How Netflix Handles Billions of Time-Series Events with a Powerful Data Layer

As Netflix continues to expand into various sectors like Video on Demand and Gaming, the company faces the challenge of efficiently managing vast amounts of temporal data. To address this, Netflix has developed the TimeSeries Data Abstraction Layer, a versatile and scalable solution designed to store and query large volumes of temporal event data with low millisecond latencies.

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Challenges in Managing Temporal Data

Netflix continuously generates and utilizes temporal data from various sources, including user interactions like video-play events, asset impressions, and complex micro-service network activities. Effectively managing this data at scale is crucial for ensuring optimal user experiences and system reliability. However, storing and querying such data presents unique challenges:

• High Throughput: Managing up to 10 million writes per second while maintaining high availability.
• Efficient Querying in Large Datasets: Storing petabytes of data while ensuring primary key reads return results within low double-digit milliseconds, and supporting searches and aggregations across multiple secondary attributes.
• Global Reads and Writes: Facilitating read and write operations from anywhere in the world with adjustable consistency models.
• Tunable Configuration: Offering the ability to partition datasets in either a single-tenant or multi-tenant datastore, with options to adjust various dataset aspects such as retention and consistency.
• Handling Bursty Traffic: Managing significant traffic spikes during high-demand events, such as new content launches or regional failovers.
• Cost Efficiency: Reducing the cost per byte and per operation to optimize long-term retention while minimizing infrastructure expenses, which can amount to millions of dollars for Netflix.

Design Principles of the TimeSeries Abstraction

The TimeSeries Abstraction was developed to meet these requirements, built around the following core design principles:

• Partitioned Data: Data is partitioned using a unique temporal partitioning strategy combined with an event bucketing approach to efficiently manage bursty workloads and streamline queries.
• Flexible Storage: The service is designed to integrate with various storage backends, including Apache Cassandra and Elasticsearch, allowing Netflix to customize storage solutions based on specific use case requirements.
• Configurability: TimeSeries offers a range of tunable options for each dataset, providing the flexibility needed to accommodate a wide array of use cases.
• Scalability: The architecture supports both horizontal and vertical scaling, enabling the system to handle increasing throughput and data volumes as Netflix expands its user base and services.
• Sharded Infrastructure: Leveraging the Data Gateway Platform, Netflix can deploy single-tenant and/or multi-tenant infrastructure with the necessary access and traffic isolation.

Real-World Applications

The TimeSeries Abstraction has been instrumental in enhancing Netflix's platform capabilities, particularly in managing temporal data at scale. By efficiently storing and querying large volumes of event data, Netflix can extract valuable insights to improve user experiences and maintain system reliability.

In summary, Netflix's TimeSeries Data Abstraction Layer represents a significant advancement in the company's data architecture, providing a robust solution for the challenges associated with managing large-scale temporal data.