1. “No Server is easier to manage than no server”
Karthi Thyagarajan AWS Solutions Architect

2. Overview: Why Serverless?

3. Challenges of server-based designs:
Why Serverless?
Challenges of server-based designs:
Operational overhead
Create and Manage VMs or EC2 Instances
Patching Operating Systems, Web Servers, App Servers
Chunkier units of scale – at the VM level
Friction when implementing microservices
The first two points are pretty straightforward.
Regarding the point about microservices – designers have to leverage containerization technologies like Docker when working in the server-based world.

4. Why Serverless? (cont’d)
Benefits of Serverless designs:
Low operational overhead
No OS patching or managing VMs or EC2 instances
More granular units of scale
Simpler to implement microservices
Lower cost
Mention
- Could be cheaper because of lower operational overhead

5. Spectrum of AWS Offerings
“On EC2”
Managed
Serverless
AWS
Lambda
Amazon Kinesis
Amazon EMR
Amazon Elasticsearch Service
Amazon EC2
Amazon ElastiCache
Amazon RDS
Amazon Redshift
Amazon S3
Amazon DynamoDB
Mention
- Could be cheaper because of lower operational overhead
Microsoft SQL Server
Amazon API Gateway
Amazon SQS
AWS IoT

6. Serverless Patterns 3 Tier Web Application Stream Processing
Operations Automation
Dynamic Data Triggers

7. Pattern 1: 3 Tier Web Application

8. Web Application: Characteristics
Typically 3-tiered:
Presentation tier
Application tier
Data tier
Presentation and application tier are typically stateless
Data tier is some sort of structured store: relational or NoSQL

9. 3 Tier Web Architecture with Servers

10. Serverless 3 Tier Web Architecture
Data tier
App tier
Presentation tier
Amazon S3
Amazon DynamoDB
AWS
Lambda
Amazon API Gateway
+ Monitoring is closer to your business needs.
Budgets and cost profile for the services. Only pay while it is running. What you pay is close to how you make money.
Amazon CloudFront

11. Demo http://bit.ly/1VD8xYa Pattern1 Web Demo:
Start with middle tier API GW. Show the Message Resource, not the Talkers resource (which is blank for now).
Show the Lambda function by clicking on the integration point in API GW.
Show DDB table.
Web page – single page app, show S3, and public permissions. Mention AngularJS.

12. Applicability SquirrelBin Augmenting an existing application
A Serverless Microservice Using AWS Lambda
Augmenting an existing application
For instance, add product search capability to an existing application
Mobile application built on AWS to complement traditional 3-tier web app

13. Pattern 2: Stream Processing & Analytics

14. 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

15. Stream Processing Architecture with Servers
Load balancer to distribute traffic
Cross-AZ replication for high availability
Auto-scale worker nodes to adjust with load
SOURCE of data
Listening to source
Pushes to queue
Pull off queue
Fleet of servers poll for changes
Fleet of servers act as workers to process the data
S3 objects
Amazon Kinesis records
DynamoDB tables
SNS messages
IoT devices …
Application code

16. Server-less Stream Processing: Sensor Data Aggregation
Lots sensors sending frequent measurements
Calculate aggregate stats over a time-window (3s) in near real-time

17. Sensor Data Aggregation: Data Flow
Amazon S3:
Visualization Web App
Amazon DynamoDB:
Aggregates Table
Amazon Kinesis:
Stream
AWS Lambda:
Stream Processor
Sensors

18. Processing a Kinesis stream with Lambda

19. Demo

20. Applicability Web Events Log processing Clickstream analytics
Analyze a Time Series in Real Time with AWS Lambda, Amazon Kinesis and Amazon DynamoDB Streams
Log processing
Real time processing of Log Data with Subscriptions (CloudWatch Logs to Lambda)
Clickstream analytics
Transaction processing

21. Customers running real-time data stream processing on Kinesis+Lambda
“I want to apply custom logic to process data being uploaded through my Kinesis stream”.
Client activity tracking
Metrics generation
Data cleansing
Log filtering
Indexing and searching
Log routing
Live alarms and notifications
AWS Lambda
Aggregate statistics
Real-time analytics
Kinesis Stream
verify data formats, audit out-of-range values, filter and copy data to other tables

