Miller 5.6.2

10-minute intro
• CSV-file examples
• Choices for printing to files
• Other-format examples
• SQL-output examples
• SQL-input examples
• Log-processing examples
• More

CSV-file examples

Sample CSV data file:

$ cat example.csv
color,shape,flag,index,quantity,rate
yellow,triangle,1,11,43.6498,9.8870
red,square,1,15,79.2778,0.0130
red,circle,1,16,13.8103,2.9010
red,square,0,48,77.5542,7.4670
purple,triangle,0,51,81.2290,8.5910
red,square,0,64,77.1991,9.5310
purple,triangle,0,65,80.1405,5.8240
yellow,circle,1,73,63.9785,4.2370
yellow,circle,1,87,63.5058,8.3350
purple,square,0,91,72.3735,8.2430

mlr cat is like cat ...

$ mlr --csv cat example.csv
color,shape,flag,index,quantity,rate
yellow,triangle,1,11,43.6498,9.8870
red,square,1,15,79.2778,0.0130
red,circle,1,16,13.8103,2.9010
red,square,0,48,77.5542,7.4670
purple,triangle,0,51,81.2290,8.5910
red,square,0,64,77.1991,9.5310
purple,triangle,0,65,80.1405,5.8240
yellow,circle,1,73,63.9785,4.2370
yellow,circle,1,87,63.5058,8.3350
purple,square,0,91,72.3735,8.2430

... but it can also do format conversion (here, to pretty-printed tabular format):

$ mlr --icsv --opprint cat example.csv
color  shape    flag index quantity rate
yellow triangle 1    11    43.6498  9.8870
red    square   1    15    79.2778  0.0130
red    circle   1    16    13.8103  2.9010
red    square   0    48    77.5542  7.4670
purple triangle 0    51    81.2290  8.5910
red    square   0    64    77.1991  9.5310
purple triangle 0    65    80.1405  5.8240
yellow circle   1    73    63.9785  4.2370
yellow circle   1    87    63.5058  8.3350
purple square   0    91    72.3735  8.2430

mlr head and mlr tail count records rather than lines. The CSV header is included either way:

$ mlr --csv head -n 4 example.csv
color,shape,flag,index,quantity,rate
yellow,triangle,1,11,43.6498,9.8870
red,square,1,15,79.2778,0.0130
red,circle,1,16,13.8103,2.9010
red,square,0,48,77.5542,7.4670

$ mlr --csv tail -n 4 example.csv
color,shape,flag,index,quantity,rate
purple,triangle,0,65,80.1405,5.8240
yellow,circle,1,73,63.9785,4.2370
yellow,circle,1,87,63.5058,8.3350
purple,square,0,91,72.3735,8.2430

Sort primarily alphabetically on one field, then secondarily numerically descending on another field:

$ mlr --icsv --opprint sort -f shape -nr index example.csv
color  shape    flag index quantity rate
yellow circle   1    87    63.5058  8.3350
yellow circle   1    73    63.9785  4.2370
red    circle   1    16    13.8103  2.9010
purple square   0    91    72.3735  8.2430
red    square   0    64    77.1991  9.5310
red    square   0    48    77.5542  7.4670
red    square   1    15    79.2778  0.0130
purple triangle 0    65    80.1405  5.8240
purple triangle 0    51    81.2290  8.5910
yellow triangle 1    11    43.6498  9.8870

Use cut to retain only specified fields, in input-data order:

$ mlr --icsv --opprint cut -f flag,shape example.csv
shape    flag
triangle 1
square   1
circle   1
square   0
triangle 0
square   0
triangle 0
circle   1
circle   1
square   0

Use cut -o to retain only specified fields, in your specified order:

$ mlr --icsv --opprint cut -o -f flag,shape example.csv
flag shape
1    triangle
1    square
1    circle
0    square
0    triangle
0    square
0    triangle
1    circle
1    circle
0    square

Use cut -x to omit specified fields:

