How Twitter processes 4 billion events in real.pdf

How Twitter processes 4 billion events in real.pdf

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  How Twitter processes4 billion events in real-time daily Twitter handles an enormous volume of data daily, with billions of events — like tweets, retweets, likes, and follows — flowing through its system. Here’s a high-level overview of how Twitter processes this massive amount of real-time data: Data Ingestion and Collection: • Event Streams: Twitter collects events from its users through APIs, mobile apps, and web clients. These events include tweets, retweets, likes, follows, and more. • Stream Processing: To handle real-time data, Twitter uses stream processing frameworks. These frameworks ingest events as they occur and process them immediately. Data Storage and Management: • Sharding: Twitter employs sharding to distribute data across multiple servers. Each shard handles a portion of the data, which allows for horizontal scaling and efficient data management.
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  • Distributed Databases:Twitter uses distributed databases and data stores like MySQL, Manhattan (Twitter’s distributed database), and others to manage and store data across its infrastructure. Real-time Processing and Analytics: • Stream Processing Systems: Technologies like Apache Kafka and Apache Samza are used for real-time stream processing. These systems process incoming data streams, perform necessary computations, and ensure that data is available for further analysis and storage. • Real-time Analytics: Twitter uses real-time analytics tools to monitor trends, user behavior, and other metrics. This helps in delivering timely content to users and providing insights for various features and improvements. Indexing and Search: • Indexing Engines: Twitter uses indexing engines like Elasticsearch to make tweets and other content searchable in real- time. This allows users to find relevant content quickly and efficiently.
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  • Caching: Toreduce latency, Twitter employs caching mechanisms that store frequently accessed data in memory, which speeds up access times and reduces load on the databases. Scalability and Load Balancing: • Load Balancers: To handle high traffic, Twitter uses load balancers to distribute incoming requests across multiple servers. This ensures that no single server becomes a bottleneck. • Auto-scaling: The infrastructure is designed to auto-scale based on demand. This means that more resources are allocated dynamically when there is a surge in activity. Fault Tolerance and Reliability: • Redundancy: Twitter’s architecture includes redundancy at multiple levels (servers, data centers, etc.) to ensure that failures do not disrupt service. • Failover Mechanisms: Automated failover mechanisms ensure that if a server or component fails, another one can take over seamlessly. Machine Learning and Personalization:
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  • Recommendation Systems:Twitter uses machine learning algorithms to personalize the user experience, such as suggesting tweets or accounts to follow based on user behavior and preferences. • Content Moderation: Machine learning models are also used for detecting and moderating inappropriate content, spam, and other issues By leveraging a combination of advanced technologies and architectural strategies, Twitter can process and manage billions of events in real-time, ensuring a responsive and scalable platform for its users. Visit website for more details ; https://adequateinfosoft.com/