data-storagelisted

# Data Storage Choose where records live, how they are keyed and queried, and how the store grows past a single machine. Storage is the hardest layer to change later: a wrong data model or shard key calcifies into a scaling ceiling, and getting replication wrong silently serves stale or lost data. ## When to reach for this Any system that persists state: picking SQL vs NoSQL, designing a schema and its access paths, adding indexes, splitting a hot table, distributing data across nodes (sharding/partitioning), adding read replicas, or deciding what to denormalize. Reach here the moment "store the data" needs a concrete key and query shape. ## When NOT to Don't shard, add replicas, or reach for NoSQL before a number forces it (YAGNI). A single well-indexed relational node handles ~1k QPS and tens of GB to low TB comfortably — most systems never outgrow it. Sharding multiplies operational cost and breaks joins/transactions; add it only when one node's write throughput or dataset size is genuinely exceeded (→ `back-of-the-envelope`). Caching reads (→ `caching`) and adding read replicas are cheaper first moves than sharding. ## Clarify first Answer these before choosing a store or topology — they decide the design: - **Data shape & relationships** — flat key-value? rich relations needing joins? document blobs? a graph of connections? Drives SQL vs NoSQL. - **Access patterns** — *how* is data read and written, not just where it lives. Point lookups by key, range scans, ad-