$ mlr --icsv --opprint cut -x -f flag,shape example.csv
color  index quantity rate
yellow 11    43.6498  9.8870
red    15    79.2778  0.0130
red    16    13.8103  2.9010
red    48    77.5542  7.4670
purple 51    81.2290  8.5910
red    64    77.1991  9.5310
purple 65    80.1405  5.8240
yellow 73    63.9785  4.2370
yellow 87    63.5058  8.3350
purple 91    72.3735  8.2430

Use filter to retain specified records:

$ mlr --icsv --opprint filter '$color == "red"' example.csv
color shape  flag index quantity rate
red   square 1    15    79.2778  0.0130
red   circle 1    16    13.8103  2.9010
red   square 0    48    77.5542  7.4670
red   square 0    64    77.1991  9.5310

$ mlr --icsv --opprint filter '$color == "red" && $flag == 1' example.csv
color shape  flag index quantity rate
red   square 1    15    79.2778  0.0130
red   circle 1    16    13.8103  2.9010

Use put to add/replace fields which are computed from other fields:

$ mlr --icsv --opprint put '$ratio = $quantity / $rate; $color_shape = $color . "_" . $shape' example.csv
color  shape    flag index quantity rate   ratio       color_shape
yellow triangle 1    11    43.6498  9.8870 4.414868    yellow_triangle
red    square   1    15    79.2778  0.0130 6098.292308 red_square
red    circle   1    16    13.8103  2.9010 4.760531    red_circle
red    square   0    48    77.5542  7.4670 10.386260   red_square
purple triangle 0    51    81.2290  8.5910 9.455127    purple_triangle
red    square   0    64    77.1991  9.5310 8.099790    red_square
purple triangle 0    65    80.1405  5.8240 13.760388   purple_triangle
yellow circle   1    73    63.9785  4.2370 15.099953   yellow_circle
yellow circle   1    87    63.5058  8.3350 7.619172    yellow_circle
purple square   0    91    72.3735  8.2430 8.779995    purple_square

Even though Miller’s main selling point is name-indexing, sometimes you really want to refer to a field name by its positional index. Use $[[3]] to access the name of field 3 or $[[[3]]] to access the value of field 3:

$ mlr --icsv --opprint put '$[[3]] = "NEW"' example.csv
color  shape    NEW index quantity rate
yellow triangle 1   11    43.6498  9.8870
red    square   1   15    79.2778  0.0130
red    circle   1   16    13.8103  2.9010
red    square   0   48    77.5542  7.4670
purple triangle 0   51    81.2290  8.5910
red    square   0   64    77.1991  9.5310
purple triangle 0   65    80.1405  5.8240
yellow circle   1   73    63.9785  4.2370
yellow circle   1   87    63.5058  8.3350
purple square   0   91    72.3735  8.2430

$ mlr --icsv --opprint put '$[[[3]]] = "NEW"' example.csv
color  shape    flag index quantity rate
yellow triangle NEW  11    43.6498  9.8870
red    square   NEW  15    79.2778  0.0130
red    circle   NEW  16    13.8103  2.9010
red    square   NEW  48    77.5542  7.4670
purple triangle NEW  51    81.2290  8.5910
red    square   NEW  64    77.1991  9.5310
purple triangle NEW  65    80.1405  5.8240
yellow circle   NEW  73    63.9785  4.2370
yellow circle   NEW  87    63.5058  8.3350
purple square   NEW  91    72.3735  8.2430

JSON output:

