The document discusses serverless state management and orchestration using AWS Step Functions. It provides examples of how companies like Xylem, Coinbase, Granular and Novartis Institutes for Biomedical Research have used Step Functions to build modern applications. Specifically, it highlights how Step Functions enabled these companies to scale workflows, integrate legacy and modern systems, improve reliability and reduce costs. The document also provides technical details on how Step Functions can be used to orchestrate tasks and process large datasets across multiple AWS services.

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Serverless State Management &
Orchestration for Modern Apps
André Dufour
Senior Manager
AWS Orchestration Services
A P I 3 0 2
Nick Whalen
Principal Software Engineer
Novartis Institutes for Biomedical Research

3. Modern application development
enables rapid innovation

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Modularity, microservices, and APIs
Function
API
API
Microservice
Function
Event
API
Microservice
Event
API
Microservice
Application

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AWS Step Functions
Visualize in the
console
Define in JSON Monitor
executions

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Xylem
Because everydrop of water counts
Challenge
• Make the data for tens of millions of endpoints available for data
scientists, engineers, and data analysts; store it forever; and do it
with a team of four people
Results
• Reduced processing time from 20+ hours to less than 2 hours
• Improved cost efficiency – hardware no longer purchased for peak
demand
• Decreased personnel costs as they no longer manage infrastructure
Patterns
• Data preparation workflows; simple state machines
• Using serverless when there is a large peak-to-average variation

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AWS Step
Functions
Amazon Aurora
Serverless
AWS Batch
Amazon
S3
Amazon Elastic
Compute Cloud
(Amazon EC2)
AWS
Lambda
function
AWS
Lambda
function
Controller
app
Fetch new files
to parse
Store
metadata
Parse job
Parse job
Parse job
Parse job
Parse job
Parse job
Parse job
Amazon EC2
Amazon EC2
Solution architecture
Amazon EC2

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Coinbase
Buy and sell digital currency
Challenge
• Secure, reliable, immutable deployments with visibility
by their engineers
• Scale to multiple AWS accounts easily and quickly
Results
• Reduced new account deployment time
• Increased the time a deployment can reliably run from 2
hours to 48 hours
• Mission critical services deployment rate rose 7%

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Solution architecture

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Workflow
How they did it
• Step Functions provides the tools for
reliability and visibility out of the box
• Integration with Codeflow configuration
management tool
• End-to-end security validation prior to
deployment into production
• Six months from idea to use for all production
deployments
Pattern
• Fail to a known state

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Granular
Farm with confidence
Challenge
• Improve scaling and management of legacy application that
performs machine data translation through ingestion and
processing from large scale agricultural machinery
Results
• Easy integration between serverless and legacy application
components
• Scalable approach to running code through multiple technology
stacks
• Self documenting executions with Step Functions execution history
Patterns
• Step Functions to bridge from legacy to modern application
development

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Workflow
How they did it
• Considered writing their own workflow engine
but discounted it as they wanted to focus on
their business logic
• Used Step Functions because the activity API
could easily combine Amazon EC2,
Kubernetes, and AWS Lambda to create a
modern application that is inclusive of useful
legacy components
C# and on
Windows
Server (EC2)
Python
lambda
function

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Novartis Institutes for Biomedical Research (NIBR)
Challenge
• Provide scientists a tool to run image analysis
workflows at scale
Results
• Reduced cluster processing times
• Improved operational efficiencies
• Delivered reliable, reproducible image analysis results
• Able to effectively support enterprise scale application
with a small team

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• The Novartis Institutes for BioMedical Research (NIBR) is
the innovation engine of Novartis
• We focus on new technologies that have the potential to
help produce therapeutic breakthroughs for patients
• 6 Research campuses across the globe
• 200+ Projects in clinical pipeline
• 500+ Clinical trials in progress

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High content screening image analysis
• Empowers scientists to directly run image
analyses without depending on cluster experts
• Provides for faster assay development and execution with
more focus on the science rather than tools

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plates
wells

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https
AWS Batch
EC2 Spot Fleet
Amazon Aurora
• Submits jobs to
AWS Batch for
processing
• Persists job states
Each batch job
sends status
updates to
queue
Image Analysis
Results
AWS Step
Functions
• Merges
partial
results
• Aggregates
• Updates job
status
• Notify
subscribed
users of
state via
email
Results from each
Batch job are written
to Amazon S3
Client
NIBR
HCSIA architecture
HCS images
us-east-1
Elastic Load
Balancing
Auto Scaling
Web
service
Retrieve job
updates
UI
JSON
Invoke PP State Machine
Array Job Complete?
Notifies subscribed
users of end state via
email through SNS

