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Real-time Stream Processing Architecture for Comcast IP Video. Strata Conference + Hadoop World 2013 Chris Lintz Gabriel Commeau. Agenda. Comcast VIPER Overview Architecture Overview Q & A. Comcast Video IP Engineering and Research (VIPER).

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Real time stream processing architecture for comcast ip video

Real-time Stream Processing Architecture for Comcast IP Video

Strata Conference + HadoopWorld 2013

Chris Lintz

Gabriel Commeau


Agenda
Agenda Video

  • Comcast VIPER Overview

  • Architecture Overview

  • Q & A


Comcast video ip engineering and research viper
Comcast Video IP Engineering and Research (VIPER) Video

Preparation Delivery Video Players

Packaging

Storage

Transcoding

Origination

Samsung

iOS

Video Players

Xbox Live

Android

Analysis

Storm


Why do we focus on real time
Why Do We Focus on Real-time? Video

  • Proactively diagnose issues

  • Form real-time intelligence

  • Help deliver best possible video experience

Viewership

Prime Time


Video player analytics protocol
Video Player Analytics Protocol Video

  • Live and On Demand

  • JSON event objects

  • Key metrics

    • Bitrate

    • Frame rate

    • Fragments

    • Errors

We collect and use all data in accordance with best consumer

privacy practices and applicable laws




Flume data collection tier
Flume: Data collection Tier Video

  • Collect, aggregate and move large amounts of data

  • Distributed, scalable, reliable, customizable

  • Multi-tier architecture



Player sessions in real time
Player Sessions in Real-time Video

  • Sessions in Flume?

    • Technical issues: consistent hash and exactly-once semantics

    • Design goals

    • Separation of concerns

  • Session write-through rate?


Flume edge tier video player analytics end point
Flume Edge Tier: Video Player Analytics End Point Video

  • Analytics events over HTTPS

  • HTTP Source

  • Re-batch with inner sink and source


Flume mid tier processing and routing data
Flume Mid Tier: Processing and Routing Data Video

  • Video Player Event processing

    • Geo-location, asset metadata, validation, to-storm

  • Replication channel processor:

    • HDFS sink

    • Storm sink


Bridging flume to storm flume2storm connector
Bridging Flume to Storm: Flume2Storm Connector Video

  • Service discovery

  • Distributed, scalable and reliable

  • Low latency



Requirements for read writes from storm bolts
Requirements for Read/Writes from Storm Bolts Video

  • Functionality beyond key/value stores

  • Real-time and historic window queries

  • Speed of in-memory writes and durability of disk


Utilizing memsql for persistence
Utilizing VideoMemSQL for Persistence

  • Distributed in-memory SQL database

  • ACID, highly available, fault tolerant

  • Aggregators route queries to leaves

  • Leaves are auto-sharded

  • Solves our intense

    read/writes



Achievements in utilizing memsql
Achievements In Utilizing VideoMemSQL

  • Complex queries in milliseconds

  • Fault-tolerant Storm bolt state

  • Joins now available outside of Storm bolts

    • Foreign key shards

  • Complex data streams

    • Dynamic alters without locks or down time

    • JSON type


Wrapping up
Wrapping Up Video

  • Real-time at Comcast scale

    • Millions of video players

    • Horizontal scale everywhere

    • Aggregated metrics across US and complex analysis

    • Real-time API

  • Builds foundation

    • Advanced real-time analytics

    • Better platform for innovation

      • Alerts on complex objects

      • Supplemental real-time data back to clients

      • Popularity-based CDN


Thank VideoYou

[email protected]

[email protected]


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