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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

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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



  • Comcast VIPER Overview

  • Architecture Overview

  • Q & A

Comcast video ip engineering and research viper

Comcast Video IP Engineering and Research (VIPER)

Preparation DeliveryVideo Players







Video Players

Xbox Live




Why do we focus on real time

Why Do We Focus on Real-time?

  • Proactively diagnose issues

  • Form real-time intelligence

  • Help deliver best possible video experience


Prime Time

Video player analytics protocol

Video Player Analytics Protocol

  • 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

Player sessions key in understanding video experience

Player Sessions: Key In Understanding Video Experience

High level architecture and data flow

High Level Architecture And Data Flow

Flume data collection tier

Flume: Data collection Tier

  • Collect, aggregate and move large amounts of data

  • Distributed, scalable, reliable, customizable

  • Multi-tier architecture

Storm stream processing tier

Storm: Stream Processing Tier

Player sessions in real time

Player Sessions in Real-time

  • 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

  • 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 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

  • Service discovery

  • Distributed, scalable and reliable

  • Low latency

Simplified video player storm topology

Simplified Video Player Storm Topology

Requirements for read writes from storm bolts

Requirements for Read/Writes from Storm Bolts

  • 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 MemSQL for Persistence

  • Distributed in-memory SQL database

  • ACID, highly available, fault tolerant

  • Aggregators route queries to leaves

  • Leaves are auto-sharded

  • Solves our intense


Isolated analysts and ingest aggregators

Isolated Analysts and Ingest Aggregators

Achievements in utilizing memsql

Achievements In Utilizing MemSQL

  • 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

  • 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

Real time stream processing architecture for comcast ip video

Thank You

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