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Smile is a computer data interchange format based on JSON.It can also be considered a binary serialization of the generic JSON data model, which means tools that operate on JSON may be used with Smile as well, as long as a proper encoder/decoder exists for the tool.
^ The primary format is binary, but text and JSON formats are available. [8] [9] ^ Means that generic tools/libraries know how to encode, decode, and dereference a reference to another piece of data in the same document. A tool may require the IDL file, but no more. Excludes custom, non-standardized referencing techniques.
JSON streaming comprises communications protocols to delimit JSON objects built upon lower-level stream-oriented protocols (such as TCP), that ensures individual JSON objects are recognized, when the server and clients use the same one (e.g. implicitly coded in). This is necessary as JSON is a non-concatenative protocol (the concatenation of ...
The JSON files that contains JData annotation tags is recommended to have a suffix of .jdt, although it can also be saved as .json. A few slight differences exist between a .jdt and a .json file, including JData .jdt file accepts multiple concatenated JSON objects inside a single file
A JSON Patch document is structured as a JSON array of objects where each object contains one of the six JSON Patch operations: add, remove, replace, move, copy, and test. This structure was influenced by the specification of XML patch.
JSON was based on a subset of the JavaScript scripting language (specifically, Standard ECMA-262 3rd Edition—December 1999 [11]) and is commonly used with JavaScript, but it is a language-independent data format. Code for parsing and generating JSON data is readily available in many programming languages.
MessagePack is more compact than JSON, but imposes limitations on array and integer sizes.On the other hand, it allows binary data and non-UTF-8 encoded strings. In JSON, map keys have to be strings, but in MessagePack there is no such limitation and any type can be a map key, including types like maps and arrays, and, like YAML, numbers.
This makes accessing data in these formats much faster than data in formats requiring more extensive processing, such as JSON, CSV, and in many cases Protocol Buffers. Compared to other serialization formats however, the handling of FlatBuffers requires usually more code, and some operations are not possible (like some mutation operations).