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Created February 4, 2018 15:58
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001evaluation.svg
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Raw
00considerations.md

Considerations

  • Forked from Artefact2
  • Tests were run in single-user mode, one at a time.
  • Data was read from and written to tmpfs.
  • I did not have the dataset from Artefact2 for zstd
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01dataset.md

Dataset 1: linux 4.13 mainline sources (many small files, good compressibility)

Benchmark date: 2017-09-09
Borg version: borg-linux64 1.1.0rc2
Python version: Python 3.6.2
Kernel version: Linux 4.12.8-2-ARCH
CPU model: AMD FX(tm)-8150 Eight-Core Processor

Borg init command: %s init --error -e repokey %s
Borg create command: %s create --error -C %s %s::foo %s
Dataset path: /tmp/borg-dataset (tmpfs)
Repository path: /tmp/borg-repo (tmpfs)

Averaging algos over 1 run(s)

           algo |    cputime |  reposize |   bytes/s | timeratio | compratio
----------------+------------+-----------+-----------+-----------+----------
           none |      31.10 |  792.654M |   25.487M |      1.00 |     1.000
            lz4 |      29.54 |  311.050M |   26.833M |      0.95 |     0.392
         zlib,0 |      33.00 |  793.244M |   24.020M |      1.06 |     1.001
         zlib,1 |      36.82 |  228.762M |   21.528M |      1.18 |     0.289
         zlib,2 |      37.74 |  221.245M |   21.003M |      1.21 |     0.279
         zlib,3 |      39.05 |  216.240M |   20.298M |      1.26 |     0.273
         zlib,4 |      41.51 |  205.451M |   19.095M |      1.33 |     0.259
         zlib,5 |      44.57 |  199.655M |   17.784M |      1.43 |     0.252
         zlib,6 |      50.48 |  197.501M |   15.702M |      1.62 |     0.249
         zlib,7 |      55.10 |  196.702M |   14.386M |      1.77 |     0.248
         zlib,8 |      66.13 |  196.170M |   11.986M |      2.13 |     0.247
         zlib,9 |      66.77 |  196.137M |   11.871M |      2.15 |     0.247
         lzma,0 |      79.57 |  201.884M | 9961.717K |      2.56 |     0.255
         lzma,1 |      96.85 |  195.178M | 8184.345K |      3.11 |     0.246
         lzma,2 |     114.55 |  192.754M | 6919.719K |      3.68 |     0.243
         lzma,3 |     154.87 |  191.959M | 5118.188K |      4.98 |     0.242
         lzma,4 |     307.70 |  189.075M | 2576.060K |      9.89 |     0.239
         lzma,5 |     487.64 |  185.676M | 1625.489K |     15.68 |     0.234
         lzma,6 |     543.37 |  184.078M | 1458.773K |     17.47 |     0.232
         lzma,7 |     764.68 |  184.058M | 1036.582K |     24.59 |     0.232
         lzma,8 |    1218.62 |  184.082M |  650.452K |     39.18 |     0.232
         lzma,9 |    1218.07 |  184.095M |  650.745K |     39.17 |     0.232
       auto,lz4 |      32.19 |  311.050M |   24.624M |      1.04 |     0.392
    auto,zlib,0 |      36.56 |  793.240M |   21.681M |      1.18 |     1.001
    auto,zlib,1 |      39.51 |  228.762M |   20.062M |      1.27 |     0.289
    auto,zlib,2 |      41.02 |  221.250M |   19.324M |      1.32 |     0.279
    auto,zlib,3 |      41.67 |  216.248M |   19.022M |      1.34 |     0.273
    auto,zlib,4 |      44.01 |  205.447M |   18.011M |      1.42 |     0.259
    auto,zlib,5 |      47.52 |  199.660M |   16.680M |      1.53 |     0.252
    auto,zlib,6 |      53.45 |  197.497M |   14.830M |      1.72 |     0.249
    auto,zlib,7 |      57.71 |  196.706M |   13.735M |      1.86 |     0.248
    auto,zlib,8 |      67.28 |  196.162M |   11.781M |      2.16 |     0.247
    auto,zlib,9 |      69.64 |  196.141M |   11.382M |      2.24 |     0.247
    auto,lzma,0 |      82.29 |  201.830M | 9632.444K |      2.65 |     0.255
    auto,lzma,1 |     100.03 |  195.105M | 7924.160K |      3.22 |     0.246
    auto,lzma,2 |     116.15 |  192.668M | 6824.397K |      3.73 |     0.243
    auto,lzma,3 |     159.19 |  192.012M | 4979.294K |      5.12 |     0.242
    auto,lzma,4 |     314.17 |  189.080M | 2523.009K |     10.10 |     0.239
    auto,lzma,5 |     486.67 |  185.709M | 1628.729K |     15.65 |     0.234
    auto,lzma,6 |     542.93 |  184.013M | 1459.955K |     17.46 |     0.232
    auto,lzma,7 |     763.56 |  184.054M | 1038.102K |     24.55 |     0.232
    auto,lzma,8 |    1207.50 |  184.029M |  656.442K |     38.83 |     0.232
    auto,lzma,9 |    1205.69 |  184.001M |  657.427K |     38.77 |     0.232
Display the source blob
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01evaluation.svg
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Raw
02dataset.md

Dataset 2: postgres dump (large text files, good compressibility)

Benchmark date: 2017-09-10
Borg version: borg-linux64 1.1.0rc2
Python version: Python 3.6.2
Kernel version: Linux 4.12.8-2-ARCH
CPU model: AMD FX(tm)-8150 Eight-Core Processor

Borg init command: %s init --error -e repokey %s
Borg create command: %s create --error -C %s %s::foo %s
Dataset path: /tmp/borg-dataset (tmpfs)
Repository path: /tmp/borg-repo (tmpfs)

