HTTP/HTTPS deeply — HTTP (Hypertext Transfer Protocol) solves the framing and semantic problem by standardizing request-response structures, caching metadata, content negotiation, and status codes.
TCP/IP basics — At the lowest physical layer, computer networks transport raw pulses of light, electricity, or radio waves. Internet Protocol (IP) operates above this layer to handle logical…
DNS — The Domain Name System (DNS) solves these problems by providing a globally distributed, hierarchical, eventually-consistent lookup mechanism that translates human-readable domain…
TLS/SSL — When two machines communicate across a public network (such as the Internet), their raw IP packets pass through multiple intermediate hops—routers, switches, internet service…
HTTP/1.1 vs HTTP/2 vs HTTP/3 — In production, you’ll see these symptoms as slow page loads, high error rates, and resource exhaustion on the load balancer or application server.
REST API Design — RESTful design is the backbone of most modern web services: Twitter, GitHub, Stripe, and many internal microservices use it.
RPC / gRPC — gRPC (Google Remote Procedure Call) addresses all of the above by providing a binary, type‑safe, streaming RPC framework that runs over HTTP/2, with automatic code generation and…
WebSockets — WebSockets are the de‑facto standard for any backend that needs to push data to clients instantly and maintain a persistent, low‑overhead channel.
Server‑Sent Events (SSE) — Real‑time data from server to client Many web apps need to push updates to the browser (chat, live scores, dashboards) without the client constantly polling the server.
Request/response lifecycle — Just as a letter travels from sender → sorting → clerk → processing → return, an HTTP request travels through a series of deterministic steps before a response is sent back.
Cookies, sessions, tokens — When a user interacts with a web application, the server needs a way to remember that the user is the same across multiple HTTP requests. HTTP is stateless, so each request is…
CORS — Motivation Web browsers enforce a same‑origin policy (SOP) to protect users from malicious cross‑site requests. Under SOP, a script running on https://app.example.com can only…
Idempotency — Motivation When a client sends a request that changes state (e.g., create an order, charge a card, update a profile), the client often wants to be sure that the operation is…
Stateless vs Stateful Services — In production, most backends aim to be stateless so that any instance can serve any request. When state is required, it is externalized (database, cache, message queue) so that…
Serialization / deserialization — Why it matters Without proper serialization you risk data corruption, security holes (e.g., code injection via eval), and brittle APIs that break when the data shape changes.
API versioning — Motivation When a backend exposes an HTTP API, clients (web, mobile, third‑party services) depend on the shape of the data and the semantics of the endpoints. As the product…
Pagination — Imagine a library with a catalog of 10 000 books. You want to find all books by a particular author. Instead of handing you the entire catalog, the librarian gives you a page of…
Filtering / Sorting — At scale, filtering/sorting becomes a bottleneck and a cost driver.
Rate limiting — Why it matters Rate limiting protects the service’s availability, performance, and cost while also providing a first line of defense against abuse.
PostgreSQL — Imagine a library where every book is a row in a table. The library’s catalog (the schema) tells you what each book contains: title, author, ISBN, etc. Shelves (the indexes) let…
Database normalization — Think of a library. If every book had its own copy of the author’s biography, the library would store the same text thousands of times. Instead, the library keeps one biography…
Joins — Think of a library where each book has a catalog record (title, author ID, genre ID). The author and genre tables hold the actual names. When you want to display a book, you look…
Indexes — Problem Databases store data in tables that are essentially unordered collections of rows. When you run a query like SELECT * FROM users WHERE email = 'alice@example.com', the…
Composite indexes — Motivation When a database receives a query, it must decide how to locate the requested rows. If the query filters on multiple columns (WHERE a = 5 AND b = 10) and the database…
Query Planning — When a backend receives a request that needs data from a database, the database must decide how to fetch that data. The query planner is the component that chooses the most…
Transactions — Motivation When a backend service performs multiple database operations that belong to a single logical unit of work, we want either all of those operations to succeed or none to…
ACID — When a backend service performs multiple database operations that belong to a single logical task—like transferring money between accounts or creating an order with inventory…
Isolation Levels — Isolation levels appear in every relational DB (PostgreSQL, MySQL, SQL Server, Oracle) and in any system that relies on ACID transactions, such as microservices that share a…
Deadlocks — Motivation When multiple concurrent processes (transactions, threads, or services) try to modify the same data, they must coordinate to keep the database consistent. If they lock…
Locks — When multiple processes or threads try to read or write the same data concurrently, they can interfere with each other. Imagine two people editing the same paragraph in a document…
MVCC — Without a mechanism to isolate concurrent operations, you either sacrifice performance (by serializing all access) or risk data corruption.
