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Hi/Lo is an algorithm and a key generation strategy used for generating unique keys for use in a database as a primary key. It uses a sequence-based hi-lo pattern to generate values. Hi/Lo is used in scenarios where an application needs its entities to have an identity prior to persistence. It is a value generation strategy.
In computer science, an associative array, map, symbol table, or dictionary is an abstract data type that stores a collection of (key, value) pairs, such that each possible key appears at most once in the collection. In mathematical terms, an associative array is a function with finite domain. [1] It supports 'lookup', 'remove', and 'insert ...
Python sets are very much like mathematical sets, and support operations like set intersection and union. Python also features a frozenset class for immutable sets, see Collection types. Dictionaries (class dict) are mutable mappings tying keys and corresponding values. Python has special syntax to create dictionaries ({key: value})
The eval() vs. exec() built-in functions (in Python 2, exec is a statement); the former is for expressions, the latter is for statements; Statements cannot be a part of an expression—so list and other comprehensions or lambda expressions, all being expressions, cannot contain statements.
Unique keys are also called alternate keys. Unique keys are an alternative to the primary key of the relation. In SQL, the unique keys have a UNIQUE constraint assigned to them in order to prevent duplicates (a duplicate entry is not valid in a unique column). Alternate keys may be used like the primary key when doing a single-table select or ...
This sort of quantification is known as uniqueness quantification or unique existential quantification, and is often denoted with the symbols "∃!" [ 2 ] or "∃ =1 ". For example, the formal statement
For any fixed set of keys, using a universal family guarantees the following properties.. For any fixed in , the expected number of keys in the bin () is /.When implementing hash tables by chaining, this number is proportional to the expected running time of an operation involving the key (for example a query, insertion or deletion).
The main approach HKDF follows is the "extract-then-expand" paradigm, where the KDF logically consists of two modules: the first stage takes the input keying material and "extracts" from it a fixed-length pseudorandom key, and then the second stage "expands" this key into several additional pseudorandom keys (the output of the KDF). [2]