Averaging algos over 1 run(s)

           algo |    cputime |  reposize |   bytes/s | timeratio | compratio
----------------+------------+-----------+-----------+-----------+----------
           none |      12.06 |  682.570M |   56.598M |      1.00 |     1.000
            lz4 |      10.04 |  222.540M |   67.985M |      0.83 |     0.326
         zlib,0 |      14.91 |  682.672M |   45.779M |      1.24 |     1.000
         zlib,1 |      15.61 |  154.948M |   43.726M |      1.29 |     0.227
         zlib,2 |      16.50 |  150.839M |   41.368M |      1.37 |     0.221
         zlib,3 |      18.30 |  147.550M |   37.299M |      1.52 |     0.216
         zlib,4 |      21.61 |  136.143M |   31.586M |      1.79 |     0.199
         zlib,5 |      25.56 |  128.610M |   26.705M |      2.12 |     0.188
         zlib,6 |      31.23 |  126.640M |   21.856M |      2.59 |     0.186
         zlib,7 |      36.63 |  125.997M |   18.634M |      3.04 |     0.185
         zlib,8 |      51.20 |  124.961M |   13.331M |      4.25 |     0.183
         zlib,9 |      53.49 |  124.903M |   12.761M |      4.44 |     0.183
         lzma,0 |      43.15 |  125.125M |   15.819M |      3.58 |     0.183
         lzma,1 |      47.43 |  115.737M |   14.391M |      3.93 |     0.170
         lzma,2 |      59.86 |  111.706M |   11.403M |      4.96 |     0.164
         lzma,3 |      77.78 |  109.818M | 8775.645K |      6.45 |     0.161
         lzma,4 |     129.02 |   98.345M | 5290.417K |     10.70 |     0.144
         lzma,5 |     174.62 |   94.564M | 3908.886K |     14.48 |     0.139
         lzma,6 |     320.48 |   89.686M | 2129.835K |     26.57 |     0.131
         lzma,7 |     322.40 |   89.711M | 2117.151K |     26.73 |     0.131
         lzma,8 |     326.85 |   89.489M | 2088.327K |     27.10 |     0.131
         lzma,9 |     329.77 |   89.518M | 2069.835K |     27.34 |     0.131
       auto,lz4 |      11.78 |  222.540M |   57.943M |      0.98 |     0.326
    auto,zlib,0 |      16.60 |  682.672M |   41.119M |      1.38 |     1.000
    auto,zlib,1 |      17.35 |  154.952M |   39.341M |      1.44 |     0.227
    auto,zlib,2 |      18.13 |  150.847M |   37.649M |      1.50 |     0.221
    auto,zlib,3 |      20.02 |  147.522M |   34.094M |      1.66 |     0.216
    auto,zlib,4 |      23.53 |  136.163M |   29.008M |      1.95 |     0.199
    auto,zlib,5 |      26.54 |  128.618M |   25.719M |      2.20 |     0.188
    auto,zlib,6 |      34.28 |  126.648M |   19.912M |      2.84 |     0.186
    auto,zlib,7 |      37.69 |  125.989M |   18.110M |      3.13 |     0.185
    auto,zlib,8 |      53.51 |  124.953M |   12.756M |      4.44 |     0.183
    auto,zlib,9 |      55.94 |  124.891M |   12.202M |      4.64 |     0.183
    auto,lzma,0 |      45.07 |  125.088M |   15.145M |      3.74 |     0.183
    auto,lzma,1 |      49.24 |  115.806M |   13.862M |      4.08 |     0.170
    auto,lzma,2 |      61.87 |  111.907M |   11.032M |      5.13 |     0.164
    auto,lzma,3 |      78.91 |  110.121M | 8649.977K |      6.54 |     0.161
    auto,lzma,4 |     135.67 |   97.944M | 5031.102K |     11.25 |     0.143
    auto,lzma,5 |     173.32 |   94.376M | 3938.205K |     14.37 |     0.138
    auto,lzma,6 |     321.33 |   89.723M | 2124.201K |     26.64 |     0.131
    auto,lzma,7 |     324.67 |   89.477M | 2102.349K |     26.92 |     0.131
    auto,lzma,8 |     325.54 |   89.559M | 2096.730K |     26.99 |     0.131
    auto,lzma,9 |     327.80 |   89.825M | 2082.274K |     27.18 |     0.132
Raw
03dataset.md

Dataset 3: RAW photo collection (large files, poor compressibility)

Benchmark date: 2017-09-10
Borg version: borg-linux64 1.1.0rc2
Python version: Python 3.6.2
Kernel version: Linux 4.12.8-2-ARCH
CPU model: AMD FX(tm)-8150 Eight-Core Processor

Borg init command: %s init --error -e repokey %s
Borg create command: %s create --error -C %s %s::foo %s
Dataset path: /tmp/borg-dataset (tmpfs)
Repository path: /tmp/borg-repo (tmpfs)

Averaging algos over 1 run(s)

           algo |    cputime |  reposize |   bytes/s | timeratio | compratio
----------------+------------+-----------+-----------+-----------+----------
           none |      10.08 |  552.243M |   54.786M |      1.00 |     1.000
            lz4 |      10.51 |  553.198M |   52.545M |      1.04 |     1.002
         zlib,0 |      12.31 |  552.329M |   44.861M |      1.22 |     1.000
         zlib,1 |      31.31 |  541.327M |   17.638M |      3.11 |     0.980
         zlib,2 |      30.76 |  541.123M |   17.953M |      3.05 |     0.980
         zlib,3 |      32.22 |  541.028M |   17.140M |      3.20 |     0.980
         zlib,4 |      32.63 |  541.639M |   16.924M |      3.24 |     0.981
         zlib,5 |      33.09 |  541.610M |   16.689M |      3.28 |     0.981
         zlib,6 |      34.04 |  541.594M |   16.223M |      3.38 |     0.981
         zlib,7 |      32.58 |  541.594M |   16.950M |      3.23 |     0.981
         zlib,8 |      33.74 |  541.594M |   16.368M |      3.35 |     0.981
         zlib,9 |      32.60 |  541.598M |   16.940M |      3.23 |     0.981
         lzma,0 |     114.12 |  539.759M | 4839.144K |     11.32 |     0.977
         lzma,1 |     128.01 |  539.451M | 4314.062K |     12.70 |     0.977
         lzma,2 |     139.48 |  539.443M | 3959.300K |     13.84 |     0.977
         lzma,3 |     144.05 |  539.423M | 3833.691K |     14.29 |     0.977
         lzma,4 |     152.77 |  536.760M | 3614.866K |     15.16 |     0.972
         lzma,5 |     150.73 |  536.711M | 3663.790K |     14.95 |     0.972
         lzma,6 |     150.52 |  536.781M | 3668.902K |     14.93 |     0.972
         lzma,7 |     148.66 |  536.748M | 3714.806K |     14.75 |     0.972
         lzma,8 |     149.84 |  536.805M | 3685.552K |     14.87 |     0.972
         lzma,9 |     149.07 |  536.736M | 3704.589K |     14.79 |     0.972
       auto,lz4 |      10.69 |  551.404M |   51.660M |      1.06 |     0.998
    auto,zlib,0 |      10.99 |  552.255M |   50.250M |      1.09 |     1.000
    auto,zlib,1 |      13.00 |  546.533M |   42.480M |      1.29 |     0.990
    auto,zlib,2 |      13.37 |  545.362M |   41.305M |      1.33 |     0.988
    auto,zlib,3 |      13.57 |  545.980M |   40.696M |      1.35 |     0.989
    auto,zlib,4 |      14.26 |  545.980M |   38.727M |      1.41 |     0.989
    auto,zlib,5 |      15.30 |  544.649M |   36.094M |      1.52 |     0.986
    auto,zlib,6 |      13.90 |  546.619M |   39.730M |      1.38 |     0.990
    auto,zlib,7 |      14.00 |  546.296M |   39.446M |      1.39 |     0.989
    auto,zlib,8 |      13.67 |  546.947M |   40.398M |      1.36 |     0.990
    auto,zlib,9 |      14.01 |  546.161M |   39.418M |      1.39 |     0.989
    auto,lzma,0 |      21.26 |  544.084M |   25.976M |      2.11 |     0.985
    auto,lzma,1 |      23.84 |  544.461M |   23.165M |      2.37 |     0.986
    auto,lzma,2 |      24.53 |  544.211M |   22.513M |      2.43 |     0.985
    auto,lzma,3 |      25.64 |  544.330M |   21.538M |      2.54 |     0.986
    auto,lzma,4 |      31.46 |  542.163M |   17.554M |      3.12 |     0.982
    auto,lzma,5 |      28.74 |  542.806M |   19.215M |      2.85 |     0.983
    auto,lzma,6 |      27.20 |  543.543M |   20.303M |      2.70 |     0.984
    auto,lzma,7 |      25.30 |  544.756M |   21.828M |      2.51 |     0.986
    auto,lzma,8 |      26.69 |  543.953M |   20.691M |      2.65 |     0.985
    auto,lzma,9 |      27.53 |  542.675M |   20.060M |      2.73 |     0.983
Raw
04dataset.md