$ mlr --icsv --ojson put '$ratio = $quantity/$rate; $shape = toupper($shape)' example.csv
{ "color": "yellow", "shape": "TRIANGLE", "flag": 1, "index": 11, "quantity": 43.6498, "rate": 9.8870, "ratio": 4.414868 }
{ "color": "red", "shape": "SQUARE", "flag": 1, "index": 15, "quantity": 79.2778, "rate": 0.0130, "ratio": 6098.292308 }
{ "color": "red", "shape": "CIRCLE", "flag": 1, "index": 16, "quantity": 13.8103, "rate": 2.9010, "ratio": 4.760531 }
{ "color": "red", "shape": "SQUARE", "flag": 0, "index": 48, "quantity": 77.5542, "rate": 7.4670, "ratio": 10.386260 }
{ "color": "purple", "shape": "TRIANGLE", "flag": 0, "index": 51, "quantity": 81.2290, "rate": 8.5910, "ratio": 9.455127 }
{ "color": "red", "shape": "SQUARE", "flag": 0, "index": 64, "quantity": 77.1991, "rate": 9.5310, "ratio": 8.099790 }
{ "color": "purple", "shape": "TRIANGLE", "flag": 0, "index": 65, "quantity": 80.1405, "rate": 5.8240, "ratio": 13.760388 }
{ "color": "yellow", "shape": "CIRCLE", "flag": 1, "index": 73, "quantity": 63.9785, "rate": 4.2370, "ratio": 15.099953 }
{ "color": "yellow", "shape": "CIRCLE", "flag": 1, "index": 87, "quantity": 63.5058, "rate": 8.3350, "ratio": 7.619172 }
{ "color": "purple", "shape": "SQUARE", "flag": 0, "index": 91, "quantity": 72.3735, "rate": 8.2430, "ratio": 8.779995 }

JSON output with vertical-formatting flags:

$ mlr --icsv --ojson --jvstack --jlistwrap tail -n 2 example.csv
[
{
  "color": "yellow",
  "shape": "circle",
  "flag": 1,
  "index": 87,
  "quantity": 63.5058,
  "rate": 8.3350
}
,{
  "color": "purple",
  "shape": "square",
  "flag": 0,
  "index": 91,
  "quantity": 72.3735,
  "rate": 8.2430
}
]

Use then to pipe commands together. Also, the -g option for many Miller commands is for group-by: here, head -n 1 -g shape outputs the first record for each distinct value of the shape field. This means we’re finding the record with highest index field for each distinct shape field:

$ mlr --icsv --opprint sort -f shape -nr index then head -n 1 -g shape example.csv
color  shape    flag index quantity rate
yellow circle   1    87    63.5058  8.3350
purple square   0    91    72.3735  8.2430
purple triangle 0    65    80.1405  5.8240

Statistics can be computed with or without group-by field(s). Also, the first of these two examples uses --oxtab output format which is a nice alternative to --opprint when you have lots of columns:

$ mlr --icsv --oxtab --from example.csv stats1 -a p0,p10,p25,p50,p75,p90,p99,p100 -f rate
rate_p0   0.013000
rate_p10  2.901000
rate_p25  4.237000
rate_p50  8.243000
rate_p75  8.591000
rate_p90  9.887000
rate_p99  9.887000
rate_p100 9.887000

$ mlr --icsv --opprint --from example.csv stats1 -a count,min,mean,max -f quantity -g shape
shape    quantity_count quantity_min quantity_mean quantity_max
triangle 3              43.649800    68.339767     81.229000
square   4              72.373500    76.601150     79.277800
circle   3              13.810300    47.098200     63.978500

$ mlr --icsv --opprint --from example.csv stats1 -a count,min,mean,max -f quantity -g shape,color
shape    color  quantity_count quantity_min quantity_mean quantity_max
triangle yellow 1              43.649800    43.649800     43.649800
square   red    3              77.199100    78.010367     79.277800
circle   red    1              13.810300    13.810300     13.810300
triangle purple 2              80.140500    80.684750     81.229000
circle   yellow 2              63.505800    63.742150     63.978500
square   purple 1              72.373500    72.373500     72.373500

Choices for printing to files

Often we want to print output to the screen. Miller does this by default, as we’ve seen in the previous examples.

Sometimes we want to print output to another file: just use '> outputfilenamegoeshere' at the end of your command:

% mlr --icsv --opprint cat example.csv > newfile.csv
# Output goes to the new file;
# nothing is printed to the screen.
% cat newfile.csv 
color  shape    flag index quantity rate
yellow triangle 1    11    43.6498  9.8870
red    square   1    15    79.2778  0.0130
red    circle   1    16    13.8103  2.9010
red    square   0    48    77.5542  7.4670
purple triangle 0    51    81.2290  8.5910
red    square   0    64    77.1991  9.5310
purple triangle 0    65    80.1405  5.8240
yellow circle   1    73    63.9785  4.2370
yellow circle   1    87    63.5058  8.3350
purple square   0    91    72.3735  8.2430

Other times we just want our files to be changed in-place: just use 'mlr -I'.

