𝑫𝒂𝒕𝒂𝒃𝒂𝒔𝒆 𝑺𝒉𝒂𝒓𝒅𝒊𝒏𝒈 - rnakidi/dsa GitHub Wiki

𝑫𝒂𝒕𝒂𝒃𝒂𝒔𝒆 𝑺𝒉𝒂𝒓𝒅𝒊𝒏𝒈

As data grows exponentially, ensuring your database keeps pace with performance demands can be challenging. Sharding, a technique that involves partitioning data horizontally, is a highly effective solution for scaling databases efficiently.

Key-Based Sharding With key-based sharding, the shard key determines where each row is stored. This method places records with similar shard key values in the same shard, optimizing performance for certain queries.

Range-Based Sharding In range-based sharding, shards are assigned based on a specific range of shard key values. For instance, one shard could store data for values between $120-$150, while another might handle values above $500, allowing fine-tuned control over data distribution.

Directory-Based Sharding Directory-based sharding introduces a lookup table to map shard keys to shards, offering greater flexibility compared to key-based sharding by allowing more dynamic allocation of data.

By adopting sharding, you can significantly improve database performance and scalability to handle vast data volumes seamlessly.

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