Serverless Architectural Patterns - Amazon Web …...Streaming data ingestion Amazon CloudWatch:...

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Monica Lora

Solutions Architect, AWS

Nils Rhode

Tech Lead & Team Lead, Haufe.Group

Serverless Architectural Patterns

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Agenda• Serverless Foundations • Serverless Architectural patterns:

• Web Application• Data Lake• Stream Processing• Automation

• Serverless at Haufe.Group

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Serverless Means …

No servers to provision or manage

Scales with usage

Never pay for idle Built-in availability and fault tolerance

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SERVICES (ANYTHING)

Changes in data state

Requests to endpoints

Changes in resource state

EVENT SOURCE FUNCTION

Node.jsPythonJavaC#Go

Serverless applications

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Amazon S3 Amazon DynamoDB

Amazon Kinesis

AWS CloudFormation

AWS CloudTrail Amazon CloudWatch

Amazon Cognito

Amazon SNSAmazonSES

Cron events

DATA STORES ENDPOINTS

DEVELOPMENT AND MANAGEMENT TOOLS EVENT/MESSAGE SERVICES

Event sources that trigger AWS Lambda

and more!

AWS CodeCommit

AmazonAPI Gateway

AmazonAlexa

AWS IoT AWS Step Functions

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Lambda Execution Model

Synchronous (push)

Asynchronous (event)

Stream-based

AmazonAPI Gateway

AWS Lambda function

/order

Amazon SNS

AWS Lambda function

Amazon S3

reqs

Amazon DynamoDB

Amazon Kinesis

changes

AWS Lambda service

function

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Common Lambda use cases

Web Applications• Static

websites

• Complex web apps

• Packages for Flask and Express

Data Processing

• Real time

• MapReduce

• Batch

Chatbots

• Powering chatbot logic

Backends

• Apps & 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

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Pattern 1: Web App/Microservice/API

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Serverless Web Application

Data stored in Amazon

DynamoDB

Dynamic content in AWS Lambda

Amazon API Gateway

Browser

Amazon CloudFront

Amazon S3

Amazon Cognito

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Serverless Web Application - Security

Amazon API Gateway AWS

LambdaAmazon

DynamoDB

AmazonS3

Amazon CloudFront

• Bucket Policies• ACLs

• OAI• Geo-Restriction• Signed Cookies• Signed URLs• DDoS Protection

IAM

AuthZ

IAM

• Throttling• Caching• Usage Plans• ACM

Static Content

Browser

Amazon Cognito

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Serverless Web Application - Security

Amazon API Gateway AWS

LambdaAmazon

DynamoDB

AmazonS3

Amazon CloudFront

• Bucket Policies• ACLs

• OAI• Geo-Restriction• Signed Cookies• Signed URLs• DDOS

IAMAuthZ IAM

• Throttling• Caching• Usage Plans

Static Content

Browser

Amazon CloudFront• HTTPS• Disable Host

Header Forwarding

AWS WAF

Amazon Cognito

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Serverless Web Application - Monitoring

Amazon API Gateway

AWSLambda

AmazonDynamoDB

AmazonS3

Amazon CloudFront

• Access Logs in S3 Bucket• Access Logs in S3 Bucket

• CloudWatch Metrics-https://aws.amazon.com/cloudfront/reporting/

AWS WAF• WebACL Testing• Total Requests• Allowed/Blocked

Requests by ACL

logslogs

• Invocations• Invocation Errors• Duration• Throttled

Invocations

• Latency• Throughput• Throttled Reqs• Returned Bytes• Documentation

• Latency• Count• Cache Hit/Miss• 4XX/5XX Errors

Streams

AWSCloudTrail

Static Content

BrowserCustom CloudWatch

Metrics & Alarms

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Serverless web app lifecycle managementAWS SAM (Serverless Application Model) - blog

AWSLambda

Amazon API Gateway

AWS CloudFormation

AmazonS3

AmazonDynamoDB

Package & Deploy

Code/Packages/Swagger

Serverless Template

Serverless Template

w/ CodeUri

package deployCI/CD Tools

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Bustle is a news, entertainment, lifestyle, and fashion website targeted towards women.

With AWS Lambda, we eliminate the need to worry

about operations

Tyler LoveCTO, Bustle

”

“ • Bustle had trouble scaling and maintaining high availability for its website without heavy management

• Moved to serverless architecture using AWS Lambda and Amazon API Gateway

• Experienced approximately 84% in cost savings

• Engineers are now focused on innovation

Bust le Achieves 84% Cost Sav ings with AWS Lambda

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Pattern 2: Data Lake

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Characteristics

• Collect/Store/Process/Consume and Analyze all organizational data

• Structured/Semi-Structured/Unstructured data• AI/ML and BI/Analytical use cases• Fast automated ingestion• Schema on Read• Complementary to EDW• Decoupled Compute and Storage

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AWS Serverless Data Lake

S3 Bucket(s)

Key Management

Service

Amazon Athena

AWSCloudTrail

AmazonCognito

AWS IAM

Amazon Kinesis

Streams

Amazon Kinesis

Firehose

Amazon ES

Amazon QuickSight

AWS GlueAmazon

DynamoDB

Amazon Macie

Amazon API Gateway

AWS IAM

Amazon Redshift

SpectrumAWS Direct Connect

Ingest

Catalog & Search

Security & Auditing

API/UI

Analytics & Processing

AWS GlueAWSLambda

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Fannie Mae Serverless Financial Modeling

Financial Modeling is a Monte-Carlo simulation process to project future cash flows , which is used for managing the mortgage risk on daily basis:

• Underwriting and valuation• Risk management • Financial reporting• Loss mitigation and loan removal

• ~10 Quadrillion (10#10$%) of cash flowprojections each month in hundreds of economic scenarios.

