# johnkerl/miller

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

Repository: https://github.com/johnkerl/miller
Canonical: https://ross.abutalabs.com/products/miller
Homepage: https://miller.readthedocs.io
Language: Go
License: NOASSERTION
License Family: other
Topics: data-processing, data-cleaning, csv, csv-format, streaming-data, streaming-algorithms, tsv, json, json-data, data-reduction, data-regression, statistics, statistical-analysis, devops, devops-tools, tabular-data, command-line, command-line-tools, unix-toolkit, miller
Last push: 2026-08-25T19:33:39+00:00

## Health v2 (maintenance only)
Score: 94/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 85, longevity 100
- inputs: {"age_days": 4140, "days_push": 8, "days_rel": 23, "gap_med": 53.0, "n_releases_24m": 9}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 10003, forks 240 (observed 2026-08-28T04:10:39.220896+00:00)

## What it is
Miller (mlr) is a command-line tool like awk, sed, cut, join, and sort but designed for name-indexed data such as CSV, TSV, JSON, JSON Lines, and YAML. It supports querying, shaping, reformatting, sorting, and statistically aggregating tabular data with familiar verbs plus a built-in expression language.

## Use cases
- convert csv files to json from the command line
- clean and reshape csv data before loading into a database
- sort and filter tsv files by named columns
- compute statistics like sums and counts over csv records
- pretty-print json lines data for inspection
- drop or rename columns in large csv files without opening a spreadsheet
- replace awk one-liners for name-indexed data

## When to choose
- you work with CSV, TSV, JSON, or JSON Lines data in the terminal
- you want awk/sed-like power but with named-field awareness
- you need fast streaming processing of large tabular files
- you want format conversion between CSV, JSON, and YAML without writing code

## When to avoid
- you need a full dataframe library with plotting and rich statistics
- your data is deeply nested binary formats like Parquet or Avro
- you prefer interactive GUI or notebook-based data exploration

## Facets
- artifact type: cli-tool
- maturity: stable
- function: cli, etl, data-science, parser, developer-tools
- domain: data-science, developer-tools, files
- platform: windows, cli, cross-platform
- tags: csv, tsv, json, json-lines, awk-alternative, data-wrangling, format-conversion, unix-tools, statistics, streaming, data-engineering, command-line, linux, macos

## Member repositories
- johnkerl/miller (main) score 94

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:39.220896+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:20:00.941432+00:00, confidence not recorded.
  - readme: https://github.com/johnkerl/miller (fetched 2026-08-28T04:10:39.220896+00:00, sha c62f44790f14)
  - homepage: https://miller.readthedocs.io (fetched 2026-08-29T08:19:45.512210+00:00, sha 2fad242d04cb)
- Data as of 2026-08-30T08:39:29.467469+00:00.