miller/README.md
2015-11-18 20:44:29 -05:00

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**Miller is like sed, awk, cut, join, and sort for name-indexed data such as CSV.**
[![Build Status](https://travis-ci.org/johnkerl/miller.svg?branch=master)](https://travis-ci.org/johnkerl/miller)
With Miller, you get to use named fields without needing to count positional
indices. Examples:
```
% mlr --csv cut -f hostname,uptime mydata.csv
% mlr --csv filter '$status != "down" && $upsec >= 10000' *.csv
% mlr --nidx put '$sum = $7 + 2.1*$8' *.dat
% grep -v '^#' /etc/group | mlr --ifs : --nidx --opprint label group,pass,gid,member then sort -f group
% mlr join -j account_id -f accounts.dat then group-by account_name balances.dat
% mlr put '$attr = sub($attr, "([0-9]+)_([0-9]+)_.*", "\1:\2")' data/*
% mlr stats1 -a min,mean,max,p10,p50,p90 -f flag,u,v data/*
% mlr stats2 -a linreg-pca -f u,v -g shape data/*
```
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** and **positionally indexed** (Unix-toolkit style)
* **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:
* Miller's license is two-clause BSD: https://github.com/johnkerl/miller/blob/master/LICENSE.txt
* 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
* See [here] (https://github.com/johnkerl/miller/issues?q=is%3Aissue+is%3Aopen+sort%3Aupdated-desc) for active issues.
* For all other information, please visit http://johnkerl.org/miller/doc