Reference: DSL

Contents:
• Overview
• Syntax
    • Expression formatting
    • Expressions from files
    • Semicolons, commas, newlines, and curly braces
• Variables
    • Built-in variables
    • Field names
    • Out-of-stream variables
    • Indexed out-of-stream variables
    • Local variables
    • Map literals
    • Type-checking
        • Type-test and type-assertion expressions
        • Type-declarations for local variables, function parameter, and function return values
    • Null data: empty and absent
    • Aggregate variable assignments
    • Keywords for filter and put
• Operator precedence
• Operator and function semantics
• Control structures
    • Pattern-action blocks
    • If-statements
    • While and do-while loops
    • For-loops
        • Key-only for-loops
        • Key-value for-loops
        • C-style triple-for loops
    • Begin/end blocks
• Output statements
    • Print statements
    • Dump statements
    • Tee statements
    • Redirected-output statements
    • Emit statements
    • Multi-emit statements
    • Emit-all statements
• Unset statements
• Filter statements
• Built-in functions for filter and put
    • +
    • -
    • *
    • /
    • //
    • %
    • **
    • |
    • ^
    • &
    • ~
    • <<
    • >>
    • ==
    • !=
    • =~
    • !=~
    • >
    • >=
    • <
    • <=
    • &&
    • ||
    • ^^
    • !
    • ? :
    • .
    • abs
    • acos
    • acosh
    • asin
    • asinh
    • asserting_absent
    • asserting_bool
    • asserting_boolean
    • asserting_empty
    • asserting_empty_map
    • asserting_float
    • asserting_int
    • asserting_map
    • asserting_nonempty_map
    • asserting_not_empty
    • asserting_not_map
    • asserting_not_null
    • asserting_null
    • asserting_numeric
    • asserting_present
    • asserting_string
    • atan
    • atan2
    • atanh
    • boolean
    • cbrt
    • ceil
    • cos
    • cosh
    • depth
    • dhms2fsec
    • dhms2sec
    • erf
    • erfc
    • exp
    • expm1
    • float
    • floor
    • fmtnum
    • fsec2dhms
    • fsec2hms
    • gmt2sec
    • gsub
    • haskey
    • hexfmt
    • hms2fsec
    • hms2sec
    • int
    • invqnorm
    • is_absent
    • is_bool
    • is_boolean
    • is_empty
    • is_empty_map
    • is_float
    • is_int
    • is_map
    • is_nonempty_map
    • is_not_empty
    • is_not_map
    • is_not_null
    • is_null
    • is_numeric
    • is_present
    • is_string
    • joink
    • joinkv
    • joinv
    • leafcount
    • length
    • log
    • log10
    • log1p
    • logifit
    • madd
    • mapdiff
    • mapsum
    • max
    • mexp
    • min
    • mmul
    • msub
    • pow
    • qnorm
    • round
    • roundm
    • sec2dhms
    • sec2gmt
    • sec2gmtdate
    • sec2hms
    • sgn
    • sin
    • sinh
    • splitkv
    • splitkvx
    • splitnv
    • splitnvx
    • sqrt
    • strftime
    • string
    • strlen
    • strptime
    • sub
    • substr
    • systime
    • tan
    • tanh
    • tolower
    • toupper
    • typeof
    • urand
    • urand32
    • urandint
• User-defined functions and subroutines
    • User-defined functions
    • User-defined subroutines
• Errors and transparency
• A note on the complexity of Miller’s expression language

Overview

Here’s comparison of verbs and put/filter DSL expressions:

Example:

$ mlr stats1 -a sum -f x -g a data/small
a=pan,x_sum=0.346790
a=eks,x_sum=1.140079
a=wye,x_sum=0.777892

  • Verbs are coded in C
  • They run a bit faster
  • They take fewer keystrokes
  • There is less to learn
  • Their customization is limited to each verb’s options
Example:

$ mlr  put -q '@x_sum[$a] += $x; end{emit @x_sum, "a"}' data/small
a=pan,x_sum=0.346790
a=eks,x_sum=1.140079
a=wye,x_sum=0.777892

  • You get to write your own DSL expressions
  • They run a bit slower
  • They take more keystrokes
  • There is more to learn
  • They are highly customizable

Please see here for information on verbs other than put and filter.

The essential usages of mlr filter and mlr put are for record-selection and record-updating expressions, respectively. For example, given the following input data:

$ cat data/small
a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533
a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776
a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463
a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729

you might retain only the records whose a field has value eks:

$ mlr filter '$a == "eks"' data/small
a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463

or you might add a new field which is a function of existing fields:

$ mlr put '$ab = $a . "_" . $b ' data/small
a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533,ab=pan_pan
a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797,ab=eks_pan
a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776,ab=wye_wye
a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463,ab=eks_wye
a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729,ab=wye_pan

The two verbs mlr filter and mlr put are essentially the same. The only differences are:

  • Expressions sent to mlr filter must end with a boolean expression, which is the filtering criterion;
  • mlr filter expressions may not reference the filter keyword within them; and
  • mlr filter expressions may not use tee, emit, emitp, or emitf.

All the rest is the same: in particular, you can define and invoke functions and subroutines to help produce the final boolean statement, and record fields may be assigned to in the statements preceding the final boolean statement.

There are more details and more choices, of course, as detailed in the following sections.

Syntax

Expression formatting

Multiple expressions may be given, separated by semicolons, and each may refer to the ones before:

$ ruby -e '10.times{|i|puts "i=#{i}"}' | mlr --opprint put '$j = $i + 1; $k = $i +$j'
i j  k
0 1  1
1 2  3
2 3  5
3 4  7
4 5  9
5 6  11
6 7  13
7 8  15
8 9  17
9 10 19

Newlines within the expression are ignored, which can help increase legibility of complex expressions:

$ mlr --opprint put '
  $nf       = NF;
  $nr       = NR;
  $fnr      = FNR;
  $filenum  = FILENUM;
  $filename = FILENAME
' data/small data/small2
a   b   i     x                    y                    nf nr fnr filenum filename
pan pan 1     0.3467901443380824   0.7268028627434533   5  1  1   1       data/small
eks pan 2     0.7586799647899636   0.5221511083334797   5  2  2   1       data/small
wye wye 3     0.20460330576630303  0.33831852551664776  5  3  3   1       data/small
eks wye 4     0.38139939387114097  0.13418874328430463  5  4  4   1       data/small
wye pan 5     0.5732889198020006   0.8636244699032729   5  5  5   1       data/small
pan eks 9999  0.267481232652199086 0.557077185510228001 5  6  1   2       data/small2
wye eks 10000 0.734806020620654365 0.884788571337605134 5  7  2   2       data/small2
pan wye 10001 0.870530722602517626 0.009854780514656930 5  8  3   2       data/small2
hat wye 10002 0.321507044286237609 0.568893318795083758 5  9  4   2       data/small2
pan zee 10003 0.272054845593895200 0.425789896597056627 5  10 5   2       data/small2

$ mlr --opprint filter '($x > 0.5 && $y < 0.5) || ($x < 0.5 && $y > 0.5)' then stats2 -a corr -f x,y data/medium
x_y_corr
-0.747994

Expressions from files

The simplest way to enter expressions for put and filter is between single quotes on the command line, e.g.

$ mlr --from data/small put '$xy = sqrt($x**2 + $y**2)'
a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533,xy=0.805299
a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797,xy=0.920998
a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776,xy=0.395376
a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463,xy=0.404317
a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729,xy=1.036584

$ mlr --from data/small put 'func f(a, b) { return sqrt(a**2 + b**2) } $xy = f($x, $y)'
a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533,xy=0.805299
a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797,xy=0.920998
a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776,xy=0.395376
a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463,xy=0.404317
a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729,xy=1.036584

You may, though, find it convenient to put expressions into files for reuse, and read them using the -f option. For example:

$ cat data/fe-example-3.mlr
func f(a, b) {
  return sqrt(a**2 + b**2)
}
$xy = f($x, $y)

$ mlr --from data/small put -f data/fe-example-3.mlr
a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533,xy=0.805299
a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797,xy=0.920998
a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776,xy=0.395376
a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463,xy=0.404317
a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729,xy=1.036584

If you have some of the logic in a file and you want to write the rest on the command line, you can use the -f and -e options together:

$ cat data/fe-example-4.mlr
func f(a, b) {
  return sqrt(a**2 + b**2)
}

$ mlr --from data/small put -f data/fe-example-4.mlr -e '$xy = f($x, $y)'
a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533,xy=0.805299
a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797,xy=0.920998
a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776,xy=0.395376
a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463,xy=0.404317
a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729,xy=1.036584

A suggested use-case here is defining functions in files, and calling them from command-line expressions.

Another suggested use-case is putting default parameter values in files, e.g. using begin{@count=is_present(@count)?@count:10} in the file, where you can precede that using begin{@count=40} using -e.

Moreover, you can have one or more -f expressions (maybe one function per file, for example) and one or more -e expressions on the command line. If you mix -f and -e then the expressions are evaluated in the order encountered. (Since the expressions are all simply concatenated together in order, don’t forget intervening semicolons: e.g. not mlr put -e '$x=1' -e '$y=2 ...' but rather mlr put -e '$x=1;' -e '$y=2' ....)

Semicolons, commas, newlines, and curly braces

Miller uses semicolons as statement separators, not statement terminators. This means you can write:

mlr put 'x=1'
mlr put 'x=1;$y=2'
mlr put 'x=1;$y=2;'
mlr put 'x=1;;;;$y=2;'

Semicolons are optional after closing curly braces (which close conditionals and loops as discussed below).

$ echo x=1,y=2 | mlr put 'while (NF < 10) { $[NF+1] = ""}  $foo = "bar"'
x=1,y=2,3=,4=,5=,6=,7=,8=,9=,10=,foo=bar

$ echo x=1,y=2 | mlr put 'while (NF < 10) { $[NF+1] = ""}; $foo = "bar"'
x=1,y=2,3=,4=,5=,6=,7=,8=,9=,10=,foo=bar

Semicolons are required between statements even if those statements are on separate lines. Newlines are for your convenience but have no syntactic meaning: line endings do not terminate statements. For example, adjacent assignment statements must be separated by semicolons even if those statements are on separate lines:

mlr put '
  $x = 1
  $y = 2 # Syntax error
'

mlr put '
  $x = 1;
  $y = 2 # This is OK
'

Trailing commas are allowed in function/subroutine definitions, function/subroutine callsites, and map literals. This is intended for (although not restricted to) the multi-line case:

$ mlr --csvlite --from data/a.csv put '
  func f(
    num a,
    num b,
  ): num {
    return a**2 + b**2;
  }
  $* = {
    "s": $a + $b,
    "t": $a - $b,
    "u": f(
      $a,
      $b,
    ),
    "v": NR,
  }
'
s,t,u,v
3,-1,5.000000,1
9,-1,41.000000,2

Bodies for all compound statements must be enclosed in curly braces, even if the body is a single statement:

mlr put 'if ($x == 1) $y = 2' # Syntax error

mlr put 'if ($x == 1) { $y = 2 }' # This is OK

Bodies for compound statements may be empty:

mlr put 'if ($x == 1) { }' # This no-op is syntactically acceptable

Variables

Miller has the following kinds of variables:

Built-in variables such as NF, NF, FILENAME, PI, and E. These are all capital letters and are read-only (although some of them change value from one record to another).

Fields of stream records, accessed using the $ prefix. These refer to fields of the current data-stream record. For example, in echo x=1,y=2 | mlr put '$z = $x + $y', $x and $y refer to input fields, and $z refers to a new, computed output field. In a few contexts, presented below, you can refer to the entire record as $*.

Out-of-stream variables accessed using the @ prefix. These refer to data which persist from one record to the next, including in begin and end blocks (which execute before/after the record stream is consumed, respectively). You use them to remember values across records, such as sums, differences, counters, and so on. In a few contexts, presented below, you can refer to the entire out-of-stream-variables collection as @*.

Local variables are limited in scope and extent to the current statements being executed: these include function arguments, bound variables in for loops, and explicitly declared local variables.

Keywords are not variables, but since their names are reserved, you cannot use these names for local variables.

Built-in variables

These are written all in capital letters, such as NR, NF, FILENAME, and only a small, specific set of them is defined by Miller.

Namely, Miller supports the following five built-in variables for filter and put, all awk-inspired: NF, NR, FNR, FILENUM, and FILENAME, as well as the mathematical constants PI and E. Lastly, the ENV hashmap allows read access to environment variables, e.g. ENV["HOME"] or ENV["foo_".$hostname].

