Access Log Data

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Essentia’s Environment

In this tutorial we just want to setup a simple Essentia environment, one that runs on our local computer and scans our local filesystem for the apache access log data data located in the casestudies/apache/accesslogs directory. If you are not already in the tutorials/woodworking/ directory, please switch into it now. We save the following commands to my_setup_script.sh:

 1     ess cluster set local
 2     ess purge local
 3
 4     ess udbd stop
 5     ess server reset
 6
 7     ess create database logsapache1 --ports=1
 8     ess create vector vector1 s,pkey:timeseg i,+add:pagecount i,+add:hitcount i,+add:pagebytes
 9     # Create a vector to aggregate (count) the number of pages, hits, and bytes seen or used by the user-specified time segment.
10
11     ess udbd start
12
13     ess select local
14
15     currentdir=`pwd`
16     echo "My current directory: " $currentdir
17     ess category add 125accesslogs "$currentdir/../../casestudies/apache/*accesslog*125-access_log*"
18
19     ess summary
20
21     ess stream 125accesslogs $1 ${2} "aq_pp $oldmod -f,qui,eok,div - -d ip:ip sep:' ' X sep:' ' X sep:' [' \
22     s:time_s sep:'] \"' X sep:' ' s,clf:accessedfile sep:' ' X sep:'\" ' i:httpstatus sep:' ' i:pagebytes sep:' \"' X \
23     sep:'\" \"' X sep:'\"' X -eval i:time 'DateToTime(time_s, \"d.b.Y.H.M.S.z\")' -filt '$5' -eval i:hitcount '1' \
24     -if -filt '(PatCmp(accessedfile, \"$4\", \"ncas\") || PatCmp(accessedfile, \"*.htm[?,#]?*\", \"ncas\") || PatCmp(accessedfile, \"*.php[?,#]?*\", \"ncas\") || PatCmp(accessedfile, \"*.asp[?,#]?*\", \"ncas\") || PatCmp(accessedfile, \"*/\", \"ncas\") || PatCmp(accessedfile, \"*.php\", \"ncas\"))' -eval i:pagecount '1' -eval s:pageurl 'accessedfile' \
25     -else -eval pagecount '0' -endif -eval s:timeseg 'TimeToDate(time,\"${3}\")' -ddef -udb -imp logsapache1:vector1" --debug
26
27     # Stream your access logs from the startdate and enddate you specify into the following command. Use logcnv to specify the format of the records in the access log and convert them to .csv format.
28     # Then pipe the data into our preprocessor (aq_pp) and specify which columns you want to keep. Filter on httpstatus so that you only include the 'good' http status codes that correspond to actual views.
29     # Create a column that you can aggregate for each record to keep track of hits and another column to group the data by. Filter on accessedfile to eliminate any viewed files that dont have certain elements in their filename.
30     # If this filter returns true, count that file as a page and save the file to a column called pageurl. If the filter returns false then the file is not counted as a page.
31     # Convert the time column to a date and extract the desired time segment. Import the modified and reduced data into the vector in the database you defined above so that the attributes defined there can be applied.

and then run

sh my_setup_script.sh 2014-11-30 2014-12-07 %d "*.html[?,#]?*" "httpstatus == 200 || httpstatus == 304"

This substitutes four parameters into the script my_setup_script.sh and then executes the essentia commands in my_setup_script.sh to load the access logs into the UDB database.

read.essentia

With the environment setup, we can now use read.essentia to execute an essenia script that exports the data from the Essentia database in csv format and then read it into R.

The read.essentia function takes one argument: file, which can contain any arguments that you could typically pass to a bash script.

The output can be saved into an R dataframe

**my_dataframe_name** <- read.essentia(file)

read.essentia requires you to store the essentia query in a bash script. Thus we save the following statement to readquery.sh:

# This statement exports the time segment vector from its database. 
ess exec "aq_udb -exp logsapache1:vector1"

and then simply have R run:

library(RESS)           # load Essentia's R Integration package 
mydata <- read.essentia('readquery.sh')           # call read.essentia to execute the essentia statement written in readquery.sh and save its output into R as a dataframe called mydata

