scaling μ-services at Gilt ade@gilt.com

San Francisco26th October 2015

Adrian Trenaman, SVP Engineering, Gilt, @adrian_trenaman

@gilttech

gilt: luxury designer brands at discounted prices

we shoot the product in our studios

we receive, store, pick, pack and ship...

we sell every day at noon...

stampede...

this is what the stampede really looks like...

rails to riches: 2007 - ruby-on-rails monolith

2011: java, loosely-typed, monolithic services

(5) Hidden linkages; buried business logic

(4) Monolithic Java App; huge bottleneck for innovation.

(2) Lots of duplicated code :(

(3) Teams focused on business lines

(1) Large loosely-typed JSON/HTTP services

enter: µ-services

“How can we arrange our teams around strategic initiatives? How can we make it fast and easy to get to change to production?”

2015: micro-services

driving forces behind gilt’s emergent architecture

● team autonomy● voluntary adoption (tools, techniques,

processes)● kpi or goal-driven initiatives● failing fast and openly● open and honest, even when it’s difficult

service growth over time: point of inflexion === scala.

anatomy of a gilt service

anatomy of a gilt service - typical choices

gilt-service-framework,

log4j, cloudwatch Cave,

, , javascript

or

service discovery: straight forward

zookeeper

Brocade Traffic Manager (aka Zeus, Stringray, SteelApp,...)

what are all these services doing?

… we used a “spread sheet”.‘The Gilt Genome Project’

It’s hard to think of architecture in one dimension.

We added ‘Functional Area’, ‘System’ and ‘Subsystem’ columns to Gilt Genome; provides a stronger (although subjective) taxonomy than the previous ‘tags’.

It turns out we have an elegant, emergent architecture.

Some services / components are deceptively simple.

Others are simply deceptive, and require knowledge of their surrounding ‘constellation’

n = 265, where n is the number of services.

Deceptively Simple - many services are small; < 2048 loc

Deceptively Simple - many services are small, < 32 files.

Gilt Admin (Legacy Ruby on Rails Application)

City

Discounts FinancialReporting

Fraud Mgmt

Gift Cards Inventory Mgmt Order Mgmt

Sales Mgmt Product Catalog

Purchase Orders

Targetting

Billing

Other Admin Applications (Scala + Play Framework)*

City Creative (2) CS

Discounts Distribution i18n Inventory (2)

Order Processing

(2)Util

Service Constellations (Scala, Java)*

Auth (1) Billing (1) City (6) Creative (4) CS (2) Discounts (1) Distribution (9) i18n (3) inventory (6)

Order Processing

(8)Payments (3) Product

Catalog (5) Referrals (1) Util (2)

Core Database - ‘db3’

Job System (Java, Ruby)

Gilt Logical Architecture - Back Office Systems

* counts denote number of service / app components.

Simply deceptive: service context only make sense in constellation.

from bare-metal...

PHXIAD

… to vapour.

Lift-and-shift + elastic teams

Existing Data Centre

Dual 10Gb direct connect line, 2ms latency.

‘Legacy VPC’

MobileCommon Person-alisation Admin Data

(1) Deploy to VPC

(2) ‘Department’ accounts for elasticity & devops

single tenant: one EC2 instance per service instance

reproducible, immutable deployments: docker

service discovery: same pattern, different LB

zookeeper

Amazon ELB

# running instances per service: ‘rule of three’

AWS instance sizing

evolution of architecture and tech organisation

Lessen dependencies between teams: faster code-to-prod

Lots of initiatives in parallel

Your favourite <tech/language/framework> here

We (heart) μ-servicesGraceful degradation of service

Disposable Code: easy to innovate, easy to fail and move on.

We (heart) cloudDo devops in a meaningful way.Low barrier of entry for new tech (dynamoDB, Kinesis, ...)Isolation

Cost visibilitySecurity tools (IAM)Well documentedResilience is easyHybrid is easyPerformance is great

seven μ-service challenges (& some solutions) no one ever said this was gonna be easy

1. staging vs test-in-prodWe find it hard to maintain staging environments across multiple teams with lots of services.

● We think TiP is the way to go: invest in automation, use dark canaries in prod.

● However, some teams have found TiP counter-productive, and use minimal staging environments.

2. ownershipWho ‘owns’ that service? What happens if that person decides to work on something else?

We have chosen for teams and departments to own and maintain their services. No throwing this stuff over the fence.

1. Software is owned by departments, tracked in ‘genome project’. Directors assign services to teams.

2. Teams are responsible for building & running their services; directors are accountable for their overall estate.

bottom-up ownership, RACI-style

‘ownership donut’ informs tech strategy

3. Ownership is classified: active, passive, at-risk.

‘done’ === 0% ‘at risk’

3. deploymentServices need somewhere to live. We’ve open-sourced tooling over docker and AWS to give:

elasticity + fast provisioning + service isolation+ fast rollback

+ repeatable, immutable deployment.

https://github.com/gilt/ionroller

4. lightweight APIsWe’ve settled on REST-style APIs, using http://apidoc.me. Separate interface from implementation; ‘an AVRO for REST” (Mike Bryzek, Gilt Founder)

We strongly recommend zero-dependency strongly-typed clients.

5. audit + alertingHow do we stay compliant while giving engineers full autonomy in prod?

Really smart alerting: http://cavellc.github.io

orders[shipTo: US].count.5m == 0

6. io explosionEach service call begets more service calls; some of which are redundant...=> unintended complexity and performance

Looking to lambda architecture for critical-path APIs: precompute, real-time updates, O(1) lookup

7. reportingMany services => many databases => data is centralized.

Solution: real-time event queues to a data-lake.

scaling μ-services at Gilt ade@gilt.com

San Francisco26th October 2015

Adrian Trenaman, SVP Engineering, Gilt, @adrian_trenaman

@gilttech