JSON (JavaScript Object Notation) and XML (Extensible Markup Language) represent two fundamental approaches to data structuring, each optimized for distinct computational paradigms. This study provides a systematic comparative analysis based on four key parameters: syntactic structure, performance metrics (file size and parsing speed), validation capabilities, and ecosystem adoption. Quantitative measurements demonstrate that JSON generates files 30-50% smaller than XML and achieves parsing speeds 2-10 times faster, making it optimal for REST APIs, mobile applications, and microservices where performance is critical. Conversely, XML’s sophisticated validation through XSD schemas, namespace support, and transformation capabilities via XSLT render it indispensable for regulated sectors requiring semantic rigor—including legal documents, standardized B2B exchanges, and long-term digital archives. The analysis reveals that these formats are not competitors but complementary tools: JSON embodies pragmatic efficiency for application-oriented exchanges, while XML provides structural integrity for complex document ecosystems. Recent evolutionary developments—including JSON Schema (RFC 8927) and JSON-LD for semantic annotations, alongside XML simplifications—demonstrate contextual adaptation rather than convergence. The findings establish that optimal format selection depends on project-specific constraints: data complexity requirements, validation needs, performance thresholds, and target ecosystem integration. This study contributes a structured decision-making framework enabling architects to select appropriate formats based on empirical criteria rather than ideological preferences.

This article focuses on the comparison of the temporal and spatial algorithmic complexities of iterative and recursive structures, through the analysis of classic cases (factorial, Fibonacci sequence, tree traversal). We demonstrate in this study that on the one hand iteration generally offers better memory efficiency (O (1) in many cases) and avoids the risks of stack overflow, and on the other hand that recursion, although more elegant and intuitive for certain problems (such as tree traversals), can generate a memory overload (O (n) in call stack) and degraded time complexity in non-optimized cases (eg: naive Fibonacci in O (2ⁿ)). The results obtained here highlight that the choice between these two approaches depends on the context the developer is in. It is therefore worth noting that iteration is better suited to linear and memory-constrained problems, and recursion to nested structures (trees, divide-and-conquer), especially if the language supports tail call optimization. This comparison provides objective criteria to guide developers in selecting the most effective approach based on needs.