Netflix is one of the largest on-demand video streaming platforms in the world, with over 220 million subscribers globally as of 2022. With such a massive userbase, Netflix generates and processes enormous amounts of data on a daily basis, including data on user behavior, recommendations, payments, content libraries and more. To support this vast data infrastructure, Netflix relies on specialized database technologies designed for massive scale and performance.
Cassandra
One of the key databases powering various parts of Netflix’s architecture is Apache Cassandra. Cassandra is a distributed NoSQL wide column store database designed to handle large amounts of structured data across many commodity servers without a single point of failure. It offers high availability, scalability and fault tolerance making it a popular choice for large-scale web applications like Netflix.
Some of the key use cases where Netflix uses Cassandra include:
- Personalization and recommendations – Stores and analyzes large amounts of user behavior and viewing history data to provide personalized recommendations for each user
- Metadata storage – Stores metadata information about Netflix’s vast catalog of titles such as movie/TV show details, cast and crew information, descriptions, subtitles, etc.
- Content lookup and discovery – Stores content hierarchy and relationships to enable quick lookup and discovery by users
- Playback metrics – Stores metrics on video starts, stops, pauses, abandoned viewings, completeness etc. to optimize streaming performance
The main reasons Cassandra is well-suited for these types of use cases include:
- Scalability across multiple data centers – Cassandra’s distributed architecture allows Netflix to scale out capacity simply by adding nodes without much downtime.
- High write throughput – Writes are spread across multiple nodes enabling high write throughput for heavy workloads like incoming streaming metrics or user activity data.
- Fast reads via caching – Netflix uses memcache to cache Cassandra data closer to the applications for faster reads.
- Tunable consistency – Cassandra enables Netflix to tune consistency levels according to their needs and optimize for performance.
Amazon DynamoDB
In addition to Cassandra, Netflix leverages Amazon DynamoDB for certain types of workloads. DynamoDB is a fully managed NoSQL database service provided by Amazon Web Services (AWS).
Some examples of how Netflix uses DynamoDB include:
- Storing session information such as bookmarks and resume points for videos the user has watched.
- Storing user account information such as profiles, preferences and settings.
- Storing metadata about content delivery networks (CDNs) eg. endpoint data.
- Leaderboards for games.
DynamoDB provides Netflix with the following advantages:
- Fully managed service – As a fully managed cloud database, DynamoDB simplifies operations for Netflix.
- High performance – DynamoDB provides predictable low-latency performance at any scale.
- Scalability – Netflix can scale capacity on demand without downtime.
- Durability – Data is replicated across multiple AWS availability zones providing high durability.
- Integration – Easy integration with other AWS services used by Netflix like S3, Redshift, EMR, etc.
MySQL
MySQL is a popular open-source relational database that has served as a key part of the Netflix cloud architecture in the past. Specifically, Netflix used MySQL for their critical mid-tier content and metadata related services.
Netflix was able to scale MySQL to massive workloads on AWS by heavily optimizing the configuration. Some optimizations included:
- Using SSD-based instances for faster I/O performance
- Tuning MySQL settings for efficiency eg. raising table cache sizes
- Tweaking Linux OS settings for better network performance
- Using connection pooling to support high connection concurrency
- Enabling MySQL query cache to improve read performance
- Master-slave replication for scaling reads
However, over time as their needs grew, Netflix migrated most of their large-scale MySQL workloads to Cassandra and DynamoDB. The main challenges with scaling MySQL further were:
- Single-server bottlenecks
- Scaling writes across servers was difficult
- Failures could not be handled gracefully
- Higher operational overhead for scaling and replication
But MySQL continues to be used at Netflix for smaller operational workloads where relational model makes sense, such as metadata about internal workflows, billing data, A/B testing data, etc.
Elasticsearch
Elasticsearch is a popular open-source search and analytics engine that Netflix uses to power search, relevance ranking and analytics for their massive content catalog.
Key use cases include:
- Search – Enables fast and relevant search across movie/TV show titles, keywords, descriptions etc.
- Personalized relevance ranking – Ranks search results according to user preferences and history.
- Analytics – Analyzes activity data to derive insights eg. popularity trends.
Benefits of Elasticsearch for Netflix include:
- Fast and scalable full-text search
- Tunable relevance ranking algorithms
- Analyzes semi-structured data
- Near real-time search and analytics
- Supports high write volumes
By integrating Elasticsearch with Cassandra and their other databases, Netflix is able to provide rich personalized search and recommendations for their users.
Atlas
Atlas is Netflix’s own in-house median datastore built on top of Cassandra. It provides metadata storage and services for Netflix’s video assets during the production workflow.
Atlas stores complex metadata like:
- Video source assets like imagery, video formats
- Episode and season information
- geographic availability
- cousin and family relationships between assets
- Title and description information
Key capabilities provided by Atlas include:
- Unified metadata storage for global video assets
- Consistent IDs and metadata across regions
- Low latency reads and writes at high scale
- Cross region replication for high availability
- Fine grained access control
By building Atlas on top of Cassandra, Netflix was able to achieve massive scale, performance and availability required for their media asset pipeline worldwide while also adding capabilities like global number sequences, access controls etc.
Conclusion
To summarize, the main databases leveraged by Netflix to power their globally scaled video streaming platform include:
- Cassandra – For recommendations, metadata, analytics, etc.
- DynamoDB – For account information, session data, CDN metadata.
- MySQL – For smaller operational workloads.
- Elasticsearch – For search, relevance ranking and analytics.
- Atlas – Internal datastore for managing media assets metadata.
By combining the strengths of these specialized databases, Netflix is able to achieve the scale, performance, reliability and innovation required to serve their 220+ million subscribers globally. The company continues to evolve their data infrastructure, optimizing it based on changing needs while leveraging cloud platforms like AWS.
