integration in the cloud, by rob davies

Integration in The Cloud

Rob Davies2016

2

Rob Davies

• Director of Middleware Engineering for iPaaS, Technical Director for Fuse

• Herd the fabric8 cats• Over 20 years experience of developing

large scale solutions for telcos and finance

• Creator of ActiveMQ and ServiceMix• Committer on open source projects,

including fabric8, Apache Camel and other stuff …

3

Fabric8 – some

background

4

Camel’s Journey to Microservices

Self-Service,On-Demand,

Elastic,Infrastructure

asCode

(Cloud)

Re-Org toDevOps

AutomationPuppet, Chef,

Ansibleand/or

ContainersFabric8 v1(2010 ish)

5

Fabric8: Cloud Engine

Agent

RuntimeRegistry

Configuration

coordination

Monitoring

Agent

Agent

Fabric v1

6

Camel’s Journey to Microservices

CI & CDDeployment

Pipeline

OneMicroservice

SiliconValley

DotComStartup

Meanwhile …

7

We had to pivot and go Kubernetes

and containers

8

Why do you want to run your application inside containers?

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● Lightweight footprint and minimal overhead,● Portability across machines,● Simplify DevOps practices,● Speeds up Continuous Integration,● Empower Microservices Architectures.● Isolation

ContainerAdvantages

10

Kubernetes: Container Orchestration

• Inspired by Google’s experience with Orchestration• Automatic bin packing• Self healing• Horizontal Scaling• Service Discovery and load balancing• Automated rollouts and rollbacks• Secret and Configuration management• Storage orchestration• 2 years old – 30,000 commits

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Master

API Server

Service Layer

VirtualPhysical Private Public

PersistentStorage

Node Node

Logger

Node

Node Node Node

Dev

Ops

SCM(Git/Svn)

CI/CD

Automation

Routing LayerRegistry

SDN Overlay Network

Controllers- Scheduler- Replication- Services- Builds- Routes- Deployment

Kubernetes

OpenShift- Deployments- Builds- ImageStreams

14

Open Source community

Version 1.3.x

Hosted on GitHub

800+ contributors

34,000+ commits

16,000+ GitHub stars

Red Hat

HP

IBM

Mesosphere

Microsoft

Project Partners

CoreOS

Pivotal

SaltStack

VMWare

http://kubernetes.io/

https://github.com/kubernetes/kubernetes





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What are Microservices

…

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Peak of Inflated

Expectations

x

Trough of Disillusionment

Serious Hotness

Techology Trigger

Slope of Enlightenment

18

Red Hat Definition

“Microservices is an architectural approach, that emphasizes the decomposition of applications into single-purpose, loosely coupled services managed by cross-functional teams, for delivering and maintaining complex software systems with the velocity and quality required by today’s digital business”

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What does that mean ?

Application is composed of multiple servicesUNIX style – do one thing, and one thing well – in one

process.Language agnosticAPI is important

Organize teams around delivery of a service

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Microservices characteristics …

1. Deployment Independence - updates to an individual microservice have no negative impact to any other component of

the system. Optimized for replacement

2. API Focused

3. Do one thing well - small enough and no smaller

4. Fit in your head

5. Decentralized Data Management

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How to do Microservices ?

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Big Team, Big Effort, High Ceremony DeploymentCode offers no value until it survives in production

24 Weeks

Monolithic System

BusinessChange

Requests

23

Programmers(18) Operators

(6)

DBAs(3)

Project Managers

(2)

Business Analysts

(4)

QualityAssurance

(6)

Compliance(2)

25

Automated TestingContinuous Integration & Build Automation

24 Weeks 8 of 3 week sprints

Monolithic System

12 Weeks

BusinessChange

Requests

26

Linux Containers (e.g. docker)Automation via Orchestration (allows Devs to become

DevOps)Infrastructure as Code


Monolithic System

12 Weeks 9 Weeks

BusinessChange

Requests

27

Continuous Delivery Pipeline


Monolithic System

612 Weeks 9 Weeks

BusinessChange

Requests

28

Zero-Downtime Deployment Strategies (Blue/Green, Canary)


Monolithic System

6 312 Weeks 9 Weeks

BusinessChange

Requests

29


Monolithic System

6 3 1 112 Weeks 9 Weeks 1

BusinessChange

Requests

30


Monolithic System

6 3 1 112 Weeks 9 Weeks 1

Deploying faster than 3-week sprint cycles?

