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Understanding JWT Authentication: A Key to Modern Web Security 🔐 As web developers, we often grapple with secure authentication. JWT (JSON Web Token) offers a robust solution. Here's a quick rundown: 1️⃣ User logs in, server verifies credentials 2️⃣ Server creates a signed JWT with user info and expiration 3️⃣ Client stores and sends JWT with each request 4️⃣ Server verifies JWT signature and extracts user info Key benefits: - Stateless: reduces server load - Scalable: works well with microservices - Secure: when implemented correctly with HTTPS Remember: - Keep your server's secret key safe - Set short expiration times - Consider using refresh tokens Have you implemented JWT auth in your projects? What challenges did you face? FLAT 70% OFF on all our courses! CODE: "SYSTEM70" Grab it now: https://interviewready.io/ #WebDevelopment #Cybersecurity #JWT #Authentication

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This system executes 2000 financial transactions per second! . . Last Month, Google Pay processed transactions worth ₹7.07 Lakh Cr. That's a little more than Spiderman's rent 😛 The system executes ~2000 transactions per second! How does Gpay manage thousands of concurrent financial transactions, with high consistency? The answer lies in an old banking practice: Maintaining ledgers. But before we get into the specifics (blog coming up!), let's try and design the Google Pay system ourselves! The following link will test your high-level design for GPay: https://lnkd.in/g_XXhQbD You have two requirements: 1. Manage payments by coordinating with participating banks. 2. Analyze the payment data to show personalized ads to users. All the best! - - - - - - - - - - - - - - - - - - - - - - Get access to 60 such HLD interview practice questions on the purchase of the System Design Simplified course. The course is now available at 70% OFF! 💥 Link: https://interviewready.io

On average, developers spend more than 30% of their time debugging. The standard method of debugging an issue is checking log files for errors. At Google's scale, this is a challenge. Google generates petabytes of request data every day. Storing logs is expensive and time-consuming. Google has a highly distributed system. A single request may hit multiple systems. Tracing the request path is difficult. Searching for strings in petabytes of log data is expensive and time-consuming. Google's solution is to standardize its request tracing, and use ingenious algorithms to reduce the volume of traced data. The result is a 1000x reduction in traces, with negligible performance impact. Read all the details in the blog here: https://lnkd.in/d-gnQA3d . . Mega 70% off on all our courses, upskill and get InterviewReady 😎

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IT IS FINALLY OUT!! Yes, we heard you. One more feature added to InterviewReady's system design judge to help you better prepare and upskill. Capacity estimation questions are now added to the system design judge!  1.⁠ ⁠Amazon SES: https://lnkd.in/grnkfXnC  2.⁠ ⁠Discord: https://lnkd.in/gz6gtfdt  3.⁠ ⁠Slido: https://lnkd.in/gpR2pNvf  4.⁠ ⁠Uber: https://lnkd.in/gru6cbGY  5.⁠ ⁠BookMyShow: https://lnkd.in/gEMpsPt7  6.⁠ ⁠Netflix CDN: https://lnkd.in/gm2NaAnM  7.⁠ ⁠DallE Generative AI: https://lnkd.in/gXHUFsYa  8.⁠ ⁠Online File Convertor like Zamzar: https://lnkd.in/gCw_bx8H  9.⁠ ⁠Canva: https://lnkd.in/gBzgKqj7 10.⁠ ⁠WhatsApp Calling: https://lnkd.in/ge2km6hN 11.⁠ ⁠Adobe PDF Manager: https://lnkd.in/ggGmV_r3 12.⁠ ⁠TikTok Trending Posts: https://lnkd.in/g_U2sqD4 13.⁠ ⁠GPay: https://lnkd.in/g_XXhQbD 14.⁠ ⁠Google drive Auth System: https://lnkd.in/gH2TAAea 15.⁠ ⁠Airbnb Search: https://lnkd.in/ga7UrCJB 16.⁠ ⁠Zomato Recommendations: https://lnkd.in/gx9svmeg 17.⁠ ⁠LiChess: https://lnkd.in/g-3wW8AU Capacity estimation (with back-of-the-envelope calculations) surfaces the feasibility of a design. Try and solve all questions. All the best! #SystemDesign #CapacityEstimation #OnlineJudge

India's Budget 2024: Microservices for Macroeconomics 🇮🇳 Will this new architecture reduce the load on the middle class? Are we going to pivot to an economy of services? Only time will tell. On that note - check out this lecture on Microservices here: https://bit.ly/3WmrZwf #budget2024 #systemdesign #India

A database principle from 1981: Write-Ahead Logging (WAL) 📊 Write-Ahead Logging (WAL) revolutionizes database reliability and replication. Let's dive deep into WAL and its critical role in PostgreSQL: 🔍 What is WAL? WAL is a protocol ensuring data integrity by logging changes before they're written to the main database files. It's the database's "black box," recording every transaction for durability and recovery. Key Concept: Changes are considered committed once they're written to the WAL, even if they haven't yet been applied to the main data files. 🔄 WAL in PostgreSQL Replication: 1. Primary server records all changes (inserts, updates, deletes) in WAL files 2. WAL files are continuously shipped to standby servers 3. Standby servers apply these WAL records to maintain an up-to-date copy Deeper Dive: - WAL Segments: For example, PostgreSQL writes WAL records in 16MB segments - LSN (Log Sequence Number): Each WAL record has a unique LSN, crucial for tracking replication progress - Checkpoints: Periodic events that flush all changed data to disk, allowing old WAL segments to be recycled 🚀 Benefits for Replication: - Consistency: Guarantees standbys are exact copies of the primary - Efficiency: Transfers only changes, not entire datasets, minimizing network traffic - Near Real-Time Sync: Standbys can trail the primary by mere milliseconds - Read Scaling: Offloads read queries to multiple standby servers - Disaster Recovery: Enables point-in-time recovery and minimal data loss 💡 Advanced WAL Features in PostgreSQL: - Streaming Replication: WAL records are streamed in real-time, reducing replication lag - Synchronous Replication: Ensures transactions are committed on standbys before acknowledging to the client - Cascading Replication: Standby servers can stream WAL to other standbys, reducing primary server load - Logical Decoding: Allows extraction of logical change sets from WAL, enabling logical replication and CDC (Change Data Capture) 🛠️ Tuning Considerations: - wal_level: Determines how much information is written to WAL - max_wal_senders: Sets the maximum number of concurrent connections for WAL streaming - wal_keep_segments: Specifies the minimum number of past log file segments to keep for standby servers Future of WAL: With the rise of distributed systems, WAL is evolving. Concepts like Conflict-free Replicated Data Types (CRDTs) and multi-master replication are pushing the boundaries of traditional WAL. WAL is a cornerstone of robust, scalable database systems. Have you implemented WAL-based replication in your projects? What challenges did you face with tuning or maintaining WAL-based systems at scale? #DatabaseArchitecture #PostgreSQL #SystemDesign #DataReplication #HighAvailability

All the best to the engineers at CrowdStrike! Our light-hearted take :D #Windows #Outage #CloudStrike

Must read to upskill yourself!