I know there are other tools around like awstats or splunk, but I wonder whether there is some serious (web)server logfile analysis going on in R. I might not be the first thought to do it in R, but still R has nice visualization capabilities and also nice spatial packages. Do you know of any? Or is there a R package / code that handles the most common log file formats that one could build on? Or is it simply a very bad idea?
Answer
In connection with a project to build an analytics toolbox for our Network Ops guys, i built one of these about two months ago. My employer has no problem if i open source it, so if anyone is interested i can put it up on my github repo. I assume it's most useful to this group if i build an R Package. I won't be able to do that straight away though because i need to research the docs on package building with non-R code (it might be as simple as tossing the python bytecode files in /exec along with a suitable python runtime, but i have no idea).
I was actually suprised that i needed to undertake a project of this sort. There are at least several excellent open source and free log file parsers/viewers (including the excellent Webalyzer and AWStats) but neither parse server error logs (parsing server access logs is the primary use case for both).
If you are not familiar with error logs or with the difference between them and access logs, in sum, Apache servers (likewsie, nginx and IIS) record two distinct logs and store them to disk by default next to each other in the same directory. On Mac OS X, that directory in /var, just below root:
$> pwd
/var/log/apache2
$> ls
access_log error_log
For network diagnostics, error logs are often far more useful than the access logs. They also happen to be significantly more difficult to process because of the unstructured nature of the data in many of the fields and more significantly, because the data file you are left with after parsing is an irregular time series--you might have multiple entries keyed to a single timestamp, then the next entry is three seconds later, and so forth.
i wanted an app that i could toss in raw error logs (of any size, but usually several hundred MB at a time) have something useful come out the other end--which in this case, had to be some pre-packaged analytics and also a data cube available inside R for command-line analytics. Given this, i coded the raw-log parser in python, while the processor (e.g., gridding the parser output to create a regular time series) and all analytics and data visualization, i coded in R.
I have been building analytics tools for a long time, but only in the past four years have i been using R. So my first impression--immediately upon parsing a raw log file and loading the data frame in R is what a pleasure R is to work with and how it is so well suited for tasks of this sort. A few welcome suprises:
Serialization. To persist working data in R is a single command (save). I knew this, but i didn't know how efficient is this binary format. Thee actual data: for every 50 MB of raw logfiles parsed, the .RData representation was about 500 KB--100 : 1 compression. (Note: i pushed this down further to about 300 : 1 by using the data.table library and manually setting compression level argument to the save function);
IO. My Data Warehouse relies heavily on a lightweight datastructure server that resides entirely in RAM and writes to disk asynchronously, called redis. The proect itself is only about two years old, yet there's already a redis client for R in CRAN (by B.W. Lewis, version 1.6.1 as of this post);
Primary Data Analysis. The purpose of this Project was to build a Library for our Network Ops guys to use. My goal was a "one command = one data view" type interface. So for instance, i used the excellent googleVis Package to create a professional-looking scrollable/paginated HTML tables with sortable columns, in which i loaded a data frame of aggregated data (>5,000 lines). Just those few interactive elments--e.g., sorting a column--delivered useful descriptive analytics. Another example, i wrote a lot of thin wrappers over some basic data juggling and table-like functions; each of these functions i would for instance, bind to a clickable button on a tabbed web page. Again, this was a pleasure to do in R, in part becasue quite often the function required no wrapper, the single command with the arguments supplied was enough to generate a useful view of the data.
A couple of examples of the last bullet:
# what are the most common issues that cause an error to be logged?
err_order = function(df){
t0 = xtabs(~Issue_Descr, df)
m = cbind( names(t0), t0)
rownames(m) = NULL
colnames(m) = c("Cause", "Count")
x = m[,2]
x = as.numeric(x)
ndx = order(x, decreasing=T)
m = m[ndx,]
m1 = data.frame(Cause=m[,1], Count=as.numeric(m[,2]),
CountAsProp=100*as.numeric(m[,2])/dim(df)[1])
subset(m1, CountAsProp >= 1.)
}
# calling this function, passing in a data frame, returns something like:
Cause Count CountAsProp
1 'connect to unix://var/ failed' 200 40.0
2 'object buffered to temp file' 185 37.0
3 'connection refused' 94 18.8
The Primary Data Cube Displayed for Interactive Analysis Using googleVis:
A contingency table (from an xtab function call) displayed using googleVis)
