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Amazon Simple Workflow

7 use cases : 7 minutes each

Sunjay Pandey, Amazon Simple Workflow

November 14, 2013

What is the Amazon SWF service?

Coordinate, track, and audit

programs

With …

Multiple steps on multiple machines

Booking travel to Vegas…

A multi-step process:

Update seat

inventory (Batch)

Select

seats

Provide

credit card

Confirm

inventory

Confirm

Trip

Update

inventory

Send confirmation

email

Search for

flights

( Web, mobile, SMS …)

Reliable, Scalable,

Auditable?

Update seat

inventory (Batch)

Select

seats

Provide

credit card

Confirm

inventory

Confirm

trip

Update

inventory

Send confirmation

email

Search for

flights

( Web, mobile, SMS …)

How to handle these challenges

Amazon SWF provides …

Control engine

Manage state, route

tasks, distribute load

APIs HTTP, task processing

instructions

Programming framework Convenient libraries

Key Amazon SWF concepts

• Discrete steps in your

application, processors

Workflow (deciders)

• Control logic for workflows:

execution order, retry policies,

timer logic, etc. – Decision tasks

Activity workers

Update seat

inventory (Batch)

Select

seats

Provide

credit card

Confirm

inventory

Confirm

trip

Update

inventory

Send confirmation

email

Search for

flights

( Web, mobile, SMS …)

Update seat

inventory (Batch)

Select

seats

Provide

credit card

Confirm

inventory

Confirm

trip

Update

inventory

Send confirmation

email

Search for

flights

( Web, mobile, SMS …)

Workflow (decider)

Activity

SWF customers are diverse

Private, venture-backed, publicly traded

Java, Ruby, C#, PHP, Python, on premises,

fully AWS

Media, e-biz, transportation, entertainment,

space exploration, life sciences, non-profit,

gaming, …

Many use cases

What they have in common…

Multiple steps

Multiple machines

Higher potential for failures

Need resilience, scale, auditability

Customers

Thousands of clients

100s of millions of pieces of content

100s of millions of unique visitors per month

10s of billions of pageviews per month

Java-based workflows

Austin San Francisco New York Engineering offices

Content moderation as a service

Content Moderation System (CMS1)

Humans read every piece of content, twice !

Content coded with tags: product flaw,

shipping issues …

Step 1 Step 1

Step 1 Step 1

Step 1 Step 1

Step 1 Step 1

Review Approve/Rej

ect

Publish &

Email

customer

Customer

review

submission

Content Moderation System (CMS1)

Design challenges

• Latent

• Rigid state machine

• Multi-cluster environment

• Balancing, scheduling, admin was hard.

• Acquiring new company to integrate didn’t help

things

Content Moderation System (CMS2)

Moderation as a service

• One service, many types of content moderated

• Fully de-coupled from other systems

• Real-time, rich prioritization of tasks

• Control logic flexible, built with SWF Flow

framework (AWS Java SDK)

• Execution managed by SWF

Moderation Workflow

50k-150k Parallel executions

3-5 activities

2-3 days duration

SWF, AWS architecture brought

Benefits

• Efficiency

Moderation time (submission – publish) down

35% since launching CMS2.

• Flexibility

Started with few activities, was able to scale

logic as we grow

4 Million+ Lessons Taught in 12 years

In 2012 > 25% of all golf lessons in US

Next Competitor < 1%

Strategic Partners: Sports Illustrated, GolfSmith

PHP-based workflows

Austin San Francisco New York Engineering offices

User generated video processing

http://s3.amazonaws.com/golftec_cloud/video/MPTG_pres_2013_02.mp4

Video capture (phone)

Upload Pre-

process Telestrate (coach)

Account updated

Post processing

Web lesson

A Golf Pro in Your Pocket ..

• Partnership with Sports Illustrated

• Mobile App = virtual golf lessons

My Pro to Go

Pre-process

Account updated

Telestrate

(coach)

Before Simple Workflow & AWS

Design challenges

• Traditional datacenter

• Limitations: scale, dynamic Growth, capital costs

• Single workflow & web server

• Patchwork of PHP scripts

• No logging, monitoring or Reporting

• Lesson processing pre- AWS (2011)

UBUNTU

APACHE

WEBSERVER

UBUNTU

MYSQL

SERVER

TWO UBUNTU

FFMPEG

TRANSCODERS

FT

P Environment challenged to

produce 88,740 lesson

videos

per month.

Lesson storage on local system was 1TB.

Regular purges of data had to be done due

to space limitations.

FIBER

CHANNE

L

SAN

CORPORATE

EXCHANGE

SERVER

EMAIL

• Lesson processing on AWS (2013)

LOAD

BALANCED

EC2 APACHE

WEBSERVERS

SIMPLE WORKFLOW

SERVICE

All uploads from centers

open a SWF workflow,

allowing all stages to be

tracked and errors to be

automatically corrected or

staff to be alerted.

PAIR OF

SWF

DECIDERS

SIMPLE STORAGE SYSTEM

(S3)

All lesson and

marketing resources

new stored on Amazon

Simple Storage Service

(S3). We now hold

multiple versions of

videos, store 100TB of

data with no fear of any

limits.

8-20 SWF workers are

deployed based on

load. We regularly

produce 655,000

lesson videos per

month.

Easy to scale and manage. Developers

able to quickly and easily spin up clones of

the production servers.

CLOUD WATCH

All systems monitored

by custom alarms,

immediately notifying

techs before

emergencies happen

RELATIONAL DATABASE

SERVICE As a managed

service, we don’t worry about

patching or

maintenance. Point

in time recovery

and the ability to

quickly spawn

clones aid

development.

CLOUD FRONT CDN

Mass market videos

now distributed via

content delivery

network.