$ mlr filter 'FNR == 2' data/small*
a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
1=pan,2=pan,3=1,4=0.3467901443380824,5=0.7268028627434533
a=wye,b=eks,i=10000,x=0.734806020620654365,y=0.884788571337605134

$ mlr put '$fnr = FNR' data/small*
a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533,fnr=1
a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797,fnr=2
a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776,fnr=3
a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463,fnr=4
a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729,fnr=5
1=a,2=b,3=i,4=x,5=y,fnr=1
1=pan,2=pan,3=1,4=0.3467901443380824,5=0.7268028627434533,fnr=2
1=eks,2=pan,3=2,4=0.7586799647899636,5=0.5221511083334797,fnr=3
1=wye,2=wye,3=3,4=0.20460330576630303,5=0.33831852551664776,fnr=4
1=eks,2=wye,3=4,4=0.38139939387114097,5=0.13418874328430463,fnr=5
1=wye,2=pan,3=5,4=0.5732889198020006,5=0.8636244699032729,fnr=6
a=pan,b=eks,i=9999,x=0.267481232652199086,y=0.557077185510228001,fnr=1
a=wye,b=eks,i=10000,x=0.734806020620654365,y=0.884788571337605134,fnr=2
a=pan,b=wye,i=10001,x=0.870530722602517626,y=0.009854780514656930,fnr=3
a=hat,b=wye,i=10002,x=0.321507044286237609,y=0.568893318795083758,fnr=4
a=pan,b=zee,i=10003,x=0.272054845593895200,y=0.425789896597056627,fnr=5

Their values of NF, NR, FNR, FILENUM, and FILENAME change from one record to the next as Miller scans through your input data stream. The mathematical constants, of course, do not change; ENV is populated from the system environment variables at the time Miller starts and is read-only for the remainder of program execution.

Their scope is global: you can refer to them in any filter or put statement. Their values are assigned by the input-record reader:

$ mlr --csv put '$nr = NR' data/a.csv
a,b,c,nr
1,2,3,1
4,5,6,2

$ mlr --csv repeat -n 3 then put '$nr = NR' data/a.csv
a,b,c,nr
1,2,3,1
1,2,3,1
1,2,3,1
4,5,6,2
4,5,6,2
4,5,6,2

The extent is for the duration of the put/filter: in a begin statement (which executes before the first input record is consumed) you will find NR=1 and in an end statement (which is executed after the last input record is consumed) you will find NR to be the total number of records ingested.

These are all read-only for the mlr put and mlr filter DSLs: they may be assigned from, e.g. $nr=NR, but they may not be assigned to: NR=100 is a syntax error.

Field names

Names of fields within stream records must be specified using a $ in filter and put expressions, even though the dollar signs don’t appear in the data stream itself. For integer-indexed data, this looks like awk’s $1,$2,$3, except that Miller allows non-numeric names such as $quantity or $hostname. Likewise, enclose string literals in double quotes in filter expressions even though they don’t appear in file data. In particular, mlr filter '$x=="abc"' passes through the record x=abc.

If field names have special characters such as . then you can use braces, e.g. '${field.name}'.

You may also use a computed field name in square brackets, e.g.

$ echo a=3,b=4 | mlr filter '$["x"] < 0.5'

$ echo s=green,t=blue,a=3,b=4 | mlr put '$[$s."_".$t] = $a * $b'
s=green,t=blue,a=3,b=4,green_blue=12

The names of record fields depend on the contents of your input data stream, and their values change from one record to the next as Miller scans through your input data stream.

Their extent is limited to the current record; their scope is the filter or put command in which they appear.

These are read-write: you can do $y=2*$x, $x=$x+1, etc.

Records are Miller’s output: field names present in the input stream are passed through to output (written to standard output) unless fields are removed with cut, or records are excluded with filter or put -q, etc. Simply assign a value to a field and it will be output.

Out-of-stream variables

These are prefixed with an at-sign, e.g. @sum. Furthermore, unlike built-in variables and stream-record fields, they are maintained in an arbitrarily nested hashmap: you can do @sum += $quanity, or @sum[$color] += $quanity, or @sum[$color][$shape] += $quanity. The keys for the multi-level hashmap can be any expression which evaluates to string or integer: e.g. @sum[NR] = $a + $b, @sum[$a."-".$b] = $x, etc.

Their names and their values are entirely under your control; they change only when you assign to them.

Just as for field names in stream records, if you want to define out-of-stream variables with special characters such as . then you can use braces, e.g. '@{variable.name}["index"]'.

You may use a computed key in square brackets, e.g.

$ echo s=green,t=blue,a=3,b=4 | mlr put -q '@[$s."_".$t] = $a * $b; emit all'
green_blue=12

Out-of-stream variables are scoped to the put command in which they appear. In particular, if you have two or more put commands separated by then, each put will have its own set of out-of-stream variables:

$ cat data/a.dkvp
a=1,b=2,c=3
a=4,b=5,c=6

$ mlr put '@sum += $a; end {emit @sum}' then put 'is_present($a) {$a=10*$a; @sum += $a}; end {emit @sum}' data/a.dkvp
a=10,b=2,c=3
a=40,b=5,c=6
sum=5
sum=50

Out-of-stream variables’ extent is from the start to the end of the record stream, i.e. every time the put or filter statement referring to them is executed.

Out-of-stream variables are read-write: you can do $sum=@sum, @sum=$sum, etc.

Indexed out-of-stream variables

Using an index on the @count and @sum variables, we get the benefit of the -g (group-by) option which mlr stats1 and various other Miller commands have:

$ mlr put -q '
  @x_count[$a] += 1;
  @x_sum[$a] += $x;
  end {
    emit @x_count, "a";
    emit @x_sum, "a";
  }
' ../data/small
a=pan,x_count=2
a=eks,x_count=3
a=wye,x_count=2
a=zee,x_count=2
a=hat,x_count=1
a=pan,x_sum=0.849416
a=eks,x_sum=1.751863
a=wye,x_sum=0.777892
a=zee,x_sum=1.125680
a=hat,x_sum=0.031442

$ mlr stats1 -a count,sum -f x -g a ../data/small
a=pan,x_count=2,x_sum=0.849416
a=eks,x_count=3,x_sum=1.751863
a=wye,x_count=2,x_sum=0.777892
a=zee,x_count=2,x_sum=1.125680
a=hat,x_count=1,x_sum=0.031442

Indices can be arbitrarily deep — here there are two or more of them:

$ mlr --from data/medium put -q '
  @x_count[$a][$b] += 1;
  @x_sum[$a][$b] += $x;
  end {
    emit (@x_count, @x_sum), "a", "b";
  }
'
a=pan,b=pan,x_count=427,x_sum=219.185129
a=pan,b=wye,x_count=395,x_sum=198.432931
a=pan,b=eks,x_count=429,x_sum=216.075228
a=pan,b=hat,x_count=417,x_sum=205.222776
a=pan,b=zee,x_count=413,x_sum=205.097518
a=eks,b=pan,x_count=371,x_sum=179.963030
a=eks,b=wye,x_count=407,x_sum=196.945286
a=eks,b=zee,x_count=357,x_sum=176.880365
a=eks,b=eks,x_count=413,x_sum=215.916097
a=eks,b=hat,x_count=417,x_sum=208.783171
a=wye,b=wye,x_count=377,x_sum=185.295850
a=wye,b=pan,x_count=392,x_sum=195.847900
a=wye,b=hat,x_count=426,x_sum=212.033183
a=wye,b=zee,x_count=385,x_sum=194.774048
a=wye,b=eks,x_count=386,x_sum=204.812961
a=zee,b=pan,x_count=389,x_sum=202.213804
a=zee,b=wye,x_count=455,x_sum=233.991394
a=zee,b=eks,x_count=391,x_sum=190.961778
a=zee,b=zee,x_count=403,x_sum=206.640635
a=zee,b=hat,x_count=409,x_sum=191.300006
a=hat,b=wye,x_count=423,x_sum=208.883010
a=hat,b=zee,x_count=385,x_sum=196.349450
a=hat,b=eks,x_count=389,x_sum=189.006793
a=hat,b=hat,x_count=381,x_sum=182.853532
a=hat,b=pan,x_count=363,x_sum=168.553807

The idea is that stats1, and other Miller verbs, encapsulate frequently-used patterns with a minimum of keystroking (and run a little faster), whereas using out-of-stream variables you have more flexibility and control in what you do.

Begin/end blocks can be mixed with pattern/action blocks. For example:

$ mlr put '
  begin {
    @num_total = 0;
    @num_positive = 0;
  };
  @num_total += 1;
  $x > 0.0 {
    @num_positive += 1;
    $y = log10($x); $z = sqrt($y)
  };
  end {
    emitf @num_total, @num_positive
  }
' data/put-gating-example-1.dkvp
x=-1
x=0
x=1,y=0.000000,z=0.000000
x=2,y=0.301030,z=0.548662
x=3,y=0.477121,z=0.690740
num_total=5,num_positive=3

Local variables

Local variables are similar to out-of-stream variables, except that their extent is limited to the expressions in which they appear (and their basenames can’t be computed using square brackets). There are three kinds of local variables: arguments to functions/subroutines, variables bound within for-loops, and locals defined within control blocks. They may be untyped using var, or typed using num, int, float, str, bool, and map.

For example:

$ # Here I'm using a specified random-number seed so this example always
# produces the same output for this web document: in everyday practice we
# would leave off the --seed 12345 part.
mlr --seed 12345 seqgen --start 1 --stop 10 then put '
  func f(a, b) {                          # function arguments a and b
      r = 0.0;                            # local r scoped to the function
      for (int i = 0; i < 6; i += 1) {    # local i scoped to the for-loop
          num u = urand();                # local u scoped to the for-loop
          r += u;                         # updates r from the enclosing scope
      }
      r /= 6;
      return a + (b - a) * r;
  }
  num o = f(10, 20);                      # local to the top-level scope
  $o = o;
'
i=1,o=14.662901
i=2,o=17.881983
i=3,o=14.586560
i=4,o=16.402409
i=5,o=16.336598
i=6,o=14.622701
i=7,o=15.983753
i=8,o=13.852177
i=9,o=15.472899
i=10,o=15.643912

Things which are completely unsurprising, resembling many other languages:

Things which are perhaps surprising compared to other languages:

  • Type declarations using var, or typed using num, int, float, str, and bool are necessary to declare local variables. Function arguments and variables bound in for-loops over stream records and out-of-stream variables are implicitly declared using var. (Some examples are shown below.)
  • Type-checking is done at assignment time. For example, float f = 0 is an error (since 0 is an integer), as is float f = 0.0; f = 1. For this reason I prefer to use num over float in most contexts since num encompasses integer and floating-point values. More information about type-checking is here.
  • Bound variables in for-loops over stream records and out-of-stream variables are implicitly local to that block. E.g. in for (k, v in $*) { ... } for ((k1, k2), v in @*) { ... } if there are k, v, etc. in the enclosing scope then those will be masked by the loop-local bound variables in the loop, and moreover the values of the loop-local bound variables are not available after the end of the loop.
  • For C-style triple-for loops, if a for-loop variable is defined using var, int, etc. then it is scoped to that for-loop. E.g. for (i = 0; i < 10; i += 1) { ... } and for (int i = 0; i < 10; i += 1) { ... }. (This is unsurprising.). If there is no typedecl and an outer-scope variable of that name exists, then it is used. (This is also unsurprising.) But of there is no outer-scope variable of that name then the variable is scoped to the for-loop only.

The following example demonstrates the scope rules:

$ cat data/scope-example.mlr
func f(a) {      # argument is local to the function
  var b = 100;   # local to the function
  c = 100;       # local to the function; does not overwrite outer c
  return a + 1;
}
var a = 10;      # local at top level
var b = 20;      # local at top level
c = 30;          # local at top level; there is no more-outer-scope c
if (NR == 3) {
  var a = 40;    # scoped to the if-statement; doesn't overwrite outer a
  b = 50;        # not scoped to the if-statement; overwrites outer b
  c = 60;        # not scoped to the if-statement; overwrites outer c
  d = 70;        # there is no outer d so a local d is created here

  $inner_a = a;
  $inner_b = b;
  $inner_c = c;
  $inner_d = d;
}
$outer_a = a;
$outer_b = b;
$outer_c = c;
$outer_d = d;    # there is no outer d defined so no assignment happens

$ cat data/scope-example.dat
n=1,x=123
n=2,x=456
n=3,x=789

$ mlr --oxtab --from data/scope-example.dat put -f data/scope-example.mlr
n       1
x       123
outer_a 10
outer_b 20
outer_c 30

n       2
x       456
outer_a 10
outer_b 20
outer_c 30

n       3
x       789
inner_a 40
inner_b 50
inner_c 60
inner_d 70
outer_a 10
outer_b 50
outer_c 60

And this example demonstrates the type-declaration rules:

$ cat data/type-decl-example.mlr
subr s(a, str b, int c) {                         # a is implicitly var (untyped).
                                                  # b is explicitly str.
                                                  # c is explicitly int.
                                                  # The type-checking is done at the callsite
                                                  # when arguments are bound to parameters.
                                                  #
    var b = 100;     # error                      # Re-declaration in the same scope is disallowed.
    int n = 10;                                   # Declaration of variable local to the subroutine.
    n = 20;                                       # Assignment is OK.
    int n = 30;      # error                      # Re-declaration in the same scope is disallowed.
    str n = "abc";   # error                      # Re-declaration in the same scope is disallowed.
                                                  #
    float f1 = 1;    # error                      # 1 is an int, not a float.
    float f2 = 2.0;                               # 2.0 is a float.
    num f3 = 3;                                   # 3 is a num.
    num f4 = 4.0;                                 # 4.0 is a num.
}                                                 #
                                                  #
call s(1, 2, 3);                                  # Type-assertion '3 is int' is done here at the callsite.
                                                  #
k = "def";                                        # Top-level variable k.
                                                  #
for (str k, v in $*) {                            # k and v are bound here, masking outer k.
  print k . ":" . v;                              # k is explicitly str; v is implicitly var.
}                                                 #
                                                  #
print "k is".k;                                   # k at this scope level is still "def".
print "v is".v;                                   # v is undefined in this scope.
                                                  #
i = -1;                                           #
for (i = 1, int j = 2; i <= 10; i += 1, j *= 2) { # C-style triple-for variables use enclosing scope, unless
                                                  # declared local: i is outer, j is local to the loop.
  print "inner i =" . i;                          #
  print "inner j =" . j;                          #
}                                                 #
print "outer i =" . i;                            # i has been modified by the loop.
print "outer j =" . j;                            # j is undefined in this scope.

