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About Miller
Miller is like sed, awk, cut, join, and sort for name-indexed data such as CSV
and tabular JSON.
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With Miller you get to use named fields without needing to count
positional indices. For example:
% mlr --csv cut -f hostname,uptime mydata.csv
% mlr --csv --rs lf filter '$status != "down" && $upsec >= 10000' *.csv
% mlr --nidx put '$sum = $7 < 0.0 ? 3.5 : $7 + 2.1*$8' *.dat
% grep -v '^#' /etc/group | mlr --ifs : --nidx --opprint label group,pass,gid,member then sort -f group
% mlr join -j account_id -f accounts.dat then group-by account_name balances.dat
% mlr --json put '$attr = sub($attr, "([0-9]+)_([0-9]+)_.*", "\1:\2")' data/*.json
% mlr stats1 -a min,mean,max,p10,p50,p90 -f flag,u,v data/*
% mlr stats2 -a linreg-pca -f u,v -g shape data/*
% mlr put -q '@sum[$a][$b] += $x; end {emit @sum, "a", "b"}' data/*
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’s natural data structure is the
insertion-ordered hash map. This encompasses a variety of data formats,
including but not limited to the familiar CSV and JSON. (Miller can handle
positionally-indexed data as a special case.)
Features:
Miller is multi-purpose: it’s useful for data
cleaning, data reduction, statistical reporting,
devops, system administration, log-file processing,
format conversion, and database-query post-processing.
You can use Miller to snarf and munge log-file data, 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.
Miller complements data-analysis tools such as R,
pandas, etc.: you can use Miller to clean and prepare your
data. While you can do basic statistics entirely in Miller, its
streaming-data feature and single-pass algorithms enable you to reduce very
large data sets.
Miller complements SQL databases: 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.
Miller also goes beyond the classic Unix tools by stepping fully into our
modern, no-SQL world: its essential record-heterogeneity property allows
Miller to operate on data where records with different schema (field names) are
interleaved.
Miller is streaming: 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 (sort,
tac, stats1), 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’s available RAM, and you can use Miller in
tail -f contexts.
Miller is pipe-friendly and interoperates with the Unix toolkit
Miller’s I/O formats include tabular pretty-printing,
positionally indexed (Unix-toolkit style), CSV, JSON, and others
Miller does conversion between formats
Miller’s processing is format-aware: e.g. CSV sort
and tac keep header lines first
Miller has high-throughput performance on par with the Unix toolkit
Not unlike jq (for JSON),
Miller is written in portable, modern C, with zero runtime dependencies.
You can download or compile a single binary, scp it to a faraway
machine, and expect it to work.
Releases and release notes:
https://github.com/johnkerl/miller/releases.
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