Microservices: A foundational approach for fully managed cloud data analytics!Sam Lightstone!CASCON 2016!

CASCON 2016

Cloud Data Services @IBM – we have a few!

CASCON 2016

Cloud Data Services Table Stakes!

‣  Things I Need!‣  Compute, Network and Storage: plentiful and

reliable

‣  Data services that can scale from tiny to massive in users, data, and associated resources.

‣  Scale my engineering team!

‣  Service Resilience

‣  For the provider (us): System management and extensibility by mere mortals

‣  Pricing and billing that starts small and scales seamlessly

‣  Security (You can trust us with your data)

‣  Quality data movement

‣  Things I Don’t!‣  Salespeople

‣  “Build an app in 5 Minutes”

‣  The last 5% of performance

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CASCON 2016

The good ol’ days…!

1:1 relationship between the application and the backend implementing it. The Monolith – one thing to deploy, and manage.

CASCON 2016

Microservices to the rescue!

‣  Small autonomous services that work together

Abstract implementation

behind API

Decentralize most

processing

Design for the

consumer

Deploy components independently

Constraint the scope of failure

(limit blast radius)

Monitor aggressively

Model a business domain

Automation (practice &

culture)

Microservices!

CASCON 2016

Starting with an opinion!

‣  Microservices ROCK! ‣  But not without problems and limitations

CASCON 2016

Why microservices work!

Source: “Service Discovery and Registration in a Microservices Architecture” F. Khaliq, F. Sanches.

CASCON 2016

Single Responsibility Principle!

‣  Group together things that change together.

‣  Corollary: Separate things that change for different reasons.

CASCON 2016

The truth will set you free!

‣  Prefer true stateless, virtual, small, highly available, microservices ‣  No concern for placement or latency

‣  Minimal specifics on physical layer

‣  Stateless is best

‣  Trust the infrastructure to connect microservices efficiently

You just need to believe….!

CASCON 2016

And here is the truth…. !

‣  Cloud Data Services are data intensive – and have one or more components that are massively stateful.

‣  BigData, in particular can store 10,000GB – 5,000,000GB in a single tenant

‣  Serious data makes ephemeral, virtual, redundant, replicated, …. all sound quaint. ‣  How many redundant copies of my 5 Petabytes do you want to store? ‣  5 Petabytes randomly placed? ‣  5 Petabytes with how much access latency?

‣  Performance makes multi-tenancy hard ‣  Share my cores with how may people? ‣  Other tenants are doing what????

CASCON 2016

An example for a generic cloud data service!

Billing Data Movement

Container Service

User Console

User Mgmt.

Service Broker

Data Engine

Spark Analytics

R Studio

Visualization

Monitoring (fault and

usage)

Key Mgmt. (encrypt)

PROBLEM

PROBLEM

CASCON 2016

PROS & CONS!

‣  Code simplicity! Easier for humans to build, understand.

‣  Update microservices independantly

‣  Enables continuous delivery

‣  Smaller, lower risk, updates to part pieces becomes possible

‣  Easier to test and deploy

‣  Enable resiliency where possible, even if other services are lacking

‣  Complicates DevOps

‣  Some microservices need to be reasonably collocated – where latency matters.

‣  Monitoring & logging more vital than ever, and harder to coordinate

‣  Small & autonomous fiction: can everything be made small?

PROS! CONS!

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