Modeling the Smart and Connected City of the Future with Kafka and Spark

Eric Frenkiel, CEO & Co-Founder, MemSQL@ericfrenkiel

MAKE DATA WORKDECEMBER 1-3, 2015 SINGAPORE

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MemSQL at a Glance

Enterprise Focused

Our Mission:

Real-time database for transactions and analytics Founded in 2011, based in San Francisco Founders are former Facebook, SQL Server

database engineers $50 million in funding to date

Make every company a real-time enterprise.

What does a Smart City Look Like?

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Our Conception

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Our Reality

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3.9b people live in cities today

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By 2050, we’ll add another 2.5b people

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We need to create sustainable cities

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We need to use technology to help us

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We don’t live in Tomorrowland

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We live here

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The good news: the Technology of Today can build smart cities.

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City-wide WiFi City App to report issues Open-Data Initiatives to

share data with the public Most importantly, an

adaptive IT department

A Smart City Should Have…

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Let’s learn how.

A Model Application: MemCityCapturing data from 1.4 million households Total AWS hardware costs at $2.35 per hour

MemCity Reach

1.4 million households (approximately the size of Chicago)

Capturing data from 8 devices in each home,

every minute

*#MemCity

186,667 transactions per secondfrom Kafka Spark MemSQL

#MemCity

1.4 Million Households8 Devices per Household186K Events per Second

The “Real-Time Trinity”

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Designing the Ideal Real-Time Pipeline

Message Queue Transformation Speed/Serving Layer

End-to-End Data Pipeline Under One Second

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A high-throughput distributed messaging system

Publish and subscribe to Kafka “topics”

Centralized data transport for the organization

Kafka

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In-memory execution engine

High level operators for procedural and programmatic analytics

Faster than MapReduce

Spark

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In-memory, distributed database

Full transactions and complete durability

Enable real-time, performant applications

MemSQL

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Subscribing to Kafka

(2015-07-06T16:43:40.33Z, 329280, 23, 60)

0111001010101111101111100000001010111100001110101100000010010010111…

Publish to Kafka Topic

0111001010101111101111100000001010111100001110101100000010010010111…

1110010101000101010001010100010111111010100011110101100011010101000…

0101111000011100101010111110001111011010111100000000101110101100000…

Event added to message queue

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Enrich and Transform the Data

Spark polling Kafka for new messages

(2015-07-06T16:43:40.33Z, 329280, 23, 60)

(2015-07-06T16:43:40.33Z, 329280, 94110, 23, ‘kitchen_appliance’, 60)

Deserialization

Enrichment

0111001010101111101111100000001010111100001110101100000010010010111…

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Persist and Prepare for Production

RDD.saveToMemSQL()

INSERT INTO memcity_table ...

time house_id zip device_id device_type watts

2015-07-

06T16:43:40.33

Z

329280 94110 23 ‘kitchen_appliance’ 60

… … … … … …

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Go to Production

Compress development timelines

SELECT ... FROM memcity_table ...

We can use In-Memory technology to build interactive applications for Cities.

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Urban planning Efficient power consumption Efficient transportation Sustainable energy practices

So We Can Optimize…

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Creating Real-Time Pipelines should be push button

easy.

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One click deployment of integrated Apache Spark

Put Spark in the Fast Lane• GUI pipeline setup• Multiple data pipelines• Real-time transformation

Eliminates batch ETL Open source on GitHub

MemSQL Streamliner for IoT Applications

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Simple Deployment Process

Application

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Cluster

1. Deploy MemSQL

In-Memory | Distributed | Relational

Application

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Cluster

2. Deploy Spark

Application

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Cluster

Kafka Connects to Each Node

Application

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Streamliner Architecture

First of many integrated Apache Spark solutions

Other Real-Time Data

Sources Application

Apache Spark

Future Solution

Future Machine Learning Solution

STREAMLINER

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Streamliner ETL Detail

Other Real-Time Data

Sources Application

Apache Spark

Future Solution

Future Machine Learning Solution

STREAMLINER

Custom

Future Extractor

JSON

Custom

Future Transformer

STREAMLINER

Extract Transform Load

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Streamliner

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Extract

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Transform

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Load

Extending Analytics with Lambda Architecture

Real-Time Analytics StreamingAnalytic ApplicationsNot Excel Reports

Financial Services Adtech eCommerce IoT Consumer Internet Energy Federal

Lambda Architecture

New Real-Time Processing

Existing Batch Processing

Msg Queue

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Multi-TB on commodity hardware