Constraints — Why it matters Without constraints, a backend could store contradictory data: two users with the same email, an order referencing a non‑existent user, or a product with a negative…
Foreign keys — When building relational data models you often have entities that belong to other entities. For example, a Post belongs to a User. In a database this relationship is represented…
Views — Views let you declare a reusable, named SELECT that the database engine can treat like a table. They provide a single source of truth for complex logic, enforce consistent…
Materialized views — Materialized views provide a database‑level, declarative way to store pre‑computed results that can be refreshed on demand or automatically, giving predictable performance and…
Stored procedures — Analogy Think of a stored procedure as a factory assembly line inside the database.
Replication — Motivation In a real‑world backend you rarely have a single database instance that can serve all traffic, guarantee uptime, and provide fast reads for users spread across the…
Read replicas — Think of a library that has one master copy of a book (the primary). Every time someone wants to read the book, they must go to the master copy. If many people come at once, the…
Sharding — Motivation When a single database instance grows beyond its hardware limits—whether in storage, I/O, or query throughput—it becomes a bottleneck. A monolithic database can no…
MongoDB — Motivation Modern web services need to store large amounts of semi‑structured data that evolve over time. Traditional relational databases force a rigid schema, which can slow…
DynamoDB concepts — In production systems, DynamoDB is often the backbone for session stores, analytics pipelines, leaderboards, and any component that requires fast, scalable key‑value access.
Cassandra concepts — Why it matters If you cannot scale writes or tolerate node failures, your service will become a bottleneck or crash under load. For example, a real‑time analytics platform that…
Document Modeling — Why the problem matters Backends that serve millions of requests per day must keep latency low and throughput high. A bad document model can become the single bottleneck, forcing…
Embedding vs Referencing — Embedding is like putting a photo inside a photo album: you can see the picture immediately, but if the photo changes you have to replace every copy. Referencing is like keeping a…
Partition keys — When a backend system grows, a single database instance can no longer hold all data or handle all traffic. Partition keys let us split the data across many machines (shards) while…
Consistency models — When a backend system is distributed across multiple nodes (databases, caches, micro‑services), a write performed on one node may not be immediately visible on another…
Why a query can be slow even when an index exists — Analogy Imagine a library with a perfect index (the card catalog). If you want a book on “Quantum Mechanics,” you look up the card, find the shelf, and grab the book. But if the…
Redis — Redis is the go‑to solution for these patterns in modern backend stacks.
