What is one way that a distributed NoSQL database usually shards data?
A. By grouping all keys numerically
B. By distributing all records that share the same key across multiple servers
C. By grouping all records that have the same data on the same server
D. By grouping all keys lexically
The correct answer and explanation is :
Correct Answer: D. By grouping all keys lexically
Explanation:
In distributed NoSQL databases, sharding refers to the practice of dividing data into smaller, more manageable parts (called shards), which are then distributed across multiple servers or nodes. This technique helps scale the database horizontally and improves performance and availability.
One common method of sharding in NoSQL databases is lexical key-based sharding, where data is distributed by grouping all keys lexically. In this method, data keys (which are often strings) are sorted in lexicographical (alphabetical) order and then split into ranges. Each range is assigned to a different shard. For example:
- Shard 1: A–F
- Shard 2: G–M
- Shard 3: N–Z
This approach is straightforward and allows for efficient key lookup using ordered range queries. However, it has some trade-offs. If the dataset has skewed key distributions—such as most keys starting with the letter “A”—then the corresponding shard may become a hotspot, receiving more load than others. This can lead to performance bottlenecks.
NoSQL systems like Apache HBase and Amazon DynamoDB sometimes implement similar strategies, although they often add mechanisms (e.g., hash-based partitioning or virtual nodes) to balance load more effectively across shards.
Let’s examine the incorrect options:
- A (Grouping all keys numerically): Similar to D, but numeric grouping is more limited and less flexible than lexical grouping for general-purpose keys.
- B (Distributing records with the same key): This would cause inconsistency, as records with the same key need to stay together to maintain data integrity.
- C (Grouping records with same data): This isn’t scalable or practical for diverse datasets, and would also be inefficient.
Hence, D is correct as it reflects a widely used and conceptually sound sharding method.