Typorithm · hard

Sharding & Replication

sharding-replicationsystem-designshardingreplicationshard-key

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Full lesson · 8 learning steps Open any exercise for focused practice

Step 1 of 8 · intro card

Learn Sharding & Replication

Data Layer

Replication (leader-follower) copies data to followers, scaling reads and adding availability. Sharding partitions data by a key to scale writes; the shard key must spread load to avoid hotspots, and consistent hashing minimizes data movement when rebalancing nodes.

Replication keeps copies of the same data on multiple machines. In leader-follower replication, the leader takes writes and streams them to followers; reads can hit any follower, which scales read throughput and keeps the system available if the leader fails.

Sharding splits the data itself by a key so each node owns only a slice. Because writes are spread across shards, this is how you scale write throughput beyond one machine.

The shard key is the critical decision: it must distribute load evenly, or one shard becomes a hotspot while others sit idle. When you add or remove nodes, consistent hashing minimizes how much data has to move, avoiding a full reshuffle.

Replication (leader-follower) scales reads and adds availability.

Sharding partitions by key to scale writes.

Consistent hashing limits data movement on rebalance.

scaling_data:
  replication: leader-follower
  read_scaling: replicas
  sharding: partition-by-key
  shard_key: avoid-hotspots
  rebalance: consistent-hashing

Read the implementation

Connect each part of the code to the concept before you type.

1. Replication

Leader-follower replication copies writes from a leader to follower replicas.

replication: leader-follower

2. Read scaling

Follower replicas serve reads, spreading read load and adding availability.

read_scaling: replicas

3. Sharding

Sharding partitions data by a key so writes scale across many nodes.

sharding: partition-by-key

4. Shard key

A good shard key spreads load evenly so no single node becomes a hotspot.

shard_key: avoid-hotspots

5. Rebalancing

Consistent hashing moves the least data when nodes are added or removed.

rebalance: consistent-hashing

A complete learning loop

Understand

Read the concept map and follow the code breakdown.

Reconstruct

Type the implementation, repair mistakes and work through every recall exercise.

Verify

Use the final check without hints; revisit weak concepts in short practice sessions.