System Design was HARD until I Learned these 30 Concepts
The 30 foundations that every system design beginner should know — collected here for quick reference.
Source: AlgoMaster- 01
Client-Server Architecture
Clients request, servers respond. The two-role split that underlies every web, mobile, and API system.
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IP Address
Numeric label that identifies a machine on a network so packets know where to land.
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DNS
Phonebook of the internet — resolves human-readable domains to IP addresses.
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Proxy / Reverse Proxy
Intermediary that forwards traffic. Forward proxies shield clients; reverse proxies shield servers and enable load balancing, caching, TLS termination.
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Latency
Time a request takes end-to-end. Network, disk, and queueing delays all add up — measure p50/p95/p99.
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HTTP/HTTPS
Request/response protocol of the web. HTTPS adds TLS for encryption and integrity.
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APIs
Contract that lets one program talk to another. Clear inputs, outputs, and error semantics.
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REST API
HTTP-based API style built around resources and standard verbs (GET, POST, PUT, DELETE).
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GraphQL
Query language for APIs — clients ask for exactly the fields they need in one round trip.
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Databases
Durable, queryable stores for application state. Relational, document, key-value, graph, columnar, time-series.
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SQL vs NoSQL
SQL: schemas, joins, ACID. NoSQL: flexible schema, horizontal scale, often eventual consistency. Pick by access pattern.
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Vertical Scaling
Make one machine bigger — more CPU/RAM/disk. Simple but capped by hardware and a single point of failure.
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Horizontal Scaling
Add more machines and spread load. Scales further but needs load balancing, statelessness, and careful data partitioning.
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Load Balancers
Distribute incoming traffic across servers using strategies like round-robin, least-connections, or consistent hashing.
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Database Indexing
Side data structure (often B-tree) that speeds reads on selected columns at the cost of slower writes and extra storage.
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Replication
Keep copies of data on multiple nodes for read scaling and failover. Trade-offs: sync vs async, leader vs leaderless.
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Sharding
Horizontal partitioning of data across machines by a shard key. Scales writes; complicates joins and rebalancing.
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Vertical Partitioning
Split a table by columns into multiple tables. Useful when some columns are large, hot, or accessed independently.
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Caching
Store hot data closer to the consumer (in-memory, CDN, browser) to cut latency and DB load. Mind invalidation.
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Denormalization
Duplicate data across tables/documents to make reads cheaper at the cost of write complexity and consistency risk.
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CAP Theorem
Under a network partition, a distributed store must choose between consistency and availability. Pick by use case.
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Blob Storage
Object stores (S3, GCS, Azure Blob) for large unstructured files — images, video, backups, logs.
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CDN
Geographically distributed cache that serves static and cacheable content from a nearby edge node.
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WebSockets
Persistent full-duplex TCP connection over HTTP for low-latency push (chat, live dashboards, multiplayer).
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Webhooks
Outbound HTTP callback fired when an event happens — push instead of poll. Needs retry and signature verification.
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Microservices
Architecture style where the system is split into small, independently deployable services with focused responsibilities.
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Message Queues
Async broker (Kafka, RabbitMQ, SQS) that decouples producers from consumers and smooths bursty workloads.
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Rate Limiting
Cap requests per client/window to protect backends. Common algorithms: token bucket, leaky bucket, sliding window.
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API Gateways
Single entry point for client traffic — handles auth, routing, rate limiting, observability for backend services.
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Idempotency
Property that the same request applied multiple times has the same effect once. Vital for retries and exactly-once semantics.
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