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[1] [2] On most modern Unix-like systems, even BSD-derived ones like FreeBSD and NetBSD, the diff3 provided is GNU's version in diffutils (formerly GNU Tools). This version has the ability to merge files on its own without relying on ed. It internally invokes diff to do the comparison, but can use any other compatible tool too. [3]
2016-7-17 (3.0.1.0b) Yes [3] No No diff, diff3: AT&T: Yes; BSD 3-clause, BSD 4-clause, CDDL, GPL, Proprietary Yes 1974 No Yes Yes ported to most platforms as part of SCCS > 2GB but less than 64 bits Eclipse (compare) Yes; Eclipse Public License: Yes 2004-07-21 2016-09-28 (4.6.1 (Neon.1)) Yes Yes Yes Anything with Java: Ediff: Michael Kifer [4 ...
Meld is a visual diff and merge tool, targeted at developers. It allows users to compare two or three files or directories visually, color-coding the different lines. Meld can be used for comparing files, directories, and version controlled repositories.
In computing, the utility diff is a data comparison tool that computes and displays the differences between the contents of files. Unlike edit distance notions used for other purposes, diff is line-oriented rather than character-oriented, but it is like Levenshtein distance in that it tries to determine the smallest set of deletions and insertions to create one file from the other.
It is a rough merging method, but widely applicable since it only requires one common ancestor to reconstruct the changes that are to be merged. Three way merge can be done on raw text (sequence of lines) or on structured trees. [2] The three-way merge looks for sections which are the same in only two of the three files.
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A right join is employed over the Target (the INTO table) and the Source (the USING table / view / sub-query)--where Target is the left table and Source is the right one. The four possible combinations yield these rules:
The sort-merge join (also known as merge join) is a join algorithm and is used in the implementation of a relational database management system. The basic problem of a join algorithm is to find, for each distinct value of the join attribute, the set of tuples in each relation which display that value.