generated by offerings, providing insights to help product
teams continuously improve the user experience
Throughput: Tested 4,000 requests / second
Growing to 10,000 requests / second or 25 Billion requests / month
Latency: new events to user dashboards in less than 10 seconds
Durable: no data loss since inception

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Pattern 4: Automation

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Automation characteristics
•Periodic jobs
•Event triggered workflows
•Enforce security policies
•Auditing and notification
•Respond to alarms
•Extend AWS functionality
•…All while being Highly Available and Scalable

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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 Instances
Amazon DynamoDB:
Task configuration & tracking
Amazon CloudWatch:
Logs
Amazon Redshift Clusters
https://aws.amazon.com/answers/infrastructure-management/ops-automator/
Tags
OpsAutomatorTaskList CreateSnapshot

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Health

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Aino Health Background
Founded in 1994(!)
We provide employee absence and wellbeing management via web-based tools.
Rapid internationalization planned from 2016 onwards
Listed on NASDAQ Stockholm First North December 2016

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The Challenge
Existing old-fashioned monolithic web server system (Spring/Hibernate/MySQL)
Main targets from strategy:
Scalability (50’000 -> millions users)
Security (personal data, partially sensitive)
Time to market (configurability and development speed)

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The Solutions
Concentrate all infrastructure on AWS
Develop everything as serverless
Always utilize AWS managed services when possible

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Scalability
Concentrate all infrastructure on AWS
• We’re not the biggest fish in the pond
Develop everything as serverless
• We ought to get near-linear scalability
Always utilize AWS managed services when possible
• We trust AWS to work with services’ scalability issues in a more efficient way than us

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Security
Concentrate all infrastructure on AWS
• We would never be able to provide the kind of security measures AWS does in its data centers
Develop everything as serverless
• Proper granularity of code makes it more readable and secure
Always utilize AWS managed services when possible
• We trust AWS to work with services’ security issues in a more efficient way than us

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Time to Market
Concentrate all infrastructure on AWS
• Immediate provisioning of infrastructure
Develop everything as serverless
• Concentrate on business logic code
Always utilize AWS managed services when possible
• Get ready-made functionality

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Overview of Solution
Security Core business services
DynamoDBCognitoCloud Directory
Step Functions
User interfaces
API Gateway
Service A Service B Service C
Supportive services, DevOps, etc.
S3
VPC
CloudFront X-Ray
CloudTrail
IAM
WAF
Shield
SES
Route 53
ElastiCache
CloudFormation
Meta
...

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Takeaways
When evaluating the various AWS services we decided on a “try it but fail fast”-methodology
Service limits do get in the way. Soft, “hard” and hard limits. Note that managed services might hit upon the limits of
underlying services.
Engagement with product management actually makes many of your worries go away.

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Summary

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Lambda Best Practices
•Minimize package size to necessities
•Separate the Lambda handler from core logic
•Use Environment Variables to modify operational behavior
•Self-contain dependencies in your function package
•Leverage “Max Memory Used” to right-size your functions
•Delete large unused functions (75GB limit)

36. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Recommendations
Use DevOps tools to automate your serverless deployments
Apply serverless patterns for common use-cases:
•Web application
•Data Lake Foundation
•Stream processing
•Operations automation

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Thank you!
What will you build with Serverless?