SIMPLE EMAIL SERVICE Automated emails go

through Amazon

Simple Email Service

(SES), increasing

deliverability and

decreasing load on

Exchange

SWF, AWS architecture brought

Benefits

• Scalability

30,000+ videos per day (30, 50, 120, ++ fps)

• Flexibility

Decouple logic (decider), limited by creativity

Loyalty marketing as a service

Connect digital offers and in-store payments

Serves: merchants, developers, publishers

Payment network < > developer apps

Ruby-based workflows

Post transaction processing

Cardspring platform connects …

Connect online behavior with in-store purchases

• Developers create card-linked apps

• Merchants choose to run campaigns “in app”

• Customers pay through POS terminals

• API receives real-time webhooks about credit card transactions

• Post-transaction data updated for merchants, developers, customers

Before Amazon SWF

Previous starting point

• Worker machines process batch file jobs +

async work outside of API requests

• Running custom job processor framework +

many ad hoc scripts

• Triggered via SNS messages, cron, & jobs

controller

Before Architecture Diagram

Jenkins API

SNS ELB

worker worker worker

After architecture diagram

Jenkins API

SWF

worker worker

worker

decider decider

decider

After Amazon SWF

Simpler, resilient architecture

• All scripts/processors consolidated into 30

distributed workflows

• Implement priorities via different task lists

• Easy to scale

• Easy to restart

Amazon SWF brought

Benefits

• Resilient

Restart in the case of failure

• Efficient consolidation, Easy Management

30 automated workflows do all the processing

CRM + ERP for non-profits, associations

Mid-to-large sized associations

100+ customers

C#/.NET based workflows

Photo Credit: flickr/hectorir

Complex order processing

CRM 360° Suite

Fully featured with asynchronous needs

• Full ERP: membership, financial, fundraising, e-

marketing portal, API platform, etc.

• File processing/Amazon S3 uploads

• Amazon CloudSearch encoding

• Long-running processes like membership approvals

Before Amazon SWF

Windows, Quartz.net

• Unreliable, no replacement

• Poor performance

MSMQ (Microsoft Message Queue) • Better throughput, but queues don’t know how to run

tasks

• Impossible to scale remote queues

• Still had to use Quartz.net for recurring tasks

After Amazon SWF

Scalable, manageable architecture

• Replaced old system with ≈ 24 workflows

• Encouraged good design – broke up code into

“activities”

• Replaced recurring activities with “always on”

workflows that woke up on scheduled timers

Post SWF architecture

SWF benefits

• Stability

System never crashes

• Replay, retry-ability

30 automated workflows do all the processing

Customers: Reebok, Jansport, Nine West,

Solutions: Shoppable images, Dynamic

imaging, Visual product customization

Java-based workflows

Chicago San Francisco New York

High-performance image processing

Before Amazon SWF

Scalability challenges

• 1 product = 1000s of images

• Generate images by rendering 3D models

• Poor performance, scalability

After architecture

• SWF brings parallelism

to both the 3D rendering

+ post-processing

• Image processing time

dropped 30 – 80%

SWF benefits

• Performance

Image processing times dropped 30 – 80%

• Costs optimized

Processing machines auto scaled as necessary

ERP for technology service providers: cloud

and on-premise. Datacenters on 4 continents

5,500+ Customers with 80,000+ end users

Trouble ticketing, time/billing, calendar/syncing

for scheduling, deployments

Back-office Automation: IT Services

Email

Processing (A-M)

Calendaring (A-M)

Email

Processing (N-Z)

Calendaring (N-Z)

Ticketing

Workflow

System

Installation

System

Corporate Data center

• Services were manually partitioned amongst customers

• Server failure resulted in customer downtime

• Server utilization was uneven – activity varied by customer

• Adding capacity was a complex process

• Logs were stored on each machine

Before SWF – customer-specific

After SWF

• Identical worker servers

• Centralized logging to

Amazon S3

Email Processing

Calendaring

Corporate datacenter

Installation System

Workflow System

Flow

Framework

Services

are

Workers

Amazon SWF

Centralized

Logging to

Amazon S3

Amazon Web Services

• Java and Flow Framework used for deciders

• .NET services act as SWF workers

• Geo-diverse workflow coordination

100K+ workflows daily with

1.5 Million+ activities, decisions

SWF benefits

• Costs/utilization

Consistent architecture/servers all customers,

server utilization more balanced

• Simplified management, reliability

Single control panel with visibility into all services.

Split complex workflows across many identical

worker servers w/o having to maintain local state.

Multi-step, long-running user enrollment

Ridesharing/virtual carpool service

80% of users are repeat customers

Average ride < $10

100,000+ users

Python-based workflows

Enrolling private citizens

Multi-step workflows

• Headshot > Car photo > Photo of license Consent to

background check > Online training, Mentoring

session (driver ridealong)

• Processes long running, can “fail” at any step –

queues, scheduled processes would have be brittle

• Built from the beginning on Amazon SWF

SWF Architecture

Workflow Workers

(Decider)

Amazon SWF

Activity

Workers

Many, many thousands of

applications per day

With 1 decider, and 1

activity worker

Driver enrollment

process, status

checking

Application updates,

Reminders via

Email and SMS ask

drivers to move

forward in their

applications

SWF benefits

• Auditability

State of user’s application consistent

Reminder emails not duplicated

• Flexibility

Application steps can be non-linear

Users complete different steps, different times

Post transaction processing Cardspring

Video processing GolfTEC

7 of many use cases

Amazon SWF

Content moderation BazaarVoice

Order processing Membersuite

Image processing Fluid

Business/IT automation Connectwise

Partner enrollment Lyft

Resilience, reliability

Flexibility

Benefits

Amazon SWF

Scalability

Performance

Simplification

Costs, efficiency

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