Miller is like awk, sed, cut, join, and sort for data formats such as CSV, TSV, JSON, JSON Lines, and positionally-indexed. 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!)
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
Project Samples
