An Extensive Study on Text Serialization Formats and Methods

TL;DR

This paper provides a comprehensive overview of text serialization formats and methods, comparing their structures, performance, and suitability for different applications to guide optimal choice in computing tasks.

Contribution

It offers an extensive analysis of prevalent text serialization formats, their methods, and performance trade-offs, serving as a valuable resource for selecting appropriate serialization techniques.

Findings

JSON often offers a good balance of readability and performance.

XML tends to be more verbose but highly structured.

YAML and CSV have specific advantages in readability and simplicity.

Abstract

Text serialization is a fundamental concept in modern computing, enabling the conversion of complex data structures into a format that can be easily stored, transmitted, and reconstructed. This paper provides an extensive overview of text serialization, exploring its importance, prevalent formats, underlying methods, and comparative performance characteristics. We dive into the advantages and disadvantages of various text-based serialization formats, including JSON, XML, YAML, and CSV, examining their structure, readability, verbosity, and suitability for different applications. The paper also discusses the common methods involved in the serialization and deserialization processes, such as parsing techniques and the role of schemas. To illustrate the practical implications of choosing a serialization format, we present hypothetical performance results in the form of tables, comparing formats based on metrics like serialization deserialization speed and resulting data size. The discussion analyzes these results, highlighting the trade offs involved in selecting a text serialization format for specific use cases. This work aims to provide a comprehensive resource for understanding and applying text serialization in various computational domains.