Cloud-native Data: Every Microservice Needs a Cache

Cloud-Native DataExtending Cloud-Native Beyond the App

Cornelia Davis Sr. Director of Technology Pivotal @cdavisafc

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

11 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

6

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

Monolithic / Layered Microservices

17

What is Cloud-native Data?

Patterns and Anti-patterns• Shared Database - Anti-pattern

• Service APIs - Anti-pattern • Data APIs • Versioning • Parallel Deployments • Database Per Service

• Caching (Look-aside, Read-through, Write-through/behind, …)

• Materialized Views • Data mirroring • Data Integration • Event-driven architecture • CQRS

Anti-Pattern: Shared Database

• While micro services appear independent, transitive dependencies in the data tier all but eliminate their autonomy

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…

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But then, haven’t we just kicked the can a bit further

down the road?

Pattern: Versioned Data API

• We are already familiar with versioned micro services…

V1 V2



V1 V2Possibly coupled with

Pattern: Parallel Deployments



Possibly coupled with

Pattern: Parallel Deployments

V1 V2

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

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

Anti-pattern: Stateless Data APIs*

27

* 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

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This was all leaning a bit toward legacy DBs

(but not strictly)

Pattern: Bounded Context

• Domain-Driven Design • Each bounded context has a single, unified

model • Relationships between models are explicitly

defined • A product team usually has a strong

correlation to a bounded context • Ideal pattern for Data APIs - do not fall into the

trap of simply projecting current data models

Pattern: Database per Service

• Supports Polyglot persistence

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

Pattern: Client Side Joins


• Joins… and data reconciliation/integration

Pattern: Microservice needs a Cache!

& Materialized Views











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

Cache is a Service Binding

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A Bit More on the Cache ItselfRequirements • Distributed

• Over various failure boundaries - Availability Zones (Racks), Regions (Data Centers)

• Data replication • Tunable consistency

• Available

• Multi-node

• Lifecycle managed (BOSH!)

• Scaleable • Ease of Provisioning

Global Load Balancing

Data Center 1 Data Center 2

WAN Replication

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

Pattern: Data Mirroring Use Cases • Scale a cluster - initialize new nodes

• Migrate to new cluster

• Lost node recovery

• Backup and Restore

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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