Building Blocks Of System Design

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Domain Name System (DNS)

• DNS (Domain Name System) is a hierarchical system that translates human-readable domain names (like www.example.com) into IP addresses (like 192.0.2.1).
• It functions like a phonebook of the internet, making it easier for users to access websites without remembering complex IP addresses.
• DNS servers store records of domain names and their corresponding IP addresses, ensuring efficient and accurate routing of internet traffic.
• DNS resolution involves several steps, including contacting root, top-level domain (TLD), and authoritative name servers to retrieve the correct IP address for a domain.

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Load Balancer

• A load balancer distributes incoming network traffic evenly across multiple servers.
• It ensures optimal resource utilization and prevents overloading any single server.
• Enhances system reliability by automatically routing traffic away from failed or unhealthy servers.
• Improves application performance by reducing downtime and improving response times.
• Can work at different layers (Layer 4 or Layer 7) based on traffic types, like TCP or HTTP.

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Database

• Data Management: Involves storing, retrieving, modifying, and deleting data as part of various data-processing tasks.
• Types of Databases: Includes relational databases (e.g., SQL databases) and non-relational databases (e.g., NoSQL databases).
• Database Replication: Ensures data redundancy and availability by copying data across multiple database instances.
• Partitioning: Distributes data across multiple servers or locations to optimize performance and scalability.
• Distributed Databases: Analyzes data across multiple sites or nodes, allowing for improved access and performance.

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Key-Value Store

A key-value store is a type of non-relational database that uses a simple key-value pair structure for storing data. Here are some key points:

• Simplicity: Key-value stores offer a straightforward way to store and retrieve data, making them easy to use.
• Scalability: They are designed to scale horizontally, allowing for the handling of large volumes of data across multiple servers.
• Performance: Key-value stores provide fast data access due to their minimalistic design.
• Flexibility: They support various data types and can be easily configured to meet specific application needs.

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Content Delivery Network (CDN)

• A CDN is a distributed network of servers that delivers content to users based on their geographic location.
• It reduces latency by caching content closer to the end-user, improving load times for websites and applications.
• CDNs enhance the reliability and availability of content by distributing the load across multiple servers.
• They help mitigate traffic spikes and DDoS attacks by distributing the network load.
• CDNs are essential for optimizing performance in media delivery, such as video streaming and gaming.

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Cloud services:

Sequencer

• Purpose: A sequencer is designed to generate unique identifiers (IDs) while ensuring the preservation of causality in distributed systems.
• Techniques: It can utilize various methods for ID generation, including UUIDs (Universally Unique Identifiers) and Snowflake ID generators, which were popularized by Twitter.
• Scalability: The system must be scalable to handle a high volume of requests without collisions in ID generation.
• Performance: The ID generation process should be efficient to minimize latency in applications requiring rapid ID creation.
• Causality Maintenance: Ensuring that the order of ID generation reflects the order of events in the system is crucial for maintaining consistency.

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Cloud services:

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Service Monitoring Overview

• Importance: Monitoring systems are essential for analyzing the performance of distributed systems.
• Alerting: They provide alerts to stakeholders in case of issues, enabling timely responses.
• Components: Both server-side and client-side monitoring systems are utilized.
• Tools: Popular tools include Amazon CloudWatch, Prometheus, and Grafana for effective monitoring.

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Distributed Caching

• Definition: Distributed caching involves multiple cache servers that work together to store and retrieve frequently accessed data, improving performance and reducing latency.
• Benefits: Enhances application speed, reduces database load, and provides a scalable solution to manage large volumes of data access.
• Coordination: Cache servers coordinate to ensure consistency and reliability of cached data across different nodes.
• Tools: Popular tools for implementing distributed caching include in-memory data stores like Redis and Memcached.

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Distributed Messaging Queue

• A distributed messaging queue allows communication between producers and consumers across multiple servers.
• It enhances system scalability by enabling independent scaling of components.
• Reliability is improved as messages can be stored and processed even if one part of the system is down.
• Common use cases include event-driven architectures, microservices, and data streaming.

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Publish-Subscribe System

• The publish-subscribe (pub-sub) system is an asynchronous messaging pattern.
• It allows multiple producers (publishers) to send messages to multiple consumers (subscribers).
• Pub-sub systems decouple message senders from receivers, enhancing scalability.
• Commonly used in serverless and microservices architectures to enable event-driven communication.

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Rate Limiter Overview

• Definition: A Rate Limiter controls the number of requests a user can make to a service within a specified time frame.
• Purpose: It protects services from being overwhelmed by excessive requests, ensuring fair usage and availability.
• Implementation: Can be integrated into APIs using gateways that provide built-in rate-limiting features.
• Benefits: Enhances security, prevents abuse, and improves service performance by managing traffic effectively.

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Distributed Search

• Definition: A system designed to process user queries and return relevant content quickly.
• Key Components: Includes crawling, indexing, and searching to ensure efficient data retrieval.
• Scalability: Supports handling large volumes of data across multiple servers, enhancing performance.
• Real-time Results: Provides timely responses to user queries, improving user experience.
• Common Tools: Elasticsearch and Amazon OpenSearch are widely used for building distributed search systems.

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Distributed Logging

• Definition: Distributed logging involves collecting logs from multiple services or applications running in different environments.
• Importance: Efficient logging is crucial in distributed systems to monitor application performance and troubleshoot issues.
• Challenges: High I/O operations can lead to bottlenecks; thus, optimizing log collection and storage is essential.
• Solutions: Cloud-based logging tools streamline the logging process, ensuring scalability and reliability.
• Usage: Logs can be aggregated, analyzed, and visualized for better insights into system behavior.

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Distributed Tracing

• Definition: Distributed tracing is a method used to monitor and track the flow of requests through various services in a microservices architecture.
• Purpose: It helps developers identify performance bottlenecks, latency issues, and failures within complex distributed systems.
• Granularity: Provides detailed insights into how long requests take at each service level, enabling effective troubleshooting and optimization.
• Visualization: Generates trace visualizations to understand the path of requests and the interactions between different services.

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Distributed Task Scheduling

• Distributed task scheduling involves managing and allocating resources to tasks across multiple nodes or systems.
• The scheduler ensures that both task-level objectives (e.g., timely completion) and system-level goals (e.g., load balancing) are achieved.
• It is essential for automating workflows, optimizing performance, and managing dependencies between tasks.
• Common in cloud environments, it allows tasks to be distributed across different regions, improving scalability and fault tolerance.
• Used for large-scale applications with complex workflows and data pipelines.

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Blob Store

• Definition: A storage solution specifically designed for unstructured data, including multimedia files, images, videos, and binary executables.
• Characteristics: Blob storage is optimized for storing large amounts of data and allows for efficient access and retrieval.
• Accessibility: Typically accessed via REST APIs, making it suitable for web and mobile applications.
• Scalability: Can scale to accommodate vast amounts of data without the need for extensive infrastructure management.
• Use Cases: Commonly used for backup and disaster recovery, big data analytics, and content distribution.

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