Microservices and Internet of ThingsBob FamiliarPractice Direct, Cloud & Services

BlueMetal, Inc.

Blog: TheUndocumentedAPI.com

Follow: @bobfamiliar

Email: bobf@bluemetal.com

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What does IoT mean to you?

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How many PCs on planet earth?

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How many mobile phones?

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By 2020 how many devices connected to the Internet?

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Device Management

Event Ingestion & Transformation

Status & Notifications

Analytics & Visualization

Integration

Components of an IoT Solution

Microservice

Microservices are…

• A microservice is a self-contained unit of functionality with

loosely coupled dependencies on other services. Autonomous

• A microservice is a unit of deployment that can be

modified, tested and deployed as a unit without impacting

other areas of a solutionIsolated

• A microservice is stateless therefore it can be horizontally

scaled up and down as neededElastic

• A microservice is fault tolerant and highly available Resilient

• A microservice responds to request in a reasonable amount

of timeResponsive

Microservices are… (cont.)

• The intelligence in a system is found in the endpoints not on the

wire. ESB is an anti-pattern to Microservices. Intelligent

• Microservices rely on asynchronous message-passing to establish a

boundary between componentsMessage Oriented

• Microservices provide API’s for access by developers and

administratorsProgrammable

• Microservices provide an API and/or a console that provides access

to administrative operationsConfigurable

• The lifecycle of a microservice is managed through automation

that includes dev, build, test, staging, production and distributionAutomated

Benefits of a Microservices Approach

• Can be developed alongside existing monolithic applications

providing a bridge to a future stateEvolutionary

• Language agnostic APIs

• Highly decoupledOpen

• No monolith to fall over

• Designed for failureResilient

• Adding, updating and maintaining services and can be done at

velocitySpeed of Development

• Reusable and Composable Reuse

Benefits of a Microservices Approach (cont.)

• Services are deployed independentlyDeployment Governance

• On-demand scaling of smaller services leads to better cost controlScale Governance

• Services can be rewritten and replaced with minimal downstream

impactReplaceable

• New API’s can be released without impacting clients that are using

previous API’sVersioned

• Microservices are typically owned by one team from development

through deploymentOwned

Microservice

Microservice Logical Architecture

• Any client Experience

• SDK for accessing serviceSDK

• Communication protocol such as

REST/HTTPProtocol

• On the wire data modelsModels

• Business Rules/LogicService

• Data AccessDAC

• Model persistent store Store

• Continuous DeliveryAutomation

Protocol

Protocol

DAC

Store

Any Client

Models

Service

SDK

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Microservice

Microservice Logical Architecture

• Any client / Administrators ConsoleExperience

• Public/Private SDKSDK

• Public / Private APIAPI

• Rules, validations, calculationsService

• On the wire data modelsModels

• Rest, DAC, Model ManagementFrameworks

• Model persistent store Store

• Continuous DeliveryAutomation

Public API Private API

Frameworks

Frameworks

Store

Any Client Admin Console

Models

Public Service

Public SDK Private SDK

Au

tom

ati

on

Private Service

IoT Microservices

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Event Hub Overview

• An event ingestor service that provides event and telemetry ingress to the cloud at massive scale, with low latency and high reliability

• Particularly useful in application instrumentation, user experience or workflow processing, and Internet of Things (IoT) scenarios

• Provides a message stream handling capability that has very different characteristics than traditional enterprise messaging

• EH differs from Enterprise messaging scenarios which commonly require a number of sophisticated capabilities such as sequencing, dead-lettering, transaction support, and strong delivery assurances

• The dominant concern for event ingestion is high throughput and processing flexibility for event streams

Partitioned Consumer Pattern

• A partition is an ordered sequence of events that is held in an Event Hub.

• As newer events arrive, they are added to the end of this sequence.

• A partition can be thought of as a “commit log.”

• Each partition can support 1MB ingress and 2MB egress per second

Partitioned Consumer Pattern

• An Event Hub contains multiple partitions.

• Each partition is independent and contains its own sequence of data.

• As a result, partitions often grow at different rates.

• The default setting for partitions is 16

• This can be lowered to 8 and increased to 32 in the portal

Stream Analytics Overview

• Stream Analytics is a fully managed, real-time stream computation service hosted in Azure

• It provides highly resilient, low-latency, and scalable complex event processing of streaming data

• It enables developers to combine streams of data with historic records or reference data to derive business insights easily and quickly

• Stream Analytics connects to Azure Event Hubs for stream ingestion and the Azure Blob service for reference data

• Data can be written from Stream Analytics to Blobs, Tables, SQL Database or sent to another Event Hub for further downstream processing

Integration with Event Hubs

• Stream Analytics is capable of handling high event throughput of up to 1GB/second.

• Integration with Azure Event Hubs allows the solution to ingest millions of events per second coming from connected devices, clickstreams, and log files, to name a few.

• In order to achieve this, SA leverages the partitioning capability of Event Hubs, which can yield 1MB/s per partition.

• Stream Analytics provides support for partitioning while processing these ingested events along both horizontal and vertical axes.

• A user can partition the computation into a number of logical steps within the query definition, each with the ability to be further partitioned into data-parallel execution elements.

• Over time, Stream Analytics will automatically scale based on the event ingestion rate, complexity of processing, and expected latencies to allow users to customize their workload appropriately.

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