DevOps(Document Repository)

Popular Redis Use Cases 1. Caching The most common use case is to utilize Redis for caching. This helps protect the database layer from overloading. Redis offers fast lookup for cached data and can help improve application performance. 2. Session Store We use Redis to share user session data among stateless servers. Redis provides a centralized place to store session dat and makes it easy to scale out servers. 3. Distributed lock We use Redis distributed locks to grant mutually exclusive access to shared resources. This prevents race conditions in distributed systems. Redis locks are easy to implement and automatically expire. 4. Counter and Rate Limiter We use Redis to track like counts, view counts etc on social media apps. Redis counters provide atomic increments/ decrements. We also use Redis to enforce rate limits on our API endpoints. This helps prevent abuse. 5. Leaderboard Sorted sets make it easy to implement gaming leaderboards in Redis. We can add, update, or remove users from the leaderboard and query ranges efficiently.

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⚙️ #SystemDesign 📈📊📊 How to reduce the latency in any software system? 1. Caching Temporarily storing frequently accessed data in memory to reduce access time. How It Helps: Data Retrieval: Fetching data from a cache (e.g., Redis, Memcached) is significantly faster than querying a database. Content Delivery: Caching static assets (like images, CSS, JS) reduces the need to retrieve them from the origin server repeatedly. 2. Load Balancing Distributing incoming network traffic across multiple servers to ensure no single server becomes a bottleneck. How It Helps: Resource Utilization: Balances the load to prevent any single server from becoming overwhelmed, which can slow down response times. Redundancy: Provides failover capabilities, ensuring requests are handled promptly even if some servers are down. 3. Asynchronous Processing Handling tasks in the background without blocking the main execution thread, allowing the system to continue processing other requests. How It Helps: Non-blocking Operations: Users don't have to wait for long-running tasks (like sending emails or processing images) to complete. 4. Data Partitioning (Sharding) Dividing a database into smaller, more manageable pieces (shards) that can be distributed across multiple servers. How It Helps: Parallelism: Queries can be executed in parallel across shards, reducing the time to retrieve data. Scalability: Distributes the load, preventing any single database instance from becoming a bottleneck. 5. Content Delivery Networks (CDNs) Distributed networks of servers that deliver web content based on the geographic location of the user. How It Helps: Proximity: Serves content from servers closest to the user, reducing the physical distance data must travel. Caching: Caches static and dynamic content to speed up delivery. 6. Database Optimization Tuning databases to perform queries more efficiently through indexing, query optimization, and proper schema design. How It Helps: Indexing: Speeds up data retrieval by allowing the database to find records without scanning entire tables. 7. Minimizing Network Hops Reducing the number of intermediary steps data must pass through and choosing efficient communication protocols. How It Helps: Fewer Hops: Each network hop introduces additional latency; minimizing them speeds up data transmission. 8. Prefetching and Predictive Loading Anticipating future data requests and loading them in advance. How It Helps: Reduced Wait Times: Data is already available when requested, eliminating retrieval delays. Smoother User Experience: Especially effective in applications with predictable access patterns. ©️ From : DesignGurus ➖➖➖➖➖➖➖➖➖➖➖➖ Group: ©️ https://t.me/DevopsDocGP Channel: ©️ https://t.me/DevopsDoc

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