Large Journal File (112 MB)

Benchmark date: 2018-04-09
Borg version: borg 1.1.5
Python version: Python 3.6.4
Kernel version: Linux 4.15.15-1-ARCH
CPU model: Intel(R) Core(TM) i5-3317U CPU @ 1.70GHz

Borg init command: %s init --error -e repokey %s
Borg create command: %s create --error -C %s %s::foo %s
Dataset path: /tmp/borgdataset (tmpfs)
Repository path: /tmp/borg-repo (tmpfs)

Averaging algos over 1 run(s)

           algo |    cputime |  reposize |   bytes/s | timeratio | compratio
----------------+------------+-----------+-----------+-----------+----------
           none |       2.36 |  117.527M |   49.799M |      1.00 |     1.000
            lz4 |       1.90 |   22.360M |   61.856M |      0.81 |     0.190
         zlib,0 |       2.47 |  117.535M |   47.582M |      1.05 |     1.000
         zlib,1 |       2.63 |   14.262M |   44.687M |      1.11 |     0.121
         zlib,2 |       2.64 |   13.529M |   44.518M |      1.12 |     0.115
         zlib,3 |       3.34 |   13.464M |   35.188M |      1.42 |     0.115
         zlib,4 |       3.37 |   13.173M |   34.874M |      1.43 |     0.112
         zlib,5 |       3.96 |   13.017M |   29.678M |      1.68 |     0.111
         zlib,6 |       5.36 |   12.931M |   21.927M |      2.27 |     0.110
         zlib,7 |       6.82 |   12.902M |   17.233M |      2.89 |     0.110
         zlib,8 |      13.77 |   12.952M | 8534.969K |      5.83 |     0.110
         zlib,9 |      36.13 |   12.980M | 3252.879K |     15.31 |     0.110
         lzma,0 |       5.07 | 8687.616K |   23.181M |      2.15 |     0.074
         lzma,1 |       5.23 | 6987.776K |   22.472M |      2.22 |     0.059
         lzma,2 |       5.85 | 7282.688K |   20.090M |      2.48 |     0.062
         lzma,3 |       6.45 | 6795.264K |   18.221M |      2.73 |     0.058
         lzma,4 |      15.95 | 6250.496K | 7368.434K |      6.76 |     0.053
         lzma,5 |      20.91 | 6037.504K | 5620.589K |      8.86 |     0.051
         lzma,6 |      27.10 | 6057.984K | 4336.772K |     11.48 |     0.052
         lzma,7 |      29.24 | 6311.936K | 4019.375K |     12.39 |     0.054
         lzma,8 |      28.81 | 5947.392K | 4079.365K |     12.21 |     0.051
         lzma,9 |      29.36 | 5926.912K | 4002.947K |     12.44 |     0.050
         zstd,1 |       2.02 | 9875.456K |   58.181M |      0.86 |     0.084
         zstd,2 |       1.92 |   10.211M |   61.212M |      0.81 |     0.087
         zstd,3 |       2.00 | 8953.856K |   58.763M |      0.85 |     0.076
         zstd,4 |       2.11 | 8671.232K |   55.700M |      0.89 |     0.074
         zstd,5 |       2.42 | 9052.160K |   48.565M |      1.03 |     0.077
         zstd,6 |       2.79 | 9318.400K |   42.124M |      1.18 |     0.079
         zstd,7 |       2.71 | 7933.952K |   43.368M |      1.15 |     0.068
         zstd,8 |       3.00 | 7680.000K |   39.176M |      1.27 |     0.065
         zstd,9 |       3.04 | 7897.088K |   38.660M |      1.29 |     0.067
        zstd,10 |       3.70 | 8794.112K |   31.764M |      1.57 |     0.075
        zstd,11 |       3.52 | 7806.976K |   33.388M |      1.49 |     0.066
        zstd,12 |       3.83 | 7483.392K |   30.686M |      1.62 |     0.064
        zstd,13 |       5.74 | 7712.768K |   20.475M |      2.43 |     0.066
        zstd,14 |       7.16 | 7593.984K |   16.414M |      3.03 |     0.065
        zstd,15 |      12.23 | 7761.920K | 9609.691K |      5.18 |     0.066
        zstd,16 |      17.84 | 7499.776K | 6587.809K |      7.56 |     0.064
        zstd,17 |      19.10 | 7294.976K | 6153.221K |      8.09 |     0.062
        zstd,18 |      27.62 | 7258.112K | 4255.124K |     11.70 |     0.062
        zstd,19 |      31.60 | 7798.784K | 3719.193K |     13.39 |     0.066
        zstd,20 |      36.01 | 7868.416K | 3263.719K |     15.26 |     0.067
        zstd,21 |      33.29 | 7118.848K | 3530.385K |     14.11 |     0.061
