On Disk Data Structures

On-disk data structures are specialized data organization methods designed for persistent storage on physical media like hard drives and SSDs. Unlike in-memory structures, they are optimized for the unique characteristics of disk storage ? slower access times, block-based I/O, and persistence across system restarts. These structures form the foundation of file systems, databases, and other storage-intensive applications.

What are On-Disk Data Structures?

On-disk data structures define how data is physically organized and accessed on storage devices. They differ from memory-based structures in several key ways:

• Block-oriented access ? Data is read/written in blocks rather than individual bytes

• Sequential vs. random access ? Sequential reads are much faster than random access

• Persistence ? Data remains intact after power loss

• Larger capacity ? Can handle datasets much larger than available RAM

Types of On-Disk Data Structures

B-Trees and B+ Trees

B-trees are the most common on-disk data structure, designed specifically for block storage devices. Each node contains multiple keys and children, minimizing disk I/O operations.

Hash Tables with Overflow Handling

Disk-based hash tables use techniques like extendible hashing or linear hashing to handle collisions and dynamic growth without full table reorganization.

Log-Structured Data

Data is written sequentially in append-only logs, optimizing for write performance. Examples include LSM-trees (Log-Structured Merge trees) used in modern NoSQL databases.

Storage Formats and Organization

Block-Based Storage

Data is organized into fixed-size blocks (typically 4KB-64KB). Each block contains:

• Header ? Metadata about block type, version, checksums

• Data section ? Actual records or index entries

• Footer ? Additional metadata for integrity checking

Record Organization

Indexing Strategies

Primary vs. Secondary Indexes

• Primary index ? Built on the ordering key of the data file

• Secondary index ? Built on non-ordering fields, points to primary records

Multi-level Indexing

For large datasets, indexes themselves are indexed, creating multiple levels to reduce search time from O(n) to O(log n).

Performance Optimization Techniques

Buffering and Caching

Frequently accessed blocks are kept in memory buffers to reduce disk I/O. Common strategies include LRU (Least Recently Used) and clock algorithms.

Sequential vs. Random Access

Disk drives perform sequential reads much faster than random access. Structures are designed to maximize sequential access patterns.

Compression

Data compression reduces storage requirements and can improve I/O performance by reducing the amount of data transferred.

Applications

Database Management Systems

Relational databases use B+ trees for indexes and heap files for data storage. NoSQL databases employ various structures like LSM-trees and hash tables.

File Systems

File systems use inodes, directory trees, and free space management structures to organize files and metadata on disk.

Search Engines

Search engines use inverted indexes stored as B-trees or hash tables to quickly locate documents containing specific terms.

Conclusion

On-disk data structures are essential for efficient persistent storage, designed around the unique characteristics of disk storage devices. The choice of structure depends on access patterns, data size, and performance requirements. Understanding these structures is crucial for building scalable storage systems and optimizing database performance.