Inner join on large datasets using mc.fast.merging function

I have two data.frames block and transactions

1. block - columns: hash, timestamp
2. transactions- columns: tx_hash,address,amount

as both data.frames have 200000000 rows inner join using

merge(x=block,y=transactions,by.x="hash",by.y="tx_hash",all.x=TRUE)

does not work

I found parallelized merge operation in r (https://xrgb.blogspot.com/2013/03/rmark-parallel-fast-merging.html). how to modify it for my use?

mc.fast.merging = function(data.list, nparts, cluster){

  if(!is.list(data.list)) stop("data.list isn't a list")


  while(length(data.list) != 1){ #Loop until everything is merged

    if(length(data.list) > nparts){
      starts = seq(1, length(data.list), nparts)
      ends = seq(nparts, length(data.list), nparts) #starts and ends are of equal size if length(data.list) divides nparts.
      if(length(ends) < length(starts)) ends = c(ends, length(data.list)) #making sure things are even
      sections = matrix(c(starts, ends), ncol=2, byrow=FALSE)
      sections = apply(sections, 1, list)
    }else{
      sections = list(c(1, length(data.list)))
    }

    if(length(sections) !=1){
      data.list = parLapply(cluster, sections, function(x, data.list){
        if(is.list(x)) x = x[[1]]
        #the standard way starts ->
        part = data.list[[x[1]]]
        for(i in x[1]:x[2]){ 
          part = merge(part, data.list[[i]], all=TRUE, sort=FALSE) 
        }   
        #<- standard way ends
        return(part)
      }, data.list = data.list)
    }else{
      data.list = lapply(sections, function(x, data.list){
        if(is.list(x)) x = x[[1]]

        part = data.list[[x[1]]]
        for(i in x[1]:x[2]){ 
          part = merge(part, data.list[[i]], all=TRUE, sort=FALSE) 
        }   
        return(part)
      }, data.list = data.list)
    }
  }
  return(data.list[[1]]) #returning the merged data frame
}