miller/doc/performance.html
2017-04-14 21:56:56 -04:00

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<br/><b>Overview:</b>
<br/>&bull;&nbsp;<a href="index.html">About Miller</a>
<br/>&bull;&nbsp;<a href="10-min.html">Miller in 10 minutes</a>
<br/>&bull;&nbsp;<a href="file-formats.html">File formats</a>
<br/>&bull;&nbsp;<a href="feature-comparison.html">Miller features in the context of the Unix toolkit</a>
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<br/>&bull;&nbsp;<a href="originality.html">How original is Miller?</a>
<br/>&bull;&nbsp;<a href="performance.html"><b>Performance</b></a>
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<center><b>Contents:</b></center>
&bull;&nbsp;<a href="#Data">Data</a><br/>
&bull;&nbsp;<a href="#Comparands">Comparands</a><br/>
&bull;&nbsp;<a href="#Raw_results">Raw results</a><br/>
&bull;&nbsp;<a href="#Analysis">Analysis</a><br/>
&bull;&nbsp;<a href="#Conclusion">Conclusion</a><br/>
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<a id="Data"/><h1>Data</h1>
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Test data were of the form
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<pre>
a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533
a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776
a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463
a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729
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a,b,i,x,y
pan,pan,1,0.3467901443380824,0.7268028627434533
eks,pan,2,0.7586799647899636,0.5221511083334797
wye,wye,3,0.20460330576630303,0.33831852551664776
eks,wye,4,0.38139939387114097,0.13418874328430463
wye,pan,5,0.5732889198020006,0.8636244699032729
</pre>
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for DKVP and CSV, respectively, where fields <tt>a</tt> and <tt>b</tt> take one of five text values,
uniformly distributed; <tt>i</tt> is a 1-up line counter; <tt>x</tt> and <tt>y</tt>
are independent uniformly distributed floating-point numbers in the unit
interval.
<p>Data files of one million lines (totalling about 50MB for CSV and 60MB for
DKVP) were used. In experiments not shown here, I also varied the file sizes;
the size-dependent results were the expected, completely unsurprising
linearities and so I produced no file-size-dependent plots for your viewing pleasure.
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<a id="Comparands"/><h1>Comparands</h1>
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The <tt>cat</tt>, <tt>cut</tt>, <tt>awk</tt>, <tt>sed</tt>, <tt>sort</tt> tools
were compared to <tt>mlr</tt> on an 8-core Darwin laptop; RAM capacity was
nowhere near challenged . The <tt>catc</tt> program is a simple line-oriented
line-printer (<a
href="https://github.com/johnkerl/miller/blob/master/c/tools/catc.c">source
here</a>) which is intermediate between Miller (which is record-aware as well
as line-aware) and <tt>cat</tt> (which is only byte-aware).
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<a id="Raw_results"/><h1>Raw results</h1>
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Note that for CSV data, the command is <tt>mlr --csvlite ...</tt> rather than <tt>mlr ...</tt>.
<p/>
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<pre>
Mac Mac Comparand
DKVP CSV
seconds seconds
0.016 0.013 cat
0.189 0.189 catc
3.657 4.388 awk -F, '{print}'
2.027 1.795 mlr cat
2.292 1.940 cut -d , -f 1,4
3.540 4.516 awk -F, '{print $1,$4}'
1.600 1.390 mlr cut -f a,x
1.694 1.648 mlr cut -x -f a,x
0.845 0.643 sed -e 's/x=/EKS=/' -e 's/b=/BEE=/'
2.076 1.842 mlr rename x,EKS,b,BEE
5.643 5.031 awk -F, '{gsub("x=","",$4);gsub("y=","",$5);print $4+$5}'
4.019 3.679 mlr put '$z=$x+$y'
2.481 2.628 mlr stats1 -a mean -f x,y -g a,b
2.587 2.389 mlr stats2 -a corr -f x,y -g a,b
23.247 14.466 sort -t, -k 1,2
3.023 5.658 mlr sort -f a,b
17.224 15.523 sort -t, -k 4,5
5.807 5.194 mlr sort -n x,y
</pre>
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<a id="Analysis"/><h1>Analysis</h1>
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<ul>
<li/> As expected, <tt>cat</tt> is very fast &mdash; it needs only stream bytes as quickly as possible; it doesn&rsquo;t even need to touch individual bytes.
<li/> My <tt>catc</tt> is also faster than Miller: it needs to read and write lines, but it doesn&rsquo;t segment lines into records; in fact it does no iteration over bytes in each line.
<li/> Miller does not outperform <tt>sed</tt>, which is string-oriented rather than record-oriented.
<li/> For the tools which do need to pick apart fields (<tt>cut</tt>,
<tt>awk</tt>, <tt>sort</tt>), Miller is comparable or outperforms. As noted above, this effect
persists linearly across file sizes.
<li/> For univariate and bivariate statistics, I didn&rsquo;t attempt to
compare to other tools wherein such computations are less straightforward;
rather, I attempted only to show that Miller&rsquo;s processing time here is comparable to its own processing time for other problems.
</ul>
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<a id="Conclusion"/><h1>Conclusion</h1>
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For record-oriented data transformations, Miller meets or beats the Unix
toolkit in many contexts. Field renames in particular are worth doing as a
pre-pipe or post-pipe using <tt>sed</tt>.
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