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

Miller Overview: • About Miller • Miller in 10 minutes • File formats • Miller features in the context of the Unix toolkit • Record-heterogeneity • Internationalization Using Miller: • FAQ • Sharing data with other languages • Cookbook part 1 • Cookbook part 2 • Cookbook part 3 • Data-diving examples • Manpage • Reference • Reference: Verbs • Reference: DSL • Documents by release • Installation, portability, dependencies, and testing Background: • Why? • Why C? • Why call it Miller? • How original is Miller? • Performance Repository: • Things to do • Contact information • GitHub repo | FAQ Contents: • No output at all • Fields not selected • Diagnosing delimiter specifications • How do I suppress numeric conversion? • How do I examine then-chaining? • I assigned $9 and it’s not 9th • How can I handle field names with special symbols in them? • How can I put single-quotes into strings? • Why doesn’t mlr cut put fields in the order I want? • NR is not consecutive after then-chaining • Why am I not seeing all possible joins occur? • What about XML or JSON file formats? # No output at all Try `od -xcv` and/or `cat -e` on your file to check for non-printable characters. If you’re using Miller version less than 5.0.0 (try `mlr --version` on your system to find out), when the line-ending-autodetect feature was introduced, please see here. # Fields not selected Check the field-separators of the data, e.g. with the command-line `head` program. Example: for CSV, Miller’s default record separator is comma; if your data is tab-delimited, e.g. `aTABbTABc`, then Miller won’t find three fields named `a`, `b`, and `c` but rather just one named `aTABbTABc`. Solution in this case: `mlr --fs tab {remaining arguments ...}`. Also try `od -xcv` and/or `cat -e` on your file to check for non-printable characters. # Diagnosing delimiter specifications
    # Use the `file` command to see if there are CR/LF terminators (in this case,
    # there are not):
    $ file data/colours.csv
    data/colours.csv: UTF-8 Unicode text

    # Look at the file to find names of fields
    $ cat data/colours.csv
    KEY;DE;EN;ES;FI;FR;IT;NL;PL;RO;TR
    masterdata_colourcode_1;Weiß;White;Blanco;Valkoinen;Blanc;Bianco;Wit;Biały;Alb;Beyaz
    masterdata_colourcode_2;Schwarz;Black;Negro;Musta;Noir;Nero;Zwart;Czarny;Negru;Siyah

    # Extract a few fields:
    $ mlr --csv cut -f KEY,PL,RO data/colours.csv
    (only blank lines appear)

    # Use XTAB output format to get a sharper picture of where records/fields
    # are being split:
    $ mlr --icsv --oxtab cat data/colours.csv
    KEY;DE;EN;ES;FI;FR;IT;NL;PL;RO;TR masterdata_colourcode_1;Weiß;White;Blanco;Valkoinen;Blanc;Bianco;Wit;Biały;Alb;Beyaz

    KEY;DE;EN;ES;FI;FR;IT;NL;PL;RO;TR masterdata_colourcode_2;Schwarz;Black;Negro;Musta;Noir;Nero;Zwart;Czarny;Negru;Siyah

    # Using XTAB output format makes it clearer that KEY;DE;...;RO;TR is being
    # treated as a single field name in the CSV header, and likewise each
    # subsequent line is being treated as a single field value. This is because
    # the default field separator is a comma but we have semicolons here.
    # Use XTAB again with different field separator (--fs semicolon):
     mlr --icsv --ifs semicolon --oxtab cat data/colours.csv
    KEY masterdata_colourcode_1
    DE  Weiß
    EN  White
    ES  Blanco
    FI  Valkoinen
    FR  Blanc
    IT  Bianco
    NL  Wit
    PL  Biały
    RO  Alb
    TR  Beyaz

    KEY masterdata_colourcode_2
    DE  Schwarz
    EN  Black
    ES  Negro
    FI  Musta
    FR  Noir
    IT  Nero
    NL  Zwart
    PL  Czarny
    RO  Negru
    TR  Siyah

    # Using the new field-separator, retry the cut:
     mlr --csv --fs semicolon cut -f KEY,PL,RO data/colours.csv
    KEY;PL;RO
    masterdata_colourcode_1;Biały;Alb
    masterdata_colourcode_2;Czarny;Negru
# How do I suppress numeric conversion? TL;DR use put -S. Within `mlr put` and `mlr filter`, the default behavior for scanning input records is to parse them as integer, if possible, then as float, if possible, else leave them as string:
    $ cat data/scan-example-1.tbl
    value
    1
    2.0
    3x
    hello
    $ mlr --pprint put '$copy = $value; $type = typeof($value)' data/scan-example-1.tbl
    value copy     type
    1     1        int
    2.0   2.000000 float
    3x    3x       string
    hello hello    string
The numeric-conversion rule is simple: Try to scan as integer (`"1"` should be int); if that doesn’t succeed, try to scan as float (`"1.0"` should be float); if that doesn’t succeed, leave the value as a string (`"1x"` is string). This is a sensible default: you should be able to put `'$z = $x + $y'` without having to write `'$z = int($x) + float($y)'`. Also note that default output format for floating-point numbers created by `put` (and other verbs such as `stats1`) is six decimal places; you can override this using `mlr --ofmt`. Also note that Miller uses your system’s C library functions whenever possible: e.g. `sscanf` for converting strings to integer or floating-point. But now suppose you have data like these:
    $ cat data/scan-example-2.tbl
    value
    0001
    0002
    0005
    0005WA
    0006
    0007
    0007WA
    0008
    0009
    0010
    $ mlr --pprint put '$copy = $value; $type = typeof($value)' data/scan-example-2.tbl
    value  copy     type
    0001   1        int
    0002   2        int
    0005   5        int
    0005WA 0005WA   string
    0006   6        int
    0007   7        int
    0007WA 0007WA   string
    0008   8.000000 float
    0009   9.000000 float
    0010   8        int
The same conversion rules as above are being used. Namely: By default field values are inferred to int, else float, else string; leading zeroes indicate octal for integers (`sscanf` semantics); since `0008` doesn't scan as integer (leading 0 requests octal but 8 isn't a valid octal digit), the float scan is tried next and it succeeds; default floating-point output format is 6 decimal places (override with `mlr --ofmt`). Taken individually the rules make sense; taken collectively they produce a mishmash of types here. The solution is to use the -S flag for `mlr put` and/or `mlr filter`. Then all field values are left as string. You can type-coerce on demand using syntax like `'$z = int($x) + float($y)'`. (See also the put documentation; see also https://github.com/johnkerl/miller/issues/150.)
    $ mlr --pprint put -S '$copy = $value; $type = typeof($value)' data/scan-example-2.tbl
    value  copy   type
    0001   0001   string
    0002   0002   string
    0005   0005   string
    0005WA 0005WA string
    0006   0006   string
    0007   0007   string
    0007WA 0007WA string
    0008   0008   string
    0009   0009   string
    0010   0010   string
# How do I examine then-chaining? Then-chaining found in Miller is intended to function the same as Unix pipes, but with less keystroking. You can print your data one pipeline step at a time, to see what intermediate output at one step becomes the input to the next step. First, look at the input data:
    $ cat data/then-example.csv
    Status,Payment_Type,Amount
    paid,cash,10.00
    pending,debit,20.00
    paid,cash,50.00
    pending,credit,40.00
    paid,debit,30.00
Next, run the first step of your command, omitting anything from the first `then` onward:
    $ mlr --icsv --opprint count-distinct -f Status,Payment_Type data/then-example.csv
    Status  Payment_Type count
    paid    cash         2
    pending debit        1
    pending credit       1
    paid    debit        1
After that, run it with the next `then` step included:
    $ mlr --icsv --opprint count-distinct -f Status,Payment_Type then sort -nr count data/then-example.csv
    Status  Payment_Type count
    paid    cash         2
    pending debit        1
    pending credit       1
    paid    debit        1
Now if you use `then` to include another verb after that, the columns `Status`, `Payment_Type`, and `count` will be the input to that verb. Note, by the way, that you’ll get the same results using pipes:
    $ mlr --csv count-distinct -f Status,Payment_Type data/then-example.csv | mlr --icsv --opprint sort -nr count
    Status  Payment_Type count
    paid    cash         2
    pending debit        1
    pending credit       1
    paid    debit        1
# I assigned $9 and it’s not 9th Miller records are ordered lists of key-value pairs. For NIDX format, DKVP format when keys are missing, or CSV/CSV-lite format with `--implicit-csv-header`, Miller will sequentially assign keys of the form `1`, `2`, etc. But these are not integer array indices: they’re just field names taken from the initial field ordering in the input data.
    $ echo x,y,z | mlr --dkvp cat
    1=x,2=y,3=z
    $ echo x,y,z | mlr --dkvp put '$6="a";$4="b";$55="cde"'
    1=x,2=y,3=z,6=a,4=b,55=cde
    $ echo x,y,z | mlr --nidx cat
    x,y,z
    $ echo x,y,z | mlr --csv --implicit-csv-header cat
    1,2,3
    x,y,z
    $ echo x,y,z | mlr --dkvp rename 2,999
    1=x,999=y,3=z
    $ echo x,y,z | mlr --dkvp rename 2,newname
    1=x,newname=y,3=z
    $ echo x,y,z | mlr --csv --implicit-csv-header reorder -f 3,1,2
    3,1,2
    z,x,y
# How can I handle field names with special symbols in them? Simply surround the field names with curly braces:
    $ echo 'x.a=3,y:b=4,z/c=5' | mlr put '${product.all} = ${x.a} * ${y:b} * ${z/c}'
    x.a=3,y:b=4,z/c=5,product.all=60
# How can I put single-quotes into strings? This is a little tricky due to the shell’s handling of quotes. For simplicity, let’s first put an update script into a file:
    $a = "It's OK, I said, then 'for now'."
    $ echo a=bcd | mlr put -f data/single-quote-example.mlr
    a=It's OK, I said, then 'for now'.
So, it’s simple: Miller’s DSL uses double quotes for strings, and you can put single quotes (or backslash-escaped double-quotes) inside strings, no problem. Without putting the update expression in a file, it’s messier:
    $ echo a=bcd | mlr put '$a="It'\''s OK, I said, '\''for now'\''."'
    a=It's OK, I said, 'for now'.
The idea is that the outermost single-quotes are to protect the `put` expression from the shell, and the double quotes within them are for Miller. To get a single quote in the middle there, you need to actually put it outside the single-quoting for the shell. The pieces are `$a="It` `\'` `s OK, I said,` `\'` `for now` `\'` `.` all concatenated together. # Why doesn’t mlr cut put fields in the order I want? Example: columns `x,i,a` were requested but they appear here in the order `a,i,x`:
    $ 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 cut -f x,i,a data/small
    a=pan,i=1,x=0.3467901443380824
    a=eks,i=2,x=0.7586799647899636
    a=wye,i=3,x=0.20460330576630303
    a=eks,i=4,x=0.38139939387114097
    a=wye,i=5,x=0.5732889198020006
The issue is that Miller’s `cut`, by default, outputs cut fields in the order they appear in the input data. This design decision was made intentionally to parallel the *nix system `cut` command, which has the same semantics. The solution is to use the `-o` option:
    $ mlr cut -o -f x,i,a data/small
    x=0.3467901443380824,i=1,a=pan
    x=0.7586799647899636,i=2,a=eks
    x=0.20460330576630303,i=3,a=wye
    x=0.38139939387114097,i=4,a=eks
    x=0.5732889198020006,i=5,a=wye
# NR is not consecutive after then-chaining Given this 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
why don’t I see `NR=1` and `NR=2` here??
    $ mlr filter '$x > 0.5' then put '$NR = NR' data/small
    a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797,NR=2
    a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729,NR=5
The reason is that `NR` is computed for the original input records and isn’t dynamically updated. By contrast, `NF` is dynamically updated: it’s the number of fields in the current record, and if you add/remove a field, the value of `NF` will change:
    $ echo x=1,y=2,z=3 | mlr put '$nf1 = NF; $u = 4; $nf2 = NF; unset $x,$y,$z; $nf3 = NF'
    nf1=3,u=4,nf2=5,nf3=3
`NR`, by contrast (and `FNR` as well), retains the value from the original input stream, and records may be dropped by a `filter` within a `then`-chain. To recover consecutive record numbers, you can use out-of-stream variables as follows:
    $ mlr --opprint --from data/small put '
      begin{ @nr1 = 0 }
      @nr1 += 1;
      $nr1 = @nr1
    ' \
    then filter '$x>0.5' \
    then put '
      begin{ @nr2 = 0 }
      @nr2 += 1;
      $nr2 = @nr2
    '
    a   b   i x                  y                  nr1 nr2
    eks pan 2 0.7586799647899636 0.5221511083334797 2   1
    wye pan 5 0.5732889198020006 0.8636244699032729 5   2
Or, simply use `mlr cat -n`:
    $ mlr filter '$x > 0.5' then cat -n data/small
    n=1,a=eks,b=pan,i=2,x=0.7586799647899636,y=0.5221511083334797
    n=2,a=wye,b=pan,i=5,x=0.5732889198020006,y=0.8636244699032729
# Why am I not seeing all possible joins occur? This section describes behavior before Miller 5.1.0. As of 5.1.0, `-u` is the default. For example, the right file here has nine records, and the left file should add in the `hostname` column — so the join output should also have 9 records:
    $ mlr --icsvlite --opprint cat data/join-u-left.csv
    hostname              ipaddr
    nadir.east.our.org    10.3.1.18
    zenith.west.our.org   10.3.1.27
    apoapsis.east.our.org 10.4.5.94
    $ mlr --icsvlite --opprint cat data/join-u-right.csv
    ipaddr    timestamp  bytes
    10.3.1.27 1448762579 4568
    10.3.1.18 1448762578 8729
    10.4.5.94 1448762579 17445
    10.3.1.27 1448762589 12
    10.3.1.18 1448762588 44558
    10.4.5.94 1448762589 8899
    10.3.1.27 1448762599 0
    10.3.1.18 1448762598 73425
    10.4.5.94 1448762599 12200
    $ mlr --icsvlite --opprint join -s -j ipaddr -f data/join-u-left.csv data/join-u-right.csv
    ipaddr    hostname              timestamp  bytes
    10.3.1.27 zenith.west.our.org   1448762579 4568
    10.4.5.94 apoapsis.east.our.org 1448762579 17445
    10.4.5.94 apoapsis.east.our.org 1448762589 8899
    10.4.5.94 apoapsis.east.our.org 1448762599 12200
The issue is that Miller’s `join`, by default (before 5.1.0), took input sorted (lexically ascending) by the sort keys on both the left and right files. This design decision was made intentionally to parallel the *nix system `join` command, which has the same semantics. The benefit of this default is that the joiner program can stream through the left and right files, needing to load neither entirely into memory. The drawback, of course, is that is requires sorted input. The solution (besides pre-sorting the input files on the join keys) is to simply use mlr join -u (which is now the default). This loads the left file entirely into memory (while the right file is still streamed one line at a time) and does all possible joins without requiring sorted input:
    $ mlr --icsvlite --opprint join -u -j ipaddr -f data/join-u-left.csv data/join-u-right.csv
    ipaddr    hostname              timestamp  bytes
    10.3.1.27 zenith.west.our.org   1448762579 4568
    10.3.1.18 nadir.east.our.org    1448762578 8729
    10.4.5.94 apoapsis.east.our.org 1448762579 17445
    10.3.1.27 zenith.west.our.org   1448762589 12
    10.3.1.18 nadir.east.our.org    1448762588 44558
    10.4.5.94 apoapsis.east.our.org 1448762589 8899
    10.3.1.27 zenith.west.our.org   1448762599 0
    10.3.1.18 nadir.east.our.org    1448762598 73425
    10.4.5.94 apoapsis.east.our.org 1448762599 12200
General advice is to make sure the left-file is relatively small, e.g. containing name-to-number mappings, while saving large amounts of data for the right file. # What about XML or JSON file formats? Miller handles tabular data, which is a list of records each having fields which are key-value pairs. Miller also doesn’t require that each record have the same field names (see also here). Regardless, tabular data is a non-recursive data structure. XML, JSON, etc. are, by contrast, all recursive or nested data structures. For example, in JSON you can represent a hash map whose values are lists of lists. Now, you can put tabular data into these formats — since list-of-key-value-pairs is one of the things representable in XML or JSON. Example:
    # DKVP
    x=1,y=2
    z=3

    # XML
    <table>
      <record>
        <field>
          <key> x </key> <value> 1 </value>
        </field>
        <field>
          <key> y </key> <value> 2 </value>
        </field>
      </record>
        <field>
          <key> z </key> <value> 3 </value>
        </field>
      <record>
      </record>
    </table>

    # JSON
    [{"x":1,"y":2},{"z":3}]
However, a tool like Miller which handles non-recursive data is never going to be able to handle full XML/JSON semantics — only a small subset. If tabular data represented in XML/JSON/etc are sufficiently well-structured, it may be easy to grep/sed out the data into a simpler text form — this is a general text-processing problem. Miller does support tabular data represented in JSON: please see File formats. See also jq for a truly powerful, JSON-specific tool. For XML, my suggestion is to use a tool like ff-extractor to do format conversion.