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grep is a command-line utility for searching plaintext datasets for lines that match a regular expression. Its name comes from the ed command g/re/p (global regular expression search and print), which has the same effect.
For example, GNU grep has the following options: "grep -E" for ERE, and "grep -G" for BRE (the default), and "grep -P" for Perl regexes. Perl regexes have become a de facto standard, having a rich and powerful set of atomic expressions.
NOTE: An application using a library for regular expression support does not necessarily support the full set of features of the library, e.g., GNU grep uses PCRE, but supports no lookahead, though PCRE does.
The complexity of the algorithm is linear in the length of the strings plus the length of the searched text plus the number of output matches. Note that because all matches are found, multiple matches will be returned for one string location if multiple substrings matched (e.g. dictionary = a, aa, aaa, aaaa and input string is aaaa).
In computer science, an algorithm for matching wildcards (also known as globbing) is useful in comparing text strings that may contain wildcard syntax. [1] Common uses of these algorithms include command-line interfaces, e.g. the Bourne shell [2] or Microsoft Windows command-line [3] or text editor or file manager, as well as the interfaces for some search engines [4] and databases. [5]
The pattern to match, however, works as follows: NR is the number of records, typically lines of input, AWK has so far read, i.e. the current line number, starting at 1 for the first line of input. % is the modulo operator. NR % 4 == 1 is true for the 1st, 5th, 9th, etc., lines of input.
This uses information gleaned during the pre-processing of the pattern in conjunction with suffix match lengths recorded at each match attempt. Storing suffix match lengths requires an additional table equal in size to the text being searched. The Raita algorithm improves the performance of Boyer–Moore–Horspool algorithm. The searching ...
A counting Bloom filter is a probabilistic data structure that is used to test whether the number of occurrences of a given element in a sequence exceeds a given threshold. As a generalized form of the Bloom filter, false positive matches are possible, but false negatives are not – in other words, a query returns either "possibly bigger or equal than the threshold" or "definitely smaller ...