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Calculate compute requirements
"Compute requirements": {
"Resource": "ComputeRequirementsArnLambda”
"ResultPath": "$.guid",
"Type": "Task",
"Next": "Submit Merge Job Array",
"Retry": [
{
"ErrorEquals": [
"Lambda.ServiceException",
"Lambda.SdkClientException"
],
"IntervalSeconds": 2,
"MaxAttempts": 5,
"BackoffRate": 2
}

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Submit merge job array
"Submit Merge Job Array": {
"Resource": “SubmitMergeJobArrayArn”,
"ResultPath": "$.guid",
"Type": "Task",
"Next": "Wait X Seconds",
"Retry": [
{
"ErrorEquals": [
"Lambda.ServiceException",
"Lambda.SdkClientException"
],
"IntervalSeconds": 2,
"MaxAttempts": 5,
"BackoffRate": 2
]
}
}

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Waiting and getting status
"Wait X Seconds": {
"SecondsPath": "$.wait_time",
"Type": "Wait",
"Next": "Get Merge Job Array Status"
},
"Get Merge Job Array Status": {
"Resource": “GetMergeJobArrayStatusArn",
"ResultPath": "$.status",
"Type": "Task",
"Next": "Merge Job Array Complete?",
"Retry": [
{
"ErrorEquals": [
"Lambda.ServiceException",
"Lambda.SdkClientException"
],
"IntervalSeconds": 2,
"MaxAttempts": 5,
"BackoffRate": 2

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Corporate
data center
HCS file transfer and job submission

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Subscribe to cluster run state changes

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• Use Amazon Simple Storage Service (Amazon S3) to
persist and iterate over large data sets and pass
object keys
• Extract certain business functionality by state
machine

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• Iterate through large data sets using for loop or
iterator
• Avoid exceeding the maximum number of 25,000
history events in an execution:

30. How many of you lead a team?

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Boss detector
Keepingthe team area safe
Challenge
• I want to be notified when my boss enters my team area and I want to know his mood

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Boss detector architecture
Reference
image
Upon
movement,
upload
snapshot
Upon new
snapshot
Start
Execution
Compare
images
Detect
emotions
Send SMS
Amazon
SNS
Amazon
S3
AWS Lambda
AWS DeepLens
Amazon
Rekognition

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Detecting emotions
def lambda_handler(event, context):
fileName=‘image.png'
bucket='boss-detector'
client=boto3.client('rekognition','us-east-1')
response =
client.detect_faces(Image={'S3Object':{'Bucket':bucket,'Name':fileName}
},Attributes=['ALL'])
# [...] Code that assembles the response into a string called emoStr
return {"jdEmotion": emoStr}

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Passing data in the state machine

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In action Detection
{'SourceImageFace': {'BoundingBox': {'Width':
0.24278846383094788, 'Height':
0.32371795177459717, 'Left':
0.4014423191547394, 'Top':
0.4246794879436493}, 'Confidence':
99.89337158203125}, 'FaceMatches':
[{'Similarity': 99.0, […]
Analysis
'AgeRange': {'Low': 39, 'High': 51}, 'Smile':
{'Value': True, 'Confidence':
87.71004486083984}, 'Sunglasses': {'Value':
False, 'Confidence': 93.51824188232422},
'Gender': {'Value': ‘Male', 'Confidence':
99.9888687133789}, 'Beard': {'Value': False,
'Confidence': 99.99958801269531}, […]
'Emotions': [{'Type': 'HAPPY', 'Confidence':
78.34557342529297}, {'Type': 'CALM',
'Confidence': 1.890808343887329}, {'Type':
'ANGRY', 'Confidence': 0.7760047912597656}],
[…]

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Quick and … clean
The costs
• 49 lines of Amazon States
Language
• $0.025 per 1,000 state
transitions (in US-East-1)
The benefits
• Modular, elastic, secure
architecture – as befits a
modern application
• Traceable, auditable
execution history
• Built-in error handling
• Visual representation of the
workflow

37. Thank you!
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
André Dufour
@arwdufour
#stepfunctions
Nick Whalen
nick.whalen@novartis.com

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