• One simulation run of ~ 20 million mortgages takes 1.4 hours, >4 times faster than the existing process.

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Pattern 3: Stream Processing

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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

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Streaming data ingestion

Amazon CloudWatch:Delivery metrics

Amazon S3:Buffered files

Kinesis Agent

Record Producers Amazon Redshift:

Table loads

Amazon Elasticsearch Service:Domain loads

Amazon S3:Source record backup

AWS Lambda:Transformations &

enrichment

Amazon DynamoDB:Lookup tables

Raw records

Lookup

Transformed records

Transformed recordsRaw records

Amazon Kinesis Firehose:Delivery stream

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Sensor data collection

IoTrules

IoTactions

MQTT

Amazon S3:Raw records

Amazon Kinesis Firehose:Delivery stream

Amazon S3:Batched records

Amazon Kinesis Streams:Real-time stream

AWS IoT:Data collection

IoT Sensors

Real-time analyticsapplications

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Thomson Reuters – Product Insight

Solution for usage analysis tracking:Capture, analyze, and visualize analytics data generated by offerings, providing insights to help product teams continuously improve the user experience

Throughput: Tested 4,000 requests / secondGrowing to 10,000 requests / second or 25 Billion requests / month

Latency: new events to user dashboards in less than 10 seconds

Durable: no data loss since inception

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Pattern 4: Automation

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Automation characteristics

• Periodic jobs • Event triggered workflows• Enforce security policies• Auditing and notification• Respond to alarms• Extend AWS functionality• …All while being Highly Available and Scalable

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AWS Ops Automator Tags

OpsAutomatorTaskList CreateSnapshot

Amazon CloudWatch:Time-based events

AWS Lambda:Event handler

AWS Lambda:Task executors

AWS SNS:Error and warning notifications

Resources in multiple AWS Regions and Accounts

Amazon EC2 Instances

Amazon DynamoDB:Task configuration & tracking

Amazon CloudWatch:Logs

Amazon Redshift Clusters

https://aws.amazon.com/answers/infrastructure-management/ops-automator/

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Serverless @ HAUFE.GROUP

Our True North Goal.

–A family company for the

next generations.

1800employees

13Locations in Germany

5International branches

370 Mio.Sales

All DAX 30

–Companies rely on our expertise

150,000

–Seminar participants at Haufe

Akademie every year

5,500,000

–Users and licences

Our SolutionsPrint

SoftwareTraining

Consulting

MarketsGermany

SwitzerlandUSA

China

1934founded as publishing house

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Our challenge- Re-Engineering the HRS

- from Monolith to Microservices- starting with the first component: EDB (Court Decision Database)

- AIM: • Future-proof by choosing state-of-the-art technics• Low maintenance effort by using services• Fast and cheap way to import millions of documents

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EDB Architecture on AWS

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Example of our serverless workflows

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How we realized the mentioned workflow

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Benefits of our work

• Cost-saving (Never pay for idle)10 imports/month with about 1.000 till 10.000 files as fast as possible (scalability)

$79.65

$42.92

$0.46

$4.52

$0.97

16 X T2.NANO 1 X T2.MEDIUM 16 LAMBDAS

Idle Needed~95% paying

for idle

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Benefits of our work

$9,558.00

$5,150.40

$55.20

$55.20

$- $2,000.00 $4,000.00 $6,000.00 $8,000.00 $10,000.00 $12,000.00

t2.nano/each function

t2.medium/all functions

Fee based on imports over all workflows per year

Lambda EC2

Cutting down the cost about

99%

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Benefits of our work

• Easy to extend and maintain• add, change or remove functions (microservice-approach)• Configuration of each step (e.g. failure-handling)• „No server is easier to manage than no server“

• Handy transformation from monolith to microservice

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How we proceed

- Our bottleneck: RDS database and the limited scaling(costs of Lambda for failover by re-trying: about:10%)

- High costs for ES(only needed for handling queries for export)

=> We‘re waiting for Aurora Serverless

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Summary

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Lambda Best Practices

• Minimize package size to necessities• Separate the Lambda handler from core logic• Use Environment Variables to modify operational behavior• Self-contain dependencies in your function package• Leverage “Max Memory Used” to right-size your functions• Delete large unused functions (75GB limit)

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Summary

Use DevOps tools to automate your serverless deployments

Apply serverless patterns for common use-cases:•Web application• Data Lake Foundation• Stream processing•Operations automation

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Thank you!What will you build with Serverless?