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// Copyright 2022 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.
// Package diffp implements a basic diff algorithm equivalent to patience diff.
// It is a copy of internal/diff from the main Go repo, renamed to diffp to avoid
// conflict with the existing golang.org/x/tools/internal/diff.
package diffp
import (
"bytes"
"fmt"
"sort"
"strings"
)
// A pair is a pair of values tracked for both the x and y side of a diff.
// It is typically a pair of line indexes.
type pair struct{ x, y int }
// Diff returns an anchored diff of the two texts old and new
// in the “unified diff” format. If old and new are identical,
// Diff returns a nil slice (no output).
//
// Unix diff implementations typically look for a diff with
// the smallest number of lines inserted and removed,
// which can in the worst case take time quadratic in the
// number of lines in the texts. As a result, many implementations
// either can be made to run for a long time or cut off the search
// after a predetermined amount of work.
//
// In contrast, this implementation looks for a diff with the
// smallest number of “unique” lines inserted and removed,
// where unique means a line that appears just once in both old and new.
// We call this an “anchored diff” because the unique lines anchor
// the chosen matching regions. An anchored diff is usually clearer
// than a standard diff, because the algorithm does not try to
// reuse unrelated blank lines or closing braces.
// The algorithm also guarantees to run in O(n log n) time
// instead of the standard O(n²) time.
//
// Some systems call this approach a “patience diff,” named for
// the “patience sorting” algorithm, itself named for a solitaire card game.
// We avoid that name for two reasons. First, the name has been used
// for a few different variants of the algorithm, so it is imprecise.
// Second, the name is frequently interpreted as meaning that you have
// to wait longer (to be patient) for the diff, meaning that it is a slower algorithm,
// when in fact the algorithm is faster than the standard one.
func Diff(oldName string, old []byte, newName string, new []byte) []byte {
if bytes.Equal(old, new) {
return nil
}
x := lines(old)
y := lines(new)
// Print diff header.
var out bytes.Buffer
fmt.Fprintf(&out, "diff %s %s\n", oldName, newName)
fmt.Fprintf(&out, "--- %s\n", oldName)
fmt.Fprintf(&out, "+++ %s\n", newName)
// Loop over matches to consider,
// expanding each match to include surrounding lines,
// and then printing diff chunks.
// To avoid setup/teardown cases outside the loop,
// tgs returns a leading {0,0} and trailing {len(x), len(y)} pair
// in the sequence of matches.
var (
done pair // printed up to x[:done.x] and y[:done.y]
chunk pair // start lines of current chunk
count pair // number of lines from each side in current chunk
ctext []string // lines for current chunk
)
for _, m := range tgs(x, y) {
if m.x < done.x {
// Already handled scanning forward from earlier match.
continue
}
// Expand matching lines as far possible,
// establishing that x[start.x:end.x] == y[start.y:end.y].
// Note that on the first (or last) iteration we may (or definitey do)
// have an empty match: start.x==end.x and start.y==end.y.
start := m
for start.x > done.x && start.y > done.y && x[start.x-1] == y[start.y-1] {
start.x--
start.y--
}
end := m
for end.x < len(x) && end.y < len(y) && x[end.x] == y[end.y] {
end.x++
end.y++
}
// Emit the mismatched lines before start into this chunk.
// (No effect on first sentinel iteration, when start = {0,0}.)
for _, s := range x[done.x:start.x] {
ctext = append(ctext, "-"+s)
count.x++
}
for _, s := range y[done.y:start.y] {
ctext = append(ctext, "+"+s)
count.y++
}
// If we're not at EOF and have too few common lines,
// the chunk includes all the common lines and continues.
const C = 3 // number of context lines
if (end.x < len(x) || end.y < len(y)) &&
(end.x-start.x < C || (len(ctext) > 0 && end.x-start.x < 2*C)) {
for _, s := range x[start.x:end.x] {
ctext = append(ctext, " "+s)
count.x++
count.y++
}
done = end
continue
}
// End chunk with common lines for context.
if len(ctext) > 0 {
n := end.x - start.x
if n > C {
n = C
}
for _, s := range x[start.x : start.x+n] {
ctext = append(ctext, " "+s)
count.x++
count.y++
}
done = pair{start.x + n, start.y + n}
// Format and emit chunk.
