Keynote Gregor Hohpe - Serverless Architectures

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O K , N O M O R E S E R V E R S – N O W W H A T ?
Serverless Architectures
Gregor Hohpe
Sr. Principal Evangelist
AWS

How modern is your cloud application?
VM Container Serverless
“Modern”
APIs?
“Traditional”
Granularity?
CI/CD?
Progr. Languages?
Managed services?
Automation?
Observability?
State management?

Serverless – Not just a run-time
AWS
Lambda
AWS
Fargate
COMPUTE
DATA STORES
Amazon Aurora
Serverless
Amazon
S3
Amazon
DynamoDB
AWS
AppSync
Amazon
API Gateway
Amazon
SNS
Amazon
SQS
AWS
Step Functions
INTEGRATION
Amazon
EventBridge

Sr. Principal Evangelist, Serverless
ArchitectElevator.com
www.linkedin.com/in/ghohpe/
Gregor Hohpe

Serverless Architectures:
Under the Lambda covers

AWS Lambda – Launched Nov 2014
• Provide your code or image; we run it as
an event when things happen
• Pay by the millisecond, up to 15mins
• Package as .zip file (250 MB) or
container image (10 GB)
• Java, Go, Node.js, .NET, Python, and
Ruby, or bring-your own
• No provisioning or managing servers
• Scale up in milliseconds in response to
traffic
Lambda is the
compression algorithm
for experience

Lambda Architecture – High-level View
Dev Tools
Event Sources
Async. Invocation
Events
Front-End
Pollers
Queue
Synchronous Invocation
Assignment
Service
Placement
Worker
Requests
Front-end
AWS X-Ray
Amazon CloudWatch
Capacity
Manager
Data
Science
Control Plane
Configuration Code
Lambda API

Our experience: Implementation considerations
• Placement (Assignment Service)
• Fault tolerant, leader-follower model
• Worker manager
• Isolation levels
• Firecracker micro-VMs
• Tenant de-correlation
• Initialization / cold starts
• SnapStart
• Chunks, read-ahead
https://www.infoq.com/news/2023/10/aws-lambda-under-the-hood/

Of boxes and lines

Serverless applications are
distributed and fine-grained.
Distribute responsibly!

Friends don’t let friends use synchronous integration
Customer
Fraud detection
service
https://...
Synchronous
response
External payment
service provider
https://...
Synchronous
response
Ride booking
service
Resource management
service
Customer loyalty
service
Ride payment
service
https://...
Synchronous
response
https://...
https://...
Synchronous
response
Synchronous
response
12
3
9
6
12
3
9
6
12
3
9
6
12
3
9
6
12
3
9
6
12
3
9
6
?

• Event-driven architectures
• Lambda has synchronous and asynchronous invocation models
• Push models, pull models, streaming
• Serverless integration provides orchestration, event routing, and
message queues
Reduced
Dependencies
Failure
Isolation
Evolvable
Architecture
Independent
Scalability
Distributed, event-driven architecture

Behold the lines!
A
B
C
D
A B
C D

Connecting two systems. How hard can it be?
A B
Sync/async?
Interaction model?
Data format?
Pub-sub or point-to-point?
Conversation State?
Error handling?
Distributed?
Partial failures? Retries?
Idempotency?
Back-off?
Data or control flow?
Polling?
Systems or instances?

Control flow vs. data flow
Data Flow
A B
Control Flow:
RPC
A B
Control Flow:
Polling
A B
Control Flow:
Messaging
A B

Control Flow
Message Transformation
Enterprise Integration Patterns
Message Routing
Content-based
Router
Message
Filter
Splitter
Message
Translator
Content
Enricher
Claim
Check
Source: EnterpriseIntegrationPatterns.com
?
Data (Message) Flow
Puller
Pusher
Driver
Queue
Event
Source
Polling
Source
Event
Target
Pulling
Target
Active
Passive
Scatter
Gather

Amazon SNS
Translator
API Destination
Filter
Amazon EventBridge Bus
EventBridge with Rate Limited Target
Driver
Invocation
Rate: 10
Control Flow
Visualizing control flow: EventBridge Bus
Source: EnterpriseIntegrationPatterns.com
Puller
Pusher
Driver
Queue
Event
Source
Polling
Source
Event
Target
Pulling
Target
Active
Passive
Queue
TTL: 24h
Amazon SNS
Translator
Filter
Amazon SQS
Amazon EventBridge Bus
EventBridge with Event Target

