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