Store the “state of the model”

Easily build applications

Avoid direct disk at all cost

In-Memory Databases Rise Up

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Comprehensive Architecture

Tran

sact

ions

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Comprehensive Architecture

Real TimeSpeed/Streaming LayerFast Updates

RowstoreTran

sact

ions

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Comprehensive Architecture

Real TimeSpeed/Streaming LayerFast Updates

Rowstore

Analytics

Tran

sact

ions

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Comprehensive Architecture

Real TimeSpeed/Streaming LayerFast Updates

Rowstore

HistoricalBatch Layer

Fast Appends

Columnstore

Analytics

Tran

sact

ions

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Comprehensive Architecture

Real TimeSpeed/Streaming LayerFast Updates

Rowstore

HistoricalBatch Layer

Fast Appends

Columnstore

Analytics

Tran

sact

ions

Execution engine that spans the data spectrum

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Comprehensive Architecture

Real TimeSpeed/Streaming LayerFast Updates

Rowstore

HistoricalBatch Layer

Fast Appends

Columnstore

Analytics

Tran

sact

ions

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Simplified Lambda Architectures with MemSQL

Layer Traditional Lambda MemSQL Lambda

Batch Hadoop MemSQL Column Store

Speed Storm, Spark Kafka > Spark > MemSQL

Serving Cassandra, HBase MemSQL

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Lambda Applies to Real-Time Data Pipelines

Message Queue

Batch

Inputs DatabaseTransformation Application

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Kafka, Spark, and MemSQL Make it Simple

Batch

Inputs Application

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Massive Ingest and Concurrent Analytics Instant accuracy to the latest repin Build real-time analytic applications 1 GB/sec totaling 72 TB/day

Real-time analytics

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Using Real-Time for Personalization

Ad Servers EC2

Real-time analytics

PostgreSQLLegacy reports

Monitoring S3 (replay) HDFSData Science

VerticaStar Schema MictoStrategy

Reach overlap and ad optimization Over 60,000 queries per second Millisecond response times

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300k events/sec

Reduced Latency from 30 minutes to Sub-Second

Real-time Analytics

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Sample Pipeline: Analyzing Twitter Data in Real Time

ApplicationApache Spark

SPARKSTREAMLINER

Public API“Garden Hose”

</>Python Extract Transform Load

SPARK STREAMLINER

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Install MemSQL and Apache Spark in < 1min With MemSQL Ops and Streamliner

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Run Kafka in Docker Container and Create a New Topic: TWITTER

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Fill Out Extract, Transform and Load Details to Set Up Pipeline

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Use Python Script to Load Tweets into Kafka Topic and Get Data Flowing

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Connect to MemSQL Database and Run SQL Queries Instantly

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Run Online Alter Table to Optimize Query Performance

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Streamliner: Dynamic Resource ManagementWithout Streamliner With StreamlinerPipeline 1

Spark Worker

Pipeline 2

Spark Worker

Executor (P2 only)

Executor (P2 only)

Executor (P1 only)

Executor (P1 only)

Driver (P1 only)

Driver (P2 only)

All Pipelines

Streamliner Driver…

…

Spark WorkerSpark Worker

Executor (P1 or P2)

Executor (P1 or P2)

Executor (P1 or P2)

Executor (P1 or P2)

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Building Real-Time Data Pipelines and Predictive Applications

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Adding Real-Time Scoring to Predictive Applications

StreamlinerInput

User JarSAS Generated PMML

Industrial Equipment

Sensor Data

S1 S2 S3 P1 P2 P3

Scoring Real-Time Data with Predictive Models

Sensor 1 Predictive Model 1

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