I use spark to do some very simple calculation. The description is like below
(pseudo code):
While timestamp == 5 minutes
df = read_hdf() # Read hdfs to get a dataframe every 5 minutes
my_dict[timestamp] = df # Put the data frame into a dict
delete_old_dataframe( my_dict ) # Delete old dataframe (timestamp is one
24 hour before)
big_df = merge(my_dict) # Merge the recent 24 hours data frame
To explain..
I have new files comes in every 5 minutes. But I need to generate report on
recent 24 hours data.
The concept of 24 hours means I need to delete the oldest data frame every
time I put a new one into it.
So I maintain a dict (my_dict in above code), the dict contains map like
timestamp: dataframe. Everytime I put dataframe into the dict, I will go
through the dict to delete those old data frame whose timestamp is 24 hour
ago.
After delete and input. I merge the data frames in the dict to a big one and
run SQL on it to get my report.
*
I want to know if any thing wrong about this model? Because it is very slow
after started for a while and hit OutOfMemory. I know that my memory is
enough. Also size of file is very small for test purpose. So should not have
memory problem.
I am wondering if there is lineage issue, but I am not sure.
*
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