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10-minute intro
File formats
Unix-toolkit context


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.)


Releases and release notes:


# 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}'