Data Structures for Indexing

3.1 B- Tree and B+ Tree

B-Tree and B+ Tree are balanced tree structures commonly used in database indexing.

• B-tree, keys are saved in each inner and leaf nodes, making it suitable for range queries with efficient insertion and deletion operations.
• B+ trees, a version, store keys most effective in the leaf nodes, improving range query overall performance and making them foremost in database structures because of their balance and predictability.

Advantages of using B- and B+ Tree:

• Efficient for range queries and equality searches.
• Balancing guarantees predictable performance.
• Well-suitable for situations in which data is often updated.

Disadvantages of using B- and B+ Tree:

• Overhead in terms of storage area.
• Maintenance operations (splitting and merging nodes) could be resource intensive.

3.2 Hash Index:

Hash indexing utilizes hash capabilities to map keys to specific locations inside the index. This method is highly efficient for equality searches, supplying quick access to targeted data on the basis hashed key.

However, its efficiency diminishes while handling variety queries, and managing collisions, in which a couple of keys hash to the same location, can introduce complexities in the indexing system. Despite those issues, hash indexes are extensively used for their speed in query instances.

Advantages of using Hash Index:

• Extremely fast for equality searches.
• Minimal storage overhead.

Disadvantages of using Hash Index:

• Inefficient for range queries.
• Handling collisions (two keys hashing to the identical region) can be complex.

3.3 Bitmap Index

Bitmap indexing represents a fixed of keys the usage of bitmaps for every distinct value in the indexed column. This indexing method is especially powerful for low-cardinality statistics, in which there are limited distinct values.

Bitmap indexes prove to be space effiecient for sparse records situations however may also face challenges with excessive-cardinality datasets, main to improved storage requirements for dense data.

Advantages of using Bitmap Index:

• Efficient for low cardinality information.
• Space-efficient for sparse data.

Disadvantages of using Bitmap Index:

• Inefficient for excessive cardinality data.
• Increased storage requirements for dense information.

System design is a complicated system that involves developing efficient and scalable solutions to satisfy the demands of modern applications. One crucial thing of system design is indexing, a way used to optimize information retrieval operations. In this article, we will delve into the idea of indexing, its significance, numerous types, and best practices for implementing indexing in system layout.