Patches to your application as well as your “stack” are also deployments.

BusinessChange

Requests

31

Microservices Fallacies…

From:http://basho.com/posts/technical/microservices-please-dont/

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#1 – Cleaner Code

Well, less code to go wrong, but

Its not going to magically fix poor engineers

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#2 – Its Easier

Only doing one thing, so it should be easier - but

The complexity still exists – its moved somewhere else – the platform

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#3 – Its Faster

Easier to optimize an isolated service, but

Network is always going to be slower than co-located code

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#5 – Better for Scalability

Microservices are better for scalability, but

Only if you carefully consider what should be architected as a Microservice in the first place

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Microservices In reality

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Its fine to mix and match monoliths and MicroservicesSome things are better left untouched

Choose carefully what to build as a Microservice

Don’t do it yourself:

38

Fabric8: Makes cloud stuff

easy – and we are building an

iPaaS too

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Microservices Platform – the Foundations

• Runtime Registry• Logging• Security• Deployment• Scalability

• Loosely coupled• Resilient• Temporal

From the platform: The services:

45

iPaaS 2.0 Microservices Platform

• Built on top of OpenShift

• Provides additional services to generate, build and test integration services

• Integration Services use Apache Camel:– deployed in Spring Boot– In a Docker Container

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iPaaS 2.0: Microservices Platform

Citizen DeveloperiPaaS Console

Expert DeveloperCan view

what’s under the

hood

AdministratorCan look at

InfrastructureOpenShift Dedicated

Component Catalog

Integration Editor

Funktion Editor

Data Mapper

ArtifactRepository

Git Repository

Application Logging

Application Metrics

Tracing Project Wizards

Code Quality

AutomatedTesting

Circuit Breaker Social

API Management

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Agility: Integrated CI/CD

• Continuous Deployment automatically, with jenkins pipelines for your integration services

• Automated tests• Hooks for manual

approval before production

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What Do we need from our

Microservices Platform?

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Logging …

50

Why Logging ?

What a system is doing

What happened

A record of Actions and Outcomes

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Traditional Application Logging

Logging strategies defined by the systemStorage and routing defined by the system

Write to local FileCentralizing

Custom configurator per system

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Container Logging

Container standardize Management of your ProcessesSo lets standardize Logging

Logs to STDOUT & STDERRCaptured by Execution EnvironmentRouted by Execution Environment

Containers are Ephemeral

CENTRALIZE

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Contextual Logging

Logs are for machines, not humansContext gives logs power

Structured Logging

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Structured Logging Example

{"STATUS": "200”,"REQUEST": "GET /apache_pb.gif

HTTP/1.0”,"HOST": ”127.0.0.1”,"REFERER": http"REFERER":

http://www.example.com/"REFERER": http://www.example.com/start.html,

"USER": "frank”,"TIME": "10/Oct/2000:13:55:36

-0700”,"USERAGENT": "Mozilla/4.08 [en]

(Win98; I ;Nav)“,"IDENTITY": "-”,"SIZE": "2326”}

Context added by the logging process

Machine ReadableFilterable and Searchable

http://www.example.com/start.html





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CENTRALIZE

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Deployment is Easy

RC and Service for ElasticSearchRC and Service for Kibana

daemon set for fluentd

Every Container’s Log is Centralized

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Can we do the same for Metrics ?

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Why collect Metrics ?