        zstd,22 |      86.91 | 6742.016K | 1352.278K |     36.83 |     0.057
       auto,lz4 |       2.15 |   22.356M |   54.664M |      0.91 |     0.190
    auto,zlib,0 |       2.07 |   22.356M |   56.776M |      0.88 |     0.190
    auto,zlib,1 |       2.80 |   14.053M |   41.974M |      1.19 |     0.120
    auto,zlib,2 |       2.84 |   13.533M |   41.383M |      1.20 |     0.115
    auto,zlib,3 |       2.90 |   13.238M |   40.526M |      1.23 |     0.113
    auto,zlib,4 |       3.35 |   13.173M |   35.083M |      1.42 |     0.112
    auto,zlib,5 |       3.76 |   13.021M |   31.257M |      1.59 |     0.111
    auto,zlib,6 |       5.44 |   12.911M |   21.604M |      2.31 |     0.110
    auto,zlib,7 |       6.80 |   12.894M |   17.283M |      2.88 |     0.110
    auto,zlib,8 |      13.74 |   12.956M | 8553.604K |      5.82 |     0.110
    auto,zlib,9 |      36.80 |   12.984M | 3193.655K |     15.59 |     0.110
    auto,lzma,0 |       5.31 | 8761.344K |   22.133M |      2.25 |     0.075
    auto,lzma,1 |       5.84 | 7966.720K |   20.124M |      2.47 |     0.068
    auto,lzma,2 |       5.74 | 6750.208K |   20.475M |      2.43 |     0.057
    auto,lzma,3 |       6.83 | 7069.696K |   17.207M |      2.89 |     0.060
    auto,lzma,4 |      15.97 | 6316.032K | 7359.206K |      6.77 |     0.054
    auto,lzma,5 |      21.95 | 6008.832K | 5354.283K |      9.30 |     0.051
    auto,lzma,6 |      28.20 | 6234.112K | 4167.607K |     11.95 |     0.053
    auto,lzma,7 |      28.30 | 6021.120K | 4152.880K |     11.99 |     0.051
    auto,lzma,8 |      30.76 | 6230.016K | 3820.758K |     13.03 |     0.053
    auto,lzma,9 |      31.45 | 6348.800K | 3736.932K |     13.33 |     0.054
    auto,zstd,1 |       2.04 | 9900.032K |   57.611M |      0.86 |     0.084
    auto,zstd,2 |       2.11 |   10.191M |   55.700M |      0.89 |     0.087
    auto,zstd,3 |       2.09 | 8773.632K |   56.233M |      0.89 |     0.075
    auto,zstd,4 |       2.14 | 8724.480K |   54.919M |      0.91 |     0.074
    auto,zstd,5 |       2.52 | 8810.496K |   46.638M |      1.07 |     0.075
    auto,zstd,6 |       2.79 | 8052.736K |   42.124M |      1.18 |     0.069
    auto,zstd,7 |       2.91 | 7888.896K |   40.387M |      1.23 |     0.067
    auto,zstd,8 |       3.17 | 8015.872K |   37.075M |      1.34 |     0.068
    auto,zstd,9 |       3.19 | 7589.888K |   36.842M |      1.35 |     0.065
   auto,zstd,10 |       3.41 | 7569.408K |   34.465M |      1.44 |     0.064
   auto,zstd,11 |       3.77 | 8028.160K |   31.174M |      1.60 |     0.068
   auto,zstd,12 |       3.83 | 7512.064K |   30.686M |      1.62 |     0.064
   auto,zstd,13 |       5.88 | 7864.320K |   19.988M |      2.49 |     0.067
   auto,zstd,14 |       7.26 | 7614.464K |   16.188M |      3.08 |     0.065
   auto,zstd,15 |      11.96 | 7663.616K | 9826.632K |      5.07 |     0.065
   auto,zstd,16 |      22.26 | 7962.624K | 5279.718K |      9.43 |     0.068
   auto,zstd,17 |      19.19 | 7237.632K | 6124.363K |      8.13 |     0.062
   auto,zstd,18 |      27.37 | 7364.608K | 4293.990K |     11.60 |     0.063
   auto,zstd,19 |      32.82 | 7909.376K | 3580.942K |     13.91 |     0.067
   auto,zstd,20 |      36.20 | 7258.112K | 3246.589K |     15.34 |     0.062
   auto,zstd,21 |      35.74 | 7950.336K | 3288.375K |     15.14 |     0.068
   auto,zstd,22 |      86.46 | 6963.200K | 1359.316K |     36.64 |     0.059
Raw
05dataset.md

Medium Sized PDFs (N = 74), Mean: 3.6 MB, Std: 8.2 MB

Benchmark date: 2018-04-09
Borg version: borg 1.1.5
Python version: Python 3.6.4
Kernel version: Linux 4.15.15-1-ARCH
CPU model: Intel(R) Core(TM) i5-3317U CPU @ 1.70GHz

Borg init command: %s init --error -e repokey %s
Borg create command: %s create --error -C %s %s::foo %s
Dataset path: /tmp/borgdataset (tmpfs)
Repository path: /tmp/borg-repo (tmpfs)

Averaging algos over 1 run(s)

