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Noggit Solr's streaming JSON parser for Java; Yajl – Yet Another JSON Library. YAJL is a small event-driven (SAX-style) JSON parser written in ANSI C, and a small validating JSON generator. ArduinoJson is a C++ library that supports concatenated JSON. GSON JsonStreamParser.java can read concatenated JSON. json-stream is a streaming JSON ...
However, parser generators for context-free grammars often support the ability for user-written code to introduce limited amounts of context-sensitivity. (For example, upon encountering a variable declaration, user-written code could save the name and type of the variable into an external data structure, so that these could be checked against ...
When deserializing, Gson navigates the type tree of the object being deserialized, which means that it ignores extra fields present in the JSON input. The user can: write a custom serializer and/or deserializer so that they can control the whole process, and even deserialize instances of classes for which the source code is inaccessible.
Code for parsing and generating JSON data is readily available in many programming languages. JSON's website lists JSON libraries by language. In October 2013, Ecma International published the first edition of its JSON standard ECMA-404. [4] That same year, RFC 7158 used ECMA-404 as a reference.
JSONiq [11] is a query and transformation language for JSON. XPath 3.1 [12] is an expression language that allows the processing of values conforming to the XDM [13] data model. The version 3.1 of XPath supports JSON as well as XML. jq is like sed for JSON data – it can be used to slice and filter and map and transform structured data.
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.
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).
It defines a comprehensive list of JSON-compatible "name":value constructs to store a wide range of data structures, including scalars, N-dimensional arrays, sparse/complex-valued arrays, maps, tables, hashes, linked lists, trees and graphs, and support optional data grouping and metadata for each data element. The generated data files are ...