Cloud-native Data

Cloud-Native DataExtending Cloud-Native Beyond the App

Cornelia Davis Sr. Director of Technology Pivotal @cdavisafc

@cdavisafc

Me?

Developer (wasn’t Ops)

Web architectures for >10 years

Cloud-native for 5 years

Cloud Foundry for 5 years

More recently

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A Seattle book store deploys code, on average,

every second

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Why?

• Scale Applications

• Scale Teams

• Independent Development Cycles

• Experimentation

• Resilience

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Continuous Delivery

of

Customer & Business Value

Obstacles

• Silos: Dev, QA, Operations is typical. No shared common goal

• Dissimilar Environments - “It works on my machine”

• Risky Deployments: Manual steps, done “off hours”

• Changes are treated as an exception →Firefighting

• Processes designed around these obstacles

Enabling Patterns

• Reinventing the Software (Delivery) Value Chain

• Cloud-native Software Architectures

• The Right Platform • Devops • Change is the Rule

(not the Exception)

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Presentation

Frontend-integration

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New LIGHTWEIGHT ARCHITECTURES are emerging Microservices addressing speed to market and cloud scale

Monolithic / Layered Microservices

12 http://highscalability.com/blog/2014/4/8/microservices-not-a-free-lunch.html

http://highscalability.com/blog/2014/4/8/microservices-not-a-free-lunch.html

Spring Cloud Services 1.0.0

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Spring Cloud Services

Config Server Service Registry Circuit Breaker Dashboard

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Operational Visibility: Distributed Tracing • Latency visibility into a request’s end-to-end call graph

• Quickly identify a problematic service in a distributed system • Zipkin is a open source distributed tracing system. It helps gather timing data

needed to troubleshoot latency problems in microservice architectures.

• Pivotal is investing in Zipkin to solve distributed tracing use cases – Apache 2.0 License – Created by Twitter in 2012. – In 2015, OpenZipkin became the primary fork

Zipkin Tracing

• PCF Developers can redirect application traffic to a desired request path in order to use logging, authentication or rate limiting systems that exist outside of PCF

• PCF’s Service API will introduce a new field: route_service_url • Developers will create a routing service instance and bind it to a

route (not an app) – Service Instance can be created by a Service Broker or can

be a user-provided service instance • Router is configured with and forwards requests to the URL

contained in the route_service_url field • The route service is expected to forward the request back to the

route • Knowing the request has already been forwarded to the route

service, the Router forwards to the associated applications

Route Servicesclient

load balancer

CF router

CF app

route service

1

2

3

4

5

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New LIGHTWEIGHT ARCHITECTURES are emerging Microservices addressing speed to market and cloud scale

Monolithic / Layered Microservices

18

What is Cloud-native Data?

It’s not this

At the Intersection of Cloud-native App Architecture & Data What is Cloud-native Data?

Patterns

• Data-services Topology

• Interfacing to existing data sources

• Caching, but rethought

• ETL, but rethought

• Micro-databases

• Event Sourcing (and CQRS)

• Personas - what happens to the DBA, Data Architect, etc.?

Data-services topology

Goals/Needs:

• Resilience

• Tenancy

• Security - networks?

• Day 2 Ops - Backup/Recovery

Data-services topology: Resilience

PCF

Data Center 1

PCF

Data Center 2

PCF PCF

Pipelines Distribute Deployments

Active/Active

Data Center 1 Data Center 2

PCF PCF


“Active/Active”


PCF PCF


“Active/Active”


PCF PCF


“Active/Active”


WAN Replication

Interfacing to Existing Data Sources

Goals/Needs:

• Cost reduction - offloading MIPS

• Agility

Pattern: Data API

• Microservices do not access data layer directly

• Except for the micro services that implement the data API

• A surface area to:

• Implement access control

• (Instead of the likes of firewall rules)

• Implement throttling

• (Fair sharing of a resource)

• Perform logging

• Other policies…

Anti-pattern: Stateless Data APIs*

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* We will maintain statelessness at the app level

This is the architecture that dominated the SOA era of the early 2000s

Culture tip: Data APIs needn't be

built by the database team

Pattern: Microservice Needs a Cache

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We’ll have a lot more to discuss with respect to caching … stay tuned

Pattern: Data API








• Perform logging


Pattern: Data API








• Perform logging


Pattern: Versioned Data API

• We are already familiar with versioned micro services…

V1 V2Possibly coupled with

Pattern: Parallel Deployments

Caching Rethought

Goals/Needs:

• Performance

• Interface to existing data sources

• Resilience

https://content.pivotal.io/white-papers/in-memory-data-caching-for-microservices-architectures

https://content.pivotal.io/white-papers/in-memory-data-caching-for-microservices-architectures

Caching Patterns

Look Aside • Attempt retrieval from cache • Client retrieves from source • Write into cache

! ?