           algo |    cputime |  reposize |   bytes/s | timeratio | compratio
----------------+------------+-----------+-----------+-----------+----------
           none |       4.23 |  269.820M |   63.787M |      1.00 |     1.000
            lz4 |       4.49 |  265.060M |   60.094M |      1.06 |     0.982
         zlib,0 |       4.36 |  269.840M |   61.885M |      1.03 |     1.000
         zlib,1 |      13.83 |  262.844M |   19.510M |      3.27 |     0.974
         zlib,2 |      13.96 |  262.779M |   19.328M |      3.30 |     0.974
         zlib,3 |      13.99 |  262.746M |   19.287M |      3.31 |     0.974
         zlib,4 |      14.37 |  262.672M |   18.777M |      3.40 |     0.974
         zlib,5 |      14.67 |  262.636M |   18.393M |      3.47 |     0.973
         zlib,6 |      14.70 |  262.595M |   18.355M |      3.48 |     0.973
         zlib,7 |      14.60 |  262.590M |   18.481M |      3.45 |     0.973
         zlib,8 |      14.77 |  262.599M |   18.268M |      3.49 |     0.973
         zlib,9 |      14.83 |  262.578M |   18.194M |      3.51 |     0.973
         lzma,0 |      75.97 |  262.431M | 3551.663K |     17.96 |     0.973
         lzma,1 |     104.71 |  261.935M | 2576.830K |     24.75 |     0.971
         lzma,2 |     104.03 |  261.390M | 2593.673K |     24.59 |     0.969
         lzma,3 |      97.22 |  261.308M | 2775.353K |     22.98 |     0.968
         lzma,4 |      97.76 |  260.997M | 2760.023K |     23.11 |     0.967
         lzma,5 |      96.46 |  260.465M | 2797.220K |     22.80 |     0.965
         lzma,6 |      92.41 |  260.923M | 2919.812K |     21.85 |     0.967
         lzma,7 |      93.01 |  260.792M | 2900.977K |     21.99 |     0.967
         lzma,8 |      95.05 |  260.813M | 2838.715K |     22.47 |     0.967
         lzma,9 |      94.23 |  261.014M | 2863.418K |     22.28 |     0.967
         zstd,1 |       4.78 |  263.500M |   56.448M |      1.13 |     0.977
         zstd,2 |       4.87 |  263.369M |   55.404M |      1.15 |     0.976
         zstd,3 |       5.12 |  263.094M |   52.699M |      1.21 |     0.975
         zstd,4 |       5.32 |  262.828M |   50.718M |      1.26 |     0.974
         zstd,5 |      10.08 |  262.345M |   26.768M |      2.38 |     0.972
         zstd,6 |      12.17 |  261.976M |   22.171M |      2.88 |     0.971
         zstd,7 |      13.71 |  261.800M |   19.681M |      3.24 |     0.970
         zstd,8 |      14.42 |  262.119M |   18.712M |      3.41 |     0.971
         zstd,9 |      14.49 |  262.001M |   18.621M |      3.43 |     0.971
        zstd,10 |      14.06 |  261.976M |   19.191M |      3.32 |     0.971
        zstd,11 |      15.71 |  261.722M |   17.175M |      3.71 |     0.970
        zstd,12 |      14.52 |  261.865M |   18.583M |      3.43 |     0.971
        zstd,13 |      21.22 |  261.648M |   12.715M |      5.02 |     0.970
        zstd,14 |      21.70 |  261.825M |   12.434M |      5.13 |     0.970
        zstd,15 |      22.60 |  261.407M |   11.939M |      5.34 |     0.969
        zstd,16 |      34.25 |  261.710M | 7877.953K |      8.10 |     0.970
        zstd,17 |      36.28 |  261.763M | 7437.152K |      8.58 |     0.970
        zstd,18 |      44.19 |  261.157M | 6105.904K |     10.45 |     0.968
        zstd,19 |      45.87 |  261.542M | 5882.273K |     10.84 |     0.969
        zstd,20 |      41.59 |  261.566M | 6487.614K |      9.83 |     0.969
        zstd,21 |      42.27 |  261.419M | 6383.248K |      9.99 |     0.969
        zstd,22 |      43.17 |  261.497M | 6250.171K |     10.21 |     0.969
       auto,lz4 |       4.48 |  264.585M |   60.228M |      1.06 |     0.981
    auto,zlib,0 |       4.39 |  264.528M |   61.462M |      1.04 |     0.980
    auto,zlib,1 |       8.29 |  263.430M |   32.548M |      1.96 |     0.976
    auto,zlib,2 |       8.54 |  263.328M |   31.595M |      2.02 |     0.976
    auto,zlib,3 |       8.26 |  263.324M |   32.666M |      1.95 |     0.976
    auto,zlib,4 |       8.64 |  263.238M |   31.229M |      2.04 |     0.976
    auto,zlib,5 |       8.66 |  263.193M |   31.157M |      2.05 |     0.975
    auto,zlib,6 |       8.61 |  263.160M |   31.338M |      2.04 |     0.975
    auto,zlib,7 |       8.73 |  263.143M |   30.907M |      2.06 |     0.975
    auto,zlib,8 |       9.12 |  263.107M |   29.586M |      2.16 |     0.975
    auto,zlib,9 |       8.74 |  263.152M |   30.872M |      2.07 |     0.975
    auto,lzma,0 |      26.77 |  262.918M |   10.079M |      6.33 |     0.974
    auto,lzma,1 |      40.72 |  262.623M | 6626.225K |      9.63 |     0.973
    auto,lzma,2 |      42.34 |  262.758M | 6372.694K |     10.01 |     0.974
    auto,lzma,3 |      39.33 |  262.550M | 6860.409K |      9.30 |     0.973
    auto,lzma,4 |      43.29 |  262.230M | 6232.846K |     10.23 |     0.972
    auto,lzma,5 |      39.78 |  262.377M | 6782.803K |      9.40 |     0.972
    auto,lzma,6 |      42.14 |  262.103M | 6402.940K |      9.96 |     0.971
    auto,lzma,7 |      40.94 |  262.463M | 6590.618K |      9.68 |     0.973
    auto,lzma,8 |      42.23 |  262.279M | 6389.294K |      9.98 |     0.972
    auto,lzma,9 |      42.08 |  262.349M | 6412.069K |      9.95 |     0.972
    auto,zstd,1 |       4.72 |  263.541M |   57.165M |      1.12 |     0.977
    auto,zstd,2 |       4.73 |  263.574M |   57.044M |      1.12 |     0.977
    auto,zstd,3 |       4.79 |  263.348M |   56.330M |      1.13 |     0.976
    auto,zstd,4 |       5.08 |  263.049M |   53.114M |      1.20 |     0.975
    auto,zstd,5 |       6.11 |  262.885M |   44.160M |      1.44 |     0.974
    auto,zstd,6 |       6.74 |  262.816M |   40.033M |      1.59 |     0.974
    auto,zstd,7 |       7.38 |  262.971M |   36.561M |      1.74 |     0.975
    auto,zstd,8 |       7.70 |  262.881M |   35.042M |      1.82 |     0.974
    auto,zstd,9 |       7.90 |  263.098M |   34.154M |      1.87 |     0.975
   auto,zstd,10 |       8.29 |  262.689M |   32.548M |      1.96 |     0.974
   auto,zstd,11 |       8.15 |  262.795M |   33.107M |      1.93 |     0.974
   auto,zstd,12 |       8.42 |  262.816M |   32.045M |      1.99 |     0.974
   auto,zstd,13 |       9.68 |  263.004M |   27.874M |      2.29 |     0.975
   auto,zstd,14 |      10.63 |  262.795M |   25.383M |      2.51 |     0.974
   auto,zstd,15 |      10.90 |  262.906M |   24.754M |      2.58 |     0.974
   auto,zstd,16 |      17.77 |  262.693M |   15.184M |      4.20 |     0.974
   auto,zstd,17 |      18.95 |  262.529M |   14.239M |      4.48 |     0.973
   auto,zstd,18 |      20.50 |  262.656M |   13.162M |      4.85 |     0.973
   auto,zstd,19 |      21.87 |  262.410M |   12.337M |      5.17 |     0.973
   auto,zstd,20 |      20.43 |  262.762M |   13.207M |      4.83 |     0.974
   auto,zstd,21 |      21.59 |  262.517M |   12.497M |      5.10 |     0.973
   auto,zstd,22 |      24.10 |  262.394M |   11.196M |      5.70 |     0.972
Raw
06dataset.md