Cache‑aside — Cache‑aside (also called lazy loading) lets an application decide when to read from or write to a cache. It keeps the cache in sync with the source of truth (the DB) while…
Write‑through cache — Motivation In a typical backend you have a primary data store (e.g., PostgreSQL) and a secondary fast store (e.g., Redis). Reads are cheap from Redis, writes are cheap to…
Write‑back cache — When a backend service reads data from a slow storage (e.g., a relational database or a distributed file system), it often keeps a copy in a fast in‑memory store (cache). The…
Read‑through cache — Motivation Backends often need to serve the same data to many users. Fetching that data from a database on every request is expensive in latency, CPU, and network usage. A cache…
TTL — Motivation Backends often serve data that changes over time: user profiles, product prices, session tokens, or computed analytics. Keeping every piece of data in memory forever is…
Cache invalidation — When a backend serves data that changes over time, we often cache that data to reduce latency and load on the database. Cache invalidation is the mechanism that tells the cache to…
Cache stampede — When a highly‑requested key is missing from the cache, many concurrent requests may all try to load the data from the backing store (DB, external API, etc.) at the same time. This…
Cache penetration — Motivation When a cache sits in front of a database, the ideal is that most read requests hit the cache. Cache penetration happens when a request asks for a key that does not…
Cache avalanche — When a cache entry expires, the next request that hits the cache must fetch the data from the origin (DB, API, etc.) and repopulate the cache. If many requests arrive at the same…
Distributed caching — Imagine a library where every patron must ask a librarian for a book. The librarian fetches the book from the shelf, reads it, and hands it over. If many patrons ask for the same…
LRU/LFU — Motivation Backends often need to serve the same data repeatedly (e.g., user profiles, product catalogs, AI model embeddings). Fetching that data from a database or recomputing it…
Redis Cluster — Redis Cluster solves these problems by sharding data across multiple nodes and providing automatic failover.
Pub/Sub — Decoupling producers and consumers In a monolithic app a function that writes to a database often calls another function that sends an email. If the email service crashes, the…
Message queues — Message queues are the backbone of many modern backend systems: order processing pipelines, event sourcing, log aggregation, background job queues, and microservice communication.
RabbitMQ — Motivation In a distributed backend, services often need to talk to each other. If every service calls another synchronously (e.g., HTTP request), the caller must wait for the…
Kafka — Imagine a large bulletin board in an office. Employees (producers) post notes (messages) on the board. Anyone (consumers) can read the notes later. The board is divided into…
Redis Streams — Problem Traditional in‑memory queues or simple Redis lists (LPUSH/RPOP) lack built‑in consumer groups, pending‑message tracking, or automatic rebalancing. They also provide only…
Consumer groups — Imagine a group of workers (consumers) sharing a pile of tasks (messages). Each task is a job card. The workers form a “task squad” (consumer group). When a new job card appears…
Message acknowledgment — When a backend service consumes a message from a queue or stream, it must confirm that it has processed the message successfully. Without this confirmation the system cannot…
Retry mechanisms — When a backend service calls another service, database, or external API, the call can fail for many reasons: transient network glitches, temporary unavailability of the target…
Dead‑letter queues — Without a DLQ, you risk silent data loss, hard‑to‑debug failures, and degraded system performance.
Event‑Driven Architecture — Event‑driven architecture (EDA) addresses these by decoupling producers and consumers through an asynchronous message bus. Producers publish events; consumers subscribe and react…
Event sourcing — Motivation In a traditional CRUD system the current state of an entity (e.g., a bank account balance) is stored in a table and updated in place. If you need to audit, replay, or…
CQRS — Motivation In many applications, the same data store is used for both reading (queries) and writing (commands). When the workload grows, the read side can become a bottleneck for…
At‑least‑once delivery — When a system sends a message (e.g., an event, a task, a notification) it wants the receiver to process it at least once. The motivation is that in real‑world distributed systems…
At-most-once delivery — When a backend system receives a message (e.g., a user signup event, a payment notification, or a sensor reading), it must decide how many times to process that message.
Exactly‑once semantics — Below is a minimal Node.js example using Kafka (kafkajs) and PostgreSQL (pg). It demonstrates an outbox pattern with a deduplication table.
Ordering — Motivation In distributed systems, operations often happen in parallel and messages can arrive out of the order they were sent. For many business scenarios the sequence of events…
Backpressure — Motivation In a distributed backend, services often produce data faster than downstream consumers can consume it. Think of a high‑traffic API that writes to a queue, but the…
Scalability — Motivation When a backend receives more traffic than it can handle, users experience slow responses, timeouts, or outages. Scalability is the set of techniques that let a system…
Availability — Goal: Build a simple two‑instance Node.js service behind NGINX with health checks, then simulate a failure.
