mlr  5.10.2
About: Miller is like sed, awk, cut, join, and sort for name-indexed data such as CSV and tabular JSON.
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mlr Documentation

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README.md

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

Community

Distributions

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

NetBSD FreeBSD

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:

county,tiv_2011,tiv_2012,line
St. Johns,29589.12,35207.53,Residential
Miami Dade,2850980.31,2650932.72,Commercial
Highlands,49155.16,47362.96,Residential
Palm Beach,1174081.5,1856589.17,Residential
Duval,1731888.18,2785551.63,Residential
Miami Dade,1158674.85,1076001.08,Residential
Seminole,22890.55,20848.71,Residential
Highlands,23006.41,19757.91,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
county,tiv_2011,tiv_2012,line
Duval,1731888.18,2785551.63,Residential
Highlands,23006.41,19757.91,Residential
Highlands,49155.16,47362.96,Residential
Miami Dade,1158674.85,1076001.08,Residential
Miami Dade,2850980.31,2650932.72,Commercial
Palm Beach,1174081.5,1856589.17,Residential
Seminole,22890.55,20848.71,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.

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 (http://stedolan.github.io/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