Small Text Files (N = 2516), Mean: 0.06 MB, Std: 0.4 MB

Benchmark date: 2018-04-09
Borg version: borg 1.1.5
Python version: Python 3.6.4
Kernel version: Linux 4.15.15-1-ARCH
CPU model: Intel(R) Core(TM) i5-3317U CPU @ 1.70GHz

Borg init command: %s init --error -e repokey %s
Borg create command: %s create --error -C %s %s::foo %s
Dataset path: /tmp/borgdataset (tmpfs)
Repository path: /tmp/borg-repo (tmpfs)

Averaging algos over 1 run(s)

           algo |    cputime |  reposize |   bytes/s | timeratio | compratio
----------------+------------+-----------+-----------+-----------+----------
           none |       3.50 |  152.375M |   43.536M |      1.00 |     1.000
            lz4 |       3.49 |  114.737M |   43.661M |      1.00 |     0.753
         zlib,0 |       3.57 |  152.400M |   42.682M |      1.02 |     1.000
         zlib,1 |       7.55 |  104.919M |   20.182M |      2.16 |     0.689
         zlib,2 |       7.75 |  104.542M |   19.661M |      2.21 |     0.686
         zlib,3 |       8.16 |  104.014M |   18.673M |      2.33 |     0.683
         zlib,4 |       8.46 |  103.633M |   18.011M |      2.42 |     0.680
         zlib,5 |       9.40 |  103.064M |   16.210M |      2.69 |     0.676
         zlib,6 |      11.25 |  102.593M |   13.544M |      3.21 |     0.673
         zlib,7 |      13.32 |  101.413M |   11.440M |      3.81 |     0.666
         zlib,8 |      21.11 |  102.236M | 7218.157K |      6.03 |     0.671
         zlib,9 |      32.94 |  102.207M | 4625.843K |      9.41 |     0.671
         lzma,0 |      29.96 |   99.594M | 5085.957K |      8.56 |     0.654
         lzma,1 |      37.16 |   99.033M | 4100.519K |     10.62 |     0.650
         lzma,2 |      37.57 |   98.804M | 4055.770K |     10.73 |     0.648
         lzma,3 |      39.16 |   98.693M | 3891.095K |     11.19 |     0.648
         lzma,4 |      56.38 |   97.382M | 2702.648K |     16.11 |     0.639
         lzma,5 |      61.89 |   97.055M | 2462.034K |     17.68 |     0.637
         lzma,6 |      64.31 |   96.936M | 2369.387K |     18.37 |     0.636
         lzma,7 |      95.28 |   96.895M | 1599.236K |     27.22 |     0.636
         lzma,8 |     126.31 |   96.932M | 1206.359K |     36.09 |     0.636
         lzma,9 |     123.99 |   96.920M | 1228.932K |     35.43 |     0.636
         zstd,1 |       3.98 |  104.194M |   38.285M |      1.14 |     0.684
         zstd,2 |       4.03 |  103.293M |   37.810M |      1.15 |     0.678
         zstd,3 |       4.19 |  102.470M |   36.366M |      1.20 |     0.672
         zstd,4 |       4.47 |  102.486M |   34.088M |      1.28 |     0.673
         zstd,5 |       5.45 |  102.052M |   27.959M |      1.56 |     0.670
         zstd,6 |       6.15 |  101.356M |   24.776M |      1.76 |     0.665
         zstd,7 |       6.70 |  101.118M |   22.743M |      1.91 |     0.664
         zstd,8 |       7.38 |  100.540M |   20.647M |      2.11 |     0.660
         zstd,9 |       7.98 |  100.475M |   19.095M |      2.28 |     0.659
        zstd,10 |       8.61 |  100.381M |   17.697M |      2.46 |     0.659
        zstd,11 |       8.92 |  100.401M |   17.082M |      2.55 |     0.659
        zstd,12 |      10.18 |  100.372M |   14.968M |      2.91 |     0.659
        zstd,13 |      14.20 |  100.192M |   10.731M |      4.06 |     0.658
        zstd,14 |      17.02 |  100.065M | 8952.720K |      4.86 |     0.657
        zstd,15 |      18.42 |  100.016M | 8272.274K |      5.26 |     0.656
        zstd,16 |      26.25 |   99.234M | 5804.773K |      7.50 |     0.651
        zstd,17 |      28.08 |   99.115M | 5426.470K |      8.02 |     0.650
        zstd,18 |      32.29 |   98.759M | 4718.962K |      9.23 |     0.648
        zstd,19 |      32.89 |   98.824M | 4632.876K |      9.40 |     0.649
        zstd,20 |      36.71 |   98.693M | 4150.784K |     10.49 |     0.648
        zstd,21 |      36.05 |   98.787M | 4226.776K |     10.30 |     0.648
        zstd,22 |      38.81 |   98.750M | 3926.186K |     11.09 |     0.648
       auto,lz4 |       3.67 |  114.565M |   41.519M |      1.05 |     0.752
    auto,zlib,0 |       3.51 |  114.561M |   43.412M |      1.00 |     0.752
    auto,zlib,1 |       5.49 |  105.312M |   27.755M |      1.57 |     0.691
    auto,zlib,2 |       5.93 |  104.825M |   25.696M |      1.69 |     0.688
    auto,zlib,3 |       6.34 |  104.346M |   24.034M |      1.81 |     0.685
    auto,zlib,4 |       6.34 |  104.055M |   24.034M |      1.81 |     0.683
    auto,zlib,5 |       7.34 |  103.408M |   20.760M |      2.10 |     0.679
    auto,zlib,6 |       9.45 |  102.887M |   16.124M |      2.70 |     0.675
    auto,zlib,7 |      11.41 |  102.687M |   13.355M |      3.26 |     0.674
    auto,zlib,8 |      19.16 |  102.662M | 7952.781K |      5.47 |     0.674
    auto,zlib,9 |      31.11 |  101.507M | 4897.952K |      8.89 |     0.666
    auto,lzma,0 |      13.53 |   99.324M |   11.262M |      3.87 |     0.652
    auto,lzma,1 |      18.34 |   99.344M | 8308.358K |      5.24 |     0.652
    auto,lzma,2 |      18.59 |   99.336M | 8196.627K |      5.31 |     0.652
    auto,lzma,3 |      20.88 |   99.000M | 7297.667K |      5.97 |     0.650