Reliability — Motivation In a distributed backend, requests rarely travel a clean, uninterrupted path. Network glitches, temporary database outages, rate limits, or downstream service hiccups…
Fault tolerance — Motivation In a distributed backend, components (services, databases, message queues, etc.) can fail for many reasons: hardware crashes, network partitions, software bugs, or…
Horizontal scaling — Horizontal scaling—adding more machines (or containers) that share the load—addresses all of the above. It lets you add capacity by deploying more instances, isolate failures to a…
Vertical scaling — Motivation In a backend system you often start with a single server that runs all your services. As traffic grows, that one machine can become a bottleneck: CPU, memory, disk I/O…
Load balancing — Motivation When a web service receives more traffic than a single server can handle, requests start queuing, latency rises, and the server can crash. Load balancing distributes…
Reverse proxies — Motivation When a client talks to a backend service, the client usually talks to a single endpoint (e.g., api.example.com). In reality, that endpoint is often a gateway that hides…
API gateways — Why it matters If you expose every micro‑service directly, you lose control over traffic routing, rate limiting, authentication, and monitoring. It also makes it hard to evolve…
Service discovery — Motivation When a micro‑service architecture grows, services are deployed on many hosts, containers, or cloud instances. Each instance has its own IP address and port, and these…
Health checks — Health checks are lightweight probes that let a system (or an orchestrator) determine whether a service is running, reachable, and able to perform its core responsibilities. They…
Failover — Motivation In a production backend you rarely have a single point of truth. Services, databases, caches, and message brokers all run on multiple machines or containers. If any of…
Replication — Without replication, a backend would be a single point of failure, would struggle to serve many concurrent reads, and would be vulnerable to data loss.
Partitioning — Motivation When a backend grows, a single database instance or a single table can become a bottleneck. Queries that scan large tables, write bursts, or high concurrency can…
Sharding — Motivation When a single database instance cannot keep up with the volume of data or traffic, you need a way to split the workload. Sharding is the technique of partitioning a…
CAP Theorem — Imagine a library that has many copies of a book spread across cities.
PACELC — In a distributed system, data is spread across multiple nodes. When a network partition occurs (some nodes become unreachable), the system must decide how to behave. The PACELC…
Consistency vs Availability — Below is a Node.js example using Redis with quorum logic for writes and reads. We’ll use ioredis for cluster support.
Distributed systems — Motivation In a single‑box application, all state lives in one process. When traffic grows, that process becomes a bottleneck: CPU, memory, disk, or network limits. A distributed…
High‑level design of WhatsApp — WhatsApp is a global instant‑messaging platform that must deliver messages, media, and presence information to millions of users with low latency, high reliability, and end‑to‑end…
High‑Level Design of Netflix — Motivation A streaming platform like Netflix must deliver video content to millions of users worldwide with minimal latency, high reliability, and personalized experiences. The…
High‑Level Design of Uber — Motivation A ride‑hailing app must connect riders with nearby drivers in real time, calculate fares, handle payments, and provide a reliable, scalable experience for millions of…
High‑level design of Instagram — The core problem is: How do you deliver a highly personalized, low‑latency feed to millions of users while keeping writes fast, reads fast, and the system cost‑effective?
