Generate Data By Using Existing Dataset As The Base Dataset

I have a dataset consisting of 100k unique data records, to benchmark the code, I need to test on data with 5 million unique records, I don't want to generate random data. I would

Solution 1:

You can generate data conforming to normal distribution easily using R, you can follow the following steps

#Read the data into a dataframe
library(data.table)
data = data = fread("data.csv", sep=",", select = c("latitude", "longitude"))

#Remove duplicate and null values
df = data.frame("Lat"=data$"latitude", "Lon"=data$"longitude")
df1 = unique(df[1:2])
df2  <- na.omit(df1)

#Determine the mean and standard deviation of latitude and longitude values
meanLat = mean(df2$Lat)
meanLon = mean(df2$Lon)
sdLat = sd(df2$Lat)
sdLon = sd(df2$Lon)

#Use Normal distribution to generate new data of 1 million records

newData = list()
newData$Lat = sapply(rep(0, 1000000), function(x) (sum(runif(12))-6) * sdLat + meanLat)
newData$Lon = sapply(rep(0, 1000000), function(x) (sum(runif(12))-6) * sdLon + meanLon)

finalData = rbind(df2,newData)

now final data contains both old records and new records

Write the finalData dataframe to a CSV file and you can read it from Scala or python

Solution 2:

If you just want to generate data only in scala, try in this way.

val r = new scala.util.Random   //create scala random object
val new_val = r.nextFloat() // for generating next random float between 0 to 1 for every call

And add this new_val to maximum value of latitude in your data. Unique latitude anyway makes pair unique.

You can try this option with Spark with Scala.

val latLongDF = ss.read.option("header", true).option("delimiter", ",").format("csv").load(mypath)   // loaded your sample data in your question as Dataframe
+---------+----------+----+-----+
| latitude| longitude|step|count|
+---------+----------+----+-----+
|25.696395|-80.297496|   1|    1|
|25.699544|-80.297055|   1|    1|
|25.698612|-80.292015|   1|    1|


val max_lat = latLongDF.select(max("latitude")).first.get(0).toString().toDouble // got max latitude value

val r = new scala.util.Random // scala random object to get random numbers

val nextLat = udf(() => (28.355484 + 0.000001 + r.nextFloat()).toFloat) // udf to give random latitude more than the existing maximum latitude

In above line toFloat rounds to float which can cause duplicate values. Remove this to get complete random values if you are fine with more decimal values(more than 6) in your latitudes. Or use same method on longitude also to get better uniqueness.

val new_df = latLongDF.withColumn("new_lat", nextLat()).select(col("new_lat").alias("latitude"),$"longitude",$"step",$"count").union(latLongDF) // creating new dataframe and Union with existing dataframe 

New generated data sample.

+----------+----------+----+-----+
|latitude| longitude|step|count|
+----------+----------+----+-----+
| 28.446129|-80.297496|   1|    1|
| 28.494934|-80.297055|   1|    1|
| 28.605234|-80.292015|   1|    1|
| 28.866316|-80.341607|   1|    1|