Overview
They solve different problems and are usually combined. Replication keeps full copies of the dataset on several nodes — great for read scaling and surviving a node failure, but every node still holds everything, so it does not help with write volume or dataset size. Sharding partitions the data by a key so each node owns a slice — that scales writes and total storage, at the cost of cross-shard queries and rebalancing.
Sharding vs Replication: key differences
| Sharding | Replication | |
|---|---|---|
| What it does | Splits data across nodes | Copies data to nodes |
| Scales | Writes + storage | Reads + redundancy |
| Each node holds | A slice | The full dataset |
| Main cost | Cross-shard queries, hot keys | Replica lag, no write scaling |
| Failure of a node | Loses that slice (unless also replicated) | Others still have the data |
When to use Sharding
The dataset or write volume is too big for one machine — split by a good partition key (and replicate each shard for safety).
When to use Replication
Reads dominate and you want redundancy/HA — add replicas of the primary; route reads to them.
Verdict
Replicate first (it is simpler and gives read scale + HA). Shard when a single node can no longer hold the data or absorb the writes — and replicate each shard so a node loss does not lose a slice.
Common questions
What is the difference between sharding and replication?
Sharding splits different data across nodes (scales writes/storage); replication copies the same data to multiple nodes (scales reads, adds redundancy). Large systems use both.
Is partitioning the same as sharding?
Closely related: partitioning splits a dataset into pieces; sharding usually means partitioning across separate machines. All sharding is partitioning, but partitioning can also be within one node.