High‑level design of Amazon — Motivation Amazon is a global e‑commerce platform that must serve millions of users, process thousands of orders per second, and keep product data up‑to‑date across thousands of…
Distributed consensus — Imagine a group of friends trying to pick a meeting time. Each friend proposes a time, and they vote. The friend with the majority becomes the leader who announces the final time…
Leader election — Motivation In a distributed system you often have many identical service instances that can perform the same work. When a task must be performed once (e.g., a cron job, a cleanup…
Distributed locks — Motivation In a distributed system, many processes or services may try to modify the same resource (a row in a database, a file, a cache entry, etc.) at the same time. Without…
Distributed transactions — If any friend says “no” at any step, the whole plan is aborted. This is similar to a two‑phase commit (2PC): a prepare phase where all participants agree, followed by a commit…
Two‑phase commit — When a business operation touches multiple independent data stores (e.g., a PostgreSQL order table and a Redis inventory cache), we want the whole operation to either succeed…
Saga pattern — Motivation In a distributed system, a single business transaction often touches many micro‑services or databases. Traditional ACID transactions guarantee atomicity only within a…
Eventual consistency — Motivation In a distributed system, data is often replicated across multiple nodes (databases, caches, micro‑services). When a client updates data, that update must be propagated…
Strong Consistency — In a distributed backend, data is often replicated across multiple nodes or services. When a client writes a value, that write must be visible to all subsequent reads, no matter…
Vector clocks — Why it matters When you build multi‑master databases, CRDT‑based collaboration tools, or event‑sourced services that run on multiple replicas, you need a lightweight mechanism to…
Logical clocks — Imagine each node is a book that writes its own page numbers. When you read a page, you can tell if it was written before or after another page by comparing numbers, even if the…
Network Partitions — Motivation In a distributed system, nodes communicate over a network that can become unreliable. A network partition happens when a subset of nodes can no longer reach another…
Split brain — In a distributed system, multiple nodes cooperate to provide a single logical service (e.g., a database cluster, a cache cluster, or a microservice that needs high availability)…
Clock Synchronization — If you ignore clock synchronization, you risk subtle bugs that surface only under load or after a few hours of operation. In production, this can lead to data loss, security…
Gossip protocols — Motivation In a distributed system you often need to keep many nodes in sync – e.g., a cluster of cache servers, a set of micro‑services that share a feature flag, or a group of…
Quorum — Motivation In a distributed system you often have multiple replicas of data or services. When a client asks for a value or performs an update, you need a single, consistent answer…
Replication strategies — Analogy: Think of a library that keeps a copy of every book in three branches. If one branch closes, the other two still have the books. Readers can go to any branch, and the…
OAuth 2.0 — Takeaway: An access token is a temporary, scoped credential that lets a client (app) act on behalf of a resource owner (user) at a resource server (API), without exposing the…
OpenID Connect — OpenID Connect (OIDC) is an authentication layer built on top of OAuth 2.0. It adds an ID token that contains identity claims and a UserInfo endpoint for additional profile data…
JWT — If you skip JWTs and rely solely on server‑side sessions, you’ll need a shared session store, which adds complexity and can become a bottleneck. JWTs let each service verify a…
Session authentication — Motivation When a user logs in, the backend must remember who that user is on subsequent requests. Without a mechanism to persist that identity across stateless HTTP calls, every…
ABAC — Send a request with Authorization: Bearer <token> and observe the result.
API keys — Run ts-node scripts/generateKey.ts to create a key. Store the secret securely (e.g., in Vault).
Password hashing — Tip: Keep the pepper out of the codebase; fetch it at runtime from a secrets manager.
Argon2 / bcrypt — Note: In production, use a dedicated auth service, store salts explicitly if needed, and never log raw passwords.
CSRF — Motivation When a user is logged into a web application, the browser automatically sends the session cookie with every request to that domain. A malicious site can trick the…
XSS — Motivation When a web application accepts user‑generated content (comments, profiles, posts, etc.) and later renders that content back to browsers, an attacker can inject…
SQL injection — Motivation When a backend receives user input and plugs it directly into an SQL query, an attacker can inject arbitrary SQL. This turns a harmless lookup into a full‑blown…
NoSQL injection — Motivation When a backend receives user data and uses it to build a query against a NoSQL database (MongoDB, Couchbase, DynamoDB, etc.), the developer often concatenates or merges…
SSRF — Motivation When a backend service fetches data from an external URL supplied by a user (or another service), the request can be redirected to internal resources that should be…
CORS security — Motivation When a web page served from one origin (scheme + host + port) wants to call an API on another origin, browsers enforce the Same‑Origin Policy (SOP). SOP protects users…
Clickjacking — Clickjacking is a malicious technique where an attacker tricks a user into clicking on a hidden or disguised UI element that performs an unintended action on a trusted site.