Map literals

Miller’s put/filter DSL has four kinds of hashmaps. Stream records are (single-level) maps from name to value. Out-of-stream variables and local variables can also be maps, although they can be multi-level hashmaps (e.g. @sum[$x][$y]). The fourth kind is map literals. These cannot be on the left-hand side of assignment expressions. Syntactically they look like JSON, although Miller allows string and integer keys in its map literals while JSON allows only string keys (e.g. "3" rather than 3).

For example, the following swaps the input stream’s a and i fields, modifies y, and drops the rest:

$ mlr --opprint put '
  $* = {
    "a": $i,
    "i": $a,
    "y": $y * 10,
  }
' data/small
a i   y
1 pan 7.268029
2 eks 5.221511
3 wye 3.383185
4 eks 1.341887
5 wye 8.636245

Likewise, you can assign map literals to out-of-stream variables or local variables; pass them as arguments to user-defined functions, return them from functions, and so on:

$ mlr --from ../c/s put '
  func f(map m): map {
    m["x"] *= 200;
    return m;
  }
  $* = f({"a": $a, "x": $x});
'

Like out-of-stream and local variables, map literals can be multi-level:

$ mlr --from data/small put -q '
  begin {
    @o = {
      "nrec": 0,
      "nkey": {"numeric":0, "non-numeric":0},
    };
  }
  @o["nrec"] += 1;
  for (k, v in $*) {
    if (is_numeric(v)) {
      @o["nkey"]["numeric"] += 1;
    } else {
      @o["nkey"]["non-numeric"] += 1;
    }
  }
  end {
    dump @o;
  }
'
{
  "nrec": 5,
  "nkey": {
    "numeric": 15,
    "non-numeric": 10
  }
}

By default, map-valued expressions are dumped using JSON formatting. If you use dump to print a hashmap with integer keys and you don’t want them double-quoted (JSON-style) then you can use mlr put --jknquoteint. See also mlr put --help.

Type-checking

Miller’s put/filter DSLs support two optional kinds of type-checking. One is inline type-tests and type-assertions within expressions. The other is type declarations for assignments to local variables, binding of arguments to user-defined functions, and return values from user-defined functions, These are discussed in the following subsections.

Use of type-checking is entirely up to you: omit it if you want flexibility with heterogeneous data; use it if you want to help catch misspellings in your DSL code or unexpected irregularities in your input data.

Type-test and type-assertion expressions

The following is... functions take a value and return a boolean indicating whether the argument is of the indicated type. The assert_... functions return their argument if it is of the specified type, and cause a fatal error otherwise:

$ mlr -F | grep ^is
is_absent
is_bool
is_boolean
is_empty
is_empty_map
is_float
is_int
is_map
is_nonempty_map
is_not_empty
is_not_map
is_not_null
is_null
is_numeric
is_present
is_string

$ mlr -F | grep ^assert
asserting_absent
asserting_bool
asserting_boolean
asserting_empty
asserting_empty_map
asserting_float
asserting_int
asserting_map
asserting_nonempty_map
asserting_not_empty
asserting_not_map
asserting_not_null
asserting_null
asserting_numeric
asserting_present
asserting_string

Please see the Cookbook part 1 for examples of how to use these.

Type-declarations for local variables, function parameter, and function return values

Local variables can be defined either untyped as in x = 1, or typed as in int x = 1. Types include var (explicitly untyped), int, float, num (int or float), str, bool, and map. These optional type declarations are enforced at the time values are assigned to variables: whether at the initial value assignment as in int x = 1 or in any subsequent assignments to the same variable farther down in the scope.

The reason for num is that int and float typedecls are very precise:

  float a = 0;   # Runtime error since 0 is int not float
  int   b = 1.0; # Runtime error since 1.0 is float not int
  num   c = 0;   # OK
  num   d = 1.0; # OK

A suggestion is to use num for general use when you want numeric content, and use int when you genuinely want integer-only values, e.g. in loop indices or map keys (since Miller map keys can only be strings or ints).

The var type declaration indicates no type restrictions, e.g. var x = 1 has the same type restrictions on x as x = 1. The difference is in intentional shadowing: if you have x = 1 in outer scope and x = 2 in inner scope (e.g. within a for-loop or an if-statement) then outer-scope x has value 2 after the second assignment. But if you have var x = 2 in the inner scope, then you are declaring a variable scoped to the inner block.) For example:

  x = 1;
  if (NR == 4) {
    x = 2; # Refers to outer-scope x: value changes from 1 to 2.
  }
  print x; # Value of x is now two
  x = 1;
  if (NR == 4) {
    var x = 2; # Defines a new inner-scope x with value 2
  }
  print x;     # Value of this x is still 1

Likewise function arguments can optionally be typed, with type enforced when the function is called:

  func f(map m, int i) {
    ...
  }
  $a = f({1:2, 3:4}, 5);     # OK
  $b = f({1:2, 3:4}, "abc"); # Runtime error
  $c = f({1:2, 3:4}, $x);    # Runtime error for records with non-integer field named x
  if (NR == 4) {
    var x = 2; # Defines a new inner-scope x with value 2
  }
  print x;     # Value of this x is still 1

Thirdly, function return values can be type-checked at the point of return using : and a typedecl after the parameter list:

  func f(map m, int i): bool {
    ...
    ...
    if (...) {
      return "false"; # Runtime error if this branch is taken
    }
    ...
    ...
    if (...) {
      return retval; # Runtime error if this function doesn't have an in-scope
                     # boolean-valued variable named retval
    }
    ...
    ...
    # In Miller if your functions don't explicitly return a value, they return absent-null.
    # So it would also be a runtime error on reaching the end of this function without
    # an explicit return statement.
  }

Null data: empty and absent

Please see here.

Aggregate variable assignments

There are three remaining kinds of variable assignment using out-of-stream variables, the last two of which use the $* syntax:

  • Recursive copy of out-of-stream variables
  • Out-of-stream variable assigned to full stream record
  • Full stream record assigned to an out-of-stream variable

Example recursive copy of out-of-stream variables:

$ mlr --opprint put -q '@v["sum"] += $x; @v["count"] += 1; end{dump; @w = @v; dump}' data/small
{
  "v": {
    "sum": 2.264762,
    "count": 5
  }
}
{
  "v": {
    "sum": 2.264762,
    "count": 5
  },
  "w": {
    "sum": 2.264762,
    "count": 5
  }
}

Example of out-of-stream variable assigned to full stream record, where the 2nd record is stashed, and the 4th record is overwritten with that:

$ mlr put 'NR == 2 {@keep = $*}; NR == 4 {$* = @keep}' data/small
a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533
a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776
a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729

Example of full stream record assigned to an out-of-stream variable, finding the record for which the x field has the largest value in the input stream:

$ cat data/small
a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533
a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776
a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463
a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729

$ mlr --opprint put -q 'is_null(@xmax) || $x > @xmax {@xmax=$x; @recmax=$*}; end {emit @recmax}' data/small
a   b   i x                  y
eks pan 2 0.7586799647899636 0.5221511083334797

Keywords for filter and put

$ mlr --help-all-keywords
all: used in "emit", "emitp", and "unset" as a synonym for @*

begin: defines a block of statements to be executed before input records
are ingested. The body statements must be wrapped in curly braces.
Example: 'begin { @count = 0 }'

bool: declares a boolean local variable in the current curly-braced scope.
Type-checking happens at assignment: 'bool b = 1' is an error.

break: causes execution to continue after the body of the current
for/while/do-while loop.

call: used for invoking a user-defined subroutine.
Example: 'subr s(k,v) { print k . " is " . v} call s("a", $a)'

continue: causes execution to skip the remaining statements in the body of
the current for/while/do-while loop. For-loop increments are still applied.

do: with "while", introduces a do-while loop. The body statements must be wrapped
in curly braces.

dump: prints all currently defined out-of-stream variables immediately
  to stdout as JSON.

  With >, >>, or |, the data do not become part of the output record stream but
  are instead redirected.

  The > and >> are for write and append, as in the shell, but (as with awk) the
  file-overwrite for > is on first write, not per record. The | is for piping to
  a process which will process the data. There will be one open file for each
  distinct file name (for > and >>) or one subordinate process for each distinct
  value of the piped-to command (for |). Output-formatting flags are taken from
  the main command line.

  Example: mlr --from f.dat put -q '@v[NR]=$*; end { dump }'
  Example: mlr --from f.dat put -q '@v[NR]=$*; end { dump >  "mytap.dat"}'
  Example: mlr --from f.dat put -q '@v[NR]=$*; end { dump >> "mytap.dat"}'
  Example: mlr --from f.dat put -q '@v[NR]=$*; end { dump | "jq .[]"}'

edump: prints all currently defined out-of-stream variables immediately
  to stderr as JSON.

  Example: mlr --from f.dat put -q '@v[NR]=$*; end { edump }'

elif: the way Miller spells "else if". The body statements must be wrapped
in curly braces.

else: terminates an if/elif/elif chain. The body statements must be wrapped
in curly braces.

emit: inserts an out-of-stream variable into the output record stream. Hashmap
  indices present in the data but not slotted by emit arguments are not output.

  With >, >>, or |, the data do not become part of the output record stream but
  are instead redirected.

  The > and >> are for write and append, as in the shell, but (as with awk) the
  file-overwrite for > is on first write, not per record. The | is for piping to
  a process which will process the data. There will be one open file for each
  distinct file name (for > and >>) or one subordinate process for each distinct
  value of the piped-to command (for |). Output-formatting flags are taken from
  the main command line.

  You can use any of the output-format command-line flags, e.g. --ocsv, --ofs,
  etc., to control the format of the output if the output is redirected. See also mlr -h.

  Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit @sums'
  Example: mlr --from f.dat put --ojson '@sums[$a][$b]+=$x; emit > "tap-".$a.$b.".dat", @sums'
  Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit @sums, "index1", "index2"'
  Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit @*, "index1", "index2"'
  Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit >  "mytap.dat", @*, "index1", "index2"'
  Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit >> "mytap.dat", @*, "index1", "index2"'
  Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit | "gzip > mytap.dat.gz", @*, "index1", "index2"'
  Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit > stderr, @*, "index1", "index2"'
  Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit | "grep somepattern", @*, "index1", "index2"'

  Please see http://johnkerl.org/miller/doc for more information.

emitf: inserts non-indexed out-of-stream variable(s) side-by-side into the
  output record stream.

  With >, >>, or |, the data do not become part of the output record stream but
  are instead redirected.

  The > and >> are for write and append, as in the shell, but (as with awk) the
  file-overwrite for > is on first write, not per record. The | is for piping to
  a process which will process the data. There will be one open file for each
  distinct file name (for > and >>) or one subordinate process for each distinct
  value of the piped-to command (for |). Output-formatting flags are taken from
  the main command line.

  You can use any of the output-format command-line flags, e.g. --ocsv, --ofs,
  etc., to control the format of the output if the output is redirected. See also mlr -h.

  Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf @a'
  Example: mlr --from f.dat put --oxtab '@a=$i;@b+=$x;@c+=$y; emitf > "tap-".$i.".dat", @a'
  Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf @a, @b, @c'
  Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf > "mytap.dat", @a, @b, @c'
  Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf >> "mytap.dat", @a, @b, @c'
  Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf > stderr, @a, @b, @c'
  Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf | "grep somepattern", @a, @b, @c'
  Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf | "grep somepattern > mytap.dat", @a, @b, @c'

  Please see http://johnkerl.org/miller/doc for more information.

emitp: inserts an out-of-stream variable into the output record stream.
  Hashmap indices present in the data but not slotted by emitp arguments are
  output concatenated with ":".

  With >, >>, or |, the data do not become part of the output record stream but
  are instead redirected.

  The > and >> are for write and append, as in the shell, but (as with awk) the
  file-overwrite for > is on first write, not per record. The | is for piping to
  a process which will process the data. There will be one open file for each
  distinct file name (for > and >>) or one subordinate process for each distinct
  value of the piped-to command (for |). Output-formatting flags are taken from
  the main command line.