"

#

Advantages

• If cache is unavailable, data source may still be

• Cache configuration is very simple

Disadvantages • Developer may be responsible for

protocol implementation (Spring Cache Abstractions do hide this from the dev)

Caching Patterns

Read-through

• Attempt retrieval from cache

• Cache retrieves from source and stores in cache

• Return value to client

! ?"

#

Advantages

• Simpler client programming model (though developer may be responsible for code running in cache)

• Less processing load on the client

Disadvantages • Cache must available

• Cache configuration, including code deployment into cache, is more complex

Caching Patterns

Write-through • Write to cache • Cache writes to source • ack sent to client

!"

#

Advantages • Simpler client programming model

• Consistent

Disadvantages

• Cache must available • Cache configuration, including code deployment, is

more complex

• Depends on connectivity to cache and cache to source • Higher latency

Caching Patterns

Write-behind

• Write to cache

• ack sent to client

• Cache writes to source asynchronously

!

"#

Advantages • Simpler client programming model

• Very low latency

Disadvantages

• Cache must available • Cache configuration, including code deployment, is

more complex

• Depends on connectivity to cache and cache to source • Eventual consistency

© Copyright 2014 Pivotal. All rights reserved.

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Why Cloud-Native Apps Need an In-Memory Cache

As an integrated service on a world class platform

Fast, available microservices

Legacy app modernization

Performance at scale

Auto-scaling High Availability Logging/Metrics Security Zero Downtime Updates …Multi-cloud

Pattern: Cache Warming

• Loading the cache can be expensive

• Spring Cloud Data Flow for modern ETL

Sources

Destination

Spring Boot Apps

Filter Microservice

Enrich Microservice

Score Microservice

Spring Boot Apps

Spring Boot Apps

IoT

Microdatabases & Event Sourcing

Goals/Needs:

• Agility ∴ Autonomy

• Resilience

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Pattern: Database per Microservice

• Supports Polyglot persistence

• Independent availability, backup/restore, access patterns, etc.

• In PCF, on-demand, dedicated clusters

My Connections

API

Posts API

New from Network

API

Client Side Join

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My Connections

API

Posts API

New from Network

API

Event-driven Systems

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My Connections

API

Posts API

New from Network

API

Independent Databases - Shared Entities

• We’ve started to break up the data monolith

• BUT our data integration “strategy” is rather brittle and bespoke

• How are changes to data in one bounded context reflected in the other?

Sales

Support

?

My Connections

API

Posts API

New from Network

API

My Connections

API

Posts API

New from Network

API

My Connections

API

Posts API

New from Network

API

Unified Log

CQRS is what allows the unified log to be the source of truth

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My Connections

API

Posts API

New from Network

API

Unified Log

But then, what about people?

Info SecSrv Build

Cap PlanNetwork

OpsMid. Eng.

SW Arch

SW Dev

Client SW Dev

Svc Govern

CUSTOMER FACING APP TEAM

Ops

Cap Plan

DCTM Eng

DCTM

Cap Plan

Ops

SW Arch

SW Dev

Client SW Dev

CUSTOMER FACING APP TEAM

Ops

Cap Plan

ENTERPRISE ARCH

Ent Arch

Proj Mgmt

Biz AnProd MgmtData Arch

DBA


SW Arch

SW Dev

Client SW Dev

LEGACY SERVICE TEAM

Ops

Cap Plan


CSO INFRA MID/ DEV BIZ ENT

APPSDATA

Change Control

PLATFORM TEAM

Ent ArchProd Mgmt

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Cloud-native Patterns Summary

Legacy Data Access

Service APIs

Data APIs

Shared DB

Database Per Service

Data Integration

Client-side “Joins”Event Sourcing

CQRSData Replication

Parallel Deployments

Caching

Cache Provisioning and Management

Look Aside

Read-through

Write-through/behind

Warming

Transforming The Way the World Builds Software

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And Software Needs Data

Thank you!

Cornelia Davis Sr. Director of Technology Pivotal @cdavisafc

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