Request validation — If you have many schemas, you can cache them in Redis to avoid re‑parsing on every request. This is rarely necessary but can help in micro‑service environments.
Input sanitization — Motivation When a backend receives data from users, that data can be malformed, malicious, or simply unexpected. Input sanitization is the process of cleaning or validating that…
Secrets management — Motivation When a backend service needs to talk to a database, an external API, or a message broker, it must authenticate itself. The credentials (passwords, API keys…
Encryption at rest — Data exposure when storage media is compromised If a server, a backup disk, or a cloud object store is stolen, lost, or accessed by an attacker, the raw data on disk can be read…
Encryption in transit — Think of sending a letter through a sealed envelope that only the recipient can open.
Key rotation — Motivation Every backend service uses cryptographic keys or secrets to protect data, authenticate users, or secure communication. If a key is compromised, all data encrypted or…
Security headers — Motivation When a browser receives a response from a server, it must decide how to treat that content. If the server does not explicitly tell the browser what is safe, the browser…
OWASP Top 10 — If any wall is weak, a siege (attack) can breach the castle. The OWASP Top 10 tells you which walls are most likely to crumble.
Authentication vs Authorization — If authentication or authorization is omitted, the system behaves like an open public API: anyone can read/write any data. In production, that translates to data breaches…
Timeouts — Analogy: Imagine a waiter in a busy restaurant who keeps a customer’s order in the kitchen. If the kitchen takes forever, the waiter will eventually give up and ask the customer…
Retries — If you never retry, the letter may never reach your friend. If you retry too aggressively, you overload the post office.
Exponential backoff — Exponential backoff mitigates these issues by spacing retries increasingly farther apart, giving the target service time to recover while still giving the caller a chance to…
Jitter — Motivation When a backend system talks to another service, the round‑trip time (RTT) is rarely constant. Network congestion, load spikes, GC pauses, or even the way a database…
Circuit breakers — Motivation In a distributed system, a service may call another service, a database, or an external API. If the callee is slow, overloaded, or down, the caller can become blocked…
Bulkheads — Motivation In a distributed backend, a single failure can ripple through the entire system. If one component consumes too many resources (CPU, memory, DB connections, external API…
Graceful Degradation — In a distributed backend, components rarely work perfectly all the time. When a downstream service (e.g., a recommendation engine, a payment gateway, or a cache) fails or becomes…
Failover — Motivation In a distributed backend, a single point of failure (SPOF) can bring the whole service down. Failover is the mechanism that detects a failure and automatically switches…
Health checks — Think of a health check like a doctor’s check‑up for a server.
Liveness probes — Motivation In a distributed backend, a process can appear to be running (the OS shows a PID, the container is up) but its internal state may be corrupted, a deadlock may have…
Readiness probes — Readiness probes let the orchestrator (e.g., Kubernetes) know when a pod is ready to receive traffic, separating liveness (is the process alive?) from readiness (is it ready to…
Error budgets — Balancing reliability and feature velocity In a production system, every change can introduce bugs or performance regressions. Teams need a systematic way to decide when to ship…
SLO — Motivation In a real‑world backend, multiple teams ship features, fix bugs, and deploy changes continuously. Clients expect a certain level of reliability, but developers often…
SLA — In a production backend, you have many moving parts: databases, caches, message queues, external APIs, and the application code itself. Clients (web browsers, mobile apps, other…
SLI — Motivation When you run a backend service, you need to know how well it is performing. Clients expect low latency, high availability, and correct results. Without a quantitative…
Structured logging — Analogy Think of a diary. A free‑text diary is great for storytelling, but if you want to find all entries about “rain” you have to read every page. A structured diary has tags…
Log levels — Log levels appear everywhere in real backend stacks: Express middleware, FastAPI logging, PostgreSQL log_min_error_statement, Redis loglevel, and even AI inference pipelines that…
Correlation IDs — In real systems, correlation IDs appear in HTTP headers (X-Request-ID, X-Correlation-ID), gRPC metadata, message queue headers, and database query tags.