  You can use any of the output-format command-line flags, e.g. --ocsv, --ofs,
  etc., to control the format of the output if the output is redirected. See also mlr -h.

  Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp @sums'
  Example: mlr --from f.dat put --opprint '@sums[$a][$b]+=$x; emitp > "tap-".$a.$b.".dat", @sums'
  Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp @sums, "index1", "index2"'
  Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp @*, "index1", "index2"'
  Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp >  "mytap.dat", @*, "index1", "index2"'
  Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp >> "mytap.dat", @*, "index1", "index2"'
  Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp | "gzip > mytap.dat.gz", @*, "index1", "index2"'
  Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp > stderr, @*, "index1", "index2"'
  Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp | "grep somepattern", @*, "index1", "index2"'

  Please see http://johnkerl.org/miller/doc for more information.

end: defines a block of statements to be executed after input records
are ingested. The body statements must be wrapped in curly braces.
Example: 'end { emit @count }'
Example: 'end { eprint "Final count is " . @count }'

eprint: prints expression immediately to stderr.
  Example: mlr --from f.dat put -q 'eprint "The sum of x and y is ".($x+$y)'
  Example: mlr --from f.dat put -q 'for (k, v in $*) { eprint k . " => " . v }'
  Example: mlr --from f.dat put  '(NR % 1000 == 0) { eprint "Checkpoint ".NR}'

eprintn: prints expression immediately to stderr, without trailing newline.
  Example: mlr --from f.dat put -q 'eprintn "The sum of x and y is ".($x+$y); eprint ""'

false: the boolean literal value.

filter: includes/excludes the record in the output record stream.

  Example: mlr --from f.dat put 'filter (NR == 2 || $x > 5.4)'

  Instead of put with 'filter false' you can simply use put -q.  The following
  uses the input record to accumulate data but only prints the running sum
  without printing the input record:

  Example: mlr --from f.dat put -q '@running_sum += $x * $y; emit @running_sum'

float: declares a floating-point local variable in the current curly-braced scope.
Type-checking happens at assignment: 'float x = 0' is an error.

for: defines a for-loop using one of three styles. The body statements must
be wrapped in curly braces.
For-loop over stream record:
  Example:  'for (k, v in $*) { ... }'
For-loop over out-of-stream variables:
  Example: 'for (k, v in @counts) { ... }'
  Example: 'for ((k1, k2), v in @counts) { ... }'
  Example: 'for ((k1, k2, k3), v in @*) { ... }'
C-style for-loop:
  Example:  'for (var i = 0, var b = 1; i < 10; i += 1, b *= 2) { ... }'

func: used for defining a user-defined function.
Example: 'func f(a,b) { return sqrt(a**2+b**2)} $d = f($x, $y)'

if: starts an if/elif/elif chain. The body statements must be wrapped
in curly braces.

in: used in for-loops over stream records or out-of-stream variables.

int: declares an integer local variable in the current curly-braced scope.
Type-checking happens at assignment: 'int x = 0.0' is an error.

map: declares an map-valued local variable in the current curly-braced scope.
Type-checking happens at assignment: 'map b = 0' is an error. map b = {} is
always OK. map b = a is OK or not depending on whether a is a map.

num: declares an int/float local variable in the current curly-braced scope.
Type-checking happens at assignment: 'num b = true' is an error.

print: prints expression immediately to stdout.
  Example: mlr --from f.dat put -q 'print "The sum of x and y is ".($x+$y)'
  Example: mlr --from f.dat put -q 'for (k, v in $*) { print k . " => " . v }'
  Example: mlr --from f.dat put  '(NR % 1000 == 0) { print > stderr, "Checkpoint ".NR}'

printn: prints expression immediately to stdout, without trailing newline.
  Example: mlr --from f.dat put -q 'printn "."; end { print "" }'

return: specifies the return value from a user-defined function.
Omitted return statements (including via if-branches) result in an absent-null
return value, which in turns results in a skipped assignment to an LHS.

stderr: Used for tee, emit, emitf, emitp, print, and dump in place of filename
  to print to standard error.

stdout: Used for tee, emit, emitf, emitp, print, and dump in place of filename
  to print to standard output.

str: declares a string local variable in the current curly-braced scope.
Type-checking happens at assignment.

subr: used for defining a subroutine.
Example: 'subr s(k,v) { print k . " is " . v} call s("a", $a)'

tee: prints the current record to specified file.
  This is an immediate print to the specified file (except for pprint format
  which of course waits until the end of the input stream to format all output).

  The > and >> are for write and append, as in the shell, but (as with awk) the
  file-overwrite for > is on first write, not per record. The | is for piping to
  a process which will process the data. There will be one open file for each
  distinct file name (for > and >>) or one subordinate process for each distinct
  value of the piped-to command (for |). Output-formatting flags are taken from
  the main command line.

  You can use any of the output-format command-line flags, e.g. --ocsv, --ofs,
  etc., to control the format of the output. See also mlr -h.

  Example: mlr --from f.dat put 'tee >  "/tmp/data-".$a, $*'
  Example: mlr --from f.dat put 'tee >> "/tmp/data-".$a.$b, $*'
  Example: mlr --from f.dat put 'tee >  stderr, $*'
  Example: mlr --from f.dat put -q 'tee | "tr [a-z\] [A-Z\]", $*'
  Example: mlr --from f.dat put -q 'tee | "tr [a-z\] [A-Z\] > /tmp/data-".$a, $*'
  Example: mlr --from f.dat put -q 'tee | "gzip > /tmp/data-".$a.".gz", $*'
  Example: mlr --from f.dat put -q --ojson 'tee | "gzip > /tmp/data-".$a.".gz", $*'

true: the boolean literal value.

unset: clears field(s) from the current record, or an out-of-stream or local variable.

  Example: mlr --from f.dat put 'unset $x'
  Example: mlr --from f.dat put 'unset $*'
  Example: mlr --from f.dat put 'for (k, v in $*) { if (k =~ "a.*") { unset $[k] } }'
  Example: mlr --from f.dat put '...; unset @sums'
  Example: mlr --from f.dat put '...; unset @sums["green"]'
  Example: mlr --from f.dat put '...; unset @*'

var: declares an untyped local variable in the current curly-braced scope.
Examples: 'var a=1', 'var xyz=""'

while: introduces a while loop, or with "do", introduces a do-while loop.
The body statements must be wrapped in curly braces.

E: the mathematical constant.

ENV: access to environment variables by name, e.g. '$home = ENV["HOME"]'

FILENAME: evaluates to the name of the current file being processed.

FILENUM: evaluates to the number of the current file being processed,
starting with 1.

FNR: evaluates to the number of the current record within the current file
being processed, starting with 1. Resets at the start of each file.

IFS: evaluates to the input field separator from the command line.

IPS: evaluates to the input pair separator from the command line.

IRS: evaluates to the input record separator from the command line,
or to LF or CRLF from the input data if in autodetect mode (which is
the default).

NF: evaluates to the number of fields in the current record.

NR: evaluates to the number of the current record over all files
being processed, starting with 1. Does not reset at the start of each file.

OFS: evaluates to the output field separator from the command line.

OPS: evaluates to the output pair separator from the command line.

ORS: evaluates to the output record separator from the command line,
or to LF or CRLF from the input data if in autodetect mode (which is
the default).

PI: the mathematical constant.

Operator precedence

Operators are listed in order of decreasing precedence, highest first.

Operators              Associativity
---------              -------------
()                     left to right
**                     right to left
! ~ unary+ unary- &    right to left
binary* / // %         left to right
binary+ binary- .      left to right
<< >>                  left to right
&                      left to right
^                      left to right
|                      left to right
< <= > >=              left to right
== != =~ !=~           left to right
&&                     left to right
^^                     left to right
||                     left to right
? :                    right to left
=                      N/A for Miller (there is no $a=$b=$c)

Operator and function semantics

  • Functions are in general pass-throughs straight to the system-standard C library.
  • The min and max functions are different from other multi-argument functions which return null if any of their inputs are null: for min and max, by contrast, if one argument is null, the other is returned.
  • Symmetrically with respect to the bitwise OR, XOR, and AND operators |, ^, &, Miller has logical operators ||, ^^, &&: the logical XOR not existing in C.
  • The exponentiation operator ** is familiar from many languages.
  • The regex-match and regex-not-match operators =~ and !=~ are similar to those in Ruby and Perl.

Control structures

Pattern-action blocks

These are reminiscent of awk syntax. They can be used to allow assignments to be done only when appropriate — e.g. for math-function domain restrictions, regex-matching, and so on:

$ mlr cat data/put-gating-example-1.dkvp
x=-1
x=0
x=1
x=2
x=3

$ mlr put '$x > 0.0 { $y = log10($x); $z = sqrt($y) }' data/put-gating-example-1.dkvp
x=-1
x=0
x=1,y=0.000000,z=0.000000
x=2,y=0.301030,z=0.548662
x=3,y=0.477121,z=0.690740

$ mlr cat data/put-gating-example-2.dkvp
a=abc_123
a=some other name
a=xyz_789

$ mlr put '$a =~ "([a-z]+)_([0-9]+)" { $b = "left_\1"; $c = "right_\2" }' data/put-gating-example-2.dkvp
a=abc_123,b=left_abc,c=right_123
a=some other name
a=xyz_789,b=left_xyz,c=right_789

This produces heteregenous output which Miller, of course, has no problems with (see Record-heterogeneity). But if you want homogeneous output, the curly braces can be replaced with a semicolon between the expression and the body statements. This causes put to evaluate the boolean expression (along with any side effects, namely, regex-captures \1, \2, etc.) but doesn’t use it as a criterion for whether subsequent assignments should be executed. Instead, subsequent assignments are done unconditionally:

$ mlr put '$x > 0.0; $y = log10($x); $z = sqrt($y)' data/put-gating-example-1.dkvp
x=-1,y=nan,z=nan
x=0,y=-inf,z=nan
x=1,y=0.000000,z=0.000000
x=2,y=0.301030,z=0.548662
x=3,y=0.477121,z=0.690740

$ mlr put '$a =~ "([a-z]+)_([0-9]+)"; $b = "left_\1"; $c = "right_\2"' data/put-gating-example-2.dkvp
a=abc_123,b=left_abc,c=right_123
a=some other name,b=left_,c=right_
a=xyz_789,b=left_xyz,c=right_789

If-statements

These are again reminiscent of awk. Pattern-action blocks are a special case of if with no elif or else blocks, no if keyword, and parentheses optional around the boolean expression:

mlr put 'NR == 4 {$foo = "bar"}'

mlr put 'if (NR == 4) {$foo = "bar"}'

Compound statements use elif (rather than elsif or else if):

mlr put '
  if (NR == 2) {
    ...
  } elif (NR ==4) {
    ...
  } elif (NR ==6) {
    ...
  } else {
    ...
  }
'

While and do-while loops

Miller’s while and do-while are unsurprising in comparison to various languages, as are break and continue:

$ echo x=1,y=2 | mlr put '
  while (NF < 10) {
    $[NF+1] = ""
  }
  $foo = "bar"
'
x=1,y=2,3=,4=,5=,6=,7=,8=,9=,10=,foo=bar

$ echo x=1,y=2 | mlr put '
  do {
    $[NF+1] = "";
    if (NF == 5) {
      break
    }
  } while (NF < 10);
  $foo = "bar"
'
x=1,y=2,3=,4=,5=,foo=bar

A break or continue within nested conditional blocks or if-statements will, of course, propagate to the innermost loop enclosing them, if any. A break or continue outside a loop is a syntax error that will be flagged as soon as the expression is parsed, before any input records are ingested.

The existence of while, do-while, and for loops in Miller’s DSL means that you can create infinite-loop scenarios inadvertently. In particular, please recall that DSL statements are executed once if in begin or end blocks, and once per record otherwise. For example, while (NR < 10) will never terminate as NR is only incremented between records.

For-loops

While Miller’s while and do-while statements are much as in many other languages, for loops are more idiosyncratic to Miller. They are loops over key-value pairs, whether in stream records, out-of-stream variables, local variables, or map-literals: more reminiscent of foreach, as in (for example) PHP. There are for-loops over map keys and for-loops over key-value tuples. Additionally, Miller has a C-style triple-for loop with initialize, test, and update statements.

As with while and do-while, a break or continue within nested control structures will propagate to the innermost loop enclosing them, if any, and a break or continue outside a loop is a syntax error that will be flagged as soon as the expression is parsed, before any input records are ingested.

