Database sharding is a scalability technique that involves horizontally dividing the data across several servers or shards. This is because each server can function more effectively with fewer data points when processing requests and changes. Each server can manage a smaller portion of the total workload and data.
C๐ผ๐บ๐บ๐ผ๐ป M๐ฒ๐๐ต๐ผ๐ฑ๐ ๐ณ๐ผ๐ฟ I๐บ๐ฝ๐น๐ฒ๐บ๐ฒ๐ป๐๐ถ๐ป๐ด D๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ S๐ต๐ฎ๐ฟ๐ฑ๐ถ๐ป๐ด?
- ๐ฅ๐ฎ๐ป๐ด๐ฒ-๐ฏ๐ฎ๐๐ฒ๐ฑ ๐๐ต๐ฎ๐ฟ๐ฑ๐ถ๐ป๐ด: This method partitions the data based on a key value, such as a user ID or a timestamp, and distributes the data among the shards according to the key value’s range. One shard, for instance, may include all user IDs in the range of 1 to 1000, while another shard would house all user IDs in the range of 1001 to 2000.
- ๐๐ฎ๐๐ต-๐ฏ๐ฎ๐๐ฒ๐ฑ ๐๐ต๐ฎ๐ฟ๐ฑ๐ถ๐ป๐ด: A hash function is employed in this strategy to distribute data among shards depending on the key value. For example, all data with the user ID 123 may be saved on one shard, while data with the user ID 456 could be stored on another.
- ๐๐ถ๐ฟ๐ฒ๐ฐ๐๐ผ๐ฟ๐-๐ฏ๐ฎ๐๐ฒ๐ฑ ๐๐ต๐ฎ๐ฟ๐ฑ๐ถ๐ป๐ด: In this approach, a central directory is used to map the key values to the specific shard where the data is stored. The directory can be used to determine which shard a piece of data belongs to, and the data can be retrieved from the appropriate shard.
- ๐๐๐๐๐ผ๐บ ๐๐ต๐ฎ๐ฟ๐ฑ๐ถ๐ป๐ด: When a database and the applications that use it have unique demands and requirements, it could be required to develop a bespoke sharding technique. This might comprise a mix of various sharding techniques or a whole original strategy.
Conclusion
Although database sharding may be a helpful strategy for enhancing a database’s efficiency and scalability, it is not always the best option for every use case and; should be carefully assessed based on a database system’s unique requirements.
Source:
- ๐๐ฟ๐ผ๐ธ๐ธ๐ถ๐ป๐ด ๐๐ต๐ฒ ๐ฆ๐๐๐๐ฒ๐บ ๐๐ฒ๐๐ถ๐ด๐ป ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐: https://lnkd.in/g4Wii9r7
- ๐๐ฟ๐ผ๐ธ๐ธ๐ถ๐ป๐ด ๐๐ต๐ฒ ๐๐ฑ๐๐ฎ๐ป๐ฐ๐ฒ๐ฑ ๐ฆ๐๐๐๐ฒ๐บ ๐๐ฒ๐๐ถ๐ด๐ป ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐: https://lnkd.in/dyCRtiec