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adds a value into an object or array. Remove removes a value from an object or array. Replace replaces a value. Logically identical to using remove and then add. Copy copies a value from one path to another by adding the value at a specified location to another location. Move moves a value from one place to another by removing from one location ...
Example of a web form with name-value pairs. A name–value pair, also called an attribute–value pair, key–value pair, or field–value pair, is a fundamental data representation in computing systems and applications. Designers often desire an open-ended data structure that allows for future extension without modifying existing code or data.
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 SQL API lets clients create, update and delete containers and items. Items can be queried with a read-only, JSON-friendly SQL dialect. [8] As Cosmos DB embeds a JavaScript engine, the SQL API also enables: Stored procedures. Functions that bundle an arbitrarily complex set of operations and logic into an ACID-compliant transaction. They are ...
Data for these collections can be imported from various file formats such as comma-separated values, JSON, Parquet, SQL database tables or queries, and Microsoft Excel. [8] A Series is a 1-dimensional data structure built on top of NumPy's array. [9]: 97 Unlike in NumPy, each data point has an associated label. The collection of these labels is ...
Find variable reference (placeholder), replace it by its variable value. This algorithm offers no cache strategy. Split and join string: splitting the string into an array, merging it with the corresponding array of values, then joining items by concatenation. The split string can be cached for reuse.
Extract, transform, load (ETL) is a three-phase computing process where data is extracted from an input source, transformed (including cleaning), and loaded into an output data container. The data can be collected from one or more sources and it can also be output to one or more destinations.
Graph databases portray the data as it is viewed conceptually. This is accomplished by transferring the data into nodes and its relationships into edges. A graph database is a database that is based on graph theory. It consists of a set of objects, which can be a node or an edge.