Key-only for-loops

The key variable is always bound to the key of key-value pairs:

$ mlr --from data/small put '
  print "NR = ".NR;
  for (key in $*) {
    value = $[key];
    print "  key:" . key . "  value:".value;
  }

'
NR = 1
  key:a  value:pan
  key:b  value:pan
  key:i  value:1
  key:x  value:0.346790
  key:y  value:0.726803
a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533
NR = 2
  key:a  value:eks
  key:b  value:pan
  key:i  value:2
  key:x  value:0.758680
  key:y  value:0.522151
a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
NR = 3
  key:a  value:wye
  key:b  value:wye
  key:i  value:3
  key:x  value:0.204603
  key:y  value:0.338319
a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776
NR = 4
  key:a  value:eks
  key:b  value:wye
  key:i  value:4
  key:x  value:0.381399
  key:y  value:0.134189
a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463
NR = 5
  key:a  value:wye
  key:b  value:pan
  key:i  value:5
  key:x  value:0.573289
  key:y  value:0.863624
a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729

$ mlr -n put '
  end {
    o = {1:2, 3:{4:5}};
    for (key in o) {
      print "  key:" . key . "  valuetype:" . typeof(o[key]);
    }
  }
'
  key:1  valuetype:int
  key:3  valuetype:map

Note that the value corresponding to a given key may be gotten as through a computed field name using square brackets as in $[key] for stream records, or by indexing the looped-over variable using square brackets.

Key-value for-loops

Single-level keys may be gotten at using either for(k,v) or for((k),v); multi-level keys may be gotten at using for((k1,k2,k3),v) and so on. The v variable will be bound to to a scalar value (a string or a number) if the map stops at that level, or to a map-valued variable if the map goes deeper. If the map isn’t deep enough then the loop body won’t be executed.

$ cat data/for-srec-example.tbl
label1 label2 f1  f2  f3
blue   green  100 240 350
red    green  120 11  195
yellow blue   140 0   240

$ mlr --pprint --from data/for-srec-example.tbl put '
  $sum1 = $f1 + $f2 + $f3;
  $sum2 = 0;
  $sum3 = 0;
  for (key, value in $*) {
    if (key =~ "^f[0-9]+") {
      $sum2 += value;
      $sum3 += $[key];
    }
  }
'
label1 label2 f1  f2  f3  sum1 sum2 sum3
blue   green  100 240 350 690  690  690
red    green  120 11  195 326  326  326
yellow blue   140 0   240 380  380  380

$ mlr --from data/small --opprint put 'for (k,v in $*) { $[k."_type"] = typeof(v) }'
a   b   i x                   y                   a_type b_type i_type x_type y_type
pan pan 1 0.3467901443380824  0.7268028627434533  string string int    float  float
eks pan 2 0.7586799647899636  0.5221511083334797  string string int    float  float
wye wye 3 0.20460330576630303 0.33831852551664776 string string int    float  float
eks wye 4 0.38139939387114097 0.13418874328430463 string string int    float  float
wye pan 5 0.5732889198020006  0.8636244699032729  string string int    float  float

Note that the value of the current field in the for-loop can be gotten either using the bound variable value, or through a computed field name using square brackets as in $[key].

Important note: to avoid inconsistent looping behavior in case you’re setting new fields (and/or unsetting existing ones) while looping over the record, Miller makes a copy of the record before the loop: loop variables are bound from the copy and all other reads/writes involve the record itself:

$ mlr --from data/small --opprint put '
  $sum1 = 0;
  $sum2 = 0;
  for (k,v in $*) {
    if (is_numeric(v)) {
      $sum1 +=v;
      $sum2 += $[k];
    }
  }
'
a   b   i x                   y                   sum1     sum2
pan pan 1 0.3467901443380824  0.7268028627434533  2.073593 8.294372
eks pan 2 0.7586799647899636  0.5221511083334797  3.280831 13.123324
wye wye 3 0.20460330576630303 0.33831852551664776 3.542922 14.171687
eks wye 4 0.38139939387114097 0.13418874328430463 4.515588 18.062353
wye pan 5 0.5732889198020006  0.8636244699032729  6.436913 25.747654

It can be confusing to modify the stream record while iterating over a copy of it, so instead you might find it simpler to use a local variable in the loop and only update the stream record after the loop:

$ mlr --from data/small --opprint put '
  sum = 0;
  for (k,v in $*) {
    if (is_numeric(v)) {
      sum += $[k];
    }
  }
  $sum = sum
'
a   b   i x                   y                   sum
pan pan 1 0.3467901443380824  0.7268028627434533  2.073593
eks pan 2 0.7586799647899636  0.5221511083334797  3.280831
wye wye 3 0.20460330576630303 0.33831852551664776 3.542922
eks wye 4 0.38139939387114097 0.13418874328430463 4.515588
wye pan 5 0.5732889198020006  0.8636244699032729  6.436913

You can also start iterating on sub-hashmaps of an out-of-stream or local variable; you can loop over nested keys; you can loop over all out-of-stream variables. The bound variables are bound to a copy of the sub-hashmap as it was before the loop started. The sub-hashmap is specified by square-bracketed indices after in, and additional deeper indices are bound to loop key-variables. The terminal values are bound to the loop value-variable whenever the keys are not too shallow. The value-variable may refer to a terminal (string, number) or it may be map-valued if the map goes deeper. Example indexing is as follows:

# Parentheses are optional for single key:
for (k1,           v in @a["b"]["c"]) { ... }
for ((k1),         v in @a["b"]["c"]) { ... }
# Parentheses are required for multiple keys:
for ((k1, k2),     v in @a["b"]["c"]) { ... } # Loop over subhashmap of a variable
for ((k1, k2, k3), v in @a["b"]["c"]) { ... } # Ditto
for ((k1, k2, k3), v in @a { ... }            # Loop over variable starting from basename
for ((k1, k2, k3), v in @* { ... }            # Loop over all variables (k1 is bound to basename)

That’s confusing in the abstract, so a concrete example is in order. Suppose the out-of-stream variable @myvar is populated as follows:

$ mlr -n put --jknquoteint -q '
  begin {
    @myvar = {
      1: 2,
      3: { 4 : 5 },
      6: { 7: { 8: 9 } }
    }
  }
  end { dump }
'
{
  "myvar": {
    1: 2,
    3: {
      4: 5
    },
    6: {
      7: {
        8: 9
      }
    }
  }
}

Then we can get at various values as follows:

$ mlr -n put --jknquoteint -q '
  begin {
    @myvar = {
      1: 2,
      3: { 4 : 5 },
      6: { 7: { 8: 9 } }
    }
  }
  end {
    for (k, v in @myvar) {
      print
        "key=" . k .
        ",valuetype=" . typeof(v);
    }
  }
'
key=1,valuetype=int
key=3,valuetype=map
key=6,valuetype=map

$ mlr -n put --jknquoteint -q '
  begin {
    @myvar = {
      1: 2,
      3: { 4 : 5 },
      6: { 7: { 8: 9 } }
    }
  }
  end {
    for ((k1, k2), v in @myvar) {
      print
        "key1=" . k1 .
        ",key2=" . k2 .
        ",valuetype=" . typeof(v);
    }
  }
'
key1=3,key2=4,valuetype=int
key1=6,key2=7,valuetype=map

$ mlr -n put --jknquoteint -q '
  begin {
    @myvar = {
      1: 2,
      3: { 4 : 5 },
      6: { 7: { 8: 9 } }
    }
  }
  end {
    for ((k1, k2), v in @myvar[6]) {
      print
        "key1=" . k1 .
        ",key2=" . k2 .
        ",valuetype=" . typeof(v);
    }
  }
'
key1=7,key2=8,valuetype=int

C-style triple-for loops

These are supported as follows:

$ mlr --from data/small --opprint put '
  num suma = 0;
  for (a = 1; a <= NR; a += 1) {
    suma += a;
  }
  $suma = suma;
'
a   b   i x                   y                   suma
pan pan 1 0.3467901443380824  0.7268028627434533  1
eks pan 2 0.7586799647899636  0.5221511083334797  3
wye wye 3 0.20460330576630303 0.33831852551664776 6
eks wye 4 0.38139939387114097 0.13418874328430463 10
wye pan 5 0.5732889198020006  0.8636244699032729  15

$ mlr --from data/small --opprint put '
  num suma = 0;
  num sumb = 0;
  for (num a = 1, num b = 1; a <= NR; a += 1, b *= 2) {
    suma += a;
    sumb += b;
  }
  $suma = suma;
  $sumb = sumb;
'
a   b   i x                   y                   suma sumb
pan pan 1 0.3467901443380824  0.7268028627434533  1    1
eks pan 2 0.7586799647899636  0.5221511083334797  3    3
wye wye 3 0.20460330576630303 0.33831852551664776 6    7
eks wye 4 0.38139939387114097 0.13418874328430463 10   15
wye pan 5 0.5732889198020006  0.8636244699032729  15   31

Notes:

  • In for (start; continuation; update) { body }, the start, continuation, and update statements may be empty, single statements, or multiple comma-separated statements. If the continuation is empty (e.g. for(i=1;;i+=1)) it defaults to true.
  • In particular, you may use $-variables and/or @-variables in the start, continuation, and/or update steps (as well as the body, of course).
  • The typedecls such as int or num are optional. If a typedecl is provided (for a local variable), it binds a variable scoped to the for-loop regardless of whether a same-name variable is present in outer scope. If a typedecl is not provided, then the variable is scoped to the for-loop if no same-name variable is present in outer scope, or if a same-name variable is present in outer scope then it is modified.
  • Miller has no ++ or -- operators.
  • As with all for/if/while statements in Miller, the curly braces are required even if the body is a single statement, or empty.

Begin/end blocks

Miller supports an awk-like begin/end syntax. The statements in the begin block are executed before any input records are read; the statements in the end block are executed after the last input record is read. (If you want to execute some statement at the start of each file, not at the start of the first file as with begin, you might use a pattern/action block of the form FNR == 1 { ... }.) All statements outside of begin or end are, of course, executed on every input record. Semicolons separate statements inside or outside of begin/end blocks; semicolons are required between begin/end block bodies and any subsequent statement. For example:

$ mlr put '
  begin { @sum = 0 };
  @x_sum += $x;
  end { emit @x_sum }
' ../data/small
a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533
a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776
a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463
a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729
a=zee,b=pan,i=6,x=0.5271261600918548,y=0.49322128674835697
a=eks,b=zee,i=7,x=0.6117840605678454,y=0.1878849191181694
a=zee,b=wye,i=8,x=0.5985540091064224,y=0.976181385699006
a=hat,b=wye,i=9,x=0.03144187646093577,y=0.7495507603507059
a=pan,b=wye,i=10,x=0.5026260055412137,y=0.9526183602969864
x_sum=4.536294

Since uninitialized out-of-stream variables default to 0 for addition/substraction and 1 for multiplication when they appear on expression right-hand sides (as in awk), the above can be written more succinctly as

$ mlr put '
  @x_sum += $x;
  end { emit @x_sum }
' ../data/small
a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533
a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776
a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463
a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729
a=zee,b=pan,i=6,x=0.5271261600918548,y=0.49322128674835697
a=eks,b=zee,i=7,x=0.6117840605678454,y=0.1878849191181694
a=zee,b=wye,i=8,x=0.5985540091064224,y=0.976181385699006
a=hat,b=wye,i=9,x=0.03144187646093577,y=0.7495507603507059
a=pan,b=wye,i=10,x=0.5026260055412137,y=0.9526183602969864
x_sum=4.536294

The put -q option is a shorthand which suppresses printing of each output record, with only emit statements being output. So to get only summary outputs, one could write

$ mlr put -q '
  @x_sum += $x;
  end { emit @x_sum }
' ../data/small
x_sum=4.536294

We can do similarly with multiple out-of-stream variables:

$ mlr put -q '
  @x_count += 1;
  @x_sum += $x;
  end {
    emit @x_count;
    emit @x_sum;
  }
' ../data/small
x_count=10
x_sum=4.536294

This is of course not much different than

$ mlr stats1 -a count,sum -f x ../data/small
x_count=10,x_sum=4.536294

Note that it’s a syntax error for begin/end blocks to refer to field names (beginning with $), since these execute outside the context of input records.

Output statements

You can output variable-values or expressions in five ways:

  • Assign them to stream-record fields. For example, $cumulative_sum = @sum. For another example, $nr = NR adds a field named nr to each output record, containing the value of the built-in variable NR as of when that record was ingested.
  • Use the print or eprint keywords which immediately print an expression directly to standard output or standard error, respectively. Note that dump, edump, print, and eprint don’t output records which participate in then-chaining; rather, they’re just immediate prints to stdout/stderr. The printn and eprintn keywords are the same except that they don’t print final newlines. Additionally, you can print to a specified file instead of stdout/stderr.
  • Use the dump or edump keywords, which immediately print all out-of-stream variables as a JSON data structure to the standard output or standard error (respectively).
  • Use tee which formats the current stream record (not just an arbitrary string as with print) to a specific file.
  • Use emit/emitp/emitf to send out-of-stream variables’ current values to the output record stream, e.g. @sum += $x; emit @sum which produces an extra output record such as sum=3.1648382.

For the first two options you are populating the output-records stream which feeds into the next verb in a then-chain (if any), or which otherwise is formatted for output using --o... flags.

For the last three options you are sending output directly to standard output, standard error, or a file.

Print statements

The print statement is perhaps self-explanatory, but with a few light caveats:

Dump statements

The dump statement is for printing expressions, including maps, directly to stdout/stderr, respectively:

Tee statements

Records produced by a mlr put go downstream to the next verb in your then-chain, if any, or otherwise to standard output. If you want to additionally copy out records to files, you can do that using tee.

