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.
- Miller in 10 minutes
- A Guide To Command-Line Data Manipulation
- A quick tutorial on Miller
- Tools to manipulate CSV files from the Command Line
- MLR for CSV manipulation
- Linux Magazine: Process structured text files with Miller
- Miller: Command Line CSV File Processing
- Miller - A Swiss Army Chainsaw for CSV Data, Data Science and Data Munging
- Pandas Killer: mlr, the Scientist
More documentation links
- Full documentation
- Miller's license is two-clause BSD
- Notes about issue-labeling in the Github repo
- Active issues
There's a good chance you can get Miller pre-built for your system:
See also 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.
- 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/
Building from source
git clone https://github.com/johnkerl/miller
- To build:
make. This takes just a few seconds and produces the Miller executable, which is
- To run tests:
- To install:
make install. This installs the executable
/usr/local/bin/mlrand manual page
/usr/local/share/man/man1/mlr.1(so you can do
- You can do
make installif you want to install somewhere other than
- To build:
- To build:
go build github.com/johnkerl/miller/cmd/mlr.
- To run tests:
go test github.com/johnkerl/miller/internal/pkg/...and
- To install:
go install github.com/johnkerl/miller/cmd/mlrwill install to GOPATH
- To build:
- See also the doc page on building from source.
- For more developer information please see README-dev.md.
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 (
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
tackeep 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,
scpit to a faraway machine, and expect it to work.
What people are saying about Miller
Today I discovered Miller—it'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
— Benjamin Wolfe (he/him) (@BenjaminWolfe) September 9, 2021
🤯@__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).
And the docs are dream. I've been reading them all morning! https://t.co/Be2pGPZK6t
Thanks to all the fine people who help make Miller better (emoji key):
This project follows the all-contributors specification. Contributions of any kind are welcome!