% cp example.csv newfile.txt

% cat newfile.txt
color,shape,flag,index,quantity,rate
yellow,triangle,1,11,43.6498,9.8870
red,square,1,15,79.2778,0.0130
red,circle,1,16,13.8103,2.9010
red,square,0,48,77.5542,7.4670
purple,triangle,0,51,81.2290,8.5910
red,square,0,64,77.1991,9.5310
purple,triangle,0,65,80.1405,5.8240
yellow,circle,1,73,63.9785,4.2370
yellow,circle,1,87,63.5058,8.3350
purple,square,0,91,72.3735,8.2430
% mlr -I --icsv --opprint cat newfile.txt

% cat newfile.txt
color  shape    flag index quantity rate
yellow triangle 1    11    43.6498  9.8870
red    square   1    15    79.2778  0.0130
red    circle   1    16    13.8103  2.9010
red    square   0    48    77.5542  7.4670
purple triangle 0    51    81.2290  8.5910
red    square   0    64    77.1991  9.5310
purple triangle 0    65    80.1405  5.8240
yellow circle   1    73    63.9785  4.2370
yellow circle   1    87    63.5058  8.3350
purple square   0    91    72.3735  8.2430

Also using mlr -I you can bulk-operate on lots of files: e.g.

mlr -I --csv cut -x -f unwanted_column_name *.csv

If you like, you can first copy off your original data somewhere else, before doing in-place operations.

Lastly, using tee within put, you can split your input data into separate files per one or more field names:

$ mlr --csv --from example.csv put -q 'tee > $shape.".csv", $*'

$ cat circle.csv
color,shape,flag,index,quantity,rate
red,circle,1,16,13.8103,2.9010
yellow,circle,1,73,63.9785,4.2370
yellow,circle,1,87,63.5058,8.3350

$ cat square.csv
color,shape,flag,index,quantity,rate
red,square,1,15,79.2778,0.0130
red,square,0,48,77.5542,7.4670
red,square,0,64,77.1991,9.5310
purple,square,0,91,72.3735,8.2430

$ cat triangle.csv
color,shape,flag,index,quantity,rate
yellow,triangle,1,11,43.6498,9.8870
purple,triangle,0,51,81.2290,8.5910
purple,triangle,0,65,80.1405,5.8240

Other-format examples

What’s a CSV file, really? It’s an array of rows, or records, each being a list of key-value pairs, or fields: for CSV it so happens that all the keys are shared in the header line and the values vary data line by data line.

For example, if you have

shape,flag,index
circle,1,24
square,0,36

then that’s a way of saying

shape=circle,flag=1,index=24
shape=square,flag=0,index=36

Data written this way are called DKVP, for delimited key-value pairs.

We’ve also already seen other ways to write the same data:

CSV                               PPRINT                 JSON
shape,flag,index                  shape  flag index      [
circle,1,24                       circle 1    24           {
square,0,36                       square 0    36             "shape": "circle",
                                                             "flag": 1,
                                                             "index": 24
                                                           },
DKVP                              XTAB                     {
shape=circle,flag=1,index=24      shape circle               "shape": "square",
shape=square,flag=0,index=36      flag  1                    "flag": 0,
                                  index 24                   "index": 36
                                                           }
                                  shape square           ]
                                  flag  0
                                  index 36

Anything we can do with CSV input data, we can do with any other format input data. And you can read from one format, do any record-processing, and output to the same format as the input, or to a different output format.

SQL-output examples

I like to produce SQL-query output with header-column and tab delimiter: this is CSV but with a tab instead of a comma, also known as TSV. Then I post-process with mlr --tsv or mlr --tsvlite. This means I can do some (or all, or none) of my data processing within SQL queries, and some (or none, or all) of my data processing using Miller — whichever is most convenient for my needs at the moment.

