1. What is Database Sharding?
- Database sharding is a data architecture strategy that increases database performance by splitting up data into chunks and then spreading these chunks intelligently across multiple database servers or database instances.
- These chunks of data are called shards, while each shard contains a subset of our data. All shards represent the entire set of data, and each row of data exists in only one shard.
2. Why do we need database sharding?
- Scalability: Sharding allows you to distribute your data across multiple servers, enabling the system to handle more data and traffic without overwhelming a single server.
- Improved Performance: Reduced load on individual servers by splitting the data into smaller shards, the query load on each server is reduced, improving response times and overall database performance.
- Avoiding Single Point of Failure: In a single instance of database, failure of a database server can lead to a complete system outage. Sharding reduces this risk by spreading the data across multiple independent servers.
- Handling Large Data: Distribute large datasets when databases grow to a size that a single server can no longer efficiently store or manage, sharding divides the data into manageable chunks.
- Cost Efficiency: Instead of investing in high-end, powerful servers for vertical scaling, sharding allows the use of multiple servers, reducing infrastructure costs.
3. Difference between database sharding and partitioning?
- Database Sharding
- Divides a database into smaller, autonomous units (shards) distributed across multiple servers.
- Shards are independent, meaning each shard is responsible for its own subset of the data.
- Ideal for large-scale distributed systems needing high scalability.
- Fault tolerance is achieved through shard distribution across multiple servers.
- Database Partitioning
- Partitions can exist on the same server or across servers, but distribution is optional.
- Partitions are part of the same database instance.
- Primarily used to improve data organization and performance in a single system.
- Fault tolerance depends on partition replication or distribution but isn’t inherent.
4. What are the different methods of database sharding?
- Key Based Database Sharding:
- A unique key (e.g., User ID or Order ID) is selected from the dataset. This key determines which shard the data will be stored in.
- A hash function is applied to the shard key to map it to a specific shard. This ensures an even distribution of data across all shards.
- The hashing process distributes data evenly across shards, helping avoid hotspots.
- Range Based Database Sharding:
- A specific key (e.g., Date, User ID) is selected, and data is partitioned into shards based on the range of this key’s values.
- Each shard holds data within a specific range of values for the sharding key. For example, Shard 1 might store data for IDs 1-1000, Shard 2 for IDs 1001-2000.
- Since each shard contains data from a well-defined range, the system can easily route queries to the correct shard based on the range of the sharding key in the query.
- Directory-based database sharding:
- Directory-based Sharding, also known as metadata-based Sharding, employs a separate service or metadata store to maintain a mapping of data to shards.
- Each piece of data contains metadata or attributes that describe which shard it belongs to.
- Directory-based Sharding offers flexibility in distributing data based on a variety of criteria, including business logic and data attributes.
5. What are the pros and cons of database sharding?
- Pros
- The sharding pattern is well suited for large, distributed enterprise applications.
- Sharding allows for the fast execution of a command or a query.
- Storage segmentation, which is a key feature of the sharding pattern, enables the physical infrastructure to scale in a controlled manner.
- Cons
- Sharding requires DBAs to have domain expertise and experience with best practices in relevant database technologies for managing servers.
- Shards distributed over many geolocations can be susceptible to performance degradation due to excessive network traffic.
- Some database technologies are better suited to the sharding pattern than others. Thus, you need to choose wisely.
- Added hardware means a higher total cost of ownership of the service.
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