    auto,lzma,4 |      36.88 |   97.886M | 4131.651K |     10.54 |     0.642
    auto,lzma,5 |      41.39 |   97.313M | 3681.451K |     11.83 |     0.639
    auto,lzma,6 |      44.97 |   97.227M | 3388.376K |     12.85 |     0.638
    auto,lzma,7 |      69.86 |   97.317M | 2181.152K |     19.96 |     0.639
    auto,lzma,8 |      97.20 |   97.251M | 1567.647K |     27.77 |     0.638
    auto,lzma,9 |      98.05 |   97.219M | 1554.057K |     28.01 |     0.638
    auto,zstd,1 |       4.10 |  104.325M |   37.165M |      1.17 |     0.685
    auto,zstd,2 |       4.20 |  103.440M |   36.280M |      1.20 |     0.679
    auto,zstd,3 |       4.40 |  102.875M |   34.631M |      1.26 |     0.675
    auto,zstd,4 |       4.59 |  102.650M |   33.197M |      1.31 |     0.674
    auto,zstd,5 |       5.24 |  102.158M |   29.079M |      1.50 |     0.670
    auto,zstd,6 |       5.85 |  101.585M |   26.047M |      1.67 |     0.667
    auto,zstd,7 |       6.30 |  101.315M |   24.187M |      1.80 |     0.665
    auto,zstd,8 |       6.63 |  100.729M |   22.983M |      1.89 |     0.661
    auto,zstd,9 |       7.00 |  100.659M |   21.768M |      2.00 |     0.661
   auto,zstd,10 |       7.84 |  100.618M |   19.436M |      2.24 |     0.660
   auto,zstd,11 |       8.09 |  100.573M |   18.835M |      2.31 |     0.660
   auto,zstd,12 |       9.44 |  100.647M |   16.141M |      2.70 |     0.661
   auto,zstd,13 |      13.02 |  100.458M |   11.703M |      3.72 |     0.659
   auto,zstd,14 |      15.79 |  100.241M | 9650.113K |      4.51 |     0.658
   auto,zstd,15 |      18.04 |  100.225M | 8446.524K |      5.15 |     0.658
   auto,zstd,16 |      20.32 |   99.521M | 7498.784K |      5.81 |     0.653
   auto,zstd,17 |      21.93 |   99.217M | 6948.257K |      6.27 |     0.651
   auto,zstd,18 |      25.25 |   99.000M | 6034.665K |      7.21 |     0.650
   auto,zstd,19 |      25.25 |   99.152M | 6034.665K |      7.21 |     0.651
   auto,zstd,20 |      28.97 |   98.841M | 5259.761K |      8.28 |     0.649
   auto,zstd,21 |      29.42 |   98.984M | 5179.309K |      8.41 |     0.650
   auto,zstd,22 |      32.85 |   98.918M | 4638.517K |      9.39 |     0.649
Raw
98borg-compressbench.php
#!/usr/bin/env php
<?php
/* Author: Romain "Artefact2" Dal Maso <artefact2@gmail.com> */
/* This program is free software. It comes without any warranty, to the
* extent permitted by applicable law. You can redistribute it and/or
* modify it under the terms of the Do What The Fuck You Want To Public
* License, Version 2, as published by Sam Hocevar. See
* http://sam.zoy.org/wtfpl/COPYING for more details. */
const BORG_BINARY = '/home/romain/Downloads/borg-linux64';
const DATASET_DIR = '/tmp/borg-dataset';
const BORG_REPO = '/tmp/borg-repo';
const STATE_FILE = '/tmp/borg-compressbench.state';
const INIT_COMMAND = '%s init --error -e repokey %s';
const CREATE_COMMAND = '%s create --error -C %s %s::foo %s';
const RUNS = 1;
$compressions = [ 'lz4' ];
foreach([ 'zlib', 'lzma' ] as $algo) {
foreach(range(0, 9) as $level) {
$compressions[] = $algo.','.$level;
}
}
foreach($compressions as $comp) {
$compressions[] = 'auto,'.$comp;
}
array_unshift($compressions, 'none');
printf("Benchmark date: %s\n", gmdate('Y-m-d'));
printf("Borg version: %s", shell_exec(sprintf('%s --version', escapeshellarg(BORG_BINARY))));
printf("Python version: %s", shell_exec('python --version'));
printf("Kernel version: %s", shell_exec('uname -sr'));
printf("CPU model: %s", shell_exec('grep "model name" /proc/cpuinfo | head -n1 | cut -d\' \' -f3-'));
printf("\nBorg init command: %s\n", INIT_COMMAND);
printf("Borg create command: %s\n", CREATE_COMMAND);
printf("Dataset path: %s (%s)\n", DATASET_DIR, find_mountpoint(DATASET_DIR));
printf("Repository path: %s (%s)\n", BORG_REPO, find_mountpoint(BORG_REPO));
printf("\nAveraging algos over %d run(s)\n", RUNS);
putenv('BORG_PASSPHRASE=foobar');
if(file_exists(STATE_FILE)) {
$results = json_decode(file_get_contents(STATE_FILE), true);
fprintf(STDERR, "\nWARNING: resuming progress from state file %s\nDelete file if you want to start over.\n", STATE_FILE);
} else {
$results = [];;
}
const FORMAT = "%15s | %10s | %9s | %9s | %9s | %9s\n";
const IFORMAT = "%15s | %10.2f | %9s | %9s | %9.2f | %9.3f\n";
printf("\n".FORMAT, "algo", "cputime", "reposize", "bytes/s", "timeratio", "compratio");
echo strtr(sprintf(FORMAT, '', '', '', '', '', '', '', ''), ' |', '-+');
foreach($compressions as $comp) {
if(!isset($results[$comp])) {
$results[$comp] = [
'runs' => 0,
'cpu' => 0,
'size' => 0,
];
}
while($results[$comp]['runs'] < RUNS) {
passthru(sprintf('rm -Rf %s', escapeshellarg(BORG_REPO)));
passthru(sprintf(INIT_COMMAND,
escapeshellarg(BORG_BINARY),
escapeshellarg(BORG_REPO)));
$times = shell_exec('bash -c '.escapeshellarg(
sprintf('time -p '.CREATE_COMMAND,
escapeshellarg(BORG_BINARY),
escapeshellarg($comp),
escapeshellarg(BORG_REPO),
escapeshellarg(DATASET_DIR))
).' 2>&1');
preg_match_all('%^(real|user|sys) (?<time>.+)$%m', $times, $matches);
$results[$comp]['cpu'] += floatval($matches['time'][1]);
$results[$comp]['cpu'] += floatval($matches['time'][2]);
