https://github.com/object88/rope-experiment
Experiment to evaluate different implementations of the rope data structure
https://github.com/object88/rope-experiment
data-structures go rope
Last synced: 8 months ago
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Experiment to evaluate different implementations of the rope data structure
- Host: GitHub
- URL: https://github.com/object88/rope-experiment
- Owner: object88
- License: mit
- Created: 2017-09-22T21:10:31.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2017-09-28T00:34:44.000Z (over 8 years ago)
- Last Synced: 2025-03-24T12:22:41.578Z (about 1 year ago)
- Topics: data-structures, go, rope
- Language: Go
- Size: 77.1 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# rope-experiment
Experiment to evaluate different implementations of the rope data structure. Conclusions of this (ongoing?) experiement are implemented in [rope](https://github.com/object88/rope).
The [rope data structure](https://en.wikipedia.org/wiki/Rope_(data_structure)) is a way to handle ad-hoc edits to largish blocks of text, as one might make in a text editor. To understand the difference in CPU usage for an edit, the project compares bare string manipulation with a basic rope implementation [translated from JavaScript](https://github.com/component/rope), and an improved version that operates over raw byte arrays.
## Implementations
`v1` is a simple string. Edits are made by reconstructing the whole string using `bytes.Buffer`.
`v2` is an actual rope implementation, using substrings as the data structure in each node in the B-Tree structure.
`v3` is similar to `v2`, except that it uses a `[]byte` as the data structure in each node. This _seems_ to have little effect on adding or removing characters to the rope, but gives a slight performance boost when reading out via an `io.Reader`.
Further work may attempt to experiment further:
* May use a fixed-size `[]byte` for each leaf. Theoretically, if changes do not reallocate the memory used even in a leaf, we may see better performance. This may be of diminishing return for the complexity, especially as there would need to be some temporary buffer allocated to shift bytes within the fixed buffer for insertions and deletions.
* May have two implementations, one for ropes which contain just ASCII characters, and one which contains UFT8 or Unicode characters. Operations on pure ASCII strings are much faster, as we don't need to map between rune offset and byte offset. The rope may scan its initial content for any non-ASCII value, and if not found, start off with faster operations. Once a non-ASCII rune is inserted, the three would need to be reassembled.
## Tests
The `Add_Small` benchmark adds a single character to the front of a rope with varying initial size, from 1kb to 15kb. `Remove_Small` removes a single character.
The `Reader` benchmark uses the `io.Reader` and `io.WriterTo` interfaces through `io.Copy` to test reading the contents.
Initial results:
``` sh
Comparing v1 and v2
benchmark old ns/op new ns/op delta
Benchmark_Alter/1000-8 187914 85284 -54.62%
Benchmark_Alter/2500-8 413970 91478 -77.90%
Benchmark_Alter/5000-8 783128 95754 -87.77%
Benchmark_Alter/7500-8 1163277 97104 -91.65%
Benchmark_Alter/10000-8 1351197 101083 -92.52%
Benchmark_Alter/12500-8 1743066 109701 -93.71%
Benchmark_Alter/15000-8 2036076 106848 -94.75%
Benchmark_Insert_Small/1000-8 101709 50010 -50.83%
Benchmark_Insert_Small/2500-8 216811 48828 -77.48%
Benchmark_Insert_Small/5000-8 411261 47513 -88.45%
Benchmark_Insert_Small/7500-8 631217 66145 -89.52%
Benchmark_Insert_Small/10000-8 734110 51753 -92.95%
Benchmark_Insert_Small/12500-8 943049 59449 -93.70%
Benchmark_Insert_Small/15000-8 1110480 81927 -92.62%
Benchmark_Reader/100000-8 4700 11775 +150.53%
Benchmark_Reader/125000-8 6133 12889 +110.16%
Benchmark_Reader/150000-8 7688 21242 +176.30%
Benchmark_Reader/175000-8 9279 22491 +142.39%
Benchmark_Reader/200000-8 10849 23263 +114.43%
Benchmark_Remove_Small/1000-8 176937 99681 -43.66%
Benchmark_Remove_Small/2500-8 382343 71238 -81.37%
Benchmark_Remove_Small/5000-8 776255 72214 -90.70%
Benchmark_Remove_Small/7500-8 1177203 98011 -91.67%
Benchmark_Remove_Small/10000-8 1357515 76813 -94.34%
Benchmark_Remove_Small/12500-8 1742483 86207 -95.05%
Benchmark_Remove_Small/15000-8 2047698 103396 -94.95%
Comparing v1 and v3
benchmark old ns/op new ns/op delta
Benchmark_Alter/1000-8 187914 88435 -52.94%
Benchmark_Alter/2500-8 413970 127396 -69.23%
Benchmark_Alter/5000-8 783128 134560 -82.82%
Benchmark_Alter/7500-8 1163277 103130 -91.13%
Benchmark_Alter/10000-8 1351197 140595 -89.59%
Benchmark_Alter/12500-8 1743066 132609 -92.39%
Benchmark_Alter/15000-8 2036076 108254 -94.68%
Benchmark_Insert_Small/1000-8 101709 54512 -46.40%
Benchmark_Insert_Small/2500-8 216811 76864 -64.55%
Benchmark_Insert_Small/5000-8 411261 79241 -80.73%
Benchmark_Insert_Small/7500-8 631217 81506 -87.09%
Benchmark_Insert_Small/10000-8 734110 82649 -88.74%
Benchmark_Insert_Small/12500-8 943049 83155 -91.18%
Benchmark_Insert_Small/15000-8 1110480 84375 -92.40%
Benchmark_Reader/100000-8 4700 10209 +117.21%
Benchmark_Reader/125000-8 6133 10820 +76.42%
Benchmark_Reader/150000-8 7688 18725 +143.56%
Benchmark_Reader/175000-8 9279 19679 +112.08%
Benchmark_Reader/200000-8 10849 20630 +90.16%
Benchmark_Remove_Small/1000-8 176937 94508 -46.59%
Benchmark_Remove_Small/2500-8 382343 95370 -75.06%
Benchmark_Remove_Small/5000-8 776255 96544 -87.56%
Benchmark_Remove_Small/7500-8 1177203 98426 -91.64%
Benchmark_Remove_Small/10000-8 1357515 100405 -92.60%
Benchmark_Remove_Small/12500-8 1742483 101078 -94.20%
Benchmark_Remove_Small/15000-8 2047698 102076 -95.02%
```