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About
Miller is like sed, awk, cut, join, and sort for name-indexed data such as CSV.
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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 sort -f hostname,uptime mydata.csv
% mlr --csv put '$z = $x + 2.7*$y' mydata.csv
% mlr --csv filter '$status != "down"' mydata.csv
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. (Miller can handle
positionally-indexed data as a special case.)
Features:
- I/O formats including tabular pretty-printing
- Conversion between formats
- Format-aware processing: e.g. CSV sort and tac keep header lines first
- High-throughput performance on par with the Unix toolkit
Miller is pipe-friendly and interoperates with Unix toolkit.
It 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 just
need to query some data in disk files in a hurry.
Miller also goes beyond classic Unix tools by stepping into our modern,
no-SQL world: its essential record-heterogeneity property allows it to
operate on data where records with different schema (field names) are
interleaved.
Not unlike jq (for JSON),
Miller is written in modern C, and it has zero runtime dependencies. You
can download or compile a single binary, scp it to a faraway machine,
and expect it to work.
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