Arthi Jaganathan, Solutions Architect, ASEAN, AWS. AWS Customer Speaker: Minh Thuong Nguyen, Full Stack Engineer, Global Fashion Group. What if you could build an application that could rapidly scale on demand to meet high-volume traffic or spikes? What if you could do this without ever having to provision or manage a server, update an operating system, or worry about availability? With serverless computing, developers can focus on building great applications and services in a cost-effective manner without the operational overhead of traditional application development and deployment. Come learn how you can use AWS Lambda, Amazon API Gateway, and other services to build the serverless applications of the future.

1. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Arthi Jaganathan
Solutions Architect, ASEAN, AWS
Minh Thuong Nguyen
Global Fashion Group, Full stack Engineer
You Don’t Need a Server For That!

2. Why are we here today?

3. Serverless Applications
Services (anything)
Changes in
data state
Requests to
endpoints
Changes in
resource state
Event source Lambda function
C#
Go
Java
Node.js
Python

4. Lambda execution model
Synchronous Asynchronous 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

5. 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
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Amazon
Alexa
• Powering
voice-enabled
apps
• Alexa
Skills Kit
IT
automation
• Policy engines
• Extending
AWS services
• Infrastructure
management

6. Serverless @ Global Fashion Group

7. Global Fashion Group (GFG)
5 regional companies 24 countries
15M monthly unique visitors3000+ international brands

8. Goal
Improve margin for our online e-commerce by suggesting
recommended price
Characteristics
• Millions of prices to be updated regularly
• Prices changes triggered by events
• Batch feeds
• Unpredictable usage spikes
Pricing Engine

9. • Microservice and event-driven
• “Generic” core engine with “adapted” integration layer with the
Regions
• Full stack (front-end / back-end) mindset
• Agile methodology with continuous deployment
Principles

10. • Full stack, asynchronous, event-driven
• Cloud based, aligned across regions
• Microservices, no DevOps in the
beginning → Lambda
• Messaging system, data storage
→
→
→
→
+
Tech Stack

11. Example Feature Flow

12. High Level Architecture
Validation Service
External Sources
Big Data
Platform
Lambda
function
Amazon
Kinesis
Publishing Service
Lambda
function
API
Gateway
API
Gateway
Amazon
Kinesis
Recommendation Service
Lambda
function
RDS -
PostgreSQL
Dashboard - Frontend
Amazon
S3
Users
DynamoDB
Integration Layer
Competitor
data
Inventory
data
Sales
data
EC2
Instance
Price Optimizer

13. • Easy to use
• Fast deployment (Serverless framework)
• Autoscaling, charged for usage
• Support of multiple triggers (API, S3, Kinesis, DynamoDB,
Lambda etc.)
Advantages

14. Lessons Learned
1000 Concurrent Invocations
3 GB Memory & 5 min
Timeout
Startup Latency
Rising Cost (DynamoDB)
Tracking & Debugging
Challenge
Well-designed Architecture
Master-Worker Approach
Warmup Plugin
Alternatives (RDS)
Correlation ID
Mitigation

15. Serverless Architecture Patterns

16. S3
bucket
object
Lambda
function
1. File put into bucket
2. Lambda invoked
Lambda
function
2. Lambda invoked
SNS
Topic
1. Data published to a topic
Data
Data Processing

17. Lambda
function
2. Lambda
function invoked
1. API call made
against API Gateway
Clients
Amazon API
Gateway
Lambda
function
Lambda
function
2. API call made directly
against backing AWS service
API clients
Amazon API
Gateway
Clients
Amazon
S3
Amazon
Kinesis
Amazon
DynamoDB
etc..
2. Step Functions
workflow is executed
1. API call made
against API Gateway
Amazon API
Gateway
Clients
AWS Step
Functions
1. API call made
against API Gateway
API Backend

18. API Backend

19. Lambda
function
2. Lambda invoked via rule
1. Error message gets sent
back to SES on delivery
attempt
Amazon
SES
Email failure
notification
Lambda
function
2. Lambda invoked
per matching trigger
1. User registration/
login/forgotten password
Amazon
Cognito
Users
Application Users

20. Databases
Lambda
function
3. Lambda invoked
1. Query executed
Amazon
Aurora - MySQL
SQL Query
2. Stored
Procedure Called
1. Items Inserted/
Updated/ Deleted
Lambda
function
3. Lambda polls
stream
Amazon
Kinesis
Stream
4. Kinesis
returns
stream data
items
DynamoDB
Table
2. Event published
to Kinesis Stream

21. Real-time Data Sync

22. Amazon CloudFrontAWS Lambda
Lambda@Edge
Lambda@Edge

23. Compliance Detection & Remediation
Lambda
function
2. Change sent to
SNS Topic
1. AWS resource
changed
AWS
Config
SNS
Topic
3. Lambda invoked

24. Compliance

25. Compliance Detection & Remediation
Lambda
function
2. S3 publishes
event to Lambda
1. AWS API calls logged
to an object in S3
AWS
CloudTrail
S3
bucket
3. Lambda invoked,
processes object in
S3
Lambda
function
2. Change sent to
SNS Topic
1. AWS resource
changed
AWS
Config
SNS
Topic
3. Lambda invoked

26. What Else?
Infrastructure Management
DevOps Pipelines
Chat Bots/IoT
CloudFormation
Template
CloudWatch
Events
CloudWatch
Logs
AWS
CodeDeploy
AWS
CodeCommit
Amazon
Lex
Alexa
skill
AWS IoT
AWS
CodePipeline

27. FIN, ACK
There are a lot of different use-cases for AWS Lambda!

28. Thank You!