The syntax is, by example, mlr --from myfile.dat put 'tee > "tap.dat", $*' then sort -n index. First is tee >, then the filename expression (which can be an expression such as "tap.".$a.".dat"), then a comma, then $*. (Nothing else but $* is teeable.)

See also the section on redirected output for examples.

Redirected-output statements

The print, dump tee, emitf, emit, and emitp keywords all allow you to redirect output to one or more files or pipe-to commands. The filenames/commands are strings which can be constructed using record-dependent values, so you can do things like splitting a table into multiple files, one for each account ID, and so on.

Details:

  • The print and dump keywords produce output immediately to standard output, or to specified file(s) or pipe-to command if present.

    $ mlr --help-keyword print
    print: prints expression immediately to stdout.
      Example: mlr --from f.dat put -q 'print "The sum of x and y is ".($x+$y)'
      Example: mlr --from f.dat put -q 'for (k, v in $*) { print k . " => " . v }'
      Example: mlr --from f.dat put  '(NR % 1000 == 0) { print > stderr, "Checkpoint ".NR}'
    

    $ mlr --help-keyword dump
    dump: prints all currently defined out-of-stream variables immediately
      to stdout as JSON.
    
      With >, >>, or |, the data do not become part of the output record stream but
      are instead redirected.
    
      The > and >> are for write and append, as in the shell, but (as with awk) the
      file-overwrite for > is on first write, not per record. The | is for piping to
      a process which will process the data. There will be one open file for each
      distinct file name (for > and >>) or one subordinate process for each distinct
      value of the piped-to command (for |). Output-formatting flags are taken from
      the main command line.
    
      Example: mlr --from f.dat put -q '@v[NR]=$*; end { dump }'
      Example: mlr --from f.dat put -q '@v[NR]=$*; end { dump >  "mytap.dat"}'
      Example: mlr --from f.dat put -q '@v[NR]=$*; end { dump >> "mytap.dat"}'
      Example: mlr --from f.dat put -q '@v[NR]=$*; end { dump | "jq .[]"}'
    

  • mlr put sends the current record (possibly modified by the put expression) to the output record stream. Records are then input to the following verb in a then-chain (if any), else printed to standard output (unless put -q). The tee keyword additionally writes the output record to specified file(s) or pipe-to command, or immediately to stdout/stderr.

    $ mlr --help-keyword tee
    tee: prints the current record to specified file.
      This is an immediate print to the specified file (except for pprint format
      which of course waits until the end of the input stream to format all output).
    
      The > and >> are for write and append, as in the shell, but (as with awk) the
      file-overwrite for > is on first write, not per record. The | is for piping to
      a process which will process the data. There will be one open file for each
      distinct file name (for > and >>) or one subordinate process for each distinct
      value of the piped-to command (for |). Output-formatting flags are taken from
      the main command line.
    
      You can use any of the output-format command-line flags, e.g. --ocsv, --ofs,
      etc., to control the format of the output. See also mlr -h.
    
      Example: mlr --from f.dat put 'tee >  "/tmp/data-".$a, $*'
      Example: mlr --from f.dat put 'tee >> "/tmp/data-".$a.$b, $*'
      Example: mlr --from f.dat put 'tee >  stderr, $*'
      Example: mlr --from f.dat put -q 'tee | "tr [a-z\] [A-Z\]", $*'
      Example: mlr --from f.dat put -q 'tee | "tr [a-z\] [A-Z\] > /tmp/data-".$a, $*'
      Example: mlr --from f.dat put -q 'tee | "gzip > /tmp/data-".$a.".gz", $*'
      Example: mlr --from f.dat put -q --ojson 'tee | "gzip > /tmp/data-".$a.".gz", $*'
    

  • mlr put’s emitf, emitp, and emit send out-of-stream variables to the output record stream. These are then input to the following verb in a then-chain (if any), else printed to standard output. When redirected with >, >>, or |, they instead write the out-of-stream variable(s) to specified file(s) or pipe-to command, or immediately to stdout/stderr.

    $ mlr --help-keyword emitf
    emitf: inserts non-indexed out-of-stream variable(s) side-by-side into the
      output record stream.
    
      With >, >>, or |, the data do not become part of the output record stream but
      are instead redirected.
    
      The > and >> are for write and append, as in the shell, but (as with awk) the
      file-overwrite for > is on first write, not per record. The | is for piping to
      a process which will process the data. There will be one open file for each
      distinct file name (for > and >>) or one subordinate process for each distinct
      value of the piped-to command (for |). Output-formatting flags are taken from
      the main command line.
    
      You can use any of the output-format command-line flags, e.g. --ocsv, --ofs,
      etc., to control the format of the output if the output is redirected. See also mlr -h.
    
      Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf @a'
      Example: mlr --from f.dat put --oxtab '@a=$i;@b+=$x;@c+=$y; emitf > "tap-".$i.".dat", @a'
      Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf @a, @b, @c'
      Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf > "mytap.dat", @a, @b, @c'
      Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf >> "mytap.dat", @a, @b, @c'
      Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf > stderr, @a, @b, @c'
      Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf | "grep somepattern", @a, @b, @c'
      Example: mlr --from f.dat put '@a=$i;@b+=$x;@c+=$y; emitf | "grep somepattern > mytap.dat", @a, @b, @c'
    
      Please see http://johnkerl.org/miller/doc for more information.
    

    $ mlr --help-keyword emitp
    emitp: inserts an out-of-stream variable into the output record stream.
      Hashmap indices present in the data but not slotted by emitp arguments are
      output concatenated with ":".
    
      With >, >>, or |, the data do not become part of the output record stream but
      are instead redirected.
    
      The > and >> are for write and append, as in the shell, but (as with awk) the
      file-overwrite for > is on first write, not per record. The | is for piping to
      a process which will process the data. There will be one open file for each
      distinct file name (for > and >>) or one subordinate process for each distinct
      value of the piped-to command (for |). Output-formatting flags are taken from
      the main command line.
    
      You can use any of the output-format command-line flags, e.g. --ocsv, --ofs,
      etc., to control the format of the output if the output is redirected. See also mlr -h.
    
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp @sums'
      Example: mlr --from f.dat put --opprint '@sums[$a][$b]+=$x; emitp > "tap-".$a.$b.".dat", @sums'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp @sums, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp >  "mytap.dat", @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp >> "mytap.dat", @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp | "gzip > mytap.dat.gz", @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp > stderr, @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emitp | "grep somepattern", @*, "index1", "index2"'
    
      Please see http://johnkerl.org/miller/doc for more information.
    

    $ mlr --help-keyword emit
    emit: inserts an out-of-stream variable into the output record stream. Hashmap
      indices present in the data but not slotted by emit arguments are not output.
    
      With >, >>, or |, the data do not become part of the output record stream but
      are instead redirected.
    
      The > and >> are for write and append, as in the shell, but (as with awk) the
      file-overwrite for > is on first write, not per record. The | is for piping to
      a process which will process the data. There will be one open file for each
      distinct file name (for > and >>) or one subordinate process for each distinct
      value of the piped-to command (for |). Output-formatting flags are taken from
      the main command line.
    
      You can use any of the output-format command-line flags, e.g. --ocsv, --ofs,
      etc., to control the format of the output if the output is redirected. See also mlr -h.
    
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit @sums'
      Example: mlr --from f.dat put --ojson '@sums[$a][$b]+=$x; emit > "tap-".$a.$b.".dat", @sums'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit @sums, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit >  "mytap.dat", @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit >> "mytap.dat", @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit | "gzip > mytap.dat.gz", @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit > stderr, @*, "index1", "index2"'
      Example: mlr --from f.dat put '@sums[$a][$b]+=$x; emit | "grep somepattern", @*, "index1", "index2"'
    
      Please see http://johnkerl.org/miller/doc for more information.
    

Emit statements

There are three variants: emitf, emit, and emitp. Keep in mind that out-of-stream variables are a nested, multi-level hashmap (directly viewable as JSON using dump), whereas Miller output records are lists of single-level key-value pairs. The three emit variants allow you to control how the multilevel hashmaps are flatten down to output records. You can emit any map-valued expression, including $*, map-valued out-of-stream variables, the entire out-of-stream-variable collection @*, map-valued local variables, map literals, or map-valued function return values.

Use emitf to output several out-of-stream variables side-by-side in the same output record. For emitf these mustn’t have indexing using @name[...]. Example:

$ mlr put -q '@count += 1; @x_sum += $x; @y_sum += $y; end { emitf @count, @x_sum, @y_sum}' data/small
count=5,x_sum=2.264762,y_sum=2.585086

Use emit to output an out-of-stream variable. If it’s non-indexed you’ll get a simple key-value pair:

$ cat data/small
a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533
a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776
a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463
a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729

$ mlr put -q '@sum += $x; end { dump }' data/small
{
  "sum": 2.264762
}

$ mlr put -q '@sum += $x; end { emit @sum }' data/small
sum=2.264762

If it’s indexed then use as many names after emit as there are indices:

$ mlr put -q '@sum[$a] += $x; end { dump }' data/small
{
  "sum": {
    "pan": 0.346790,
    "eks": 1.140079,
    "wye": 0.777892
  }
}

$ mlr put -q '@sum[$a] += $x; end { emit @sum, "a" }' data/small
a=pan,sum=0.346790
a=eks,sum=1.140079
a=wye,sum=0.777892

$ mlr put -q '@sum[$a][$b] += $x; end { dump }' data/small
{
  "sum": {
    "pan": {
      "pan": 0.346790
    },
    "eks": {
      "pan": 0.758680,
      "wye": 0.381399
    },
    "wye": {
      "wye": 0.204603,
      "pan": 0.573289
    }
  }
}

$ mlr put -q '@sum[$a][$b] += $x; end { emit @sum, "a", "b" }' data/small
a=pan,b=pan,sum=0.346790
a=eks,b=pan,sum=0.758680
a=eks,b=wye,sum=0.381399
a=wye,b=wye,sum=0.204603
a=wye,b=pan,sum=0.573289

$ mlr put -q '@sum[$a][$b][$i] += $x; end { dump }' data/small
{
  "sum": {
    "pan": {
      "pan": {
        "1": 0.346790
      }
    },
    "eks": {
      "pan": {
        "2": 0.758680
      },
      "wye": {
        "4": 0.381399
      }
    },
    "wye": {
      "wye": {
        "3": 0.204603
      },
      "pan": {
        "5": 0.573289
      }
    }
  }
}

$ mlr put -q '@sum[$a][$b][$i] += $x; end { emit @sum, "a", "b", "i" }' data/small
a=pan,b=pan,i=1,sum=0.346790
a=eks,b=pan,i=2,sum=0.758680
a=eks,b=wye,i=4,sum=0.381399
a=wye,b=wye,i=3,sum=0.204603
a=wye,b=pan,i=5,sum=0.573289

Now for emitp: if you have as many names following emit as there are levels in the out-of-stream variable’s hashmap, then emit and emitp do the same thing. Where they differ is when you don’t specify as many names as there are hashmap levels. In this case, Miller needs to flatten multiple map indices down to output-record keys: emitp includes full prefixing (hence the p in emitp) while emit takes the deepest hashmap key as the output-record key:

$ mlr put -q '@sum[$a][$b] += $x; end { dump }' data/small
{
  "sum": {
    "pan": {
      "pan": 0.346790
    },
    "eks": {
      "pan": 0.758680,
      "wye": 0.381399
    },
    "wye": {
      "wye": 0.204603,
      "pan": 0.573289
    }
  }
}

$ mlr put -q '@sum[$a][$b] += $x; end { emit @sum, "a" }' data/small
a=pan,pan=0.346790
a=eks,pan=0.758680,wye=0.381399
a=wye,wye=0.204603,pan=0.573289

$ mlr put -q '@sum[$a][$b] += $x; end { emit @sum }' data/small
pan=0.346790
pan=0.758680,wye=0.381399
wye=0.204603,pan=0.573289

$ mlr put -q '@sum[$a][$b] += $x; end { emitp @sum, "a" }' data/small
a=pan,sum:pan=0.346790
a=eks,sum:pan=0.758680,sum:wye=0.381399
a=wye,sum:wye=0.204603,sum:pan=0.573289

$ mlr put -q '@sum[$a][$b] += $x; end { emitp @sum }' data/small
sum:pan:pan=0.346790,sum:eks:pan=0.758680,sum:eks:wye=0.381399,sum:wye:wye=0.204603,sum:wye:pan=0.573289

$ mlr --oxtab put -q '@sum[$a][$b] += $x; end { emitp @sum }' data/small
sum:pan:pan 0.346790
sum:eks:pan 0.758680
sum:eks:wye 0.381399
sum:wye:wye 0.204603
sum:wye:pan 0.573289

Use --oflatsep to specify the character which joins multilevel keys for emitp (it defaults to a colon):

