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220 lines
8.8 KiB
HTML
220 lines
8.8 KiB
HTML
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<html lang="en">
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<!-- PAGE GENERATED FROM template.html and content-for-index.html BY poki. -->
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<head>
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<meta name="description" content="Miller documentation"/>
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content="John Kerl, Kerl, Miller, miller, mlr, OLAP, data analysis software, regression, correlation, variance, data tools, " />
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<title> About Miller </title>
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The background image is from a screenshot of a Google search for "data analysis
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tools", lightened and sepia-toned. Over this was placed a Mac Terminal app with
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very light-grey font and translucent background, in which a few statistical
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Miller commands were run with pretty-print-tabular output format.
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-->
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<body background="pix/sepia-overlay.jpg">
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<table width="100%">
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<tr>
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<td width="15%">
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<!--
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<img src="pix/mlr.jpg" />
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<img style="border-width:1px; color:black;" src="pix/mlr.jpg" />
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-->
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<div class="pokinav">
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<center><titleinbody>Miller</titleinbody></center>
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<!-- PAGE LIST GENERATED FROM template.html BY poki -->
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<br/>• <a href="index.html"><b>About Miller</b></a>
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<br/>• <a href="file-formats.html">File formats</a>
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<br/>• <a href="feature-comparison.html">Miller features in the context of the Unix toolkit</a>
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<br/>• <a href="record-heterogeneity.html">Record-heterogeneity</a>
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<br/>• <a href="reference.html">Reference</a>
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<br/>• <a href="data-examples.html">Data examples</a>
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<br/>• <a href="cookbook.html">Cookbook</a>
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<br/>• <a href="faq.html">FAQ</a>
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<br/>• <a href="internationalization.html">Internationalization</a>
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<br/>• <a href="build.html">Compiling, portability, dependencies, and testing</a>
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<br/>• <a href="performance.html">Performance</a>
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<br/>• <a href="whyc.html">Why C?</a>
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<br/>• <a href="etymology.html">Why call it Miller?</a>
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<br/>• <a href="originality.html">How original is Miller?</a>
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<br/>• <a href="to-do.html">Things to do</a>
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<br/>• <a href="release-docs.html">Documents by release</a>
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<br/>• <a href="contact.html">Contact information</a>
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<br/>• <a href="https://github.com/johnkerl/miller">GitHub repo</a>
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<br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/>
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<br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/>
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<br/> <br/> <br/> <br/> <br/> <br/>
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</div>
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</td>
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<td>
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This is a visually gorgeous feature (here & in the CSS): it allows for
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independent scroll of the nav and body panels. In particular the nav
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stays on-screen as you scroll the body.
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However, two problems:
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(1) In Firefox & Chrome both I get janky end-of-body scrolls: there is
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more content but I can't scroll down to it unless I repeatedly retry the
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scrolldown. Which is weird.
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(2) Worse, only the first page renders in PDF (again, Firefox & Chrome).
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For now I'm disabling this separate-scroll feature. A frontender, I am
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not ... maybe someday I'll find a config which gets *all* the features
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I want; for now, it's a tradeoff.
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-->
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<!-- Implementation details: one bit is right here:
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div style="overflow-y:scroll;height:1500px"
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and the other bit is in css/poki-callbacks.css:
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.pokinav {
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display: inline-block;
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background: #e8d9bc;
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border: 1;
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box-shadow: 0px 0px 3px 3px #C9C9C9;
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margin: 10px;
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padding-top: 10px;
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padding-bottom: 10px;
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padding-left: 10px;
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padding-right: 10px;
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overflow-y: scroll; < - - - - - - here
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height: 1500px;
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}
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-->
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<div>
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<center> <titleinbody> About Miller </titleinbody> </center>
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<p/>
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<!-- BODY COPIED FROM content-for-index.html BY poki -->
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<center><table border=1 width="60%"><tr><td><center>
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<b>
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Miller is like sed, awk, cut, join, and sort for name-indexed data such as CSV.
