Miller is like sed, awk, cut, join, and sort for name-indexed data such as CSV.

(See also [here] (https://github.com/johnkerl/miller/issues?q=is%3Aissue+is%3Aopen+sort%3Aupdated-desc) for active issues.)

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
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.
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 the classic Unix tools by stepping 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.
Not unlike jq (http://stedolan.github.io/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.

Documentation:

Build information including dependencies: http://johnkerl.org/miller/doc/build.html
Notes about issue-labeling in the Github repo: https://github.com/johnkerl/miller/wiki/Issue-labeling
For all other information, please visit http://johnkerl.org/miller/doc

License

0-wiz-0/miller

ErrorLooks like something went wrong!

About

Resources

License

Uh oh!

Stars

Watchers

Forks

Releases

Packages 0

Languages

Error
Looks like something went wrong!

Packages