About Miller
Description: Miller documentation
Keywords: John Kerl, Kerl, Miller, miller, mlr, OLAP, data analysis software, regression, correlation, variance, data tools,

Miller Overview: • About Miller • Miller in 10 minutes • File formats • Miller features in the context of the Unix toolkit • Record-heterogeneity • Internationalization Using Miller: • FAQ • Sharing data with other languages • Cookbook part 1 • Cookbook part 2 • Cookbook part 3 • Data-diving examples • Manpage • Reference • Reference: Verbs • Reference: DSL • Documents by release • Installation, portability, dependencies, and testing Background: • Why? • Why C? • Why call it Miller? • How original is Miller? • Performance Repository: • Things to do • Contact information • GitHub repo | About Miller 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. Releases and release notes: https://github.com/johnkerl/miller/releases. Examples:
    # Column select
    % mlr --csv cut -f hostname,uptime mydata.csv
    # Add new columns as function of other columns
    % mlr --nidx put '$sum = $7 < 0.0 ? 3.5 : $7 + 2.1*$8' *.dat
    # Row filter
    % mlr --csv filter '$status != "down" && $upsec >= 10000' *.csv
    # Apply column labels and pretty-print
    % grep -v '^#' /etc/group | mlr --ifs : --nidx --opprint label group,pass,gid,member then sort -f group
    # Join multiple data sources on key columns
    % mlr join -j account_id -f accounts.dat then group-by account_name balances.dat
    # Multiple formats including JSON
    % mlr --json put '$attr = sub($attr, "([0-9]+)_([0-9]+)_.*", "\1:\2")' data/*.json
    # Aggregate per-column statistics
    % mlr stats1 -a min,mean,max,p10,p50,p90 -f flag,u,v data/*
    # Linear regression
    % mlr stats2 -a linreg-pca -f u,v -g shape data/*
    # Aggregate custom per-column statistics
    % mlr put -q '@sum[$a][$b] += $x; end {emit @sum, "a", "b"}' data/*
    # Iterate over data using DSL expressions
    % mlr --from estimates.tbl put '
      for (k,v in $*) {
        if (is_numeric(v) && k =~ "^[t-z].*$") {
          $sum += v; $count += 1
        }
      }
      $mean = $sum / $count # no assignment if count unset
    '
    # Run DSL expressions from a script file
    % mlr --from infile.dat put -f analyze.mlr
    # Split/reduce output to multiple filenames
    % mlr --from infile.dat put 'tee > "./taps/data-".$a."-".$b, $*'
    # Compressed I/O
    % mlr --from infile.dat put 'tee | "gzip > ./taps/data-".$a."-".$b.".gz", $*'
    # Interoperate with other data-processing tools using standard pipes
    % mlr --from infile.dat put -q '@v=$*; dump | "jq .[]"'
    # Tap/trace
    % mlr --from infile.dat put  '(NR % 1000 == 0) { print > stderr, "Checkpoint ".NR}'