Indexing and B+ Trees Practice Questions for GATE DA: 0+ Solved Questions with Step-by-Step Solutions

Solve 0+ Indexing and B+ Trees practice questions for GATE DA with answers and detailed solutions. Free sample questions below.

Chapter Roadmap: Indexing and B+ Trees

Chapter Journey

1. B+ Tree Structure, Insertion and Fanout Calculation

Current Topic • High Importance • Foundation for all indexing and disk I/O questions

2. Index Selection for SQL Query Processing

Upcoming • Moderate Importance • Applies B+ tree mechanics to query execution plans

What you will master: Deriving node capacity limits from block sizes, executing step-by-step B+ tree insertions, and avoiding classic split traps.

Why B+ Trees? The Core Intuition

The Disk I/O Problem

Database systems minimize slow disk accesses by reading data in fixed-size blocks (or pages). A good index must minimize the number of blocks read to find a record.

Why Not Binary Search Trees?

A binary tree has a maximum of two children per node. For a million records, the height is around 20, meaning up to 20 disk reads. This is inefficient.

The B+ Tree Solution

High Fanout: Each node can hold hundreds of keys, drastically reducing the tree height (typically to 3 or 4 levels for millions of records).
Balanced: All leaf nodes are at the exact same depth, guaranteeing predictable, worst-case $O (lo g_{p} N)$ search time.
Data at Leaves Only: Internal nodes act purely as a "map" or routing guide. Only leaf nodes contain Data Record Pointers.
Linked Leaves: Leaf nodes are connected via sequential pointers, making range queries highly efficient without traversing back up the tree.

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