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Cascading can be implemented in terms of chaining by having the methods return the target object (receiver, this, self).However, this requires that the method be implemented this way already – or the original object be wrapped in another object that does this – and that the method not return some other, potentially useful value (or nothing if that would be more appropriate, as in setters).
The final release date of the JPA 1.0 specification was 11 May 2006 as part of Java Community Process JSR 220. The JPA 2.0 specification was released 10 December 2009 (the Java EE 6 platform requires JPA 2.0 [2]). The JPA 2.1 specification was released 22 April 2013 (the Java EE 7 platform requires JPA 2.1 [3]). The JPA 2.2 specification was ...
Cascading can be implemented using method chaining by having the method return the current object itself. Cascading is a key technique in fluent interfaces , and since chaining is widely implemented in object-oriented languages while cascading isn't, this form of "cascading-by-chaining by returning this " is often referred to simply as "chaining".
BEA Systems acquired SolarMetric in 2005, where Kodo was expanded to be an implementation of both the JDO (JSR 12) [2] and JPA (JSR 220) [3] specifications. In 2006, BEA donated a large part of the Kodo source code to the Apache Software Foundation under the name OpenJPA.
In computer science, lazy deletion refers to a method of deleting elements from a hash table that uses open addressing. In this method, deletions are done by marking an element as deleted, rather than erasing it entirely. Deleted locations are treated as empty when inserting and as occupied during a search.
In his database textbook, Beynon-Davies explains the three ways that RDBMS handle deletions of target and related tuples: Restricted Delete - the user cannot delete the target row until all rows that point to it (via foreign keys) have been deleted. This means that all Housewares employees would need to be deleted, or their departments changed ...
In predictive analytics, data science, machine learning and related fields, concept drift or drift is an evolution of data that invalidates the data model.It happens when the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways.
Prolog, for both atoms (predicate names, function names, and constants) and variables [20] Python, for variable names, function names, method names, and module or package (i.e. file) names [3] PHP uses SCREAMING_SNAKE_CASE for class constants; PL/I [21] R, for variable names, function names, and argument names, especially in the tidyverse style ...