Serverless Design Patterns with AWS Lambda: Data with ... · Using AWS Lambda Bring your own code • Node 4.3, 6.1 • Java 8 • Python 2.7, 3.6 • .NET Core 1.0.1 • Bring your

© 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

Dr. Tim Wagner, General Manager, AWS Lambda and Amazon API GatewayApril 27, 2017

Serverless Design Patterns with AWS Lambda:

Big Data with Little EffortCRAFT-Conf 2017

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

Agenda

What isserverless?Build and run applicationswithout thinking about servers

Let’s take a look at the evolution of computing

Physical serversin data centers

Virtual serversin data centers

Virtual serversin the cloud

Each progressive step was better

Physical serversdata centers

Virtual serversdata centers

• Higher utilization• Faster provisioning speed• Improved uptime• Disaster recovery• Hardware independence

• Trade CAPEX for OPEX• More scale• Elastic resources• Faster speed and agility• Reduced maintenance• Better availability and fault

tolerance


But there are still limitations

Physical serversdata centers

Virtual serversdata centers

• Trade CAPEX for OPEX• More scale• Elastic resources• Faster speed and agility• Reduced maintenance• Better availability and fault

tolerance

• Still need to administer virtual servers

• Still need to manage capacity and utilization

• Still need to size workloads

• Still need to manage availability, fault tolerance

• Still expensive to run intermittent jobs


Evolving to serverless

SERVERLESS


Physical serversin data centers

Virtual serversin data centers

No server is easier to manage than no server

All of these responsibilitiesgo awayProvisioning and utilizationAvailability and fault toleranceScalingOperations and management

EVENT DRIVEN CONTINUOUS SCALING PAY BY USAGE

Deliver on demand, never pay for idle

There is a spectrum of compute options

Size of Deployable Unit

FunctionAppContainerVirtual Machine

IaaS Docker

PaaS

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Function as a Service (FaaS)

There is a spectrum of compute options


Amazon EC2 Amazon ECS

AWS Elastic Beanstalk


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FaaS services differ in operational burden


FaaS

FaaS



FaaS



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Serverless means no management burden


FaaS

FaaS



Serverless



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Serverless means no management burden


FaaS

FaaS





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AWS Lambda: Run code in response to events

FUNCTION SERVICES (ANYTHING)

Changes in data state

Requests to endpoints

Changes in resource state

NodePythonJavaC#

EVENT SOURCE

Amazon S3 Amazon DynamoDB

Amazon Kinesis

AWS CloudFormation

AWS CloudTrail

Amazon CloudWatch

Amazon Cognito

Amazon SNS

AmazonSES

Cron events

DATA STORES ENDPOINTS

CONFIGURATION & MANAGEMENT EVENT/MESSAGE SERVICES

Example event sources that trigger AWS Lambda

AWS CodeCommit

AmazonAPI Gateway

AmazonAlexa

AWS IoT

AWS Step Functions

Using AWS Lambda

Bring your own code• Node 4.3, 6.1• Java 8• Python 2.7, 3.6• .NET Core 1.0.1• Bring your own libraries

(even native ones)

Simple resource model• Select power rating from

128 MB to 1.5 GB• CPU and network

allocated proportionately

Flexible use• Synchronous or

asynchronous• Integrated with other

AWS services

Flexible authorization• Securely grant access to

resources and VPCs• Fine-grained control for

invoking your functions

Using AWS Lambda

Authoring functions• WYSIWYG editor or

upload packaged .zip• Third-party plugins

(Eclipse, Visual Studio)

Monitoring and logging• Metrics for requests,

errors, and throttles• Built-in logs to Amazon

CloudWatch Logs

Programming model• Use processes, threads,

/tmp, sockets normally• AWS SDK built in

(Python and Node.js)

Stateless• Persist data using

external storage• No affinity or access to

underlying infrastructure

Mapping owned by Event SourceInvokes Lambda asynchronously

AmazonS3

Amazon SNS

ASYNCHRONOUS PUSH MODEL

Async invocation

Mapping owned by Lambda

Lambda function called when new records found on stream

Lambda polls the source

HOW IT WORKS

AmazonAlexa

AWSIoT

SYNCHRONOUS PUSH MODEL

Sync invocation

Amazon DynamoDB

Amazon Kinesis

STREAM PULL MODEL

Sync invocation

How event sources work

Mapping owned by Event SourceCalls Lambda synchronously

Building blocks for serverless

AWS Lambda Amazon DynamoDB

Amazon SNS

Amazon API GatewayAmazon SQS

Amazon Kinesis

Amazon S3

Orchestration and State Management

API Proxy Messaging and Queues Analytics

Monitoring and Debugging

Compute Storage Database

AWS X-RayAWS Step Functions

Edge Compute

AWS Greengrass

Lambda@Edge

Serverless changes how you deliver

Speeds uptime to market

Dedicated timeto innovation

Increases developer productivity

Eliminates operational complexity

Serverless use cases

Chatbots

• Powering chatbot logic

• Alexa Skills for Amazon Echo

Common use cases

Web applications• Static

websites

• Dynamic web apps

• Packages for Flask and Express

Backends

• Apps & services

• Mobile

• IoT

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Media & Log Processing• Real-time data

