IT Comparison

Overview REST and GraphQL are both HTTP-based API paradigms that differ fundamentally in how clients request data. REST exposes multiple URLs with server-defined response shapes; GraphQL exposes a single endpoint where the client’s query body dictates exactly which fields and relationships to return. The distinction drives decisions around over-fetching, N+1 latency, HTTP cacheability, and schema contracts. Comparison Diagram RESTmultiple endpoints, fixed shapeClientGET /usersGET /postsGET /comments3 requestsEach response = full object:{ id, name, email, avatarUrl, createdAt, role, prefs }only name+email needed → over-fetchRelated data → N+1 round-tripsBreaking change → new URL, e.g. /v2/GET · cacheable · status codesGraphQLone endpoint, client-declared shapeClientPOST /graphql1 requestquery (client-authored)query { user(id: 1) { name email } posts { title }}SDL schema (server)type User { name: String! email: String posts: [Post]}Response: exactly what was declared{ user: { name, email }, posts: [{ title }] }Schema evolves via @deprecated, no versioningPOST · typed schema · introspectable Comparison Table Aspect REST GraphQL Endpoints One URL per resource type — GET /users, POST /orders, etc. Single URL (POST /graphql); resource selection is in the query body Response shape Fixed by the server; client receives all fields the endpoint defines Declared by the client per query; only the requested fields are returned Over/under-fetching Chronic: server returns full objects; client discards unused fields or must make extra requests for missing ones Eliminated by design: resolvers return only fields the query specifies HTTP caching Native: GET responses cache at CDN and browser level by URL + headers Non-trivial: queries are POST bodies; requires persisted queries or APQ for cache-key stability Type system Optional — enforced only if you add OpenAPI/JSON Schema; not validated at runtime by default Mandatory SDL schema; all queries are parsed and validated against it before execution Versioning Breaking changes typically require new URL paths (/v1/, /v2/) or custom Accept headers Additive schema evolution with @deprecated directives; a single endpoint surface stays stable Error handling HTTP status codes carry semantic meaning: 200, 404, 422, 500, etc. Always 200 OK; errors surface in an errors[] array alongside any partial data Introspection / discoverability Requires an external spec (OpenAPI/Swagger); not built into the protocol Built-in: query __schema or __type to get the full type graph at runtime Key Differences REST has one endpoint per resource; GraphQL has one endpoint for the entire API, and the query body — not the URL — determines what data comes back. REST responses return all server-defined fields for a resource (over-fetch), and fetching related data requires additional round-trips (N+1 problem). A single GraphQL query can traverse multiple types and return exactly the fields requested. REST uses standard HTTP verbs and status codes, making GET-based CDN and browser caching trivially available. GraphQL queries travel as POST bodies, breaking HTTP cache semantics by default and requiring explicit workarounds. GraphQL’s SDL provides a mandatory, introspectable type contract enforced at parse time. REST type contracts are optional add-ons (OpenAPI); the server can return anything without violating the protocol. REST breaking changes typically force a new URL version (/v2/); GraphQL prefers additive schema evolution with @deprecated, keeping a single endpoint surface over the API’s lifetime. When to Use Each REST — Use REST when HTTP semantics matter: public APIs where CDN caching, stateless GET-based reads, and broad ecosystem compatibility (browsers, curl, API gateways) are first-class requirements. Also the natural default when each resource type is consumed independently and maps cleanly to standard CRUD verbs. ...

Overview The stack and heap are two distinct memory regions used for different allocation strategies at runtime. The stack manages function call frames automatically via a single pointer increment/decrement, while the heap handles dynamic allocations with flexible lifetimes through an allocator. The distinction directly affects allocation speed, data lifetime, size constraints, and thread safety — core considerations in systems, embedded, and performance-sensitive programming. Comparison Diagram STACKgrows downward, LIFOhighlowframe: main()frame: compute(n)frame: factorial(3)SPunusedauto-managed · O(1) · fast~1-8 MB per threadHEAPunordered, explicit lifetimeObjectVec<T>freeHashMapBoxfreeString dataArcmanual/GC · flexible · fragmentslimited by OS / RAM Comparison Table Aspect Stack Heap Allocation mechanism Pointer decrement — O(1), no bookkeeping Allocator call (malloc/new) — higher constant, free-list bookkeeping Deallocation Automatic on scope/frame exit Explicit (free/delete) or garbage collector Data lifetime Bound to the declaring scope or call frame Arbitrary; can outlive any function call Size constraint Fixed at thread creation (typically 1–8 MB) Limited only by available virtual memory / RAM Size known at compile time Required — compiler must know the type’s layout Not required — length/capacity decided at runtime Fragmentation None — LIFO order keeps allocation contiguous Yes — internal and external fragmentation accumulate over time Thread ownership Each thread has its own stack; no synchronization needed Shared across threads; allocator must serialize internally Failure mode Stack overflow → immediate crash (SIGSEGV/signal) OOM → null / exception / OOM-killer; potentially recoverable Key Differences Stack allocation is a single SP register decrement; heap allocation invokes an allocator with metadata updates, free-list traversal, and possible OS syscalls. Stack lifetime is strictly scoped to the function frame — data cannot be returned by pointer from the stack safely; heap memory can be returned, stored globally, or transferred across threads. Each thread has its own stack and needs no locking; the heap is process-wide and requires allocator-level synchronization on every alloc/free. Stack size is fixed and small (set by the OS or linker script); heap can grow dynamically, making it the only viable region for large buffers or runtime-sized collections. Heap fragmentation is a real operational concern in long-lived or allocation-heavy processes; the stack never fragments because it always grows and shrinks from one end in LIFO order. When to Use Each Stack — Prefer the stack for local variables, function arguments, and fixed-size value types whose lifetime is bounded by the enclosing scope — it is faster, deterministic, and eliminates entire classes of memory bugs. ...