mlr  5.10.2
About: Miller is like sed, awk, cut, join, and sort for name-indexed data such as CSV and tabular JSON.
  Fossies Dox: mlr-5.10.2.tar.gz  ("unofficial" and yet experimental doxygen-generated source code documentation)  

mlr Documentation

Some Fossies usage hints in advance:

  1. To see the Doxygen generated documentation please click on one of the items in the steelblue colored "quick index" bar above or use the side panel at the left which displays a hierarchical tree-like index structure and is adjustable in width.
  2. If you want to search for something by keyword rather than browse for it you can use the client side search facility (using Javascript and DHTML) that provides live searching, i.e. the search results are presented and adapted as you type in the Search input field at the top right.
  3. Doxygen doesn't incorporate all member files but just a definable subset (basically the main project source code files that are written in a supported language). So to search and browse all member files you may visit the Fossies mlr-5.10.2.tar.gz contents page and use the Fossies standard member browsing features (also with source code highlighting and additionally with optional code folding).

What is Miller?

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

Build status

Linux build status Windows build status License Docs



There's a good chance you can get Miller pre-built for your system:

Ubuntu Ubuntu 16.04 LTS Fedora Debian Gentoo

Pro-Linux Arch Linux


Homebrew/MacOSX MacPorts/MacOSX Chocolatey

OS Installation command
Linux yum install miller
apt-get install miller
Mac brew install miller
port install miller
Windows choco install miller

See also building from source.

What can Miller do for me?

With Miller, you get to use named fields without needing to count positional indices, using familiar formats such as CSV, TSV, JSON, and positionally-indexed.

For example, suppose you have a CSV data file like this:

St. Johns,29589.12,35207.53,Residential
Miami Dade,2850980.31,2650932.72,Commercial
Palm Beach,1174081.5,1856589.17,Residential
Miami Dade,1158674.85,1076001.08,Residential

Then, on the fly, you can add new fields which are functions of existing fields, drop fields, sort, aggregate statistically, pretty-print, and more. A simple example:

$ mlr --csv sort -f county flins.csv
Miami Dade,1158674.85,1076001.08,Residential
Miami Dade,2850980.31,2650932.72,Commercial
Palm Beach,1174081.5,1856589.17,Residential
St. Johns,29589.12,35207.53,Residential

A more powerful example:

$ mlr --icsv --opprint --barred \
  put '$tiv_delta = int($tiv_2012 - $tiv_2011); unset $tiv_2011, $tiv_2012' \
  then sort -nr tiv_delta flins.csv 
| county     | line        | tiv_delta |
| Duval      | Residential | 1053663   |
| Palm Beach | Residential | 682508    |
| St. Johns  | Residential | 5618      |
| Highlands  | Residential | -1792     |
| Seminole   | Residential | -2042     |
| Highlands  | Residential | -3249     |
| Miami Dade | Residential | -82674    |
| Miami Dade | Commercial  | -200048   |

This is something the Unix toolkit always could have done, and arguably always should have done.

  • Miller 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.

  • Miller handles a variety of data formats, including but not limited to the familiar CSV, TSV, and JSON. (Miller can handle positionally-indexed data too!)

For a few more examples please see Miller in 10 minutes.


  • 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.

Documentation links