Behind the Scenes at Netflix: Distributed Systems & NoSQL Architecture.

Ever wondered how Netflix streams seamlessly to millions of users worldwide? This presentation, “Behind the Scenes at Netflix: Distributed Systems & NoSQL Architecture” by Dr. Ayesha Butalia, explores the cutting-edge technologies and architectural strategies that power one of the world’s largest streaming platforms.

From distributed data storage to fault tolerance and scalability, discover how Netflix leverages NoSQL databases and distributed system design to ensure high availability, low latency, and exceptional user experience.

Key highlights include:

Core principles of distributed systems in large-scale environments

Netflix’s approach to data replication, consistency, and fault tolerance

Real-world use of NoSQL databases for scalability and flexibility

Insights into microservices, caching, and global content delivery

Lessons and best practices for modern cloud-native applications

Perfect for students, researchers, and professionals, this case study blends theory with practical insights from Netflix’s engineering excellence.

1. Distributed Data Storage and Retrieval Welcome to this presentation on Distributed Data Storage and Retrieval, a foundational topic in modern distributed systems. We'll explore how data is managed across vast networks to ensure reliability and performance.

2. Distributed Systems Distributed Data Storage and Retrieval This presentation is part of the Distributed Systems curriculum. Course Coordinator: Dr. Ayesha Butalia

3. Presentation Agenda 1 Introduction to Distributed Storage Understanding the core concepts and benefits. 2 Data Replication & Consistency Ensuring data integrity and availability. 3 Distributed Hash Tables (DHTs) Decentralized key-value storage architectures. 4 Distributed File Systems Managing files across interconnected servers. 5 NoSQL Databases Exploring diverse data models for scalability.

4. Introduction to Distributed Data Storage Distributed data storage involves storing data across multiple independent computing nodes. This fundamental approach underpins the reliability, scalability, and performance of modern applications. Key Benefits: High Availability: Data remains accessible even if some nodes fail. Fault Tolerance: System continues to operate despite component failures. Scalability: Easily expand storage capacity and processing power by adding more nodes.

5. Replication in Distributed Systems Data replication is the process of creating and maintaining multiple copies of data across different nodes. It's crucial for enhancing system performance, availability, and fault tolerance. Primary-Backup One primary node handles all writes; backups receive updates. Simpler consistency. Multi-Primary (Active-Active) Multiple nodes can handle writes simultaneously. Complex conflict resolution. Passive/Active Replication Primary processes requests, active replicas are ready to take over quickly. Despite its benefits, replication introduces challenges in managing update propagation and ensuring data consistency across all replicas.

6. Consistency Models Consistency models define the rules for visibility of updates and data state across distributed nodes. The choice of model impacts data integrity, performance, and availability. Strong Consistency All reads return the most recent write, ensuring all replicas are identical at any given moment. This typically involves coordination overhead. Eventual Consistency Reads may return stale data, but eventually all replicas will converge to the same state. Commonly used in highly available NoSQL databases. Causal Consistency Preserves the order of causally related operations. If A caused B, then B will be seen after A by all nodes. CAP Theorem: A fundamental theorem stating that a distributed system cannot simultaneously guarantee Consistency, Availability, and Partition Tolerance. Systems must choose two out of three.

7. Distributed Hash Tables (DHTs) DHTs are decentralized distributed systems that provide a lookup service similar to a hash table. They are designed for extreme scalability and fault tolerance by distributing ownership of data across all participating nodes. Decentralized: No central coordinator, all nodes are peers. Key-Value Storage: Maps keys to values (e.g., file hashes to file locations). Hashing Principle: Keys are hashed to determine which node stores the corresponding value. Examples: Chord Kademlia Pastry Applications: Peer-to-peer file sharing (BitTorrent), decentralized web (IPFS), and various decentralized storage networks.

8. Distributed File Systems Distributed File Systems (DFS) allow multiple users on multiple machines to share files and storage resources. They abstract the underlying physical storage, presenting a unified view of files to users. File Replication Copies of files are stored on different nodes to ensure availability and durability. Load Balancing Distributes client requests and data access across nodes to prevent bottlenecks. Fault Recovery Mechanisms to detect and recover from node failures, ensuring data integrity. Prominent Examples: Google File System (GFS), Hadoop Distributed File System (HDFS), Ceph, and GlusterFS, which are foundational for big data and cloud storage solutions.

9. NoSQL Databases Overview NoSQL databases offer flexible schemas and horizontal scalability, diverging from the traditional relational model. They are optimized for modern applications that handle massive volumes of unstructured or semi-structured data. Key-Value Stores Simple, high-performance databases for storing data as collections of key-value pairs (e.g., Redis, DynamoDB). Document Stores Store data in flexible, semi-structured documents (e.g., JSON, BSON), ideal for evolving data models (e.g., MongoDB). Column-Family Stores Organize data into column families, optimized for fast writes and analytical queries (e.g., Cassandra, HBase). Graph Databases Designed to store and query highly interconnected data, perfect for relationships and network analysis (e.g., Neo4j).

10. Applications of NoSQL Databases NoSQL databases excel in scenarios demanding high throughput, low latency, and flexible data models. Their diverse structures cater to specific application requirements. Real-time Analytics: Cassandra's high write throughput makes it suitable for IoT sensor data and real-time dashboards. E-commerce Inventory: MongoDB's flexible document model handles varying product attributes and rapid catalog changes. Session Storage & Caching: Redis provides fast in-memory key-value storage for user sessions and frequently accessed data. Social Networks: Neo4j efficiently manages complex friend graphs and relationship queries for social media platforms.