For example, using default output formatting in mysql we get formatting like Miller’s --opprint --barred:

$ mysql --database=mydb -e 'show columns in mytable'
+------------------+--------------+------+-----+---------+-------+
| Field            | Type         | Null | Key | Default | Extra |
+------------------+--------------+------+-----+---------+-------+
| id               | bigint(20)   | NO   | MUL | NULL    |       |
| category         | varchar(256) | NO   |     | NULL    |       |
| is_permanent     | tinyint(1)   | NO   |     | NULL    |       |
| assigned_to      | bigint(20)   | YES  |     | NULL    |       |
| last_update_time | int(11)      | YES  |     | NULL    |       |
+------------------+--------------+------+-----+---------+-------+

Using mysql’s -B we get TSV output:

$ mysql --database=mydb -B -e 'show columns in mytable' | mlr --itsvlite --opprint cat
Field            Type         Null Key Default Extra
id               bigint(20)   NO  MUL NULL -
category         varchar(256) NO  -   NULL -
is_permanent     tinyint(1)   NO  -   NULL -
assigned_to      bigint(20)   YES -   NULL -
last_update_time int(11)      YES -   NULL -

Since Miller handles TSV output, we can do as much or as little processing as we want in the SQL query, then send the rest on to Miller. This includes outputting as JSON, doing further selects/joins in Miller, doing stats, etc. etc.

$ mysql --database=mydb -B -e 'show columns in mytable' | mlr --itsvlite --ojson --jlistwrap --jvstack cat 
[
  {
    "Field": "id",
    "Type": "bigint(20)",
    "Null": "NO",
    "Key": "MUL",
    "Default": "NULL",
    "Extra": ""
  },
  {
    "Field": "category",
    "Type": "varchar(256)",
    "Null": "NO",
    "Key": "",
    "Default": "NULL",
    "Extra": ""
  },
  {
    "Field": "is_permanent",
    "Type": "tinyint(1)",
    "Null": "NO",
    "Key": "",
    "Default": "NULL",
    "Extra": ""
  },
  {
    "Field": "assigned_to",
    "Type": "bigint(20)",
    "Null": "YES",
    "Key": "",
    "Default": "NULL",
    "Extra": ""
  },
  {
    "Field": "last_update_time",
    "Type": "int(11)",
    "Null": "YES",
    "Key": "",
    "Default": "NULL",
    "Extra": ""
  }
]
$ mysql --database=mydb -B -e 'select * from mytable' > query.tsv

$ mlr --from query.tsv --t2p stats1 -a count -f id -g category,assigned_to
category assigned_to id_count
special  10000978    207
special  10003924    385
special  10009872    168
standard 10000978    524
standard 10003924    392
standard 10009872    108
...

Again, all the examples in the CSV section apply here — just change the input-format flags.

SQL-input examples

One use of NIDX (value-only, no keys) format is for loading up SQL tables.

Create and load SQL table:

mysql> CREATE TABLE abixy(
  a VARCHAR(32),
  b VARCHAR(32),
  i BIGINT(10),
  x DOUBLE,
  y DOUBLE
);
Query OK, 0 rows affected (0.01 sec)

bash$ mlr --onidx --fs comma cat data/medium > medium.nidx

mysql> LOAD DATA LOCAL INFILE 'medium.nidx' REPLACE INTO TABLE abixy FIELDS TERMINATED BY ',' ;
Query OK, 10000 rows affected (0.07 sec)
Records: 10000  Deleted: 0  Skipped: 0  Warnings: 0

mysql> SELECT COUNT(*) AS count FROM abixy;
+-------+
| count |
+-------+
| 10000 |
+-------+
1 row in set (0.00 sec)

mysql> SELECT * FROM abixy LIMIT 10;
+------+------+------+---------------------+---------------------+
| a    | b    | i    | x                   | y                   |
+------+------+------+---------------------+---------------------+
| pan  | pan  |    1 |  0.3467901443380824 |  0.7268028627434533 |
| eks  | pan  |    2 |  0.7586799647899636 |  0.5221511083334797 |
| wye  | wye  |    3 | 0.20460330576630303 | 0.33831852551664776 |
| eks  | wye  |    4 | 0.38139939387114097 | 0.13418874328430463 |
| wye  | pan  |    5 |  0.5732889198020006 |  0.8636244699032729 |
| zee  | pan  |    6 |  0.5271261600918548 | 0.49322128674835697 |
| eks  | zee  |    7 |  0.6117840605678454 |  0.1878849191181694 |
| zee  | wye  |    8 |  0.5985540091064224 |   0.976181385699006 |
| hat  | wye  |    9 | 0.03144187646093577 |  0.7495507603507059 |
| pan  | wye  |   10 |  0.5026260055412137 |  0.9526183602969864 |
+------+------+------+---------------------+---------------------+