Control flow is essential to
understanding dynamic system
behavior such as latency,
scaling, batching, time-outs

Queues invert flow control, shape traffic
Arrival Rate Consumption Rate
Arrival Rate Consumption Rate
Queue empty
Queue fills up
Producer Consumer

Queues require flow control
Back Pressure:
Slow down arrival rate
Time-to-live (TTL):
Shed old messages
TTL is an SQS setting,
backpressure has to be built.
Even valuable messages have a
meaningful time to live.
Fast
Producer
Slow
Consumer

Queues decouple control flow
but require flow control.
Hard-earned experience from an integration architect

Coordination

Choreography vs. Orchestration
 Centralized business logic in Orchestrator
 Transaction management across services
 End-to-end monitoring and error handling easier
 Tightly coupled
 Orchestrator can become single point of failure / limitation
 Single point of maintenance (can be good or bad)
 Business logic can evolve easily (add, update services)
 Services can scale independently
 Transactional consistency across services is difficult
 End-to-end monitoring ,handling errors, and timeout
is difficult
Original Design
 Lambda must publish events
Refactored Design

Automation

Cloud
− Automation
= Just another data center
An opinionated architect

Automation focused on infrastructure (“IaC”)
Development
teams
Infra / Ops
teams
- Functionality, UX
- Application architecture
- Write code
- Non-functional “-ilities”
- Sizing, procurement
- Click-Ops -> Write scripts
Automation
Application
“Build”
“Run”
• Resource-oriented
• Prefer simple syntax (YAML)
• Strings and integers
• Text editor
• Object-oriented or functional
• Powerful languages
• Type systems
• Automated testing, TDD, IDE
https://architectelevator.com/cloud/iac-ifc-trends/

As application architecture changes,
so does the role of cloud automation.
Limiting cloud automation to
infrastructure is no longer adequate.

Composition with cloud automation
Data change triggers workflow
Type: AWS::Pipes::Pipe
Properties:
Description: String
Name: String
RoleArn: String
Source: String
SourceParameters:
PipeSourceParameters
YAML (CloudFormation)
Amazon DynamoDB AWS Step Functions
Source
this.sourceArn = table.tableStreamArn;
this.sourceParameters = {
dynamoDbStreamParameters: {
startingPosition: LATEST,
maximumBatchingWindowInSeconds: 5,
maximumRetryAttempts: 10
},
};
Object-Oriented (CDK)
Target
Amazon EventBridge
Pipes
Architecture
Implementation
(Services)
Automation
Code

From IaC to AaC: Architecture as Code
Classic IaC (incl. CDK):
Resource hierarchy
Serverless Application Architecture:
Data and control flow
Well suited for
infrastructure resources
Well suited for fine-grained and
loosely coupled applications
Source: architectelevator.com/cloud/iac-ifc-trends/

Coding in 202x: Fluent APIs, Pipes-and-Filters
const pipeline = new PipelineBuilder<IPipelineSource, Filter, Channel, IPipelineTarget>()
.fromSource(new DynamoDBStreamSource(ordersTable))
.with(new MessageSplitter('$.tickets', ['$.id', '$.userId'], 'common_'))
.with(new ContentEnricher(lookupTable, { "id": "id" }, "detail"))
.toTarget(new StepFunctionsTarget(…);
deployPipe(this, "myPipeline", pipeline, LogLevel.ALL);
Implementation with CDK (Experimental )
Amazon DynamoDB AWS Step Functions
Amazon DynamoDB
Message
Splitter
Content
Enricher
Architecture
Automation
Code
Implementation Icons: EnterpriseIntegrationPatterns.com

Program to your architecture, not cloud services
Manual
Translation
Manual
Translation
Automatic
Translation
Direct
Mapping
Architecture
Automation
Code
Implementation
Traditional Model
Architecture
Automation
Code
Implementation
Cloud Compiler Approach

as Code
Actual
Next-gen Serverless Automation:
Application Architecture
Infrastructure

Want more?
Gregor Hohpe
@ghohpe
www.linkedin.com/in/ghohpe

Thank you!
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Thank you!
Please complete the session
survey in the mobile app
Gregor Hohpe
@ghohpe
Linkedin.com/in/ghohpe

Keynote Gregor Hohpe - Serverless Architectures

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