In a relational database management system (RDBMS), which data structure is commonly used internally to implement indexes efficiently for range queries and ordered access?

Introduction / Context:
Relational database management systems must store and retrieve data efficiently, especially when handling large tables and complex queries. Indexes are special structures that speed up search and range queries on table columns. The choice of internal data structure for indexes affects performance and scalability. This question asks which data structure is widely used in RDBMS implementations for this purpose.

Given Data / Assumptions:

• We are interested in the internal representation of indexes in disk based RDBMS products.
• The data structure must support efficient search, insertion, deletion, and range queries.
• The structure should minimise disk I O by grouping keys into blocks or pages.
• Many commercial and open source databases use similar tree based strategies for indexing.

Concept / Approach:
B plus trees are a variation of B trees that are optimised for disk storage and range queries. In a B plus tree, all actual data records or pointers reside in the leaf nodes, and internal nodes contain only keys and child pointers. Leaves are typically linked together to support fast in order traversal, making range queries very efficient. Because nodes correspond to disk pages, B plus trees minimise the number of I O operations required to find a key. This makes them ideal for implementing both clustered and non clustered indexes in many RDBMS engines.

Step-by-Step Solution:
Step 1: Eliminate stacks and queues, which are linear data structures used for last in first out and first in first out processing rather than ordered indexing.Step 2: Recognise that self balancing trees are good candidates for search operations, but the specific tree design matters for disk based systems.Step 3: Compare AVL trees and B plus trees; AVL trees are binary with strict height balancing, which can lead to many pointer traversals and frequent rebalancing.Step 4: B plus trees, on the other hand, have high fan out, reducing tree height and mapping nodes naturally to disk blocks, which is more efficient for RDBMS workloads.Step 5: Conclude that B plus tree structure is the most appropriate answer for internal index representation in relational databases.

Verification / Alternative check:
Documentation for database systems such as MySQL InnoDB, PostgreSQL, and others frequently mentions B tree or B plus tree based indexing. In particular, many implementations describe indexes as B plus trees because data values are stored only at the leaves, with leaf level linked lists providing fast range scans. Database textbooks also present B plus trees as the standard index structure for relational systems, confirming this choice.

Why Other Options Are Wrong:
Option A, stack, and option B, queue, are simple linear structures that support push or pop operations but do not support efficient ordered searches or range queries. Option D, AVL tree, is a balanced binary search tree that works well in memory but is less suited to disk I O patterns due to its small branching factor and frequent rotations. While some in memory databases might use such trees, mainstream disk based RDBMS products rely on B plus trees for indexing.

Common Pitfalls:
A common pitfall is to assume that binary search trees are sufficient for all indexing needs without considering disk layout and cache behaviour. In real systems, reducing disk seeks is critical, and B plus trees achieve this through high fan out and page oriented design. Another mistake is overlooking how leaf level links make range queries efficient. Understanding why B plus trees are preferred helps you interpret query plans and index design recommendations from database documentation.

Final Answer:
Correct answer: B+ tree structure