Database.Middle.Explain the concept of sharding in databases.

Sharding is a database scaling technique where you split a large database into smaller, faster, more manageable pieces called “shards.”

Each shard contains a subset of the data, and all shards together make up the full dataset.


🧠 Why Use Sharding?

  • Improves performance: Reduces load per server
  • 🌍 Increases scalability: More shards = more capacity
  • 🧩 Enables horizontal scaling: Add servers to scale out

🗂️ Example

Suppose you have a users table with 1 billion users:

Instead of storing all users in one huge table on one server, you could:

ShardRange
1Users with ID 1–100M
2Users with ID 100M–200M
3Users with ID 200M–300M

Each shard lives on a separate server or database instance.

🛠️ Types of Sharding

TypeDescriptionExample
Horizontal shardingSplit rowsusers by region or ID range
Vertical shardingSplit columnsusers_basic vs users_private
Directory-based shardingUse a lookup tableKeeps track of which user is in which shard

⚙️ How It Works

  • Shard key: A column (e.g. user_id, region, tenant_id) used to decide which shard data goes into
  • Application logic or middleware routes queries to the right shard

📦 Benefits

  • ✅ Load is spread across multiple machines
  • ✅ Parallelism improves throughput
  • ✅ Reduces contention and I/O bottlenecks

⚠️ Challenges

IssueDescription
Cross-shard joinsHarder to query across shards
ReshardingMoving data when scaling or changing strategy
Complex logicApp must handle routing and aggregation
ConsistencyMore difficult to ensure strong consistency across shards

🔄 Example Sharding Strategy

// Pseudo-code for picking a shard:
shard_id = hash(user_id) % num_shards

Then route the query to shard[shard_id].

🧰 Common Use Cases

  • Large-scale social networks
  • Multi-tenant SaaS apps
  • High-volume e-commerce platforms
  • Large log/event stores

🧱 Summary

ConceptDescription
ShardingSplitting data across DB instances
Shard KeyField used to determine shard
GoalScale out performance + storage
Trade-offsComplexity, joins, consistency
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