$ mlr put -q --oflatsep / '@sum[$a][$b] += $x; end { emitp @sum, "a" }' data/small
a=pan,sum/pan=0.346790
a=eks,sum/pan=0.758680,sum/wye=0.381399
a=wye,sum/wye=0.204603,sum/pan=0.573289

$ mlr put -q --oflatsep / '@sum[$a][$b] += $x; end { emitp @sum }' data/small
sum/pan/pan=0.346790,sum/eks/pan=0.758680,sum/eks/wye=0.381399,sum/wye/wye=0.204603,sum/wye/pan=0.573289

$ mlr --oxtab put -q --oflatsep / '@sum[$a][$b] += $x; end { emitp @sum }' data/small
sum/pan/pan 0.346790
sum/eks/pan 0.758680
sum/eks/wye 0.381399
sum/wye/wye 0.204603
sum/wye/pan 0.573289

Multi-emit statements

You can emit multiple map-valued expressions side-by-side by including their names in parentheses:

$ mlr --from data/medium --opprint put -q '
  @x_count[$a][$b] += 1;
  @x_sum[$a][$b] += $x;
  end {
      for ((a, b), _ in @x_count) {
          @x_mean[a][b] = @x_sum[a][b] / @x_count[a][b]
      }
      emit (@x_sum, @x_count, @x_mean), "a", "b"
  }
'
a   b   x_sum      x_count x_mean
pan pan 219.185129 427     0.513314
pan wye 198.432931 395     0.502362
pan eks 216.075228 429     0.503672
pan hat 205.222776 417     0.492141
pan zee 205.097518 413     0.496604
eks pan 179.963030 371     0.485076
eks wye 196.945286 407     0.483895
eks zee 176.880365 357     0.495463
eks eks 215.916097 413     0.522799
eks hat 208.783171 417     0.500679
wye wye 185.295850 377     0.491501
wye pan 195.847900 392     0.499612
wye hat 212.033183 426     0.497730
wye zee 194.774048 385     0.505907
wye eks 204.812961 386     0.530604
zee pan 202.213804 389     0.519830
zee wye 233.991394 455     0.514267
zee eks 190.961778 391     0.488393
zee zee 206.640635 403     0.512756
zee hat 191.300006 409     0.467726
hat wye 208.883010 423     0.493813
hat zee 196.349450 385     0.509999
hat eks 189.006793 389     0.485879
hat hat 182.853532 381     0.479931
hat pan 168.553807 363     0.464336

What this does is walk through the first out-of-stream variable (@x_sum in this example) as usual, then for each keylist found (e.g. pan,wye), include the values for the remaining out-of-stream variables (here, @x_count and @x_mean). You should use this when all out-of-stream variables in the emit statement have the same shape and the same keylists.

Emit-all statements

Use emit all (or emit @* which is synonymous) to output all out-of-stream variables. You can use the following idiom to get various accumulators output side-by-side (reminiscent of mlr stats1):

$ mlr --from data/small --opprint put -q '@v[$a][$b]["sum"] += $x; @v[$a][$b]["count"] += 1; end{emit @*,"a","b"}'
a   b   sum      count
pan pan 0.346790 1
eks pan 0.758680 1
eks wye 0.381399 1
wye wye 0.204603 1
wye pan 0.573289 1

$ mlr --from data/small --opprint put -q '@sum[$a][$b] += $x; @count[$a][$b] += 1; end{emit @*,"a","b"}'
a   b   sum
pan pan 0.346790
eks pan 0.758680
eks wye 0.381399
wye wye 0.204603
wye pan 0.573289

a   b   count
pan pan 1
eks pan 1
eks wye 1
wye wye 1
wye pan 1

$ mlr --from data/small --opprint put -q '@sum[$a][$b] += $x; @count[$a][$b] += 1; end{emit (@sum, @count),"a","b"}'
a   b   sum      count
pan pan 0.346790 1
eks pan 0.758680 1
eks wye 0.381399 1
wye wye 0.204603 1
wye pan 0.573289 1

Unset statements

You can clear a map key by assigning the empty string as its value: $x="" or @x="". Using unset you can remove the key entirely. Examples:

$ cat data/small
a=pan,b=pan,i=1,x=0.3467901443380824,y=0.7268028627434533
a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776
a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463
a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729

$ mlr put 'unset $x, $a' data/small
b=pan,i=1,y=0.7268028627434533
b=pan,i=2,y=0.5221511083334797
b=wye,i=3,y=0.33831852551664776
b=wye,i=4,y=0.13418874328430463
b=pan,i=5,y=0.8636244699032729

This can also be done, of course, using mlr cut -x. You can also clear out-of-stream or local variables, at the base name level, or at an indexed sublevel:

$ mlr put -q '@sum[$a][$b] += $x; end { dump; unset @sum; dump }' data/small
{
  "sum": {
    "pan": {
      "pan": 0.346790
    },
    "eks": {
      "pan": 0.758680,
      "wye": 0.381399
    },
    "wye": {
      "wye": 0.204603,
      "pan": 0.573289
    }
  }
}
{
}

$ mlr put -q '@sum[$a][$b] += $x; end { dump; unset @sum["eks"]; dump }' data/small
{
  "sum": {
    "pan": {
      "pan": 0.346790
    },
    "eks": {
      "pan": 0.758680,
      "wye": 0.381399
    },
    "wye": {
      "wye": 0.204603,
      "pan": 0.573289
    }
  }
}
{
  "sum": {
    "pan": {
      "pan": 0.346790
    },
    "wye": {
      "wye": 0.204603,
      "pan": 0.573289
    }
  }
}

If you use unset all (or unset @* which is synonymous), that will unset all out-of-stream variables which have been defined up to that point.

Filter statements

You can use filter within put. In fact, the following two are synonymous:

$ mlr filter 'NR==2 || NR==3' data/small
a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776

$ mlr put 'filter NR==2 || NR==3' data/small
a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776

The former, of course, is much easier to type. But the latter allows you to define more complex expressions for the filter, and/or do other things in addition to the filter:

$ mlr put '@running_sum += $x; filter @running_sum > 1.3' data/small
a=wye,b=wye,i=3,x=0.20460330576630303,y=0.33831852551664776
a=eks,b=wye,i=4,x=0.38139939387114097,y=0.13418874328430463
a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729

$ mlr put '$z = $x * $y; filter $z > 0.3' data/small
a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797,z=0.396146
a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729,z=0.495106

Built-in functions for filter and put

Each function takes a specific number of arguments, as shown below, except for functions marked as variadic such as min and max. (The latter compute min and max of any number of numerical arguments.) There is no notion of optional or default-on-absent arguments. All argument-passing is positional rather than by name; arguments are passed by value, not by reference.

You can get a list of all functions using mlr -F.

+

+ (class=arithmetic #args=2): Addition.
+ (class=arithmetic #args=1): Unary plus.

-

- (class=arithmetic #args=2): Subtraction.
- (class=arithmetic #args=1): Unary minus.

*

* (class=arithmetic #args=2): Multiplication.

/

/ (class=arithmetic #args=2): Division.

//

// (class=arithmetic #args=2): Integer division: rounds to negative (pythonic).

%

% (class=arithmetic #args=2): Remainder; never negative-valued (pythonic).

**

** (class=arithmetic #args=2): Exponentiation; same as pow, but as an infix
operator.

|

| (class=arithmetic #args=2): Bitwise OR.

^

^ (class=arithmetic #args=2): Bitwise XOR.

&

& (class=arithmetic #args=2): Bitwise AND.

~

~ (class=arithmetic #args=1): Bitwise NOT. Beware '$y=~$x' since =~ is the
regex-match operator: try '$y = ~$x'.

<<

<< (class=arithmetic #args=2): Bitwise left-shift.

>>

>> (class=arithmetic #args=2): Bitwise right-shift.

==

== (class=boolean #args=2): String/numeric equality. Mixing number and string
results in string compare.

!=

!= (class=boolean #args=2): String/numeric inequality. Mixing number and string
results in string compare.

=~

=~ (class=boolean #args=2): String (left-hand side) matches regex (right-hand
side), e.g. '$name =~ "^a.*b$"'.

!=~

!=~ (class=boolean #args=2): String (left-hand side) does not match regex
(right-hand side), e.g. '$name !=~ "^a.*b$"'.

>

> (class=boolean #args=2): String/numeric greater-than. Mixing number and string
results in string compare.

>=

>= (class=boolean #args=2): String/numeric greater-than-or-equals. Mixing number
and string results in string compare.

<

< (class=boolean #args=2): String/numeric less-than. Mixing number and string
results in string compare.

<=

<= (class=boolean #args=2): String/numeric less-than-or-equals. Mixing number
and string results in string compare.

&&

&& (class=boolean #args=2): Logical AND.

||

|| (class=boolean #args=2): Logical OR.

^^

^^ (class=boolean #args=2): Logical XOR.

!

! (class=boolean #args=1): Logical negation.

? :

? : (class=boolean #args=3): Ternary operator.

.

. (class=string #args=2): String concatenation.

abs

abs (class=math #args=1): Absolute value.

acos

acos (class=math #args=1): Inverse trigonometric cosine.

acosh

acosh (class=math #args=1): Inverse hyperbolic cosine.

asin

asin (class=math #args=1): Inverse trigonometric sine.

asinh

asinh (class=math #args=1): Inverse hyperbolic sine.

asserting_absent

asserting_absent (class=typing #args=1): Returns argument if it is absent in the input data, else
throws an error.

asserting_bool

asserting_bool (class=typing #args=1): Returns argument if it is present with boolean value, else
throws an error.

asserting_boolean

asserting_boolean (class=typing #args=1): Returns argument if it is present with boolean value, else
throws an error.

asserting_empty

asserting_empty (class=typing #args=1): Returns argument if it is present in input with empty value,
else throws an error.

asserting_empty_map

asserting_empty_map (class=typing #args=1): Returns argument if it is a map with empty value, else
throws an error.

asserting_float

asserting_float (class=typing #args=1): Returns argument if it is present with float value, else
throws an error.

asserting_int

asserting_int (class=typing #args=1): Returns argument if it is present with int value, else
throws an error.

asserting_map

asserting_map (class=typing #args=1): Returns argument if it is a map, else throws an error.

asserting_nonempty_map

asserting_nonempty_map (class=typing #args=1): Returns argument if it is a non-empty map, else throws
an error.

asserting_not_empty

asserting_not_empty (class=typing #args=1): Returns argument if it is present in input with non-empty
value, else throws an error.

asserting_not_map

asserting_not_map (class=typing #args=1): Returns argument if it is not a map, else throws an error.

asserting_not_null

asserting_not_null (class=typing #args=1): Returns argument if it is non-null (non-empty and non-absent),
else throws an error.

asserting_null

asserting_null (class=typing #args=1): Returns argument if it is null (empty or absent), else throws
an error.

asserting_numeric

asserting_numeric (class=typing #args=1): Returns argument if it is present with int or float value,
else throws an error.

asserting_present

asserting_present (class=typing #args=1): Returns argument if it is present in input, else throws
an error.

asserting_string

asserting_string (class=typing #args=1): Returns argument if it is present with string (including
empty-string) value, else throws an error.

atan

atan (class=math #args=1): One-argument arctangent.

atan2

atan2 (class=math #args=2): Two-argument arctangent.

atanh

atanh (class=math #args=1): Inverse hyperbolic tangent.

boolean

boolean (class=conversion #args=1): Convert int/float/bool/string to boolean.

cbrt

cbrt (class=math #args=1): Cube root.

ceil

ceil (class=math #args=1): Ceiling: nearest integer at or above.

cos

cos (class=math #args=1): Trigonometric cosine.

cosh

cosh (class=math #args=1): Hyperbolic cosine.

depth

depth (class=maps #args=1): Prints maximum depth of hashmap: ''. Scalars have depth 0.

dhms2fsec

dhms2fsec (class=time #args=1): Recovers floating-point seconds as in
dhms2fsec("5d18h53m20.250000s") = 500000.250000

dhms2sec

dhms2sec (class=time #args=1): Recovers integer seconds as in
dhms2sec("5d18h53m20s") = 500000

erf

erf (class=math #args=1): Error function.

erfc

erfc (class=math #args=1): Complementary error function.

exp

exp (class=math #args=1): Exponential function e**x.

expm1

expm1 (class=math #args=1): e**x - 1.

float

float (class=conversion #args=1): Convert int/float/bool/string to float.

floor

floor (class=math #args=1): Floor: nearest integer at or below.

fmtnum

fmtnum (class=conversion #args=2): Convert int/float/bool to string using
printf-style format string, e.g. '$s = fmtnum($n, "%06lld")'.

fsec2dhms

fsec2dhms (class=time #args=1): Formats floating-point seconds as in
fsec2dhms(500000.25) = "5d18h53m20.250000s"

fsec2hms

fsec2hms (class=time #args=1): Formats floating-point seconds as in
fsec2hms(5000.25) = "01:23:20.250000"

gmt2sec

gmt2sec (class=time #args=1): Parses GMT timestamp as integer seconds since
the epoch.

gsub

gsub (class=string #args=3): Example: '$name=gsub($name, "old", "new")'
(replace all).