Aggregate counts within SQL:

mysql> SELECT a, b, COUNT(*) AS count FROM abixy GROUP BY a, b ORDER BY COUNT DESC;
+------+------+-------+
| a    | b    | count |
+------+------+-------+
| zee  | wye  |   455 |
| pan  | eks  |   429 |
| pan  | pan  |   427 |
| wye  | hat  |   426 |
| hat  | wye  |   423 |
| pan  | hat  |   417 |
| eks  | hat  |   417 |
| pan  | zee  |   413 |
| eks  | eks  |   413 |
| zee  | hat  |   409 |
| eks  | wye  |   407 |
| zee  | zee  |   403 |
| pan  | wye  |   395 |
| wye  | pan  |   392 |
| zee  | eks  |   391 |
| zee  | pan  |   389 |
| hat  | eks  |   389 |
| wye  | eks  |   386 |
| wye  | zee  |   385 |
| hat  | zee  |   385 |
| hat  | hat  |   381 |
| wye  | wye  |   377 |
| eks  | pan  |   371 |
| hat  | pan  |   363 |
| eks  | zee  |   357 |
+------+------+-------+
25 rows in set (0.01 sec)

Aggregate counts within Miller:


$ mlr --opprint uniq -c -g a,b then sort -nr count data/medium
a   b   count
zee wye 455
pan eks 429
pan pan 427
wye hat 426
hat wye 423
pan hat 417
eks hat 417
eks eks 413
pan zee 413
zee hat 409
eks wye 407
zee zee 403
pan wye 395
hat pan 363
eks zee 357

Pipe SQL output to aggregate counts within Miller:


$ mysql -D miller -B -e 'select * from abixy' | mlr --itsv --opprint uniq -c -g a,b then sort -nr count
a   b   count
zee wye 455
pan eks 429
pan pan 427
wye hat 426
hat wye 423
pan hat 417
eks hat 417
eks eks 413
pan zee 413
zee hat 409
eks wye 407
zee zee 403
pan wye 395
wye pan 392
zee eks 391
zee pan 389
hat eks 389
wye eks 386
hat zee 385
wye zee 385
hat hat 381
wye wye 377
eks pan 371
hat pan 363
eks zee 357

Log-processing examples

Another of my favorite use-cases for Miller is doing ad-hoc processing of log-file data. Here’s where DKVP format really shines: one, since the field names and field values are present on every line, every line stands on its own. That means you can grep or what have you. Also it means not every line needs to have the same list of field names (“schema ”).

Again, all the examples in the CSV section apply here — just change the input-format flags. But there’s more you can do when not all the records have the same shape.

Writing a program — in any language whatsoever — you can have it print out log lines as it goes along, with items for various events jumbled together. After the program has finished running you can sort it all out, filter it, analyze it, and learn from it.

Suppose your program has printed something like this:

$ cat log.txt
op=enter,time=1472819681
op=cache,type=A9,hit=0
op=cache,type=A4,hit=1
time=1472819690,batch_size=100,num_filtered=237
op=cache,type=A1,hit=1
op=cache,type=A9,hit=0
op=cache,type=A1,hit=1
op=cache,type=A9,hit=0
op=cache,type=A9,hit=0
op=cache,type=A1,hit=1
time=1472819705,batch_size=100,num_filtered=348
op=cache,type=A4,hit=1
op=cache,type=A9,hit=0
op=cache,type=A9,hit=0
op=cache,type=A9,hit=0
op=cache,type=A9,hit=0
op=cache,type=A4,hit=1
time=1472819713,batch_size=100,num_filtered=493
op=cache,type=A9,hit=1
op=cache,type=A1,hit=1
op=cache,type=A9,hit=0
op=cache,type=A9,hit=0
op=cache,type=A9,hit=1
time=1472819720,batch_size=100,num_filtered=554
op=cache,type=A1,hit=0
op=cache,type=A4,hit=1
op=cache,type=A9,hit=0
op=cache,type=A9,hit=0
op=cache,type=A9,hit=0
op=cache,type=A4,hit=0
op=cache,type=A4,hit=0
op=cache,type=A9,hit=0
time=1472819736,batch_size=100,num_filtered=612
op=cache,type=A1,hit=1
op=cache,type=A9,hit=0
op=cache,type=A9,hit=0
op=cache,type=A9,hit=0
op=cache,type=A9,hit=0
op=cache,type=A4,hit=1
op=cache,type=A1,hit=1
op=cache,type=A9,hit=0
op=cache,type=A9,hit=0
time=1472819742,batch_size=100,num_filtered=728

Each print statement simply contains local information: the current timestamp, whether a particular cache was hit or not, etc. Then using either the system grep command, or Miller’s having-fields, or is_present, we can pick out the parts we want and analyze them:

$ grep op=cache log.txt \
  | mlr --idkvp --opprint stats1 -a mean -f hit -g type then sort -f type
type hit_mean
A1   0.857143
A4   0.714286
A9   0.090909

$ mlr --from log.txt --opprint \
  filter 'is_present($batch_size)' \
  then step -a delta -f time,num_filtered \
  then sec2gmt time
time                 batch_size num_filtered time_delta num_filtered_delta
2016-09-02T12:34:50Z 100        237          0          0
2016-09-02T12:35:05Z 100        348          15         111
2016-09-02T12:35:13Z 100        493          8          145
2016-09-02T12:35:20Z 100        554          7          61
2016-09-02T12:35:36Z 100        612          16         58
2016-09-02T12:35:42Z 100        728          6          116

Alternatively, we can simply group the similar data for a better look:

$ mlr --opprint group-like log.txt
op    time
enter 1472819681

op    type hit
cache A9   0
cache A4   1
cache A1   1
cache A9   0
cache A1   1
cache A9   0
cache A9   0
cache A1   1
cache A4   1
cache A9   0
cache A9   0
cache A9   0
cache A9   0
cache A4   1
cache A9   1
cache A1   1
cache A9   0
cache A9   0
cache A9   1
cache A1   0
cache A4   1
cache A9   0
cache A9   0
cache A9   0
cache A4   0
cache A4   0
cache A9   0
cache A1   1
cache A9   0
cache A9   0
cache A9   0
cache A9   0
cache A4   1
cache A1   1
cache A9   0
cache A9   0

time       batch_size num_filtered
1472819690 100        237
1472819705 100        348
1472819713 100        493
1472819720 100        554
1472819736 100        612
1472819742 100        728

$ mlr --opprint group-like then sec2gmt time log.txt
op    time
enter 2016-09-02T12:34:41Z

op    type hit
cache A9   0
cache A4   1
cache A1   1
cache A9   0
cache A1   1
cache A9   0
cache A9   0
cache A1   1
cache A4   1
cache A9   0
cache A9   0
cache A9   0
cache A9   0
cache A4   1
cache A9   1
cache A1   1
cache A9   0
cache A9   0
cache A9   1
cache A1   0
cache A4   1
cache A9   0
cache A9   0
cache A9   0
cache A4   0
cache A4   0
cache A9   0
cache A1   1
cache A9   0
cache A9   0
cache A9   0
cache A9   0
cache A4   1
cache A1   1
cache A9   0
cache A9   0

time                 batch_size num_filtered
2016-09-02T12:34:50Z 100        237
2016-09-02T12:35:05Z 100        348
2016-09-02T12:35:13Z 100        493
2016-09-02T12:35:20Z 100        554
2016-09-02T12:35:36Z 100        612
2016-09-02T12:35:42Z 100        728

More

Please see the reference for complete information, as well as the FAQ and the cookbook for more tips.