Logs tell you what’s happeningMetrics measure behavior

Measure improvement of change

Adapt to runtime changes – e.g. auto-scaling

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Kubernetes Infra … Kublet

Runs on each node

Manages Pod and container lifecycle

Key Elements:

• Docker client

• cAdvisor client• Etcd client

• Docker client

• Root directory

masterNode

NodeNode

NodeNode

Node

Pod

Container

Container

Pod

Container

Container

Kublet

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cAdvisor

Daemon that collects, aggregates processes and exports information about containers

Collects metrics for both system components and user containers

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Heapster

One per cluster

Aggregates all metrics exposed by cAdvisor

Has Pluggable backend for persistent storage:

• InfluxDB

• Kafka

• Hawkular etc.

masterNode

NodeNode

NodeNode

Kublet

Heapster

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Microservices Platform – Application Metrics

• Historical metrics required for diagnosis,trends, and auto-scaling

• Uses Prometheus for storage

• Grafana for front end

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In Summary

Logs tell you what’s happeningMetrics measure how it happened

Use Microservices to capture

CENTRALIZE

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What about Tracing ??

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Why do Tracing?

Latency optimization – where are the bottlenecks ?Root cause analysis

Measure improvement of change

Continuous Analysis for Continuous Improvement

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Open Tracing

Vendor neutral Open APIs

Support for Go, Python, Java and Javascript

No lock in, easy to develop, easy to switch

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Microservices Platform – Tracing: Zipkin

• Zipkin: distributed tracing framework:

• Manages both the tracing and lookup of the data.

• All routes for iPaaS use camel-zipkin to record incoming and outgoing Camel messages

• OpenTracing

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Microservices Platform – Tracing: Zipkin Architecture

70

Wait – there’s more

…

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Writing an Integration easily: Funktion

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Funktion

Event driven lambda style Microservices, built on top of Kubernetes

Polygot - supports Java, Node.js, Groovy, Kotlin, Go …Supports hundreds of trigger endpoint URLs

Trigger endpoint defined in funktion.yml:

rules: - trigger: http://0.0.0.0:8080 action: io.fabric8.funktion.sample.Main

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Funktion supports same URLs as Camel:

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Funktion rules:

rules: name: foo - trigger: URL action: io.fabric8.funktion.sample.Producer::message chain: URL

Optional code to call

Optional

Mandatory – the input

Out – can have multiple of these

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Funktion how it works:

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Funktion how it works:

http://

Pod

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Funktion: part of the Fabric8 family

Code: https Code: https://github.com/fabric8io/ Code: https://github.com/fabric8io/funktion

Examples: in quickstarts: https://github.com/fabric8-Examples: in quickstarts: https://github.com/fabric8-quickstarts

• funktion-java-example• funktion-nodejs-example• funktion-kotlin-example• funktion-nats-example• funktion-java-kafka-example

https://github.com/fabric8io/funktion



https://github.com/fabric8-quickstarts

https://github.com/fabric8-quickstarts

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iPaaS:

A lot is happening under the hood …

82

But … the user just sees this:

83

iPaaS is being built on

fabric8 and OpenShift

84

Red Hat Registry

86

SERVICE CATALOG(LANGUAGE RUNTIMES, MIDDLEWARE, DATABASES, …)

SELF-SERVICE

APPLICATION LIFECYCLE MANAGEMENT(CI / CD)

BUILD AUTOMATION DEPLOYMENT AUTOMATION

CONTAINER CONTAINERCONTAINER CONTAINER CONTAINER

NETWORKING SECURITYSTORAGE REGISTRYLOGS &

METRICS

CONTAINER ORCHESTRATION & CLUSTER MANAGEMENT(KUBERNETES)

RED HAT ENTERPRISE LINUX

CONTAINER RUNTIME & PACKAGING(DOCKER)

ATOMIC HOST

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Single Sign On By RH SSO

Integration ServicesBy JBoss Fuse

Intelligent Process Server

By JBoss BPM Suite

Real time Decision Service

By JBoss BRMS

In Memory Data GridBy JBoss Data Grid

Messaging ServicesBy JBoss A-MQ

Java EE Application Server

By JBoss EAP

TomcatBy JBoss Web Server

API ManagementData Services

By JBoss Data Virtualization

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Links:

http://camel.apache.orghttp://fabric8.iohttp://http://kubernetes.iohttps://https://www.openshift.org

http://fabric8.io

http://fabric8.io

http://kubernetes.io



https://www.openshift.org




integration in the cloud, by rob davies

Software