Decision tree model running for long time

I am running my decision tree model using rpart package in R. Here is what I am doing,

1. Loading my data using read.csv
2. Remove unwanted columns
3. Split my dataset in to training and test
4. Fitting my model on the training set -- This is running for whole day.

Here is the summary of my dataset.

'data.frame':   117919 obs. of  7 variables:
 $ Database          : Factor w/ 2 levels "DBIL","DBPD": 1 1 1 1 1 1 1 1 1 1 ...
 $ Market_Description: Factor w/ 1 level "MY (PM)": 1 1 1 1 1 1 1 1 1 1 ...
 $ Manufacturer      : Factor w/ 21 levels "21 Century","Abbott Lab",..: 4 3 4 4 4 4 3 3 3 3 ...
 $ Brand             : Factor w/ 133 levels "","21 Century",..: 34 26 34 34 34 34 26 26 26 26 ...
 $ Sub_Brand         : Factor w/ 194 levels "","0-6 Bulan",..: 9 6 9 9 9 9 6 6 6 6 ...
 $ Age_Group         : Factor w/ 5 levels "","Adultenr",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ FMT_Category      : Factor w/ 10 levels "Adult Powders (excl Super Bev)",..: 5 5 5 5 5 5 5 5 5 5 ...

Here is my script for model..

fit <- rpart(FMT_Category~Database+Market_Description+Manufacturer+Brand+Sub_Brand+Age_Group, data=trainingset)

It has only 117919 observations. I checked the memory.limit in my R and it says 8065 , also mem_used says 40 MB. I am not getting any error, but the model keeps running for a day. so I am not sure what to check here. I expected R to give at least some crappy tree, so I can start from there. I thought it has something to do with the factors, so I read the data with stringAsFactors=FALSE. Still it runs forever. I tried the same data in my python script and weka and it runs fast without any error. Please let me know what I am missing or point me in a right direction on what I should be checking.

Edit -- I just noticed that the issue is the number of levels in Brand and Sub_Brand , this is making the model run forever as it has to run recursively. Any suggestion to handle this?