This document summarizes a presentation given at AWS re:Invent 2017 about Cisco's journey from monolithic architecture to microservices. The presentation discusses Cisco's migration of thousands of services to microservices running across over 100 instances. It describes the four pillars of Cisco's approach: automation, cloud services, microservices, and continuous delivery pipelines. Specific microservices patterns for data management are also summarized, including patterns for shared static data, shared mutable data, and distributed transactions.

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AWS re:INVENT
CISCO’S JOURNEY FROM MONOLITH TO MICROSERVICES
ADVANCED ARCHITECTURAL PATTERNS
Ronnen Slasky, AWS Solutions Architect
Avi Fruchter, Cisco CTO IVP
November 28, 2017
EDCS-11803578

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AGENDA
Microservices Persistency Patterns
Cisco’s Migration to Microservices

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Technology Organization Processes
Modern Software Development
Microservices Service Teams Continuous Delivery

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In 2014:
• Thousands of service teams
• Building microservices
• Practicing continuous delivery
50 Million Deployments
Microservices at Amazon

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Centralized Data Management
DB
cart accountuser
Monolithic Service

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Principle: Each Microservice has its own Database
• Choose the right technology for each service
• Operational ownership
Decentralized Data Management
user-svc
Redis Amazon RDS
cart-svc search-svc
Elasticsearch
Amazon
DynamoDB

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Microservices Persistency Patterns
Shared Static Data
Shared Mutable Data
Transactions

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Scenario #1: Shared Static Data
Challenge: Sharing Static Data Across Multiple Services
cart-svc account-svcuser-svc
Zip Codes

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Pattern A: Duplicate Table
Scenario #1: Shared Static Data
user-svc cart-svc account-
svc
ZipCodes ZipCodes ZipCodes

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Pattern A: Duplicate Table Pattern B: Property File
Scenario #1: Shared Static Data
use-svc
ZipCodes ZipCodes ZipCodes
user-svc cart-svc account-
svc
user-svc cart-svc account-
svc
ZipCodes ZipCodes ZipCodes

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Pattern C: New ServicePattern A: Duplicate Table Pattern B: Property File
Scenario #1: Shared Static Data
use-svc
ZipCodes ZipCodes ZipCodes
user-svc cart-svc account-
svc
user-svc cart-svc account-
svc
ZipCodes ZipCodes ZipCodes
ZipCode-svc
ZipCodes
account-
svc
cart-svcuser-svc

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Scenario #2: Shared Mutable Data
Challenge
Monolithic database makes it easy to access shared data.
This is not so simple in a Microservice Architecture
cart-svcuser-svc shipping-svc
Users Shipping

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Pattern A: Synchronous Lookup
Scenario #2: Shared Mutable Data
shipping-svc user-svc
RDS

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Pattern B: Lookup, Cache + Asynchronous
Event
Pattern A: Synchronous Lookup
Scenario #2: Shared Mutable Data
shipping-svc user-svc
RDS
shipping-svc user-svc
RDSCache

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Scenario #3: Transactions
Challenge
Monolithic database enable transactions across multiple entities
Splitting data to their own datastores makes this more complex
Monolithic Service
Single
Transaction Boundary
Account-svc Order-svc
RDS
Monolithic Database
Account Table Order Table

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Scenario #3: Transactions
Pattern A: Queue and Retry
• Eventually consistent
• Simple and robust implementation
• Gracefully handles failure
Separate Transaction Boundary
Microservice
Order-svc
Order Table
Microservice
Account-svc
Account Table

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Scenario #3: Transactions
Transaction
Manager
User-svc
Cart-svc
Account-svc
Pattern B: Two Phased Commit
• Atomic Commit Protocol = Guarantees Consistency

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Scenario #3: Transactions
Propose User
Propose Cart
Propose Account
User-svc
Cart-svc
Account-svc
Transaction
Manager
Pattern B: Two Phased Commit
• Atomic Commit Protocol = Guarantees Consistency
• Two distinct phases:
• Voting phase

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Scenario #3: Transactions
User-svc
Cart-svc
Account-svc
Transaction
Manager
Propose User
Propose Cart
Propose Account
Commit/Abort
Commit/Abort
Commit/Abort
Pattern B: Two Phased Commit
• Atomic Commit Protocol = Guarantees Consistency
• Two distinct phases:
• Voting phase
• Commit phase

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Scenario #3: Transactions
User-svc
Cart-svc
Account-svc
Transaction
Manager
Propose User
Propose Cart
Propose Account
Commit/Abort
Commit/Abort
Commit/Abort
Pattern B: Two Phased Commit
• Atomic Commit Protocol = Guarantees Consistency
• Two distinct phases:
• Voting phase
• Commit phase
• Locks resources between phases
• Fit for short lived transactions

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Scenario #3: Transactions
Pattern C: Saga Pattern
A Saga is a long lived transaction that can be written as a sequence of
transactions that can be interleaved with other transactions. The database
management system guarantees that either all the transactions of a saga
are successfully completed or compensated transactions are run
to amend a partial execution.Hector Garcia-Molina and Keneth Salem

