CSV 和 JSON 文檔處理工具 - Miller
% mlr --csv cut -f hostname,uptime mydata.csv % mlr --csv --rs lf filter '$status != "down" && $upsec >= 10000' *.csv % mlr --nidx put '$sum = $7 < 0.0 ? 3.5 : $7 + 2.1*$8' *.dat % grep -v '^#' /etc/group | mlr --ifs : --nidx --opprint label group,pass,gid,member then sort -f group % mlr join -j account_id -f accounts.dat then group-by account_name balances.dat % mlr put '$attr = sub($attr, "([0-9]+)_([0-9]+)_.*", "\1:\2")' data/*.json % mlr stats1 -a min,mean,max,p10,p50,p90 -f flag,u,v data/* % mlr stats2 -a linreg-pca -f u,v -g shape data/
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 and JSON. (Miller can handle positionally-indexed data as a special case.)
Features:
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I/O formats including tabular pretty-printing and positionally indexed (Unix-toolkit style)
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Conversion between formats
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Format-aware processing: e.g. CSV
sortandtackeep header lines first -
High-throughput performance on par with the Unix toolkit
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Miller is pipe-friendly and interoperates with Unix toolkit
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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 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.
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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. You can 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.
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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.
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Not unlike
jq(http://stedolan.github.io/jq/) for JSON, Miller is written in portable, modern C, with zero runtime dependencies. You can download or compile a single binary,scpit to a faraway machine, and expect it to work.
Documentation:
- For full documentation, please visit http://johnkerl.org/miller/doc
- Note: head docs at http://johnkerl.org/miller/doc/index.html match head code at https://github.com/johnkerl/miller. Release-specific docs at http://johnkerl.org/miller/doc/release-docs.html match release-specific code athttps://github.com/johnkerl/miller/tags.
- Miller's license is two-clause BSD: https://github.com/johnkerl/miller/blob/master/LICENSE.txt
- Build information including dependencies: http://johnkerl.org/miller/doc/build.html
- Notes about issue-labeling in the Github repo: https://github.com/johnkerl/miller/wiki/Issue-labeling
- See here for active issues.