Database Sharding Explained inn 5 Minutes

In database terms, sharding is the practice of dividing a large dataset into smaller, more manageable chunks, called shards, each stored on a different server. Instead of one large database handling all requests, each shard handles a subset of data, making the system faster and more efficient.
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Database sharding is a technique that partitions a large database into smaller, more manageable units called "shards," distributed across multiple servers to improve performance, scalability, and availability.
Here's a more detailed explanation:
What is Sharding?
Horizontal Partitioning:
Sharding, also known as horizontal partitioning, divides a database into smaller, independent parts (shards).
Data Distribution:
Each shard contains a subset of the total data and is responsible for serving a portion of the overall workload.
Scalability:
Sharding allows databases to scale horizontally by adding more servers (shards) to handle increased data volume and traffic.
Performance:
By distributing data across multiple servers, sharding reduces the load on any single server, leading to improved performance and faster query times.
Fault Tolerance:
If one shard fails, the rest of the system can continue to operate, improving fault tolerance.
Benefits of Sharding:
Improved Performance:
Distributing data across multiple servers reduces the load on any single server, leading to faster query times and improved overall performance.
Enhanced Scalability:
Sharding allows you to scale your database horizontally by adding more servers (shards) as needed, without disrupting the system.
Increased Availability:
If one shard fails, the rest of the system can continue to operate, improving availability and reducing downtime.
Cost Savings:
Sharding can help reduce costs by allowing you to use smaller, cheaper servers instead of investing in a large, expensive one.
Improved Resource Utilization:
Sharding distributes the workload across multiple servers, leading to better resource utilization and reduced risk of overloading a single server.
Common Sharding Strategies:
Range-Based Sharding:
Divides data based on specified ranges of values (e.g., sharding customer data based on purchase amounts).
Hash-Based Sharding:
Applies a hash function to a chosen shard key (e.g., sharding user data based on user IDs).
List-Based Sharding:
Explicitly specifies which shard will store certain data based on a predefined list of values (e.g., sharding chat history based on country code).
Shard Synchronization: Maintaining consistency across multiple shards can be challenging.
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