22. Indexing DynamoDB using Elastic Search and Lambda

23. Pattern 3: Operations Automation

24. Operations Automation: Characteristics
Respond to alarms or events
Periodic jobs
Auditing and Notification
Extend AWS functionality
…All while being Highly Available and Scalable

25. Dynamic DNS using Route53 using CloudWatch Events and Lambda
Amazon EC2 Instance
State Changes
Amazon CloudWatch Events:
Rule Triggered
AWS Lambda:
Update R53
Amazon Route53:
Private Hosted Zone
CNAME = ‘xyz.example.com’
xyz.example.com A
From:
Amazon DynamoDB:
EC2 Instance Properties

26. Demo

27. Applicability Scheduled Jobs Auditing and Notification
Scheduling EBS Snapshots (Part 1)
Scheduling EBS Snapshots (Part 2) – Delete old snapshots
Scheduling SSH Jobs using Lambda
Auditing and Notification
AWS Config Rules (AWS Managed) and Rule Repository
Sample Rule “Ensure IAM User has MFA Enabled”
Sample Rule “Ensure all EC2 Instances are of a Given Type”

28. Applicability Respond to Alarms and Events
Building a Dynamic DNS for Route 53 using CloudWatch Events and Lambda
Scaling Amazon ECS Services Automatically Using Amazon CloudWatch and AWS Lambda
Auto Scaling Group lifecycle hooks
Using AWS Lambda with Auto Scaling Lifecycle Hooks (attach a second ENI to every server in an ASG)
Extending CloudFormation using Custom Resources
Look up AMI IDs:
Refer to resources in another stack:

29. Pattern 4: Dynamic Data Triggers

30. Characteristics Perform actions upon data ingestion Highly available
Scalable
Event driven, avoid polling

31. Image Thumbnail Creation

33. Demo

34. Applicability Image / Video transcoding Indexing S3
Tutorial: Using AWS Lambda with S3
Indexing S3
Building and Maintaining an Amazon S3 Metadata Index without Servers
Automated log analysis
AWS Service Logs: Access Logs from S3, ELB, CloudFront Access Logs, CloudTrail
How to Configure Rate-Based Blacklisting with AWS WAF and AWS Lambda
How to Use AWS WAF to Block IP Addresses That Generate Bad Requests
Billing analysis
Process AWS Detailed Billing Reports

35. Indexing S3 Objects in DynamoDB
From:

36. Indexing tables or notifications
Customers running dynamic data ingestion and processing using S3+Lambda
“I want to apply custom logic to process content being uploaded to my data store”.
Watermarking / thumbnailing
Transcoding
Indexing and deduplication
Aggregation and filtering
Pre processing
Content validation
Processed
files
verify data formats, audit out-of-range values, filter and copy data to other tables
Amazon S3 Bucket Events
AWS Lambda
Indexing tables or notifications

37. Further Reading JAWS / Serverless at http://www.serverless.com/
Getting Started with JAWS on Amazon Web Services
AWS Serverless Multi-Tier Architectures
Microservices without the Servers
The Serverless Start-up

38. Further Watching
AWS re:Invent 2015 | (ARC308) The Serverless Company Using AWS Lambda
AWS re:Invent 2015 | (CMP301) AWS Lambda and the Serverless Cloud
AWS re:Invent 2015 | (MBL302) Building Scalable, Serverless Mobile and IoT Back Ends
AWS re:Invent 2015 | (GAM401) Build a Serverless Mobile Game with Amazon Cognito, Lambda & DynamoDB
AWS re:Invent 2015 | (CMP407) Lambda as Cron: Scheduling Invocations in AWS Lambda
AWS re:Invent 2014 | Netflix Gains New Efficiencies using AWS Lambda
AWS re:Invent 2015 | (DEV203) Amazon API Gateway & AWS Lambda to Build Secure and Scalable APIs
AWS re:Invent 2015 | (CMP403) AWS Lambda: Simplifying Big Data Workloads
AWS re:Invent 2015 | (DVO209) JAWS: The Monstrously Scalable Serverless Framework