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Scenario #3: Transactions
A Saga is a long lived transaction that can be written as a sequence of
transactions that can be interleaved with other transactions. The database
management system guarantees that either all the transactions of a saga
are successfully completed or compensated transactions are run
to amend a partial execution.Hector Garcia-Molina and Keneth Salem
T1, T2, T3….Tn
Pattern C: Saga Pattern

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Scenario #3: Transactions
A Saga is a long lived transaction that can be written as a sequence of
transactions that can be interleaved with other transactions. The database
management system guarantees that either all the transactions of a saga
are successfully completed or compensated transactions are run
to amend a partial execution.Hector Garcia-Molina and Keneth Salem
Cn… C3, C2, C1
Pattern C: Saga Pattern

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Scenario #3: Transactions
A Saga is a long lived transaction that can be written as a sequence of
transactions that can be interleaved with other transactions. The database
management system guarantees that either all the transactions of a saga
are successfully completed or compensated transactions are run
to amend a partial execution.Hector Garcia-Molina and Keneth Salem
Cn Semantically Undoes Tn
Pattern C: Saga Pattern

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Scenario #3: Transactions
A Saga is a long lived transaction that can be written as a sequence of
transactions that can be interleaved with other transactions. The database
management system guarantees that either all the transactions of a saga
are successfully completed or compensated transactions are run
to amend a partial execution.Hector Garcia-Molina and Keneth Salem
Either T1, T2, T3….Tn or
T1, T2, ….Tn, Cn….C2, C1
Pattern C: Saga Pattern Guarantee

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Takeaways
Breaking your Monolith can help move faster
Select the right microservices persistency design pattern
Learn from others

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Cisco’s Migration to Microservices

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Rebirth in the cloud
WaterfallBare Metal Monoliths

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Our Journey

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Thousands
of instances
>100 microservices

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The four pillars
Automation PipelineCloud ServicesMicroservices

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Automation

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Automation
Click to Deploy
$./deploy.sh FULL 01.17.40.17 nc_pipeline
Click to Upgrade
$./deploy.sh UPGRADE 01.17.40.17 nc_pipeline

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Benefits
Development Velocity
New Customer Onboarding
Short Feedback Cycles to Production Environment

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Microservices

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Two types of monoliths
Functional Platforms

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Platform monoliths
Scale
Log
Database
Deploy
APP
Monitor

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“Un-peel-able”
Application

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Success!
99.7% Code reduction
1,250,000
4,000
Lines of Code
860 ms
130 ms
Latency
5 XL
Machines
5 Small
BOM

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44. VOD
Linear
Entitlements
Search
TV
VOD
Functional monolith

45. Functional monolith
Development velocity Operational costs Scale

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Success!
97.5% Code reduction
2,000,000
50,000
Lines of Code
70 ms
42 ms
Latency
150 Core
550 RAM
50 Core
85 RAM
BOM

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Domain-driven design

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VOD navigation
{
“Title”:…
“Synopsis”:…
“Episode/season”:…
“SellPrice”:…
“Classification”:…
“Credits”:…
“Thumnail”:…
“playableURI”:…
“offers”:…
}
{
“Title”:…
“Synopsis”:…
“Episode/season”:…
“SellPrice”:…
“Classification”:…
“Credits”:…
“Thumnail”:…
“playableURI”:…
}

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Search
{
“Title”:…
“Synopsis”:…
“Episode/season”:…
“SellPrice”:…
“Classification”:…
“Credits”:…
“Thumnail”:…
“playableURI”:…
“offers”:…
}
{
“Title”:…
“Synopsis”:…
“Episode/season”:…
}

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Sharing technology

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Replacement in flight
Fork Ingest Recache Sync

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Cloud platform services

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Cloud platform services
Deployment
Load Balancing
Messaging
Service Registry
Monitoring
Smart Routing
Object Storage
Alerts
Logging

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Smart routing
GW
GW
µs1
µs1
µs1.1
µs2
µs2
µs2
µs4
µs4
µs4.1

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Smart routing

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Pipeline

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Pipeline runs 24/7
>4,300 runs past year/~12 releases per day

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Continuous delivery flow
CONTINUOUS UPGRADE PIPELINE
DAILY UPGRADE + CLEAN DEPLOY
Sunday
Service 1
Rel.3
Service 2
Rel.3
Service 4
Rel.3
Service 1
Rel.2
Service 2
Rel.2
Service 3
Rel.2
Service 1
Rel.3
Service 2
Rel.3
Service 4
Rel.3
Monday
Service 2
Rel.2
Service 3
Rel.2
… Service 3
Rel.3

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What’s next?

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Independent pipelines

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Leverage native cloud services
Applications
Platform
Development

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Prod A
AWS
CodeDeploy
AWS
CodeBuild
Prod B
Prod C
Dev
AWS
CodeDeploy
AWS
CodePipeline

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+( + )=

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