an experiment with aws lambda
TRANSCRIPT
The Problem• Map the skills and interests of our developers
• Easy to interact with and no registration
• Enable finding patterns in the data
The Graph Database
Amazon EC2t2.micro with
8GB SSD
+ +
AWS Lambda
Lambda
• Language runtime vs. container or VM
• Functions are unit of deployment
• Java 8, Node.js or Python 2.7
• Linux containers + packing algorithm
• Scaling handling for you
• Stateless – permanent storage is
elsewhere
• Pay per request
Getting email in
SES(Simple Email Service)
Lambda SNS(Simple Notification Service)
To: [email protected]: nodejs aws-lambda
Processing email
SNS(Simple Notification Service)
Lambda Graph Database
Lambda
DynamoDB
Gathering more information
SNS(Simple Notification Service)
Graph Database
Lambda
Stack Overflow API
EBI Intranet
Getting information out
API Gateway Lambda Graph Database
Serving the UI
Route 53(gcscc.site)
Webpages on S3 API Gateway
The Result
The Graph
What I liked• Low costs
• Very nearly free and will be even with thousands of users
• Clean architecture• Forced to have independent functions that do one
thing• Hugely powerful and flexible toolkit• Feels productive as a developer
• ~1 feature per hour (40 hours total)
What I didn’t like• Tools are still emerging
• node-lambda used for local execution, testing and deployment
• Can see managing many functions and will get tricky
• Setting up plumbing like SNS events and permissions not automated (for me)
• A slight delay in cold start of Node.js functions but better than Java
• Error handling needs a bit of thought
Future plans• Cloud Formation for Lambda• API Caching (very small $ cost)• Tuning (turn up the dial)• Editing web frontend with login and API
authentication• I am optimistic Lambda will become production
ready
Thanks to
Jenny ChamUX, prototyping and testing
Ken HawkinsVisual design and styling
