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RDFLib is a Python library for working with RDF, [2] a simple yet powerful language for representing information. This library contains parsers/serializers for almost all of the known RDF serializations, such as RDF/XML, Turtle, N-Triples, & JSON-LD, many of which are now supported in their updated form (e.g. Turtle 1.1).
Zorba is usable through different host languages: C++, C, XQJ / Java, PHP, Python, C#, Ruby, and even XQuery/JSONiq. Zorba is also available as a command-line tool. XQDT is an XQuery plugin for the Eclipse (IDE). It fully supports Zorba API and syntax.
FlatBuffers can be used in software written in C++, C#, C, Go, Java, JavaScript, Kotlin, Lobster, Lua, PHP, Python, Rust, Swift, and TypeScript. The schema compiler runs on Android , Microsoft Windows , macOS , and Linux , [ 3 ] but games and other programs use FlatBuffers for serialization work on many other operating systems as well ...
While JSON provides a syntactic framework for data interchange, unambiguous data interchange also requires agreement between producer and consumer on the semantics of specific use of the JSON syntax. [25] One example of where such an agreement is necessary is the serialization of data types that are not part of the JSON standard, for example ...
C, C#, Go, Java, JavaScript, Python, Rust — Java serialization Oracle Corporation — Yes Java Object Serialization: Yes No Yes No Yes — JSON: Douglas Crockford: JavaScript syntax: Yes STD 90/RFC 8259 (ancillary: RFC 6901, RFC 6902), ECMA-404, ISO/IEC 21778:2017: No, but see BSON, Smile, UBJSON: Yes
Length-prefixed JSON is also well-suited for TCP applications, where a single "message" may be divided into arbitrary chunks, because the prefixed length tells the parser exactly how many bytes to expect before attempting to parse a JSON string. This example shows two length-prefixed JSON objects (with each length being the byte-length of the ...
Flow diagram. In computing, serialization (or serialisation, also referred to as pickling in Python) is the process of translating a data structure or object state into a format that can be stored (e.g. files in secondary storage devices, data buffers in primary storage devices) or transmitted (e.g. data streams over computer networks) and reconstructed later (possibly in a different computer ...
The "streaming parser" is particularly useful when one of more of the JSON inputs is too large to fit into memory, since its memory requirements are typically quite small. For example, for an arbitrarily large array of JSON objects, the peak memory requirement is not much more than required to handle the largest top-level object.