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

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.)


  • 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.