We are now free to analyze this data using the massive variety of R functions and methods. To get a few quick plots of this data, we run:

barplot(t(as.matrix(mydata[,2])), names.arg=mydata[,1], col=c("black"), beside=TRUE, main="# of Pages",xlab="Time",axes=TRUE,las=2,ylim=c(0,max(mydata[,2]))) 
legend("topright",legend=c("Pages"),fill=c("black"),bty="n") 

barplot(t(as.matrix(mydata[,3])), names.arg=mydata[,1], col=c("blue"), beside=TRUE, main="# of Hits",xlab="Time",axes=TRUE,las=2,ylim=c(0,max(mydata[,3]))) 
legend("topright",legend=c("Hits"),fill=c("blue"),bty="n") 

barplot(t(as.matrix(mydata[,4])), names.arg=mydata[,1], col=c("red"), beside=TRUE, main="Bandwidth",xlab="Time",axes=TRUE,las=2,ylim=c(0,max(mydata[,4])),cex.axis=.7,cex.names=.8) 
legend("topright",legend=c("Bandwidth"),fill=c("red"),bty="n")

This created plots of the number of pages, hits, and bandwidth versus the day of the week.

essQuery

We could also have used essQuery to export the data from the UDB database and into an R dataframe.

The essQuery function takes three arguments: essentia, aq, and flags.

The output can be saved into an R dataframe

**my_dataframe_name** <- essQuery(essentia, aq = "", flags = "")

or directly analyzed in R. For demonstration purposes, we’ll save the output of the essQuery call to a dataframe.

First we must open an R script or the R interactive prompt and type

library(RESS)

to tell R to use the installed RESS package. Then we run

mydata <- essQuery("ess exec", "aq_udb -exp logsapache1:vector1")

to import the apache files into R and save them as a dataframe called mydata.

Here essentia is an ess exec command executing the aq_command. This aq_command then pulls all of the aggregated and grouped apache access log data from the UDB database.

We can now run:

barplot(t(as.matrix(mydata[,2])), names.arg=mydata[,1], col=c("black"), beside=TRUE, main="# of Pages",xlab="Time",axes=TRUE,las=2,ylim=c(0,max(mydata[,2]))) 
legend("topright",legend=c("Pages"),fill=c("black"),bty="n") 

barplot(t(as.matrix(mydata[,3])), names.arg=mydata[,1], col=c("blue"), beside=TRUE, main="# of Hits",xlab="Time",axes=TRUE,las=2,ylim=c(0,max(mydata[,3]))) 
legend("topright",legend=c("Hits"),fill=c("blue"),bty="n") 

barplot(t(as.matrix(mydata[,4])), names.arg=mydata[,1], col=c("red"), beside=TRUE, main="Bandwidth",xlab="Time",axes=TRUE,las=2,ylim=c(0,max(mydata[,4])),cex.axis=.7,cex.names=.8) 
legend("topright",legend=c("Bandwidth"),fill=c("red"),bty="n")

in R to create the same plots as before.

capture.essentia

An alternative way to send the data to R is to use capture.essentia.

capture.essentia requires you to store the essentia queries in a bash script and then store that script’s filename as file in R. Thus we save the following statements to capture_essentia_query.sh:

ess exec "aq_udb -exp logsapache1:vector1" #Rinclude #R#mydata#R#

and then simply have R run:

library(RESS)           # load Essentia's R Integration package 
capture.essentia("capture_essentia_query.sh")           # call capture.essentia to execute the essentia statement written in capture_essentia_query.sh and save them to the R dataframe mydata 

barplot(t(as.matrix(mydata[,2])), names.arg=mydata[,1], col=c("black"), beside=TRUE, main="# of Pages",xlab="Time",axes=TRUE,las=2,ylim=c(0,max(mydata[,2]))) 
legend("topright",legend=c("Pages"),fill=c("black"),bty="n") 

barplot(t(as.matrix(mydata[,3])), names.arg=mydata[,1], col=c("blue"), beside=TRUE, main="# of Hits",xlab="Time",axes=TRUE,las=2,ylim=c(0,max(mydata[,3]))) 
legend("topright",legend=c("Hits"),fill=c("blue"),bty="n") 

barplot(t(as.matrix(mydata[,4])), names.arg=mydata[,1], col=c("red"), beside=TRUE, main="Bandwidth",xlab="Time",axes=TRUE,las=2,ylim=c(0,max(mydata[,4])),cex.axis=.7,cex.names=.8) 
legend("topright",legend=c("Bandwidth"),fill=c("red"),bty="n")

and we get the same three plots as before.

Next Steps

This tutorial was meant to continue to familiarize the user with Essentia’s R Integration and demonstrated how to use the functions inside the RESS package to send data through Essentia’s preprocessor and into R. The next tutorial, Aq Log Data, will work on similar log data that needs to be converted and analyzed before being loaded into R. It will focus on using read.essentia to load the data into R. To see more analysis of complex datasets, please read through our R Apache Analysis use case.

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