Some popular iterations of an on-demand video streaming service today are: YouTube Netflix Vimeo TikTok Requirements The user can upload video files The user can stream video content The user can search for videos based on the video title Data storage Database schema The primary entities are the videos, the users, and the comments tables The relationship between the users and the videos is 1-to-many The relationship between the users and the comments table is 1-to-many The relationship between the videos and the comments table is 1-to-many Type of data stored The wide-column data store ( tree-based) such as is used to persist thumbnail images for clumping the files together, fault-tolerance, and replication LSM Apache HBase A cache server such as is used to store the metadata of popular video content Redis Message queue such as is used for the asynchronous processing (encoding) of videos Apache Kafka A relational database such as MySQL stores the metadata of the users and the videos The video files are stored in a managed such as AWS object storage S3 Lucene-based inverted-index data store such as Apache Solr is used to persist the video index data to provide search functionality High-level design Popular video content is streamed from CDN Video encoding ( ) is the process of converting a video format to other formats (MPEG, HLS) to provide the best stream possible on multiple devices and bandwidth transcoding A message queue can be configured between services for parallelism and improved fault tolerance Codecs (H.264, VP9, HEVC) are compression and decompression algorithms used to reduce video file size while preserving video quality The popular video streaming protocols (data transfer standard) are (Moving Pictures Experts Group — Dynamic Adaptive Streaming over HTTP), (HTTP Live Streaming), , and (HTTP Dynamic Streaming) MPEG-DASH Apple HLS Microsoft Smooth Streaming Adobe HDS Video upload workflow The user ( ) executes a DNS query to identify the server client The client makes an HTTP connection to the load balancer The video upload requests are rate limited to prevent malicious clients The load balancer delegates the client’s request to an API server ( ) with free capacity webserver The web server delegates the client’s request to an app server that handles the API endpoint The ID of the uploaded video is stored on the message queue for asynchronous processing of the video file The title and description ( ) of the video are stored in the metadata database metadata The app server queries the object store service to generate a pre-signed URL for storing the raw video file The client uploads the raw video file directly to the object store using the pre-signed URL to save the system network bandwidth The transcoding servers query the message queue using the publish-subscribe pattern to get notified on uploaded videos The transcoding server fetches the raw video file by querying the raw object store The transcoding server transcodes the raw video file into multiple codecs and stores the transcoded content on the transcoded object store The thumbnail server generates on average five thumbnail images for each video file and stores the generated images on the thumbnail store The transcoding server persists the ID of the transcoded video on the message queue for further processing The upload handler service queries the message queue through the publish-subscribe pattern to get notified on transcoded video files The upload handler service updates the metadata database with metadata of transcoded video files The upload handler service queries the notification service to notify the client of the video processing status The database can be partitioned through (key = user ID or video ID) consistent hashing or algorithms can be used to detect the duplicate video content Block matching Phase correlation The web server (API server) must be kept stateless for scaling out through replication The video file is stored in multiple resolutions and formats in order to support multiple devices and bandwidth The video can be split into smaller chunks by the client before upload to support the resume of broken uploads Watermarking and encryption can be used to protect video content The data centers are added to improve latency and data recovery at the expense of increased maintenance workflows Dead letter queue can be used to improve fault tolerance and error handling Chaos engineering is used to identify the failures on networks, servers, and applications Load testing and chaos engineering are used to improve fault tolerance configuration improves the hardware throughput RAID The data store is partitioned to spread the writes and reads at the expense of difficult joins, transactions, and fat client Federation and sharding are used to scale out the database The write requests are redirected to the leader and the read requests are redirected to the followers of the database is a storage middleware for scaling out MySQL Vitess Vitess redirects the read requests that require fresh data to the leader (For example, update user profile operation) Vitess uses a lock server (Apache Zookeeper) for automatic sharding and leader election on the database layer Vitess supports RPC-based joins, indexing, and transactions on SQL database Vitess allows to offload of partitioning logic from the application and improves database queries by caching Video streaming workflow The client executes a DNS query to identify the server The client makes an HTTP connection on the load balancer The CDN is queried to verify if the requested video content is on the CDN cache The CDN queries the transcoded object stored on a cache miss The load balancer delegates the client’s request to a web server with free capacity using the weighted round-robin algorithm The web server delegates the client’s request to an app server using consistent hashing The app server queries the metadata cache to fetch the metadata of the video The app server queries the metadata database on a cache miss The app server queries the thumbnail store to fetch the relevant thumbnail images of the video The app server queries the transcoded object store to fetch the video content The app server delegates the search queries of the client to the inverted index store The read and write traffic are segregated for high throughput Popular video content is streamed from CDN (in memory) The push CDN model can be used for caching videos uploaded by users with a significant number of subscribers The moderately streamed video content can be served from the video server directly (disk IO) Consistent hashing can be used to load balance cache servers Caching can be implemented on multiple levels to improve latency LRU cache eviction policy can be used Entropy or jitter is implemented on cache expiration to prevent the thundering herd failure The video files are distributed to the data centers closer to the client when the client starts streaming The traffic should be prioritized between the video streaming cluster (higher priority) and the general cluster to improve reliability The videos can be recommended to the client based on geography, watch history (KNN algorithm), and results A/B testing The video file is split into chunks for streaming and improved fault tolerance The chunks of a video file are joined together when the client starts streaming The video chunks allow adaptive streaming by switching to lower-quality chunks if the high-quality chunks are slower to download Different streaming protocols support different video encodings and playback players The video content is streamed on TCP through buffering The video offset is stored on the server to resume playback from any client device The video resolution on playback depends on the client's device The video file should be encoded into compatible bitrates (quality) and formats for smoother streaming and compatibility The counter for likes on a video can be non-accurate to improve performance (transactions executed at sampled intervals) The comments on video content are shown to the comment owner by data fetching from the leader (database) while other users can fetch from followers with a slight delay The services can be prescaled for extremely popular channels as autoscaling might not meet the requirements of concurrent clients The autoscaling requirements can be predicted by performing machine learning on traffic patterns The autoscaling service should keep a time buffer due to the anticipated delays in the provisioning of resources Fully baked container images (no additional script execution required after provisioning) improve the startup time of services The infrastructure can be prewarmed before the peak hours using benchmark data through load testing The emergency mode should shut down non-critical services to free up resources, and skip the execution of failed services for improved reliability References Cuong Do, , Google Talks YouTube Scalability , vitess.io Vitess Architecture Also published . here Featured image . source

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