Centralized logging — Analogy Think of each service as a department in a company. Each department keeps its own notebook (local logs). A CEO (the ops team) needs a single company ledger that records…
CPU metrics — Motivation When a backend runs, the CPU is the engine that turns code into work. If the engine is over‑worked, requests slow down, queues grow, and the system can crash. If the…
Memory metrics — They are the foundation for capacity planning, alerting, and performance tuning.
Database Replication & Sharding Explained — Think of Swiggy’s order system on a busy night. Har minute mein lakhon orders aate hain, aur har order ke liye payment, inventory, aur delivery status update karna hota hai. Agar…
Message Queues in System Design — Yehhi reason hai jiske liye message queues introduce ki jaati hain: asynchronous, decoupled, aur scalable processing.
REST API Basics & Best Practices Explained — If each request goes through a monolithic API layer, the latency spikes, throughput drops, and a single buggy endpoint can bring the whole service down. Yahi problem hai…
6 System Design Interview Concepts — Yeh hi wajah hai ki interviewers aapse poochte hain: “Aap system ko kaise design karenge taaki yeh sab problems solve ho?” Hum 6 core concepts pe focus karte hain jo har design ke…
The Ultimate Guide to Caching and CDNs — Imagine Swiggy ke app pe ek menu page. Har user click karta hai “See all restaurants” – ye ek GET request hai. Agar har request pe backend ko database se 200 restaurants fetch…
Database Scaling Basics Explained — Swiggy ka order system socho. Har din millions of orders aate hain, aur har order ko realtime mein confirm karna hota hai. Agar database ek single server pe run kare, to latency…
Load Balancers Explained in 7 Minutes — Imagine Swiggy ke ek din ka peak time jab 10,000 orders ek sath aa rahe ho. Har ek order ka request ek single server pe direct jata, toh kya hoga?
Transport Layer Explained: TCP vs UDP — Soch lo tum Swiggy se order kar rahe ho. Order place karte hi ek confirmation message tumhare phone pe aata hai aur delivery driver ko location milti hai. Agar packet loss ho…
Data Centers in System Design — Socho aap Swiggy ke delivery manager ho. Har roz lakhon orders aate hain, aur log chahte hain ki unka food exactly 30‑45 minutes mein pohonch jaye. Agar aapke servers ek hi data…
Proxy Servers: How Proxies Work — Imagine Swiggy’s order system during a big sale. Every user’s request (GET /menu, POST /order, etc.) hits the public API gateway. If we let every request go directly to the…
SQL Tutorial for Beginners — SQL (Structured Query Language) humen structured, ACID compliant databases provide karta hai jisse hum high traffic ke liye reliable aur fast data access kar sakte hain.
SQL Transactions and ACID Properties — If koi bhi step fail ho jaata (e.g., payment gateway down, inventory out of stock), humko ya to partial data chahiye (order placed but payment not captured) ya inconsistent state…
REST vs GraphQL — Yeh 5 alag HTTP requests hote hain, network latency badh jaati hai, mobile data consume hota hai, aur backend ko multiple database hits karne padte hain. Agar user slow network pe…
Every AWS Service You Need To Know — If you ignore these, you get timeouts, data races, and disgruntled users. AWS services are the tools to solve these problems at scale.
How Message Queues Handle Traffic Spikes — Picture Swiggy during the 5‑am breakfast rush. Orders keep pouring in, but the backend services (order‑processor, payment, inventory) can’t keep up instantly. If we process…