Making Every Drop Count: How i2O Addresses the Water Crisis with the IOT and Apache Cassandra

Mike Williams, Software and IT Director, i2O Water

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Webinar Housekeeping

1 About i2O Water & The State of Water Consumption Today

2 Database Technology for Time Series and IOT

3 Relational vs NoSQL

4 Why NoSQL and Cassandra?

5 Impact of Cassandra

6 Q&A

Agenda

About i2O Water

• Smart pressure management systems for water

distribution networks

• Hardware/Firmware/Software

• IoT

• Reduces leakages, burst frequency and energy

waste

• Saves over 235 million litres of water per day

around the world

Water Consumption v.s. Supply

• A staggering 46 billion litres (~12.15 billion gallons ) of drinking water are lost globally every day

• It’s not a problem restricted only to the developing world either – Montreal, for example, loses 40% of the water it produces

• 40% global shortfall between water demand and supply by 2030• The World Economic Forum ranked water crises as the top global risk in its 2015 Global

Risks Report

How i2O Works

How do we do it?

Safety margin

Head loss

The Challenge with Relational Databases

• Massive volumes of time series data (1.5TB and growing) that needs to be stored and analyzed in close to real time

• Low energy, battery powered devices

• Must be efficient in protocol design

• Must be efficient in message sizes

• Relational database (SQL Server) couldn't adequately handle time-series data and IOT needs at scale

• Re-indexing tables causes loss of performance

• The need to scale without impacting performance of availability

• Migration from existing data management platform

Key Database Requirements

Strong real-time search and query capability

Support high reliability and security

Structured & unstructured real-time data

Flexible data model with affordable scalability

1 2

3 4

How Cassandra Compared to RDBMS

• Wide rows allowed better modeling of time series– time sharding and rollup aggregations

• Smaller data footprint on “disk”• Much faster write performance• Faster read performance• Scalable by design / architecture• Encourages us to duplicate data

• Model what you need to query efficiently

How Cassandra Compared to other NoSQL

• Compared it to HBASE

– Similar architectures

– C* was a better supported product

• Compared it to MongoDB

– C* was much more scalable by design

– Concerned over sharding and lossy writes

• Compared it to RavenDB

– C* was far more robust

– C* was far more performant

– C* is better supported

Why NoSQL and Apache Cassandra?

• Database platform built for IOT applications

• Optimized for storage and retrieval of time-series data

• Streaming and real time analytics (Spark integration)

• Best performance based on internal benchmark testing

• Strong search & real-time query capability on unstructured data (Solr integration)

• Supports 100% uptime through masterless architecture and multi-datacenter replication

• Linear scalability across commodity hardware makes supporting high velocity data a reality (and affordable)

i2O Technical Environment

SSL OffloadingLoad Balancing

In memory Cache

Development Stack

Message Broker

QueuingProtocols

Message Encoding

Data Stores

AMQP

Cassandra

Postgresql

PostGIS

The Results

>15,000 devices

>70 Water Utilities globally

+235M litres of water saved per day

20+ countries

>99.9% Uptime since launch

even during upgrades & node failuresSecurity audited / tested

Recommendations

• Think Security at all times - everywhere• Think about Scalability early• Think about API’s and Protocols early• Use test infrastructures to practice changes to tech

– Cheap to do, consider Container technologies like Docker

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Q&A

Thank you!