haskey

haskey (class=maps #args=2): True/false if map has/hasn't key, e.g. 'haskey($*, "a")' or
'haskey(mymap, mykey)'. Error if 1st argument is not a map.

hexfmt

hexfmt (class=conversion #args=1): Convert int to string, e.g. 255 to "0xff".

hms2fsec

hms2fsec (class=time #args=1): Recovers floating-point seconds as in
hms2fsec("01:23:20.250000") = 5000.250000

hms2sec

hms2sec (class=time #args=1): Recovers integer seconds as in
hms2sec("01:23:20") = 5000

int

int (class=conversion #args=1): Convert int/float/bool/string to int.

invqnorm

invqnorm (class=math #args=1): Inverse of normal cumulative distribution
function. Note that invqorm(urand()) is normally distributed.

is_absent

is_absent (class=typing #args=1): False if field is present in input, false otherwise

is_bool

is_bool (class=typing #args=1): True if field is present with boolean value. Synonymous with is_boolean.

is_boolean

is_boolean (class=typing #args=1): True if field is present with boolean value. Synonymous with is_bool.

is_empty

is_empty (class=typing #args=1): True if field is present in input with empty string value, false otherwise.

is_empty_map

is_empty_map (class=typing #args=1): True if argument is a map which is empty.

is_float

is_float (class=typing #args=1): True if field is present with value inferred to be float

is_int

is_int (class=typing #args=1): True if field is present with value inferred to be int 

is_map

is_map (class=typing #args=1): True if argument is a map.

is_nonempty_map

is_nonempty_map (class=typing #args=1): True if argument is a map which is non-empty.

is_not_empty

is_not_empty (class=typing #args=1): False if field is present in input with empty value, false otherwise

is_not_map

is_not_map (class=typing #args=1): True if argument is not a map.

is_not_null

is_not_null (class=typing #args=1): False if argument is null (empty or absent), true otherwise.

is_null

is_null (class=typing #args=1): True if argument is null (empty or absent), false otherwise.

is_numeric

is_numeric (class=typing #args=1): True if field is present with value inferred to be int or float

is_present

is_present (class=typing #args=1): True if field is present in input, false otherwise.

is_string

is_string (class=typing #args=1): True if field is present with string (including empty-string) value

joink

joink (class=maps #args=2): Makes string from map keys. E.g. 'joink($*, ",")'.

joinkv

joinkv (class=maps #args=3): Makes string from map key-value pairs. E.g. 'joinkv(@v[2], "=", ",")'

joinv

joinv (class=maps #args=2): Makes string from map keys. E.g. 'joinv(mymap, ",")'.

leafcount

leafcount (class=maps #args=1): Counts total number of terminal values in hashmap. For single-level maps,
same as length.

length

length (class=maps #args=1): Counts number of top-level entries in hashmap. Scalars have length 1.

log

log (class=math #args=1): Natural (base-e) logarithm.

log10

log10 (class=math #args=1): Base-10 logarithm.

log1p

log1p (class=math #args=1): log(1-x).

logifit

logifit (class=math #args=3): Given m and b from logistic regression, compute
fit: $yhat=logifit($x,$m,$b).

madd

madd (class=math #args=3): a + b mod m (integers)

mapdiff

mapdiff (class=maps variadic): With 0 args, returns empty map. With 1 arg, returns copy of arg.
With 2 or more, returns copy of arg 1 with all keys from any of remaining argument maps removed.

mapsum

mapsum (class=maps variadic): With 0 args, returns empty map. With >= 1 arg, returns a map with
key-value pairs from all arguments. Rightmost collisions win, e.g. 'mapsum({1:2,3,4},{1:5})' is '{1:5,3:4}'.

max

max (class=math variadic): max of n numbers; null loses

mexp

mexp (class=math #args=3): a ** b mod m (integers)

min

min (class=math variadic): Min of n numbers; null loses

mmul

mmul (class=math #args=3): a * b mod m (integers)

msub

msub (class=math #args=3): a - b mod m (integers)

pow

pow (class=math #args=2): Exponentiation; same as **.

qnorm

qnorm (class=math #args=1): Normal cumulative distribution function.

round

round (class=math #args=1): Round to nearest integer.

roundm

roundm (class=math #args=2): Round to nearest multiple of m: roundm($x,$m) is
the same as round($x/$m)*$m

sec2dhms

sec2dhms (class=time #args=1): Formats integer seconds as in sec2dhms(500000)
= "5d18h53m20s"

sec2gmt

sec2gmt (class=time #args=1): Formats seconds since epoch (integer part)
as GMT timestamp, e.g. sec2gmt(1440768801.7) = "2015-08-28T13:33:21Z".
Leaves non-numbers as-is.

sec2gmt (class=time #args=2): Formats seconds since epoch as GMT timestamp with n
decimal places for seconds, e.g. sec2gmt(1440768801.7,1) = "2015-08-28T13:33:21.7Z".
Leaves non-numbers as-is.

sec2gmtdate

sec2gmtdate (class=time #args=1): Formats seconds since epoch (integer part)
as GMT timestamp with year-month-date, e.g. sec2gmtdate(1440768801.7) = "2015-08-28".
Leaves non-numbers as-is.

sec2hms

sec2hms (class=time #args=1): Formats integer seconds as in
sec2hms(5000) = "01:23:20"

sgn

sgn (class=math #args=1): +1 for positive input, 0 for zero input, -1 for
negative input.

sin

sin (class=math #args=1): Trigonometric sine.

sinh

sinh (class=math #args=1): Hyperbolic sine.

splitkv

splitkv (class=maps #args=3): Splits string by separators into map with type inference.
E.g. 'splitkv("a=1,b=2,c=3", "=", ",")' gives '{"a" : 1, "b" : 2, "c" : 3}'.

splitkvx

splitkvx (class=maps #args=3): Splits string by separators into map without type inference (keys and
values are strings). E.g. 'splitkv("a=1,b=2,c=3", "=", ",")' gives
'{"a" : "1", "b" : "2", "c" : "3"}'.

splitnv

splitnv (class=maps #args=2): Splits string by separator into integer-indexed map with type inference.
E.g. 'splitnv("a,b,c" , ",")' gives '{1 : "a", 2 : "b", 3 : "c"}'.

splitnvx

splitnvx (class=maps #args=2): Splits string by separator into integer-indexed map without type
inference (values are strings). E.g. 'splitnv("4,5,6" , ",")' gives '{1 : "4", 2 : "5", 3 : "6"}'.

sqrt

sqrt (class=math #args=1): Square root.

strftime

strftime (class=time #args=2): Formats seconds since the epoch as timestamp, e.g.
strftime(1440768801.7,"%Y-%m-%dT%H:%M:%SZ") = "2015-08-28T13:33:21Z", and
strftime(1440768801.7,"%Y-%m-%dT%H:%M:%3SZ") = "2015-08-28T13:33:21.700Z".
Format strings are as in the C library (please see "man strftime" on your system),
with the Miller-specific addition of "%1S" through "%9S" which format the seocnds
with 1 through 9 decimal places, respectively. ("%S" uses no decimal places.)

string

string (class=conversion #args=1): Convert int/float/bool/string to string.

strlen

strlen (class=string #args=1): String length.

strptime

strptime (class=time #args=2): Parses timestamp as floating-point seconds since the epoch,
e.g. strptime("2015-08-28T13:33:21Z","%Y-%m-%dT%H:%M:%SZ") = 1440768801.000000,
and  strptime("2015-08-28T13:33:21.345Z","%Y-%m-%dT%H:%M:%SZ") = 1440768801.345000.

sub

sub (class=string #args=3): Example: '$name=sub($name, "old", "new")'
(replace once).

substr

substr (class=string #args=3): substr(s,m,n) gives substring of s from 0-up position m to n 
inclusive. Negative indices -len .. -1 alias to 0 .. len-1.

systime

systime (class=time #args=0): Floating-point seconds since the epoch,
e.g. 1440768801.748936.

tan

tan (class=math #args=1): Trigonometric tangent.

tanh

tanh (class=math #args=1): Hyperbolic tangent.

tolower

tolower (class=string #args=1): Convert string to lowercase.

toupper

toupper (class=string #args=1): Convert string to uppercase.

typeof

typeof (class=conversion #args=1): Convert argument to type of argument (e.g.
MT_STRING). For debug.

urand

urand (class=math #args=0): Floating-point numbers on the unit interval.
Int-valued example: '$n=floor(20+urand()*11)'.

urand32

urand32 (class=math #args=0): Integer uniformly distributed 0 and 2**32-1
inclusive.

urandint

urandint (class=math #args=2): Integer uniformly distributed between inclusive
integer endpoints.

User-defined functions and subroutines

As of Miller 5.0.0 you can define your own functions, as well as subroutines.

User-defined functions

Here’s the obligatory example of a recursive function to compute the factorial function:

$ mlr --opprint --from data/small put '
    func f(n) {
        if (is_numeric(n)) {
            if (n > 0) {
                return n * f(n-1);
            } else {
                return 1;
            }
        }
        # implicitly return absent-null if non-numeric
    }
    $ox = f($x + NR);
    $oi = f($i);
'
a   b   i x                   y                   ox         oi
pan pan 1 0.3467901443380824  0.7268028627434533  0.467054   1
eks pan 2 0.7586799647899636  0.5221511083334797  3.680838   2
wye wye 3 0.20460330576630303 0.33831852551664776 1.741251   6
eks wye 4 0.38139939387114097 0.13418874328430463 18.588349  24
wye pan 5 0.5732889198020006  0.8636244699032729  211.387310 120

Properties of user-defined functions:

User-defined subroutines

Example:

$ mlr --opprint --from data/small put -q '
  begin {
    @call_count = 0;
  }
  subr s(n) {
    @call_count += 1;
    if (is_numeric(n)) {
      if (n > 1) {
        call s(n-1);
      } else {
        print "numcalls=" . @call_count;
      }
    }
  }
  print "NR=" . NR;
  call s(NR);
'
NR=1
numcalls=1
NR=2
numcalls=3
NR=3
numcalls=6
NR=4
numcalls=10
NR=5
numcalls=15

Properties of user-defined subroutines:

Errors and transparency

As soon as you have a programming language, you start having the problem What is my code doing, and why? This includes getting syntax errors — which are always annoying — as well as the even more annoying problem of a program which parses without syntax error but doesn’t do what you expect.

The syntax error message is cryptic: it says syntax error at followed by the next symbol it couldn’t parse. This is good, but (as of 5.0.0) it doesn’t say things like syntax error at line 17, character 22. Here are some common causes of syntax errors:

  • Don’t forget ; at end of line, before another statement on the next line.
  • Miller’s DSL lacks the ++ and -- operators.
  • Curly braces are required for the bodies of if/while/for blocks, even when the body is a single statement.

Now for transparency:

  • As in any language, you can do print (or eprint to print to stderr). See also dump and emit.
  • The -v option to mlr put and mlr filter prints abstract syntax trees for your code. While not all details here will be of interest to everyone, certainly this makes questions such as operator precedence completely unambiguous.
  • The -T option prints a trace of each statement executed.
  • The -t and -a options show low-level details for the parsing process and for stack-variable-index allocation, respectively. These will likely be of interest to people who enjoy compilers, and probably less useful for a more general audience.
  • Please see the type-checking section for type declarations and type-assertions you can use to make sure expressions and the data flowing them are evaluating as you expect. I made them optional because one of Miller’s important use-cases is being able to say simple things like mlr put '$y = $x + 1' myfile.dat with a minimum of punctuational bric-a-brac — but for programs over a few lines I generally find that the more type-specification, the better.

A note on the complexity of Miller’s expression language

One of Miller’s strengths is its brevity: it’s much quicker — and less error-prone — to type mlr stats1 -a sum -f x,y -g a,b than having to track summation variables as in awk, or using Miller’s out-of-stream variables. And the more language features Miller’s put-DSL has (for-loops, if-statements, nested control structures, user-defined functions, etc.) then the less powerful it begins to seem: because of the other programming-language features it doesn’t have (classes, execptions, and so on).

When I was originally prototyping Miller in 2015, the decision I had was whether to hand-code in a low-level language like C or Rust, with my own hand-rolled DSL, or whether to use a higher-level language (like Python or Lua or Nim) and let the put statements be handled by the implementation language’s own eval: the implementation language would take the place of a DSL. Multiple performance experiments showed me I could get better throughput using the former, and using C in particular — by a wide margin. So Miller is C under the hood with a hand-rolled DSL.

I do want to keep focusing on what Miller is good at — concise notation, low latency, and high throughput — and not add too much in terms of high-level-language features to the DSL. That said, some sort of customizability is a basic thing to want. As of 4.1.0 we have recursive for/while/if structures on about the same complexity level as awk; as of 5.0.0 we have user-defined functions and map-valued variables, again on about the same complexity level as awk along with optional type-declaration syntax. While I’m excited by these powerful language features, I hope to keep new features beyond 5.0.0 focused on Miller’s sweet spot which is speed plus simplicity.