# What is Miller?
**Miller is like awk, sed, cut, join, and sort for data formats such as CSV, TSV, JSON, JSON Lines, and positionally-indexed.**
# 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, JSON Lines, and
positionally-indexed. Then, on the fly, you can add new fields which are
functions of existing fields, drop fields, sort, aggregate statistically,
pretty-print, and more.

* 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**/**JSON Lines**.
(Miller can handle **positionally-indexed data** too!)
In the above image you can see how Miller embraces the common themes of
key-value-pair data in a variety of data formats.
# Getting started
* [Miller in 10 minutes](https://miller.readthedocs.io/en/latest/10min)
* [A Guide To Command-Line Data Manipulation](https://www.smashingmagazine.com/2022/12/guide-command-line-data-manipulation-cli-miller)
* [A quick tutorial on Miller](https://www.ict4g.net/adolfo/notes/data-analysis/miller-quick-tutorial.html)
* [Miller Exercises](https://github.com/GuilloteauQ/miller-exercises)
* [Tools to manipulate CSV files from the Command Line](https://www.ict4g.net/adolfo/notes/data-analysis/tools-to-manipulate-csv.html)
* [www.togaware.com/linux/survivor/CSV_Files.html](https://www.togaware.com/linux/survivor/CSV_Files.html)
* [MLR for CSV manipulation](https://guillim.github.io/terminal/2018/06/19/MLR-for-CSV-manipulation.html)
* [Linux Magazine: Process structured text files with Miller](https://www.linux-magazine.com/Issues/2016/187/Miller)
* [Miller: Command Line CSV File Processing](https://onepointzero.app/posts/miller-command-line-csv-file-processing/)
* [Miller - A Swiss Army Chainsaw for CSV Data, Data Science and Data Munging](https://fuzzyblog.io/blog/data_science/2022/05/13/miller-a-swiss-army-chainsaw-for-csv-data-data-science-and-data-munging.html)
* [Pandas Killer: mlr, the Scientist](https://xvzftube.xyz/posts/pandas_killers/#mlr%3A-the-scientist)
# More documentation links
* [**Full documentation**](https://miller.readthedocs.io/)
* [Miller's license is two-clause BSD](https://github.com/johnkerl/miller/blob/main/LICENSE.txt)
* [Notes about issue-labeling in the Github repo](https://github.com/johnkerl/miller/wiki/Issue-labeling)
* [Active issues](https://github.com/johnkerl/miller/issues?q=is%3Aissue+is%3Aopen+sort%3Aupdated-desc)
# Installing
There's a good chance you can get Miller pre-built for your system:
[](https://launchpad.net/ubuntu/+source/miller)
[](https://launchpad.net/ubuntu/xenial/+package/miller)
[](https://packages.fedoraproject.org/pkgs/miller/miller/)
[](https://packages.debian.org/stable/miller)
[](https://packages.gentoo.org/packages/sys-apps/miller)
[](http://www.pro-linux.de/cgi-bin/DBApp/check.cgi?ShowApp..20427.100)
[](https://aur.archlinux.org/packages/miller-git)
[](http://pkgsrc.se/textproc/miller)
[](https://www.freshports.org/textproc/miller/)
[](https://anaconda.org/conda-forge/miller/)
[](https://snapcraft.io/)
[](https://formulae.brew.sh/formula/miller)
[](https://www.macports.org/ports.php?by=name&substr=miller)
[](https://chocolatey.org/packages/miller)
[](https://github.com/microsoft/winget-pkgs/tree/master/manifests/m/Miller/Miller)
|OS|Installation command|
|---|---|
|Linux|`yum install miller`
`apt-get install miller`
`snap install miller`|
|Mac|`brew install miller`
`port install miller`|
|Windows|`choco install miller`
`winget install Miller.Miller`
`scoop install main/miller`|
See also [README-versions.md](./README-versions.md) for a full list of package versions. Note that long-term-support (LtS) releases will likely be on older versions.
See also [building from source](https://miller.readthedocs.io/en/latest/build.html).
