What is Sharding in DBMS?
Sharding is a very important concept that helps the system to keep data in different resources according to the sharding process. The word “Shard” means “a small part of a whole“. Hence Sharding means dividing a larger part into smaller parts. In DBMS, Sharding is a type of DataBase partitioning in which a large database is divided or partitioned into smaller data and different nodes. These shards are not only smaller, but also faster and hence easily manageable.
Need for Sharding:
Consider a very large database whose sharding has not been done. For example, let’s take a DataBase of a college in which all the student’s records (present and past) in the whole college are maintained in a single database. So, it would contain a very very large number of data, say 100, 000 records. Now when we need to find a student from this Database, each time around 100, 000 transactions have to be done to find the student, which is very very costly. Now consider the same college students records, divided into smaller data shards based on years. Now each data shard will have around 1000-5000 students records only. So not only the database became much more manageable, but also the transaction cost each time also reduces by a huge factor, which is achieved by Sharding. Hence this is why Sharding is needed.
How does Sharding work?
In a sharded system, the data is partitioned into shards based on a predetermined criterion. For example, a sharding scheme may divide the data based on geographic location, user ID, or time period. Once the data is partitioned, it is distributed across multiple servers or nodes. Each server or node is responsible for storing and processing a subset of the data.
To query data from a sharded database, the system needs to know which shard contains the required data. This is achieved using a shard key, which is a unique identifier that is used to map the data to its corresponding shard. When a query is received, the system uses the shard key to determine which shard contains the required data and then sends the query to the appropriate server or node.
Features of Sharding:
- Sharding makes the Database smaller
- Sharding makes the Database faster
- Sharding makes the Database much more easily manageable
- Sharding can be a complex operation sometimes
- Sharding reduces the transaction cost of the Database
- Each shard reads and writes its own data.
- Many NoSQL databases offer auto-sharding.
- Failure of one shard doesn’t effect the data processing of other shards.
Benefits of Sharding:
- Improved Scalability: Sharding allows the system to scale horizontally by adding more servers or nodes as the data grows. This improves the system’s capacity to handle large volumes of data and requests.
- Increased Performance: Sharding distributes the data across multiple servers or nodes, which improves the system’s performance by reducing the load on each server or node. This results in faster response times and better throughput.
- Fault Tolerance: Sharding provides a degree of fault tolerance as the system can continue to function even if one or more servers or nodes fail. This is because the data is replicated across multiple servers or nodes, and if one fails, the others can continue to serve the requests.
- Reduced Costs: Sharding allows the system to scale horizontally, which can be more cost-effective than scaling vertically by upgrading hardware. This is because horizontal scaling can be done using commodity hardware, which is typically less expensive than high-end servers.
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