// Convert line numbers to 1-indexed.
// Special case: empty file shows up as 0,0 not 1,0.
if count.x > 0 {
chunk.x++
}
if count.y > 0 {
chunk.y++
}
fmt.Fprintf(&out, "@@ -%d,%d +%d,%d @@\n", chunk.x, count.x, chunk.y, count.y)
for _, s := range ctext {
out.WriteString(s)
}
count.x = 0
count.y = 0
ctext = ctext[:0]
}
// If we reached EOF, we're done.
if end.x >= len(x) && end.y >= len(y) {
break
}
// Otherwise start a new chunk.
chunk = pair{end.x - C, end.y - C}
for _, s := range x[chunk.x:end.x] {
ctext = append(ctext, " "+s)
count.x++
count.y++
}
done = end
}
return out.Bytes()
}
// lines returns the lines in the file x, including newlines.
// If the file does not end in a newline, one is supplied
// along with a warning about the missing newline.
func lines(x []byte) []string {
l := strings.SplitAfter(string(x), "\n")
if l[len(l)-1] == "" {
l = l[:len(l)-1]
} else {
// Treat last line as having a message about the missing newline attached,
// using the same text as BSD/GNU diff (including the leading backslash).
l[len(l)-1] += "\n\\ No newline at end of file\n"
}
return l
}
// tgs returns the pairs of indexes of the longest common subsequence
// of unique lines in x and y, where a unique line is one that appears
// once in x and once in y.
//
// The longest common subsequence algorithm is as described in
// Thomas G. Szymanski, “A Special Case of the Maximal Common
// Subsequence Problem,” Princeton TR #170 (January 1975),
// available at https://research.swtch.com/tgs170.pdf.
func tgs(x, y []string) []pair {
// Count the number of times each string appears in a and b.
// We only care about 0, 1, many, counted as 0, -1, -2
// for the x side and 0, -4, -8 for the y side.
// Using negative numbers now lets us distinguish positive line numbers later.
m := make(map[string]int)
for _, s := range x {
if c := m[s]; c > -2 {
m[s] = c - 1
}
}
for _, s := range y {
if c := m[s]; c > -8 {
m[s] = c - 4
}
}
// Now unique strings can be identified by m[s] = -1+-4.
//
// Gather the indexes of those strings in x and y, building:
// xi[i] = increasing indexes of unique strings in x.
// yi[i] = increasing indexes of unique strings in y.
// inv[i] = index j such that x[xi[i]] = y[yi[j]].
var xi, yi, inv []int
for i, s := range y {
if m[s] == -1+-4 {
m[s] = len(yi)
yi = append(yi, i)
}
}
for i, s := range x {
if j, ok := m[s]; ok && j >= 0 {
xi = append(xi, i)
inv = append(inv, j)
}
}
// Apply Algorithm A from Szymanski's paper.
// In those terms, A = J = inv and B = [0, n).
// We add sentinel pairs {0,0}, and {len(x),len(y)}
// to the returned sequence, to help the processing loop.
J := inv
n := len(xi)
T := make([]int, n)
L := make([]int, n)
for i := range T {
T[i] = n + 1
}
for i := 0; i < n; i++ {
k := sort.Search(n, func(k int) bool {
return T[k] >= J[i]
})
T[k] = J[i]
L[i] = k + 1
}
k := 0
for _, v := range L {
if k < v {
k = v
}
}
seq := make([]pair, 2+k)
seq[1+k] = pair{len(x), len(y)} // sentinel at end
lastj := n
for i := n - 1; i >= 0; i-- {
if L[i] == k && J[i] < lastj {
seq[k] = pair{xi[i], yi[J[i]]}
k--
}
}
seq[0] = pair{0, 0} // sentinel at start
return seq
}