Component explorer

Distributes incoming traffic across a pool of servers for scale and fault tolerance.

Single entry point that handles auth, rate limiting, routing, and protocol translation for downstream services.

A globally distributed cache that serves static and cacheable dynamic content close to the user.

An in-memory key-value store used for caching, pub/sub, rate limiting, distributed locks, and simple queues.

A row-oriented, ACID-compliant relational database — the default for transactional workloads.

The world's most-deployed open-source relational database — clustered PK index, InnoDB MVCC, Vitess for sharding at billions-of-rows scale.

The extensible, standards-compliant relational database — MVCC via xmin/xmax, richest type system in SQL, extensions from PostGIS to pgvector.

Umbrella for document, wide-column, and key-value stores optimized for horizontal scale over strict schema.

A distributed, partitioned, replicated commit log for event streaming, high-throughput ingest, and decoupled services.

Async task queue that decouples producers from consumers and smooths bursts. Each message is processed exactly once (per-message ACK model).

Durable, cheap, flat-namespace storage for blobs — images, videos, backups, logs, and any large binary object.

Inverted-index-based full-text and structured search — the right tool for 'find me records matching this query' when SQL LIKE isn't fast enough.

Continuous computation over event streams — windowed aggregations, joins, ML features, and event-driven derived views.

Enforces per-caller (per-user, per-IP, per-tenant) request budgets to protect downstream systems from abuse and overload.

Runs jobs at a time or interval — the simplest form of eventual asynchronous computation.

Orchestrates long-running, stateful multi-step processes — the right tool when a business flow involves many services and can span days.

Cross-node mutual exclusion — 'only one node can do this at a time' when you can't rely on a single-node lock.

How services find each other in a dynamic fleet — because IPs change, nodes come and go, and hardcoding is a losing game.

Horizontally-scaled database with automatic partitioning and replication — the answer when one node can't hold the data or the traffic.