Database Scaling

Copies of the same data on multiple nodes: primary handles writes, replicas serve reads and take over on failure.

7 min 1 diagram practice

Splitting one logical dataset into smaller slices so each slice can live (and be queried) on its own node.

7 min 1 diagram practice

Partitioning across separate physical databases — the real 'put more money in the machine' of writes.

7 min 1 diagram 1 code practice

High cardinality, even distribution, and alignment with real queries — the three criteria.

6 min practice

When one shard drowns in traffic while siblings idle — the celebrity/whale/one-tenant problem.

6 min practice

The ring-based mapping that makes adding/removing nodes move only a tiny slice of keys — the trick behind Redis clusters, Cassandra, and LB affinity.

8 min 1 diagram 1 code practice

The decision matrix: joins/transactions/flexibility vs scale/schema/consistency — and the honest 'mostly SQL' answer.

7 min 1 code practice

MongoDB-style: JSON documents, embedded nesting, flexible schema, secondary indexes, and Atlas-scale ops.

6 min practice

DynamoDB/Redis-class stores: O(1) by key, insane scale, and the access patterns that justify them.

6 min practice

Cassandra/Bigtable: rows with dynamic columns, partition-key-clustered tables, tuned for planetary write scale.

6 min practice