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Database Scaling
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In a primary-replica (master-slave) database setup, how are reads and writes typically distributed?
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┌──── Replica 1 (reads)
Primary ──┤──── Replica 2 (reads)
(writes) └──── Replica 3 (reads)AAll reads and writes go to all nodes simultaneously to ensure consistency.
BWrites go to the primary; reads can be served by both the primary and replicas, distributing read load.
CReads go exclusively to the primary; replicas only receive writes.
DThe primary handles odd-numbered requests; replicas handle even-numbered requests.
What is the main consistency risk of multi-primary (master-master) replication compared to primary-replica?
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Client A ──▶ Primary 1 ──sync──▶ Primary 2 ◀── Client BABoth primaries accept writes simultaneously, which can create write conflicts when both modify the same record concurrently.
BMulti-primary replication cannot handle concurrent reads from multiple clients.
CMulti-primary replication requires twice the storage because data is duplicated.
DOnly one primary can be active at a time, defeating the purpose of having two primaries.
What does database federation mean and what problem does it solve?
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Before federation:
┌────────────────────────────────┐
│ Monolithic DB │
│ users + orders + products │ ← single bottleneck
│ + reviews + analytics + ... │
└────────────────────────────────┘
After federation:
┌──────────┐ ┌──────────┐ ┌───────────┐
│ Users DB │ │ Orders DB│ │Products DB│
└──────────┘ └──────────┘ └───────────┘
Each database is smaller, has fewer connections,
fits more data in memory → better cache localityAFederation combines multiple databases into a single schema accessed through one connection string.
BFederation replicates the entire database across multiple geographic regions.
CFederation splits one large database into multiple smaller databases by domain/function (e.g., users DB, orders DB, products DB), reducing load on each.
DFederation is a NoSQL pattern that replaces JOINs with denormalized documents.
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