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Distributed SQL Databases

Distributed SQL databases store relational data across several machines while presenting one SQL database to applications. They coordinate replicas and transactions so the database can keep consistent data as it scales or survives machine and site failures.

itDatabases and data storage

Distributed SQL Databases

A distributed SQL database is a relational database whose data, query work, and transaction coordination span a cluster of machines. Applications still use tables, indexes, constraints, and SQL transactions. The database divides rows into partitions, maintains replicated copies, and coordinates changes across those partitions.

This combination addresses a hard operational problem. A single relational server offers familiar semantics, but its write throughput and storage eventually meet one machine's limits. Manual sharding spreads data across servers, but application code must route requests and coordinate work that crosses shards. Distributed SQL moves much of that routing, replication, and transaction logic into the database.

The result is not a faster form of every relational workload. Coordination adds network delays and failure modes that a single process does not have. Distributed SQL is most useful when horizontal scale, multi-zone survival, or geographic placement matters enough to pay that cost.

The logical database and the physical cluster

The application sees one logical database. Beneath that interface, most systems contain several layers:

  • SQL layer: parses statements, checks schemas and permissions, chooses plans, and turns relational operations into reads and writes against distributed data.
  • Distribution layer: maps keys or key ranges to partitions. Products call these units ranges, splits, tablets, or shards.
  • Replication layer: keeps several copies of each partition and uses a consensus protocol to agree on an ordered log of changes.
  • Transaction layer: provides isolation and atomicity when one transaction touches multiple keys or partitions.
  • Placement and metadata services: track partition boundaries, replica locations, leaders, health, and placement constraints.
  • Storage engine: persists keys and versions on each node. Multi-version concurrency control, or MVCC, retains timestamped versions so reads can use a consistent snapshot.

These responsibilities may run in one process or in separate compute and storage services. The boundary affects scaling and failure behavior, but the logical problem stays the same: route each operation to the correct replicated partition and preserve the promised transaction semantics.

How a write moves through the cluster

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