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Miller John Kerl
csv csv-format data-cleaning data-processing data-reduction data-regression devops devops-tools json json-data statistical-analysis statistics streaming-algorithms streaming-data tabular-data tsv
Use this command to install Miller:
winget install --id=Miller.Miller -e Miller is like awk, sed, cut, join, and sort for name-indexed data such as CSV, TSV, and tabular JSON. You get to work with your data using named fields, without needing to count positional column indices. 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, TSV, and JSON. (Miller can handle positionally-indexed data as a special case.) ## 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. (Examples here and here). 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 (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.
Miller is a versatile command-line tool designed for processing structured data formats such as CSV, TSV, and JSON using named fields, eliminating the need to track positional indices. Offering multi-purpose capabilities, Miller supports data cleaning, reduction, statistical reporting, log-file processing, and more. Its streaming architecture allows efficient handling of large datasets with minimal memory usage, making it suitable for real-time processing scenarios. With support for various formats including CSV, TSV, JSON, and JSON Lines, Miller ensures seamless data manipulation across different structures. Additionally, its ability to handle record heterogeneity enables processing of data with interleaved schemas, a feature beyond traditional Unix tools. Ideal for developers, system administrators, and data scientists, Miller provides a powerful yet intuitive solution for structured data tasks, enhancing productivity and efficiency in data workflows. It can be installed via winget.
README
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.
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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.
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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
- A Guide To Command-Line Data Manipulation
- A quick tutorial on Miller
- Miller Exercises
- Tools to manipulate CSV files from the Command Line
- www.togaware.com/linux/survivor/CSV_Files.html
- 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