# Community
[](https://github.com/johnkerl/miller/stargazers)
[](https://formulae.brew.sh/formula/miller)
[](https://anaconda.org/conda-forge/miller)
[](#contributors-)
* Discussion forum: https://github.com/johnkerl/miller/discussions
* Feature requests / bug reports: https://github.com/johnkerl/miller/issues
* How to contribute: [https://miller.readthedocs.io/en/latest/contributing/](https://miller.readthedocs.io/en/latest/contributing/)
# Build status
[](https://github.com/johnkerl/miller/actions/workflows/go.yml)
[](https://github.com/johnkerl/miller/actions/workflows/codeql-analysis.yml)
[](https://github.com/johnkerl/miller/actions/workflows/codespell.yml)
# Building from source
* First:
* `cd /where/you/want/to/put/the/source`
* `git clone https://github.com/johnkerl/miller`
* `cd miller`
* With `make`:
* To build: `make`. This takes just a few seconds and produces the Miller executable, which is `./mlr` (or `.\mlr.exe` on Windows).
* To run tests: `make check`.
* To install: `make install`. This installs the executable `/usr/local/bin/mlr` and manual page `/usr/local/share/man/man1/mlr.1` (so you can do `man mlr`).
* You can do `./configure --prefix=/some/install/path` before `make install` if you want to install somewhere other than `/usr/local`.
* Without `make`:
* To build: `go build github.com/johnkerl/miller/v6/cmd/mlr`.
* To run tests: `go test github.com/johnkerl/miller/v6/pkg/...` and `mlr regtest`.
* To install: `go install github.com/johnkerl/miller/v6/cmd/mlr@latest` will install to _GOPATH_`/bin/mlr`.
* See also the doc page on [building from source](https://miller.readthedocs.io/en/latest/build).
* For more developer information please see [README-dev.md](./README-dev.md).
# For developers
* [README-dev.md](README-dev.md)
* [How to contribute](https://miller.readthedocs.io/en/latest/contributing/)
# License
[License: BSD2](https://github.com/johnkerl/miller/blob/main/LICENSE.txt)
# 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, TSV, JSON, JSON Lines, 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.
* Miller is written in portable, modern Go, with **zero runtime dependencies**.
You can download or compile a single binary, `scp` it to a faraway machine,
and expect it to work.
# What people are saying about Miller
Today I discovered Millerβit's like jq but for CSV: https://t.co/pn5Ni241KM
— Adrien Trouillaud (@adrienjt) September 24, 2020
Also, "Miller complements data-analysis tools such as R, pandas, etc.: you can use Miller to clean and prepare your data." @GreatBlueC @nfmcclure
Underappreciated swiss-army command-line chainsaw.
— Dirk Eddelbuettel (@eddelbuettel) February 28, 2017
"Miller is like awk, sed, cut, join, and sort for [...] CSV, TSV, and [...] JSON." https://t.co/TrQqSUK3KK
Miller looks like a great command line tool for working with CSV data. Sed, awk, cut, join all rolled into one: http://t.co/9BBb6VCZ6Y
— Mike Loukides (@mikeloukides) August 16, 2015
Miller is like sed, awk, cut, join, and sort for name-indexed data such as CSV: http://t.co/1zPbfg6B2W - handy tool!
— Ilya Grigorik (@igrigorik) August 22, 2015
Btw, I think Miller is the best CLI tool to deal with CSV. I used to use this when I need to preprocess too big CSVs to load into R (now we have vroom, so such cases might be rare, though...)https://t.co/kUjrSSGJoT
— Hiroaki Yutani (@yutannihilat_en) April 21, 2020
Miller: a *format-aware* data munging tool By @__jo_ker__ to overcome limitations with *line-aware* workshorses like awk, sed et al https://t.co/LCyPkhYvt9
— Donny Daniel (@dnnydnl) September 9, 2018
The project website is a fantastic example of good software documentation!!
Holy holly data swiss army knife batman! How did no one suggest Miller https://t.co/JGQpmRAZLv for solving database cleaning / ETL issues to me before
— James Miller (@japanlawprof) June 12, 2018
Congrats to @__jo_ker__ for amazingly intuitive tool for critical data management tasks!#DataScienceandLaw #ComputationalLaw
## Contributors β¨ Thanks to all the fine people who help make Miller better ([emoji key](https://allcontributors.org/docs/en/emoji-key)):π€―@__jo_ker__'s Miller easily reads, transforms, + writes all sorts of tabular data. It's standalone, fast, and built for streaming data (operating on one line at a time, so you can work on files larger than memory).
— Benjamin Wolfe (he/him) (@BenjaminWolfe) September 9, 2021
And the docs are dream. I've been reading them all morning! https://t.co/Be2pGPZK6t