For the past 3 years, ASOS has been on a journey of moving its monolithic architecture to Microservices - and what has been driving this change is not just the buzzword: as with any monolith, the spiraling cost of change stifles the business and innovation. And in this market, advancing your competitive edge by constant improvement is a big factor in the overall success of your business. Probably not many know that ASOS website drives more traffic (and way more bandwidth) than the showcase Stackoverflow. Some of the services are built to serve up to 10K RPS (request/second). And the services are spread around the globe currently on more than 4 Azure DCs. And on the top, we have pretty thick data pipelines moving many GBs of data to enable traditional BI - as well as the trendy Machine Learning algorithms powering recommendations and personalisation. This talk will be a brief intro to the overall view of what +20 2-pizza teams are doing and in specific, goes into some of the details of ML-enabled recommendations platform. Underneath the success of the transition, has been a Logging/Monitoring/Alerting system (Elasticsearch+ConverorBelt+Kibana) to empower platform teams to ensure health of the system and keeping Mean-Time-to-Recovery low. As with any such talk, there will be a section on lessons learned...

1. Microservice Architecture at ASOS
Ali Kheyrollahi, ASOS
@aliostad aliostad@gmail.com #devsum17

2. @aliostad
: @aliostad
email: the same @gmail
http://byterot.blogspot.com
Ali Kheyrollahi,
Solutions Architect at ASOS

3. @aliostad
/// mini-agenda
> Microservice: aspects you do not hear enough about
> Observability
> Patterns for building microservices
> ASOS Recommendations as an example of Microservices
> ASOS: Company and the tech

4. @aliostad
> stackoverflow
> £1.5 bln
global fashion
destination
> 35% year-on-year

5. @aliostad

6. /// ASOS in numbers
2 0 1 6 T u r n O v e r → £1.5 bln
A c t i v e C u s t o m e r s → 12 M
E m p l o y e e s → 2500
U n i q u e V i s i t s / m o → 123 M
P a g e V i e w s / d a y → 95 M
V i r t u a l S t o r e s → 9
A z u r e D C s → 5

7. @aliostad
Nick Beighton - CEO
from “IT Cascade” slides - Oct 2014
© 2017 ||| ASOS Plc

8. @aliostad
/// stock price: 2009-2013
More than 2000% growth!

9. @aliostad
/// stock price: 2009-2014
- Multiple IT project failures
- Tech issues disrupting promotions
- Overdue features were not delivered

10. @aliostad
/// asos journey

11. @aliostad© 2017 ||| ASOS Plc

12. @aliostad
Domain Modelling
Operating
Model
People
Successful
Architecture

13. @aliostad
Indicative Team Numbers
6 Digital Domains
19 Platforms
35 Dev / Scrum teams
24 Solution Architects
700+ people in Technology
(2500 in Asos total)
Avg +20 people per month over last year
/// operating model
© 2017 ||| ASOS Plc

14. @aliostad
/// asos tech stack

15. @aliostad
ASOS ❤

16. @aliostad
/// Microservices
Architecture

17. @aliostad
/// microservices
Actor-basedAPI-based
Adrian Cockcroft (Netflix) Fred George

18. @aliostad
/// why microservices
> Scaling people not the solution
> Decentralising decision centres => Agility
> Frequent deployment => Agility
> Reduced complexity of each ms (Divide/Conquere) => Agility
> Cost of mistakes and bad decisions smaller ...
> Overall complexity is smaller

19. @aliostad
/// microservices vs soa
SOA Microservices
Main Goal Architectual Decoupling Business Agility
Set out to solve Architectural Coupling
Scaling People,
Frequent Deployment
Audience Mainly Architecture Business (Everyone)
Law Conway’s Reverse Conway’s
Impact on Structure of
Organisation
Minimal Huge
Service Cardinality Usually up to a dozen >40 (Commonly >100)
When to do Always teams > ~5**
** Debateable. There are articles and discussions on this very topic

20. @aliostad
/// microservice challenges
> Very difficult to build a complete mental picture of solution
> When things go wrong, need to know where before why
> Potentially increased latency
> Performance outliers intractable to solve
> A complete mind-shift requiring a new operating model

21. @aliostad
/// probability distribution
Response Time
Probabilty

22. @aliostad
/// performance outliers
Microservice
A
Microservie
B
99th Percentile = 500ms 99th Percentile = 500ms
A B Total
<1s 99% 99% 98.01%
>500m 1% 99% 0.99%
>500m 99% 1% 0.99%
>1s 1% 1% 0.01%

23. @aliostad
/// probability dist
Service A Prob.
10 ms 33%
15 ms 33%
25 ms 33%
Service B Prob.
15 ms 33%
20 ms 33%
30 ms 33%
A + B Prob.
10 + 15 11%
10 + 20 11%
10 + 30 11%
15 + 15 11%
15 + 20 11%
15 + 30 11%
25 + 15 11%
25 + 20 11%
25 + 30 11%
X
=
66th=15ms 66th=20ms
44th=35ms
66th=40ms

24. @aliostad
/// where do you start?

