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JSON Schema specifies a JSON-based format to define the structure of JSON data for validation, documentation, and interaction control. It provides a contract for the JSON data required by a given application and how that data can be modified. [29] JSON Schema is based on the concepts from XML Schema (XSD) but is JSON-based. As in XSD, the same ...
For example, PKIX uses such notation in RFC 5912. With such notation (constraints on parameterized types using information object sets), generic ASN.1 tools/libraries can automatically encode/decode/resolve references within a document. ^ The primary format is binary, a json encoder is available. [10]
JSON-LD is designed around the concept of a "context" to provide additional mappings from JSON to an RDF model. The context links object properties in a JSON document to concepts in an ontology. In order to map the JSON-LD syntax to RDF, JSON-LD allows values to be coerced to a specified type or to be tagged with a language.
[4] [5] It was described as being "like sed for JSON data". [6] Support for regular expressions was added in jq version 1.5. A "wrapper" program for jq named yq adds support for YAML, XML and TOML. It was first released in 2017. [7] The Go implementation, gojq, was initially released in 2019. [8] gojq notably extends jq to include support for YAML.
This could be modeled in an object-oriented implementation by a "Person object" with an attribute/field to hold each data item that the entry comprises: the person's name, a list of phone numbers, and a list of addresses. The list of phone numbers would itself contain "PhoneNumber objects" and so on.
[4] [5] The topmost element in the structure must be of type BSON object and contains 1 or more elements, where an element consists of a field name, a type, and a value. Field names are strings. Types include: Unicode string (using the UTF-8 encoding) 32-bit integer; 64-bit integer; double (64-bit IEEE 754 floating point number, including NaN/Inf)
File metadata, including the schema definition. The 16-byte, randomly-generated sync marker for this file. For data blocks Avro specifies two serialization encodings: [6] binary and JSON. Most applications will use the binary encoding, as it is smaller and faster. For debugging and web-based applications, the JSON encoding may sometimes be ...
Entity Types form the class of objects entities conform to, with the Entities being instances of the entity types. Entities represent individual objects that form a part of the problem being solved by the application and are indexed by a key. For example, converting the physical schema described above, we will have two entity types: