FAQ

No output at all

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 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.