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</b>
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</center> </td></tr></table></center>
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<p/> With Miller you get to use named fields without needing to count
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positional indices. For example:
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<p/>
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<div class="pokipanel">
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<pre>
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% mlr --csv cut -f hostname,uptime mydata.csv
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% mlr --csv --rs lf filter '$status != "down" && $upsec >= 10000' *.csv
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% mlr --nidx put '$sum = $7 + 2.1*$8' *.dat
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% grep -v '^#' /etc/group | mlr --ifs : --nidx --opprint label group,pass,gid,member then sort -f group
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% mlr join -j account_id -f accounts.dat then group-by account_name balances.dat
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% mlr put '$attr = sub($attr, "([0-9]+)_([0-9]+)_.*", "\1:\2")' data/*
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% mlr stats1 -a min,mean,max,p10,p50,p90 -f flag,u,v data/*
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% mlr stats2 -a linreg-pca -f u,v -g shape data/*
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</pre>
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</div>
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<p/>
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<p/>This is something the Unix toolkit always could have done, and arguably
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always should have done. It operates on key-value-pair data while the familiar
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Unix tools operate on integer-indexed fields: if the natural data structure for
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the latter is the array, then Miller’s natural data structure is the
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insertion-ordered hash map. This encompasses a <b>variety of data formats</b>,
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including but not limited to the familiar CSV. (Miller can handle
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positionally-indexed data as a special case.)
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<p/> Features:
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<ul>
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<li> I/O formats including <b>tabular pretty-printing</b> and <b>positionally indexed</b> (Unix-toolkit style)
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<li> <b>Conversion</b> between formats
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<li> <b>Format-aware processing</b>: e.g. CSV sort and tac keep header lines first
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<li> High-throughput <b>performance</b> on par with the Unix toolkit
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<li/> Miller is <b>pipe-friendly</b> and interoperates with Unix toolkit.
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<li/> Miller is <b>streaming</b>: most operations need only a single record in
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memory at a time, rather than ingesting all input before producing any output.
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For those operations which require deeper retention (<tt>sort</tt>,
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<tt>tac</tt>, <tt>stats1</tt>), Miller retains only as much data as needed.
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This means that whenever functionally possible you can operate on files which
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are larger than your system’s available RAM, and you can use Miller in
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<tt>tail -f</tt> contexts.
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<li/> Miller complements SQL <b>databases</b>: you can slice, dice, and reformat
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data on the client side on its way into or out of a database. You can also reap
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some of the benefits of databases for quick, setup-free one-off tasks when just
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need to query some data in disk files in a hurry.
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<li/> Miller complements <b>data-analysis tools</b> such as <b>R</b>,
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<b>pandas</b>, etc.: you can use Miller to <b>clean</b> and <b>prepare</b> your
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data. While you can do basic statistics entirely in Miller, its streaming-data
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feature and single-pass algorithms enable you to reduce very large data sets.
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You can snarf and munge log-file data, including selecting out relevant
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substreams, then produce CSV format and load that into all-in-memory/data-frame
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utilities for further statistical and/or graphical processing.
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<li/>Miller also goes beyond classic Unix tools by stepping into our modern,
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<b>no-SQL</b> world: its essential record-heterogeneity property allows it to
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operate on data where records with different schema (field names) are
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interleaved.
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<li/> Not unlike <a href="http://stedolan.github.io/jq/">jq</a> (for JSON),
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Miller is written in modern C, and it has <b>zero runtime dependencies</b>. You
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can download or compile a single binary, <tt>scp</tt> it to a faraway machine,
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and expect it to work.
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</ul>
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<p>Releases and release notes:
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<a href="https://github.com/johnkerl/miller/releases">https://github.com/johnkerl/miller/releases</a>.
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</div>
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</td>
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</table>
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</body>
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</html>
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