go implementation of timsort

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Utilities timsort

timsort Build Status codecov

timsort is a Go implementation of Tim Peters's mergesort sorting algorithm.

For many input types it is 2-3 times faster than Go's built-in sorting.

The main drawback of this sort method is that it is not in-place (as any mergesort), and may put extra strain on garbage collector.

This implementation was derived from Java's TimSort object by Josh Bloch, which, in turn, was based on the original code by Tim Peters.


$ go get -u github.com/psilva261/timsort/v2


Inside the source directory, type

go test

to run test harness.


Inside the source directory, type

go test -test.bench=.*

to run benchmarks. Each combination of input type/size is presented to timsort, and, for comparison, to the standard Go sort (sort.Sort for ints or sort.Stable otherwise). See BENCHMARKS.md for more info and some benchmarking results.


As drop-in replacement for sort.Sort

package main

import (

func main() {
	l := []string{"c", "a", "b"}
	fmt.Printf("sorted array: %+v\n", l)

Explicit "less" function

package main

import (

type Record struct {
	ssn  int
	name string

func BySsn(a, b interface{}) bool {
	return a.(Record).ssn < b.(Record).ssn

func ByName(a, b interface{}) bool {
	return a.(Record).name < b.(Record).name

func main() {
	db := make([]interface{}, 3)
	db[0] = Record{123456789, "joe"}
	db[1] = Record{101765430, "sue"}
	db[2] = Record{345623452, "mary"}

	// sorts array by ssn (ascending)
	timsort.Sort(db, BySsn)
	fmt.Printf("sorted by ssn: %v\n", db)

	// now re-sort same array by name (ascending)
	timsort.Sort(db, ByName)
	fmt.Printf("sorted by name: %v\n", db)
  • Broken algorithm?

    Broken algorithm?

    After a quick review of the code, I've noticed a few suspects.

    1) Binary search integer overflow


    This fails for large values of low and high. If the sum overflows to a negative value, this will fail with an out of bounds error. There are two ways to fix this.

    mid := left + (right - left) / 2
    mid := int(uint(left + right) >> 1) // Used in Go package sort.Search

    See: Extra, Extra - Read All About It: Nearly All Binary Searches and Mergesorts are Broken

    2) Timsort invariant violation

    I noticed that the implementation of mergeCollapse is similar to the one in an old version of CPython.


    See: Proving that Android’s, Java’s and Python’s sorting algorithm is broken (and showing how to fix it)

    opened by dsamarin 3
  • Some small changes

    Some small changes

    1. In makeVector of bench_test, I added a line to allocation a slice for v before appending it, this avoids small slices allocated. Without this change, I can't run all benchmark successfully under default go configs.
    2. records.Less is modified to use less function call, which makes the comparison more fair.
    3. Some lines are rewrite in go idiomic style.(exchange values)


    opened by daviddengcn 3
  • TimSort for sort.Interface

    TimSort for sort.Interface

    In this merge:

    1. An Ints function is provided. It sorts []int. It is faster than normal sort.Sort in every shape.
    2. TimSort sorts data with sort.Interface. It is generally faster than Sort, if the data is stored in a slice of the original data-structure(e.g. []record). Faster than sort.Sort if the data is partially sorted (or reverse). But slower than build-in sort.Sort for totally random data.
    3. If the data to be sorted is in []interface{}, Sort is the best choice. It is faster than build-in sort.
    4. I fixed a vital bug in IsSorted.

    In the benchmarks, I let the standard-sort use []record instead of []interface{}, since I think that is a more commonly used pattern and runs much faster.

    opened by daviddengcn 2
  • Using timsort on sort.Interface

    Using timsort on sort.Interface

    1. The timsort algorithm was rewritten on int slice([]int)(The Ints function in timsortint.go). The benchmarks(in benchint_test.go) show it's faster than build-in sort
    2. The function TimSort(), defined in timsortint.go, creates an index slice(an int slice), and sort the index slice with timsort (Ints function). Finally swapping on the original data with the sorted indexes.
    3. The benchmarks was changed.
      1. Standard-sort is performed on a record slice, instead of an interface{} slice.
      2. The record slice is also sorted by TimSort function
      3. Benchmark results show the interface{} has quite a lot overhead, which makes the standand sorting on random data the fastest. (You can run the benchmarks to see details)
    opened by daviddengcn 2
  • why comparing with quicksort?

    why comparing with quicksort?

    It seems that the benchmark is comparing with GO's implementation of quicksort (which is standard in go)


    Given that timsort is a stable sort, would not it be right to compare with sort.Stable() instead?

    I got much worse results with that:

    RevSorted100: 1500          (7806)        1582          (7759)        1469          (7773)        
    Xor100:       4838          (5802)        4828          (5845)        4819          (5834)        
    Random100:    5244          (7891)        5250          (7887)        5242          (7911)        
    Sorted1K:     5597          (4837)        5584          (4838)        5633          (4846)        
    RevSorted1K:  6588          (87094)       6523          (85785)       6562          (88508)       
    Xor1K:        45714         (114406)      45331         (113812)      46041         (114143)      
    Random1K:     114125        (202941)      132139        (203927)      114355        (202147)      
    Sorted1M:     4129627       (8783352)     4129406       (8771254)     4148070       (8678536)     
    RevSorted1M:  6239082       (114131971)   6189573       (115857436)   6199218       (114664717)   
    Xor1M:        99852677      (224848697)   98523482      (228193397)   100662027     (222984886)   
    Random1M:     362650134     (665379425)   348801121     (669130321)   340476927     (670888160)```
    opened by andrewmed 1
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