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At datadolph.in, we are constantly crunching through large amounts of data.  We are designing unique and innovative ways to process large datasets on a single node and use distributed computing only when single node computing becomes time consuming and less efficient.  

We are happy to share with the R community one such unique map-reduce like approach we designed in R for a single node to process flights data (available here) which has  ~122 million records and occupies 12GB of space when uncompressed.  We used Mathew Dowle’s data.table package heavily to load and analyze large datasets.

It took us few days to stabilize and optimize this approach and we are very proud to share this approach and source code with you.  The full source code can be found and downloaded from datadolph.in’s git repository.

Here is how we approached this problem:  First, before loading the datasets in R, we compressed each of the 22 CSV files using gunzip for faster reading in R.  The method read.csv can read gzip files faster than it can read uncompressed files:

# load list of all files
 flights.files <- list.files(path=flights.folder.path, pattern="*.csv.gz")

# read files in data.table
 flights <- data.table(read.csv(flights.files[i], stringsAsFactors=F))
Next, we mapped the analysis we wanted to run to extract insights from each of the datasets.  This approach included extracting flight level, airlines level and airport level aggregated analysis and generating intermediate results.  Here is example code to get stats for each airline by year:


Here is a copy of the map function:

#map all calculations 
mapFlightStats <- function(){
  for(j in 1:period) {
      yr <- as.integer(gsub("[^0-9]", "", gsub("(.*)(\\.csv)", "\\1", flights.files[j])))
      flights.data.file <- paste(flights.folder.path, flights.files[j], sep="")
      if(verbose) cat(yr, “: Reading : “, flights.data.file, “\n”)
      flights <- data.table(read.csv(flights.data.file, stringsAsFactors=F))
      setkeyv(flights, c(“year”, “uniquecarrier”, “dest”, “origin”, “month”)) 
     # call functions
      getFlightStatsForYear(flights, yr)
      getFlightsStatusByAirlines(flights, yr)
      getFlightsStatsByAirport(flights, yr)
    }
} 

As one can see, we are generating intermediate results by airlines (and by airports /  flights) for each year and storing it on the disk.  The map function takes less than 2 hours to run on a MacBook Pro which had 2.3 GHZ dual core processor and 8 GB of memory and generated 132 intermediate datasets containing aggregated analysis. 

And finally, we call the reduce function to aggregate intermediate datasets into final output (for flights, airlines and airports):

#reduce all results
reduceFlightStats <- function(){
  n <- 1:6
  folder.path <- paste("./raw-data/flights/stats/", n, "/", sep="")
  print(folder.path)
  for(i in n){
    filenames <- paste(folder.path[i], list.files(path=folder.path[i], pattern="*.csv"), sep="") 
    dt <- do.call("rbind", lapply(filenames, read.csv, stringsAsFactors=F))
    print(nrow(dt))
    saveData(dt, paste(“./raw-data/flights/stats/”, i, “.csv”, sep=””))
  }
}

datadolph.in users are using this analysis to produce micro-stories (we call them datagrams) on airport and airlines performance and sharing it on various social networks.  Here is a sample micro-story:


This is what we are doing at datadolph.in – making data discovery, data visualization and telling data-stories as easy as 1-2-3.  Happy Coding, Happy Analyzing and Happy Friday!

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