abdw17-lightning talks track-thinking in streaming

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1 Thinking in Streaming Yogi Devendra [email protected]

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Page 1: ABDW17-Lightning Talks track-Thinking in Streaming

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Thinking in Streaming

Yogi Devendra [email protected]

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Outline

● What?

● Why?

● How?

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What is streaming?

Image ref: [1]

Streaming

“Process on-the-go

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Why streaming?

• Reducing the end to end latency• Effective use of resources

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Significance

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Traffic Signal vs Flyover

Image ref: [3]

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Stock exchange

Auction style trading Online trading

Image ref: [4] Image ref: [5]

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Taxi booking

Cab drivers waiting for customers

Riders waiting for a cab

Ride on the go

Image ref: [6]

Image ref: [7]

Image ref: [8]

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• Infrastructure availability• CPU, disks, memory• Distributed systems

•Datasets• Bigger datasets• More data sources• Ongoing data

• SLAs• Results expected as soon as possible

• Newer types of data sources• Kafka• REST• websockets

Key changes

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Problem statement

● Input○ 1MB / record○ 1TB data / day○ Monthly/Daily file

● Output○ Process entire dataset and give

the answer at the end.○ E.g. : Aggregate CDR for

customer billing, generate detailed billing report

● Input○ 1MB / record○ 1 Million records / sec○ Continuous Stream

● Output○ Process data as it comes and

output current answer. ○ E.g. User dashboard with current

billing.

Batch Streaming

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Characteristics of streaming

•On the go ⇒ Incremental logic•while (true) : current state = function ( current state , current record)result = function (current state)•Need to store only the current state (NOT DATA!!)

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Current :• Rates are based on TRP ratings• TRP ratings are offline

•Bids for ad slots happen offline

Example : TV advertising

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Impact of TRP on-the-go

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How to stream?

Ad Server

Ad Ad Ad Ad Ad

Viewership of ESPN

Service provider 1

Service provider 2

Set top box

Publish Viewership

Bid for Ad slot

To Broadcaster

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Streaming pipeline

Read from Input

Source

Cleanse the Data

Aggregate

Data

Ad Server

Viewership Output

Accept Winner

Ad Bidding

Input

Output to Broadcaste

r

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• Incremental logic•Scalability, fault tolerance

• Modular design• No. of channels• No. of users

•Operability

Key considerations

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Further possibilities

• Real time bidding for ad-slots based on viewership• Pay per use billing plans for users• User profiling, targeted ads

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Streaming : Game Changer

• Streaming adds value to existing applications by reducing latencies.

•Streaming opens up more possibilities for applications.

Image ref: [9]

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Questions

Questions

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1. One piece flow LuciArsene slideshare | https://www.slideshare.net/LuciArsene/leandlarsene 2. Are you series Boomsbeat | http://images.boomsbeat.com/

3.Flyovers World Amazing pictures | https://worldamazingpictures.wordpress.com4. NYSE Alamy | http://www.alamy.com/5. NASDAQ Goforex | http://www.goforex.eu/6. Cabs driver colebegone | http://colebegone.blogspot.in/

7.Riders frpo | https://www.frpo.org/lobby-view/8.App based booking Officechai | https://officechai.com/news/9.Game changer gurussolutions | https://gurussolutions.com/

References