The document discusses the shift towards serverless computing, highlighting its advantages such as faster feature releases, increased agility, and reduced overhead for organizations. It emphasizes the adoption of services like AWS Lambda and Fargate, which allow developers to focus on business logic without managing servers. Additionally, it covers the operational models and tools provided by AWS to facilitate serverless application development and manageability.

1. Primeros pasos en desarrollo
serverless
Javier Ramírez
Developer Advocate - Amazon Web Services
@supercoco9 ramirez

2. We are witnessing a paradigm shift
Experiment,
innovate
more often
Release
features
faster
Build better
products
Focus on
business
logic
Decouple
software
systems
Win
customers
Win
customers
75% of organizations use or plan to use serverless
technologies within the next two years.1
* 451 Research, The Journey to Serverless-First: Enterprise Stories
https://aws.amazon.com/resources/analyst-reports/451-serverless-first-enterprises-on-aws/

3. Sumo Logic’s analysis of its AWS customers’ activities found a modest increase in
those running Lambdas, going from 36% in 2019 to 39% in 2020. In early 2020,
Datadog reported that more than 40% of its AWS customers had adopted Lambda,
with that figure almost doubling among AWS customers that also used containers.
https://thenewstack.io/adoption-of-aws-lambda-serverless-stalls/
https://www.sumologic.com/brief/continuous-intelligence-report/

4. Development transformation at Amazon (and everywhere else)
1994-2001 2002+
Monolithic architecture +
hierarchical organization
Decoupled services +
Two-pizza teams

5. Amazon S3 at launch:

6. Amazon S3 @ re:Invent 2018:
In 2020 S3 stored over 100 trillion (1014,
or 100,000,000,000,000) objects, and
regularly peaks at tens of millions of
requests per second.

7. Deployment at scale
1000s of
teams
Micro-
services
CI/CD Serverless
>60 million deployments a year*

8. Computing evolution – A paradigm shift
LEVEL
OF
ABSTRACTION
FOCUS ON BUSINESS LOGIC
PHYSICAL MACHINES
Requires “guess” planning
Lives for years on-premises
Heavy investments (capex)
Low innovation factor
Deploy in months

9. Computing evolution – A paradigm shift
LEVEL
OF
ABSTRACTION
FOCUS ON BUSINESS LOGIC
VIRTUAL MACHINES
Hardware independence
Faster provisioning speed (minutes/hours)
Trade capex for opex
More scale
Elastic resources
Faster speed and agility
Reduced maintenance

10. Computing evolution – A paradigm shift
LEVEL
OF
ABSTRACTION
FOCUS ON BUSINESS LOGIC
CONTAINERIZATION
Platform independence
Consistent runtime environment
Higher resource utilization
Easier and faster deployments
Isolation and sandboxing
Start speed (deploy in seconds)

11. No server is easier to manage than
"no server.”
Werner Vogels—Amazon CTO

12. Computing evolution – A paradigm shift
AWS Lambda
AWS Fargate
LEVEL
OF
ABSTRACTION
FOCUS ON BUSINESS LOGIC
Continuous scaling
Fault tolerance built-in
Pay for value
Zero maintenance
SERVERLESS

13. AWS operational responsibility models
On-Premises Cloud
Less More
Compute Virtual Machine
EC2 Elastic Beanstalk AWS Lambda
Fargate
Databases MySQL MySQL on EC2
RDS MySQL RDS Aurora Aurora Serverless DynamoDB
Storage Storage
S3
Messaging ESBs
Amazon MQ Kinesis SQS / SNS
Analytics
Hadoop Hadoop on EC2 EMR Elasticsearch Service Athena

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

15. AWS approach to reliability

16. Serverless means:
Greater agility
Less overhead
Better focus
Increased scale
More flexibility
Faster time to market

17. AWS Serverless Portfolio (some missing services)
APPLICATION PRIMITIVES – COMPUTE AND DATASTORES
AWS
Lambda
AWS
Fargate
Amazon
API Gateway
Amazon
SNS
Amazon
MQ
Amazon
SQS
AWS
Step Functions
APPLICATION INTEGRATION
DEVELOPER TOOLS
SECURITY AND ADMINISTRATION
Amazon Aurora
Serverless
Amazon
S3
Amazon
DynamoDB
AWS
AppSync
AWS
IAM
Amazon
Cognito
Amazon
Inspector
Amazon
VPC
Amazon
GuardDuty
AWS
CloudFormation
AWS
Cloud9
AWS
CloudTrail
Amazon
CloudWatch
AWS
X-Ray
AWS
CodePipeline
AWS
Config
AWS
SSO
AWS
Shield
AWS
WAF
Amazon
Kinesis
AWS Serverless
Application
Repository
Amazon
EventBridge
ANALYTICS
Amazon
Athena
AWS
Glue
AWS Glue
Elastic Views
AWS Glue
DataBrew
Amazon
Kinesis
Amazon
Quicksight
Amazon
Timestream

18. Introduction to AWS Lambda
• Function-as-a-Service
• Run code without provisioning or managing servers
• Pay only for the compute time you consume
• Automatically runs your code with high availability
• Scale with usage

19. Lambda handles
• Load balancing
• Auto scaling
• Handling failures
• Security isolation
• OS management
• Managing utilization
(and many other things) for you