25. @aliostad
/// some guidelines
1
2
3
4
Break-up your business to domains and subdomains
Plan for your system’s observability
Define a set of key constraints on microservices
Start building by chiselling or strangling parts of monolith

26. @aliostad
/// some guidelines
1
2
3
4
Break-up your business to domains and subdomains
Plan for your system’s observability
Define a set of key constraints between microservices
Start building by chiselling or strangling parts of monolith

27. @aliostad
Tracing
Logging
Credit: Peter Bourgon
Events
Aggregations
Request Scope
Telemetry
Alerting
/// observability

28. @aliostad
Logging Telemetry Tracing Alerting
Log4Net ✓
Time-series DBs ✓ ✓ ✓
Zipkin ✓ ✓ ✓ ✓
Prometheus ✓ ✓ ✓
Elasticsearch ✓ ✓ ✓ ✓
New Relic* ✓ ✓ ✓
Circonus* ✓ ✓ ✓
* paid services
/// comparison

29. @aliostad
/// our stack
Performance
Counters
ConveyorBelt
Azure
WAD logs
ETW Logs
Elasticsearch
Instrumentation
Logs
IIS Logs
Woodpecker
Outputs (Pull Logs)
Sources Config
Up to 2TB/day

30. @aliostad
3x client
2x kibana
20x data (hot)
traffic
traffic
10x data (warm)
/// our setup
3x master
ARM
Template
Desire State
Configuration

31. @aliostad
/// some guidelines
1
2
3
4
Break-up your business to domains and subdomains
Plan for your system’s observability
Define a set of key constraints between microservices
Start building by chiselling or strangling parts of monolith

32. @aliostad
/// some of asos constraints
a
b
c
d
All commands across MS are synchronous REST calls
Microservices do send ansychronous events
OpenId connect for Identity and Azure AD for x-MS calls
All REST calls send a set of standard headers such as
correlation-id, client version, etc

33. @aliostad
/// types of microservices
Microservice
Data Store
Functional
API
Microservice
Compositional
(sometimes BEFFE, also Gateway)
APIservice
service

34. @aliostad
/// exposing data
> All data needs to be exposed only via API
> For data-intensive operations or when the origin of the
request is a system favour data integration
> On exceptional performance-related cases, giving read-
only access to a secondary data source (e.g. soft store) is OK
> For analytics purpose, all data shared via Data Lake

35. @aliostad
/// some guidelines
1
2
3
4
Break-up your business to domains and subdomains
Plan for your system’s observability
Define a set of key constraints between microservices
Start building by chiselling or strangling parts of monolith

36. @aliostad
/// chiselling pattern
a
b
C
A
B
D
b

37. @aliostad
/// strangler pattern
E
D
C
A
B
https://www.martinfowler.com/bliki/StranglerApplication.html
Martin Fowler
D’

38. @aliostad
/// asos
recommendations

39. @aliostad
/// recommendations
Business
Influence
Machine
Learning
Algorithms: MF, FM, etc
Business rules:
Filtering and boosting

40. @aliostad
/// lambda architecture
Serving
Layer
Realtime
Batch
Batch
What customer
likes? (taste)
Realtime
What customer
wants? (session
and context)
Serving Layer
Merge two models
Influence Context
MVT, model selection
Enrichment
Filtering, faceting, boosting
Pagination
Caching

41. @aliostad
/// Wrap-up
> If you have more than 4 teams, consider Microservices
> Response outliers the most intractable problems in Microservices
> “Nevermind the Microservices” if you don’t have observability
> Define a set of key constraints in/between your microservices
> Chisel and strangulate the monolith

42. @aliostad
>>> Thank You!
special thanks to Dave Green
Q U E S T I O N S

43. @aliostad
: @aliostad
email: the same @gmail
http://byterot.blogspot.com
Ali Kheyrollahi,
Solutions Architect at ASOS

44. @aliostad
Thomas Wood: Daisy Picture
Thomas Au: Thermometer Picture
Torbakhopper: Cables Picture
Dam Picture - Japan
Hsiung: Lights Picture
Health Endpoint in API Design