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  Designing a distributed log analytics system involves several key components and considerations to ensure it can handle large volumes of log data efficiently and reliably. Here’s a high-level overview of the design: 1.  Requirements Gathering Functional Requirements : Log Collection : Collect logs from various sources. Log Storage : Store logs in a distributed and scalable manner. Log Processing : Process logs for real-time analytics. Querying and Visualization : Provide tools for querying and visualizing log data. Non-Functional Requirements : Scalability : Handle increasing volumes of log data. Reliability : Ensure data is not lost and system is fault-tolerant. Performance : Low latency for log ingestion and querying. Security : Secure log data and access. 2.  Architecture Components Log Producers : Applications, services, and systems generating logs. Log Collectors : Agents or services that collect logs from producers (e.g., Fluentd, Logstash). Message Queue : A distributed queue

  Designing a distributed cache system to handle 1 billion queries per minute for both read and write operations is a complex task. Here’s a high-level overview of how you might approach this: 1.   Requirements Gathering Functional Requirements : Read Data : Quickly retrieve data from the cache. Write Data : Store data in the cache. Eviction Policy : Automatically evict least recently/frequently used items. Replication : Replicate data across multiple nodes for fault tolerance. Consistency : Ensure data consistency across nodes. Node Management : Add and remove cache nodes dynamically. Non-Functional Requirements : Performance : Low latency for read and write operations. Scalability : System should scale horizontally by adding more nodes. Reliability : Ensure high availability and fault tolerance. Durability : Persist data if required. Security : Secure access to the cache system. 2.   Capacity Estimation Traffic Estimate : Read Traffic : Estimate the number of read requests per second

 WebSockets and Server-Sent Events (SSE) are both technologies used for real-time communication between a server and a client, but they have some key differences: WebSockets Full Duplex Communication : WebSockets provide full-duplex communication, meaning data can be sent and received simultaneously between the client and server. Two-Way Communication : Both the client and server can initiate messages. This makes WebSockets suitable for applications where real-time updates are needed from both sides, such as chat applications, online gaming, or collaborative tools. Protocol : WebSockets establish a single long-lived connection over TCP. They start as an HTTP handshake, then switch to the WebSocket protocol. Binary and Text Data : WebSockets can send binary and text data, making them versatile for various applications. Use Cases : Ideal for real-time applications where both the client and server need to send messages independently, such as chat applications, live gaming, financial ticke

 Designing a real-time system for a stock market and trading application involves several critical components to ensure low latency, high availability, and security. Here's a structured approach to designing such a system: 1. Requirements Gathering Functional Requirements: Real-time stock price updates Trade execution Portfolio management User authentication and authorization Historical data access Notification and alert system Non-functional Requirements: Low latency High availability and scalability Data security Fault tolerance Compliance with regulatory requirements 2. System Architecture Frontend: Web and Mobile Apps: Use frameworks like React for web and React Native for mobile to ensure a responsive and dynamic user interface. Real-time Data Display: WebSockets or Server-Sent Events (SSE) for real-time updates. Backend: API Gateway: Central point for managing API requests. Tools like Kong or Amazon API Gateway. Microservices Architecture: Different services for user mana

To calculate the required network bandwidth, you need to consider the estimated traffic volume and data transfer sizes. Here's a step-by-step guide to help you estimate the bandwidth requirements: 1. Identify the Traffic Volume Determine the number of users or devices that will be using the network and how frequently they will be sending or receiving data. 2. Determine Data Transfer Sizes Estimate the size of the data each user or device will transfer during each session or over a specific period (e.g., per second, minute, or hour). 3. Calculate Total Data Transfer Multiply the number of users or devices by the data transfer size to get the total data transferred over the period. Total Data Transfer=Number of Users/Devices×Data Transfer Size 4. Convert Data Transfer to Bits Convert the data transfer size from bytes to bits (since bandwidth is usually measured in bits per second). Bits=Bytes×8 5. Determine the Period Decide the time period over which the data