20. Comparison of operational responsibility
AWS Lambda
Serverless functions
AWS Fargate
Serverless containers
ECS/EKS
Container-management as a service
EC2
Infrastructure-as-a-Service
More opinionated
Less opinionated
AWS manages Customer manages
• Data source integrations
• Physical hardware, software, networking,
and facilities
• Provisioning
• Application code
• Container orchestration, provisioning
• Cluster scaling
• Physical hardware, host OS/kernel,
networking, and facilities
• Application code
• Data source integrations
• Security config and updates, network config,
management tasks
• Container orchestration control plane
• Physical hardware software, networking,
and facilities
• Application code
• Data source integrations
• Work clusters
• Security config and updates, network config,
firewall, management tasks
• Physical hardware software,
networking, and facilities
• Application code
• Data source integrations
• Scaling
• Security config and updates, network config,
management tasks
• Provisioning,managing scaling and
patching of servers

21. Customer uses cases: Lambda at scale
processes 4,000 requests
per second
processes half a trillion
validations of stock trades daily
triggers 1.2 billion Lambda
requests each month
Scaled up to 20,000
concurrent Lambda executions
in testing

22. Anatomy of a Lambda Function

23. Serverless applications
AWS
Lambda

24. Serverless applications
Function
Node.js
Python
Java
C#
Go
Ruby
PowerShell
Runtime API

25. Serverless Applications
Event source Function
Node.js
Python
Java
C#
Go
Ruby
PowerShell
Runtime API
Changes in
data state
Requests to
endpoints
Changes in
Resource state

26. AWS Lambda integrations
https://docs.aws.amazon.com/lambda/lates
t/dg/lambda-services.html#intro-core-
components-event-sources

27. Serverless Applications
Event source Services
Changes in
data state
Requests to
endpoints
Changes in
Resource state
Function
Node.js
Python
Java
C#
Go
Ruby
PowerShell
Runtime API

28. Lambda Execution Models
Synchronous
(push)
Asynchronous
(event)
Stream
(Poll-based)
Amazon
DynamoDB
Amazon
SNS
/order
Amazon
S3
reqs
Amazon
Kinesis
changes
AWS Lambda
service
Amazon API
Gateway
Lambda
function
Lambda
function
function

29. Dead-Letter Queue
• Asynchronous Lambda invocations are retried two more
times (3 times total)
• Lambda can forward payloads that were not processed
to a dead-letter queue (IF configured!)
• A mechanism to handle exceptions and failures
gracefully

30. Monitoring and debugging Lambda functions
• AWS Lambda console includes a dashboard for functions
• Lists all Lambda functions
• Easy editing of resources, event sources and other settings
• At-a-glance metrics
• Metrics automatically reported to Amazon CloudWatch for each Lambda
function
• Requests
• Errors
• Latency
• Throttles

31. AWS X-Ray
Profile and troubleshoot
serverless applications:
• Lambda instruments
incoming requests for all
supported languages and can
capture calls made in code
• API Gateway inserts a tracing
header into HTTP calls as
well as reports data back to
X-Ray itself
var AWSXRay = require(‘aws-xray-sdk-core‘);
var AWS = AWSXRay.captureAWS(require(‘aws-sdk’));
S3Client = AWS.S3();

32. 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, more)
import json
def lambda_handler(event, context):
# TODO implement
return {
'statusCode': 200,
'body': json.dumps('Hello World!')
}

33. Lambda Layers
Lets functions easily share code: Upload layer
once, reference within any function
Promote separation of responsibilities, lets
developers iterate faster on writing business logic
Built in support for secure sharing by ecosystem

34. Lambda Layers: Uses cases
• Custom code, that is used by more than one function
• Libraries, modules, frameworks to simplify the
implementation of your business logic
• Security/monitoring service
• Shared code that does not change frequently
• Bring your own Runtime
• C++
• Rust
• PHP

35. Fine-grained Pricing
Buy compute time in 1ms
increments
Low request charge
No hourly, daily, or monthly
minimums
No per-device fees
Never pay for idle
Free Tier
1M requests and 400,000 GBs of compute.
Every month, every customer.

36. Let’s see this in action

37. AWS Compute Offerings
Service
I want to
configure
servers, storage,
networking, and
my OS
I want to run
servers,
configure
applications, and
control scaling
Run my code
when it’s
needed
How do I
choose?
Amazon EC2 Amazon ECS AWS Lambda
AWS Fargate
I want to run my
containers

38. Serverless compute engine
for containers
Long-running
Bring existing code
Fully-managed orchestration
AWS Fargate
The two Serverless compute options
Serverless event-driven
code execution
Short-lived
All language runtimes
Data source integrations
AWS Lambda

39. Picking a Compute Platform: Containers vs. Lambda
10 GB

40. But distributed systems can be harder
to coordinate and debug

41. Orchestration for serverless apps
“I want to sequence functions”
“I want to select functions based on data”
“I want to run functions in parallel”
“I want to retry functions”
“I want to try/catch/finally”
“I want to run code for hours”
AWS Step Functions

42. AWS Step Functions
Visualize in the
Console
Define in JSON Monitor
Executions
Easily coordinate multiple Lambda functions using visual workflows

43. Processing a new bank account application
requires some service coordination
Data
checking
Verify identity documents
Check address
Human review
List flagged applications
Handle human decisions
Account applications
Accept new application
Consolidate data checks
Human review?
Approve or reject

44. Example workflow:
opening an account
Wait for a callback
Parallel steps
Branching choice
Tasks

45. There are still a lot of things to think about
• Deployment and Infrastructure as code
• Secrets/configuration management
• Simplifying code management
• Debugging/troubleshooting
• Performance controls
• Security

46. X-Ray trace example

47. Para saber más
• https://aws.amazon.com/serverless
• https://bit.ly/ServerlessWorkshopIL
• https://step-functions-workshop.go-aws.com/

49. Gracias
Javier Ramírez
Developer Advocate - Amazon Web Services
@supercoco9 ramirez