$du = shell_exec(sprintf('du -B 1 -s %s', escapeshellarg(BORG_REPO)));
preg_match('%^(?<total>[0-9]+)\s%m', $du, $match);
$results[$comp]['size'] += intval($match['total']);
++$results[$comp]['runs'];
$f = fopen(STATE_FILE, 'wb');
fwrite($f, json_encode($results));
fflush($f);
fclose($f);
}
printf(
IFORMAT,
$comp,
$results[$comp]['cpu'] / $results[$comp]['runs'],
format_bytes($results[$comp]['size'] / $results[$comp]['runs']),
format_bytes(($results['none']['size'] / $results['none']['runs']) / ($results[$comp]['cpu'] / $results[$comp]['runs'])),
($results[$comp]['cpu'] / $results[$comp]['runs']) / ($results['none']['cpu'] / $results['none']['runs']),
($results[$comp]['size'] / $results[$comp]['runs']) / ($results['none']['size'] / $results['none']['runs'])
);
}
function format_bytes(int $b, string $prec = '8.3') {
static $suffixes = [ 'B', 'K', 'M', 'G', 'T', 'P', 'E' ];
list($total, $decimals) = explode('.', $prec);
$cutoff = pow(10, $total - $decimals - 1);
$i = 0;
while($b >= $cutoff) {
$b /= 1000;
++$i;
}
return sprintf("%".$prec."f%s", $b, $suffixes[$i]);
}
function find_mountpoint($path) {
if($del = !file_exists($path)) {
touch($path);
}
$dev = shell_exec(sprintf('df %s | tail -n1 | cut -d\' \' -f1', escapeshellarg($path)));
if($del) unlink($path);
return substr($dev, 0, -1);
}
Raw
99autobench.sh
#!/bin/sh
# Run me in single-user mode:
# systemctl isolate rescue.target
for i in 1 2 3; do
rm -Rf /tmp/borg-dataset /tmp/borg-repo /tmp/borg-compressbench.state
cp -a /tmp/borg-dataset$i /tmp/borg-dataset
./borg-compressbench | tee bcb$i.txt
mv /tmp/borg-compressbench.state bcb$i.state
done
Raw
eval.py
#!/usr/bin/python3
import sys
import numpy as np
import matplotlib.pyplot as plt
data = {}
stats = {}
average = {}
infiles = sys.argv[1:]
Ninfiles = len(infiles)
for file, n in zip( infiles, range(Ninfiles) ):
data[n] = np.genfromtxt(file, skip_header=19, skip_footer=1, delimiter="|", dtype='U20,f4,U20,U20,f4,f4')
data[n] = data[n].tolist()
data[n] = [ [cell.strip() if type(cell) == str else cell for cell in row] for row in data[n] ]
data[n] = np.array(data[n])
stats[n] = {}
stats[n]['algo'] = np.array(data[n][:,0], dtype=str)
stats[n]['timeratio'] = np.array(data[n][:,4], dtype=float)
stats[n]['compratio'] = np.array(data[n][:,5], dtype=float)
stats[n]['eff'] = [ stats[n]['timeratio'][i] * stats[n]['compratio'][i] for i in range( len( stats[n]['timeratio'] ) ) ]
stats[n]['eff'] = [ -i+2 for i in stats[n]['eff'] ]
with open(file) as f:
stats[n]['description'] = f.readline()
################## Calculate Average ##################
last = len(stats)
stats[last] = {}
stats[last]['algo'] = stats[0]['algo'].copy()
stats[last]['timeratio'] = stats[0]['timeratio'].copy()
stats[last]['compratio'] = stats[0]['compratio'].copy()
stats[last]['eff'] = stats[0]['eff'].copy()
stats[last]['description'] = 'Mean'
for i in range(len(stats[0]['algo'])):
stats[last]['timeratio'][i] = np.mean( [ stats[file]['timeratio'][i] for file in range(Ninfiles) ] )
stats[last]['compratio'][i] = np.mean( [ stats[file]['compratio'][i] for file in range(Ninfiles) ] )
stats[last]['eff'][i] = np.mean( [ stats[file]['eff'][i] for file in stats ] )
#######################################################
Nplots = [ stats[key]['description'] for key in stats.keys() ]
fig = plt.figure(figsize=(30,5))
xticklabels = data[0][:,0]
plotx = range( len( xticklabels ) )
ax_size = fig.add_subplot(111)
ax_size.set_xticks(plotx)
ax_size.set_xticklabels(xticklabels)
ax_size.set_title('Borg Compression Comparison')
ax_size.set_ylim([0,1])
xlabel = 'Compr. Algorithm[, Compr. Factor]'
ylabel_size = 'Size Relative to No Compression'
ylabel_time = 'Compression Time Relative to No Compression'
ylabel_efficiency = r'Efficiency: $(-1)\cdot$ Fraction Time $\cdot$ Fraction Size $+ 2$'
ax_time = ax_size.twinx()
ax_efficiency = ax_size.twinx()
colorfracs = [i / (Ninfiles+.1) if i > 0 else 0 for i in range( len(Nplots) )]
# Actual plotting
lns = []
for cfrac, n in zip( colorfracs, range(Ninfiles+1) ):
color = plt.cm.viridis(cfrac)
l_size, = ax_size.plot(plotx, stats[n]['compratio'],
color=color, linestyle='-.',
label='Size Ratio: {}'.format(stats[n]['description']))
l_time, = ax_time.plot(plotx, stats[n]['timeratio'],
color=color, linestyle=':',
label='Time Ratio: {}'.format(stats[n]['description']))
l_eff, = ax_efficiency.plot(plotx, stats[n]['eff'],
color=color, linestyle='-',
label='Efficiency: {}'.format(stats[n]['description']))
lns.append(l_size)
lns.append(l_time)
lns.append(l_eff)
ax_efficiency.spines['right'].set_position(('outward', 60))
ax_size.xaxis.grid(color='.85', linestyle='-', linewidth=.5)
for tick in ax_size.get_xticklabels():
tick.set_rotation(45)
tick.set_horizontalalignment('right')
ax_size.set_xlabel(xlabel)
ax_size.set_ylabel(ylabel_size)
ax_efficiency.set_ylabel(ylabel_efficiency)
ax_time.set_ylabel(ylabel_time)
lgd = ax_time.legend(handles=lns, loc='upper center', ncol=2,
bbox_to_anchor=(0.5, -.35), facecolor='#FFFFFF',
prop={'family': 'monospace','size': 'medium'})
fig.savefig('eval.svg', bbox_inches='tight', dpi=300)
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