The document discusses the advantages and challenges of using microservices in cloud data analytics, highlighting their ability to enhance scalability and resilience while offering autonomy for component deployment. It emphasizes the importance of true statelessness and minimal latency in cloud services, especially for data-intensive applications. Despite their benefits, the document acknowledges complications in development and operations due to the need for effective monitoring and potential limitations in service collocation.

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

2. CASCON 2016
Cloud Data Services @IBM – we have a few!

3. 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
3

4. 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.

5. 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!

6. CASCON 2016
Starting with an opinion!
‣ Microservices ROCK!
‣ But not without problems and limitations

7. CASCON 2016
Why microservices work!
Source: “Service Discovery and Registration in a Microservices Architecture” F. Khaliq, F. Sanches.

8. CASCON 2016
Single Responsibility Principle!
‣ Group together things that change together.
‣ Corollary: Separate things that change for different reasons.

9. 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….!

10. 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????

11. 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

12. 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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