Estimate the number of lines in a file.
Project description
lcw is like wc -l but faster, less precise, and equally accurate.
usage: lcw [-h] [--sample-size N] [--page-size PAGE_SIZE] [--pattern PATTERN]
[--regex]
file [file ...]
Estimate how many lines are in a file.
positional arguments:
file
optional arguments:
-h, --help show this help message and exit
--sample-size N, -n N
How many pages to count (default: 1000)
--page-size PAGE_SIZE, -p PAGE_SIZE
Size of an observation (default: 16384)
--pattern PATTERN, -e PATTERN
The pattern to match (default: b'\n')
--regex, -r Use regular expressions (statistically unsound)
(default: False)
Speed
It’s faster than wc -l on big files.
$ wc -c big-file.csv 1071895374 big-file.csv $ time lcw big-file.csv 2386238 ± 22903 lines (99% confidence) real 0m0.172s user 0m0.140s sys 0m0.027s $ time wc -l big-file.csv 2388430 big-file.csv real 0m1.379s user 0m1.170s sys 0m0.197s
Math
lcw uses elementary statistics to perform unbiased estimates of the number of lines in a file. It takes a random sample of “pages” within the file and counts how many newlines are in each page.
It multiplies the average count by the number of pages in the file in order to get its best guess at the number of lines in the file (the maximum likelihood estimate) and then computes a 99% normal confidence interval, applying a finite population correction for the estimate the standard deviation of sample totals.
Tuning
It is best to use the page size that your storage medium uses; modern storage media read entire pages at once, so using a page size that is too small will be bad for performance.
The sample size is set with -n, and typical rules of thumb say that this should be at least 20 for the confidence level to be valid. The page size is set with -p and should be something like 2048, 4096, 8192, or 16384.
Matching things other than newline
You can count occurrences of a string other than newline; specify the string with -e. It will be interpreted as a regular expression if you pass -r. The statistical estimates do not account for the variable length of regular expression matches, so you are better off using plain strings if you care about accuracy.
Future work
I have been thinking about how to quickly sample from lots of files. Things like lcw can help us with samples within files, but it can be could be part of a broader survey plan, with cluster sampling or stratification on directories or filenames and with multistage sampling, using pilot tests to estimate the costs of the sampling of different files.
lcw presently uses a simple random sample. Because data in text files often vary with their position within the file, (Later lines often correspond to later dates.) systematic sampling would be appropriate.
Or, does this already exist so I don’t have to write it?
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