modern sso using the mean stack

I | ProSiebenSat.1 Digital & Adjacent | Central Technology Initiatives | October 2015

Modern SSO using MEAN stackLessons learned from a live project

Dan Harabagiu, Head of Platform Development

Munich October 20, 2015


AGENDA

Introduction

Technology stack

Performance tuning and results

Conclusions


Introduction


P7S1 Group snapshot – a leading media corporation in Europe

TV & Distribution

Digital Entertainment, Digital Commerce & Music

Content Production & Sales

• #1 commercial broadcaster2 in Germany and Austria

• Reaching 42m TV households in DACH1 region

• #1 in German TV ad market with ~44%3 market share

• 7 FTA TV channels with premier thematic channels franchise

• Growing distribution (HD) and Pay-TV business (3 channels)

• #1 Pay VOD platform4 and #1 Instream Video AdSales5

• Leading media investor in Germany with a portfolio of promising ventures

• Digital commerce business in travel, fashion, beauty, home & living

• Largest independent music label in Germany, artist management & events

• Within top 10 production and distribution groups globally

• 15 production assets targeting top TV markets worldwide

• >800 formats currently in development; global sales into >180 countries6

Broadcasting

German-speaking

Digital & Adjacent

Content Production

& Global Sales

1) DACH = Germany, Austria and Switzerland; 2) #1 by audience share in Germany (A14-49y) and Austria (A12-49y); 3) 9M 2014 according to Nielsen Media Research

4) 9M 2014 SVOD market share according to GfK; 5) Nielsen Media Rearch (w/o YouTube); 6) 9M 2014


Who are we?

■ Central team under the CIO of ProSiebenSat.1 Media SE

■ Very skilled developers, software architects and IT specialists

■ Working with modern and scalable technologies

With the mission to provide central technology solutions for digital business units

in the Digital and Adjacent area.


Who am I?

■ One of the founders of Central Technology Foundation team

■ Worked in multiple industry fields

■ Broad coverage of technologies


SSO solution and our requirements

Single Sign-On provides the possibility of a user to be easy recognized and

authenticated across multiple web platforms and digital assets

■ Have a mature OpenID Connect implementation

■ A web service that needs to be integrated on multiple platforms

■ Web, Smart TV, Over-The-Top, mobile devices

■ A very high load to be expected

■ A minimum of 300.000 Logins / Registration per minute

■ Based on Node.js in order to handle at lower costs higher payload


Technology Stack


What technologies could take the challenge?

After several evaluations, we have put our bets on the MEAN stack.

■ MongoDB (Replicated and sharded structure)

■ Express (Easy framework for web services)

■ Angular (Fast and structured Frontend application)

■ Node.js (Asynchronous structure provides high load capabilities)


The resulting landscape


Transforming ideas into reality

■ Developed within 8 months

■ Team of 4 developers

■ Complete OpenID Connect implementation in JavaScript

■ Used Scrum methodology

■ Complete Continuous Integration process


Performance tuning and results


Pushing the limits once done

■ Testing was required once development reached a finite goal

■ What have we done?

■ Client simulator

■ Automated deployment

■ Aggregated metrics centrally


Benchmarking Round 1

Environment

■ Regular MongoDB cluster (3 x 3 x 3)

■ 16 instances of Node.js application

server

■ Allowed ELB to gracefully scale over

time (10 – 15 minutes)

Results

■ 150,000 requests / minute

■ Avg response time – 220 ms0

100

200

300

400

500

600

Round1

Th

ou

sa

nd

s

Requests/minute

Handled Perf. goal


Findings and solutions

Findings

■ Scatter gather

Solutions

■ Re-factory the data model

■ Sharding the whole data model


Benchmarking Round 2

Environment

■ Increased MongoDB deployment

■ Added 3 more shards

■ Increased IOPS on data nodes

■ Increased Node.js deployment

■ Doubled the size (to 32 servers)

Results

■ 400.000 requests / minute

■ Average response time – 300 ms

0

100

200

300

400

500

600

Round1 Round2

Th

ou

sa

nd

s

Requests/minute

Handled Perf. goal

+167%


Findings and solutions

Findings

■ Hot Shard issue -> Sharding based on artificial strings

■ Issues with connection handling in NodeJS

Solutions

■ Presplitting

■ Shuffle the connection points to MongoS


Final results

Environment

■ Added 2 more shards to MongoDB

■ Total of 8 Shards

■ Same large deployment of Node.js

■ Total of 32 Servers

Results

■ 520.000 requests / minute

■ Average response time – 320 ms 0

100

200

300

400

500

600

Round1 Round2 Round3

Th

ou

sa

nd

s

Requests/minute

Handled Perf. goal

+30%


Conclusions


Conclusions

■ We were very happy to choose MEAN stack

■ Node.js + MongoDB = Winner

■ We have reached our targeted performance

■ Register / Authenticate the whole population of Germany in less than 3

Hours


Learnings

■ Know your data model and analyze where the stress point could be

■ Create your data model in ways to provide you flexibility in changing at later

points in time

■ Always stress test your projects to know your limits


Thank you!Questions?

Let us connect:

Email: [email protected]

Linkedin: https://de.linkedin.com/in/danharabagiu

mailto:[email protected]