# Scaling Reads and Partitioning Strategies — System Design

Source: https://www.geekswithgeeks.com/en/system-design/sd-scaling-reads-partitioning

> Read replicas for read load; partitioning strategies for write load.

## Read replicas

Adding **read replicas** off a single primary scales read throughput cheaply — but replication lag means a replica read can be milliseconds to seconds stale. Route read-your-own-write requests to the primary.

## Partitioning strategies

**Range partitioning** (by date or ID range) keeps related rows together for range scans but risks hotspots on the latest range. **Hash partitioning** spreads load evenly but scatters range queries across shards. **Directory-based** partitioning uses a lookup service for full flexibility at the cost of an extra hop.

## Branch libraries vs one warehouse

Read replicas are branch libraries holding copies of the same books — convenient, but a just-published book might not have reached every branch yet. Partitioning is splitting one huge warehouse into regional ones, each fully responsible for its own slice of inventory.

**Quiz:** Reading data you just wrote, immediately after, is most at risk of appearing missing when reading from:

- [ ] The primary
- [x] A read replica
- [ ] A local cache you just wrote

*Answer:* A read replica. Replication lag means a replica may not yet have the write that just committed on the primary.