<ol><li>A viewer likes a video and sends an HTTP request to the Likes backend, which stores it in a database.</li><li>The backend forwards the like to any random Dispatcher node using an HTTP request.</li><li>Dispatcher looks up in the key-value store to find out which Frontend nodes are subscribed to likes from that video.</li><li>Dispatcher sends the like to the corresponding Frontend nodes.</li><li>Frontend nodes send the like to client devices which are subscribed to that video.</li></ol>

Final architecture

<ul><li>Each connection is managed by an Akka actor.</li><li>Actors are so lightweight that there can be millions of them on a single system. Moreover, all of them can be served by a small number of threads, proportional to the number of cores. This is possible because a thread is assigned to an actor only when it has work to do.</li><li>Actors are managed by an Akka supervisor actor that sends them events (likes, comments etc.) which need to be forwarded to user devices.</li></ul>Akka is a toolkit for building highly concurrent, distributed, and resilient message-driven apps.

Challenge 2: Connection Management

<ul><li>Clients are subscribing to events for a particular live video i.e. they are telling the server which live video they are watching.</li><li>The Frontend server stores all subscriptions in an in-memory table.</li><li>Every time a new event is published, the supervisor actor does a lookup in the in-memory table to determine which actors need to receive this event.</li></ul>

Challenge 3: Multiple Live Videos

LinkedIn has built the Realtime Platform to distribute multiple types of data in real-time such as:<ul><li>Likes, comments and viewer count for Live Videos</li><li>Typing indicators and Read receipts for Instant Messaging</li><li>Presence i.e. the green online indicators</li></ul>Their goal is to increase user engagement by enabling dynamic instant experiences between users, such as: likes, comments, polls, discussions etc.

The Realtime Platform

Having a lot of people interact on live videos poses many technical challenges. Mainly because viewers generate a lot of interactions that need to be delivered fast.To get a sense of the scale, the top live streams in the world gathered millions of concurrent users:<ol><li>Cricket World Cup Semifinal 2019 - 25M concurrent viewers</li><li>British Royal Wedding 2018 - 18M concurrent viewers</li></ol>

Interactive Live Videos

<ul><li>Each Frontend node handles 100k persistent connections. It handles only this many connections because the server is doing a lot of work processing multiple types of data (likes, comments, instant messaging etc.).</li><li>Each Dispatcher can publish 5k events per second to the Frontend nodes.</li><li>End-to-end Publish Latency is 75ms at p90, from the moment the Like is received until the Like is sent to a viewer. There are only two lookups in the subscriptions tables and a series of network calls.</li><li>The system is completely horizontally scalable. You can add more Dispatchers and Frontend nodes to handle more viewers and more events.</li></ul>

Performance and scale

<ul><li>Scale horizontally to handle more concurrent viewers -> Add multiple Frontend nodes and coordinate them using a Dispatcher node.</li><li>In a similar fashion to the Frontend node, the Dispatcher has a subscriptions table to know which frontend nodes should receive which events.</li><li>This table is populated when Frontend nodes send subscription requests to tell the Dispatcher which live videos they're interested in (i.e. which live videos its connections are subscribed to).</li></ul>

Challenge 4: 10K Concurrent Viewers

<ul><li>How LinkedIn displays Presence indicators in real-time: https://engineering.linkedin.com/blog/2018/01/now-you-see-me--now-you-dont--linkedins-real-time-presence-platf</li><li>How LinkedIn measures end-to-end latency across systems: https://engineering.linkedin.com/blog/2018/04/samza-aeon--latency-insights-for-asynchronous-one-way-flows</li><li>How LinkedIn scaled one server to handle hundreds of thousands of persistent connections: https://engineering.linkedin.com/blog/2016/10/instant-messaging-at-linkedin--scaling-to-hundreds-of-thousands-</li></ul>

Challenge 1: The Delivery Pipe

<ul><li>Scale horizontally again to handle more events -> Add multiple Dispatchers and move the subscription table into a key-value store so it's accessible to all Dispatchers.</li><li>Dispatchers are independent from Frontend nodes and don't have persistent connections between them.</li><li>Any Frontend node can subscribe to any Dispatcher.</li><li>Any Dispatcher can publish events to any Frontend node.</li></ul>

Challenge 5: 100 Likes/second

Expanding to other regions requires two steps:<ol><li>Replicate the setup in each data-center.</li><li>Have the Dispatcher broadcast the events to its peer Dispatchers from the other data-centers.</li></ol>

Bonus Challenge: Multiple data-centers

Video with transcript included: https://bit.ly/3nwZkBt Akhilesh Gupta does a technical deep-dive into how LinkedIn uses the Play/Akka Framework and a scalable distributed system to enable live interactions like likes/comments at massive scale at extremely low costs across multiple data centers. This presentation was recorded at QCon London 2020: http://bit.ly/2VfRldq #LinkedIn #DistributedSystems #Scalability

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Video with transcript included: https://bit.ly/3nwZkBt             

Akhilesh Gupta does a technical deep-dive into how LinkedIn uses the Play/Akka Framework and a scalable distributed system to enable live interactions like likes/comments at massive scale at extremely low costs across multiple data centers.

This presentation was recorded at QCon London 2020: http://bit.ly/2VfRldq 

#LinkedIn #DistributedSystems #Scalability

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Challenge 4: 10K Concurrent Viewers

Similar ideas to Challenge 4: 10K Concurrent Viewers

Challenge 5: 100 Likes/second

Performance and scale

Challenge 3: Multiple Live Videos