• Streaming data

Big Data

• MapReduce

• Batch

Lambda + S3

Common use cases

Common use cases

• Lambda processes 200-300 images uploaded per minute

• Peak processing of 6,000 images per minutes

• Reduced image processing time from hours to only 10+ seconds

Common use cases

http://www.vogue.it/

• Lambda performs image processing for PhotoVogue, which hosts 400,000+ photos online

• User experience up to 90% faster

Lambda + Kinesis + DynamoDB

Common use cases

Common use cases

• Processes 4,000 requests per second

• Built the solution in only 2.5 months

• Handled spikes in traffic of 2x normal load

Lambda + API Gateway + S3 + DynamoDB

Serverless web applications

Chalice

Frameworks for building serverless apps

Serverless JavaContainer

Serverless app lifecycle

Capabilities of a serverless platform

Application modeling framework

Monolithic application

Microservices

But what happens when you have an entire app made up of many functions?

Composing serverless applications

Meet SAM

AWS Serverless Application Model (SAM)

Standard model for representing serverless applications on AWS

Functions, APIs, event sources, and data stores

Simplifies deployment and management for serverless applications

AWS Serverless Application Model (SAM)

• Natively supported by AWS CloudFormation

• Export any function as a SAM template

• Package and deploy SAM templates using AWS CLI

• Open spec under Apache 2.0 for community extensions

CI/CD for serverless applications

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AWS CodePipeline + SAM

GitHubAmazon S3AWS CodeCommit

AWS CodeBuild AWS CodeBuildThird-party tools

AWS CloudFormation

Commit Build Test Deployto Prod

AWS CodeStar New!

Big Data and AWS Lambda!

Big Data

• MapReduce

• Batch

Big data

Map Phase Reduce PhaseInputs Results

Demos

Focus on two techniques• Amazon S3 – persistent data• Amazon Kinesis Firehose – streaming data

Many more options and topics!• SQL and NoSQL data triggers• Sharded streams• Queues, notifications, working with 3rd party systems, …

Why Lambda + S3?

• Easy – both scale automatically• Common – S3 is one of the most-used AWS services• Persistent – data sticks around• Can be immutable (i.e., keep both original and

transformed data)• Two ways to work:

• Read/write from S3 in a Lambda function• Hook up S3 bucket events to a Lambda function

Why Lambda + Firehose?

• Easy – both scale automatically, no sharding needed• 24 hour record retention• Works great for real-time analytics pipelines• Data can go to Elasticache, S3, or Redshift• Data can come from anywhere

• …including Lambda!• Data can be transformed in flight

• …using a simple Lambda function

Demos

Focus on two techniques• Amazon S3 – persistent data

1. Parallel data processing: Lambda à Amazon S32. In-place object transforms: S3 à Lambda à S3

• Amazon Kinesis Firehose – streaming data3. Streaming data: Lambda à Amazon Kinesis Firehose4. Streaming transforms: Firehose à Lambda à Firehose

1. Parallel data processing using Lambda

Amazon S3

Amazon S3

Animal sightings data

{

“squirrel”, 33,

“raccoon”, 11,

“hummingbird”, 1,

…

}

Three levels:

200 parallel functions X200 S3 objects per function X500 entries per S3 object =

20 million entries

2. In-place S3 data transformation

Amazon S3

Lambda

Object written to S3

World’s simplest map-reduce!

• Each S3 object is mapped (aggregated) by the event processor

• We run N parallel reducers, one per S3 directory• Final serial reduction over the directories (could be

parallelized, but N is small here)

Serverless Map/Reduce with Lambda

https://github.com/awslabs/lambda-refarch-mapreduce

Capture DataStreams

IoT Data

Financial Data

Log Data

Clickstream Data

OutputData

DATABASE

CLOUDSERVICES

EVENT SOURCE

Process Data Streams

FUNCTION

Real-time streaming data

3. Streaming data ingestion

Kinesis Firehose

Kinesis Firehose

4. Inline streaming transformation

Lambda

Kinesis Firehose

…and the cool part?

That was free.

All the infrastructure on which it ran is gone.

• Buy compute time in 100 ms increments

• Low request charge• No hourly, daily, or

monthly minimums• No per-device fees

Never pay for idle!

Free Tier1 million requests and 400,000 GBs of compute every month, every customer

AWS Lambda pricing

Serverless is a fundamental component of modern applications

Conclusion

Lambda is a fundamental component of modern application architectures

It has a place in everything from data processing to simple web apps

Documents

Serverless Design Patterns with AWS Lambda: Data with ... · Using AWS Lambda Bring your own code • Node 4.3, 6.1 • Java 8 • Python 2.7, 3.6 • .NET Core 1.0.1 • Bring your