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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.
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 ...
An invocation of gethash actually returns two values: the value or substitute value for the key and a boolean indicator, returning T if the hash table contains the key and NIL to signal its absence. ( multiple-value-bind ( value contains-key ) ( gethash "Sally Smart" phone-book ) ( if contains-key ( format T "~&The associated value is: ~s ...
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 ...
The main difference is that XPath 1.0 was more relaxed about type conversion, for example comparing two strings ("4" > "4.0") was quite possible but would do a numeric comparison; in XPath 2.0 this is defined to compare the two values as strings using a context-defined collating sequence.
The two most widespread approaches to this problem are separate chaining and open addressing. [3] [4] [5] [11] In separate chaining, the array does not store the value itself but stores a pointer to another container, usually an association list, that stores all the values matching the hash. By contrast, in open addressing, if a hash collision ...
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 ...
Relational databases are very well suited to flat data layouts, where relationships between data are only one or two levels deep. For example, an accounting database might need to look up all the line items for all the invoices for a given customer, a three-join query. Graph databases are aimed at datasets that contain many more links.