miller/doc/content-for-index.html
2016-02-09 09:18:31 -05:00

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<center><table border=1 width="60%"><tr><td><center>
<b>
Miller is like sed, awk, cut, join, and sort for name-indexed data such as CSV
and tabular JSON.
</b>
</center> </td></tr></table></center>
<p/> With Miller you get to use named fields without needing to count
positional indices. For example:
POKI_INCLUDE_ESCAPED(index-snippet.txt)HERE
<p/>This is something the Unix toolkit always could have done, and arguably
always should have done. It operates on key-value-pair data while the familiar
Unix tools operate on integer-indexed fields: if the natural data structure for
the latter is the array, then Miller&rsquo;s natural data structure is the
insertion-ordered hash map. This encompasses a <b>variety of data formats</b>,
including but not limited to the familiar CSV and JSON. (Miller can handle
<b>positionally-indexed data</b> as a special case.)
<p/> Features:
<ul>
<li> I/O formats including <b>tabular pretty-printing</b> and <b>positionally
indexed</b> (Unix-toolkit style)
<li> <b>Conversion</b> between formats
<li> <b>Format-aware processing</b>: e.g. CSV sort and tac keep header lines first
<li> High-throughput <b>performance</b> on par with the Unix toolkit
<li/> Miller is <b>pipe-friendly</b> and interoperates with Unix toolkit.
<li/> Miller is <b>streaming</b>: most operations need only a single record in
memory at a time, rather than ingesting all input before producing any output.
For those operations which require deeper retention (<tt>sort</tt>,
<tt>tac</tt>, <tt>stats1</tt>), Miller retains only as much data as needed.
This means that whenever functionally possible you can operate on files which
are larger than your system&rsquo;s available RAM, and you can use Miller in
<b>tail -f</b> contexts.
<li/> Miller complements SQL <b>databases</b>: you can slice, dice, and
reformat data on the client side on its way into or out of a database. You can
also reap some of the benefits of databases for quick, setup-free one-off tasks
when you just need to query some data in disk files in a hurry.
<li/> Miller complements <b>data-analysis tools</b> such as <b>R</b>,
<b>pandas</b>, etc.: you can use Miller to <b>clean</b> and <b>prepare</b> your
data. While you can do <b>basic statistics</b> entirely in Miller, its streaming-data
feature and single-pass algorithms enable you to <b>reduce very large data sets</b>.
You can snarf and munge <b>log-file data</b>, including selecting out relevant
substreams, then produce CSV format and load that into all-in-memory/data-frame
utilities for further statistical and/or graphical processing.
<li/>Miller also goes beyond classic Unix tools by stepping fully into our
modern, <b>no-SQL</b> world: its essential record-heterogeneity property allows
it to operate on data where records with different schema (field names) are
interleaved.
<li/> Not unlike <a href="http://stedolan.github.io/jq/">jq</a> (for JSON),
Miller is written in portable, modern C, with <b>zero runtime dependencies</b>.
You can download or compile a single binary, <tt>scp</tt> it to a faraway
machine, and expect it to work. </ul>
<p>Releases and release notes:
<a href="https://github.com/johnkerl/miller/releases">https://github.com/johnkerl/miller/releases</a>.