I'm new to scala/spark and am trying to loop through a dataframe and assign the results as the loop progresses. The following code works but can only print the results to screen.
traincategory.columns.foreach { x=>
val test1 = traincategory.select("Id", x)
import org.apache.spark.ml.feature.{OneHotEncoder, StringIndexer}
//CODE TO PERFORM ONEHOT TRANSFORMATION
val encoded = encoder.transform(indexed)
encoded.show()
}
As val is immutable I have attempted to append the vectors from this transformation onto another variable, as might be done in R.
//var ended = traincategory.withColumn(x,encoded(0))
I suspect Scala has a more idiomatic way of processing this.
Thank you in advance for your help.
A solution was available at :
https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/examples/mllib/Correlations.scala
If anyone has similar issues with Scala MLIB there is great example code at :
https://github.com/apache/spark/tree/master/examples/src/main/scala/org/apache/spark/examples/mllib
Related
I'm having some trouble refactoring a spark dataframe to not use expr but instead use dataframe filters and when conditionals.
My code is this:
outDF = outDF.withColumn("MAIN_TYPE", expr
("case when 'TYPE_A' in (GROUP_A,GROUP_B,GROUP_C,GROUP_D) then 'TYPE_A'" +
"when 'TYPE_B' in (GROUP_A,GROUP_B,GROUP_C,GROUP_D) then 'TYPE_B'" +
"when 'TYPE_C' in (GROUP_A,GROUP_B,GROUP_C,GROUP_D) then 'TYPE_C'" +
"when 'TYPE_D' in (GROUP_A,GROUP_B,GROUP_C,GROUP_D) then 'TYPE_D' else '0' end")
.cast(StringType))
The only solution that I could think of, so far is a series of individual .when().otherwise() chains, but that would require mXn lines, where m the number of Types and n the number of Groups that I need.
Is there any better way to do this kind of operation?
Thank you very much for your time!
So, this is how I worked this out, in case anyone is interested:
I used a helper column for the groups which I later dropped.
This is how this worked:
outDF = outDF.withColumn("Helper_Column", concat(col("Group_A"),col("Group_B"),
col("Group_C"),col("Group_D")))
outDF = outDF.withColumn("MAIN_TYPE", when(col("Helper_Column").like("%Type_A%"),"Type_A").otherwise(
when(col("Helper_Column").like("%Type_B%"),"Type_B").otherwise(
when(col("Helper_Column").like("%Type_C%"),"Type_C").otherwise(
when(col("Helper_Column").like("%Type_D%"),"Type_D").otherwise(lit("0")
)))))
outDF = outDF.drop("Helper_Column")
Hope this helps someone.
I have a file in HDFS containing paths of various other files. Here is the file called file1:
path/of/HDFS/fileA
path/of/HDFS/fileB
path/of/HDFS/fileC
.
.
.
I am using a for loop in Scala Spark as follows to read each line of the above file and process it in another function:
val lines=Source.fromFile("path/to/file1.txt").getLines.toList
for(i<-lines){
i.toString()
val firstLines=sc.hadoopFile(i,classOf[TextInputFormat],classOf[LongWritable],classOf[Text]).flatMap {
case (k, v) => if (k.get == 0) Seq(v.toString) else Seq.empty[String]
}
}
when I run the above loop, it runs through without returning any errors and I get the Scala prompt in a new line: scala>
However, when I try to see a few lines of output which should be stored in firstLines, it does not work:
scala> firstLines
<console>:38: error: not found: value firstLines
firstLine
^
What is the problem in the above loop that is not producing the output, however running through without any errors?
Additional info
The function hadoopFile accepts a String path name as its first parameter. That is why I am trying to pass each line of file1 (each line is a path name) as a String in the first parameter i. The flatMap functionality is taking the first line of the file that has been passed to hadoopFile and stores that alone and dumps all the other lines. So the desired output (firstLines) should be the first line of all the files that are being passed to hadoopFile through their path names (i).
I tried running the function for just a single file, without a looop, and that produces the output:
val firstLines=sc.hadoopFile("path/of/HDFS/fileA",classOf[TextInputFormat],classOf[LongWritable],classOf[Text]).flatMap {
case (k, v) => if (k.get == 0) Seq(v.toString) else Seq.empty[String]
}
scala> firstLines.take(3)
res27: Array[String] = Array(<?xml version="1.0" encoding="utf-8"?>)
fileA is an XML file, so you can see the resulting first line of that file. So I know the function works fine, it is just a problem with the loop that I am not able to figure out. Please help.
The variable firstLines is defined in the body of the for loop and its scope is therefore limited to this loop. This means you cannot access the variable outside of the loop, and this is why the Scala compiler tells you error: not found: value firstLines.
From your description, I understand you want to collect the first line of every file which are listed in lines.
The every here can translate into different construct in Scala. We can use something like the for loop you wrote or even better adopt a functional approach and use a map function applied on the list of files. In the code below I put inside the map the code you used in your description, which creates an HadoopRDD and applies flatMap with your function to retrieve the first line of a file.
We then obtain a list of RDD[String] of lines. At this stage, note that we have not started to do any actual work. To trigger the evaluation of the RDDs and collect the result, we need an addition call to the collect method for each of the RDD we have in our list.
// Renamed "lines" to "files" as it is more explicit.
val fileNames = Source.fromFile("path/to/file1.txt").getLines.toList
val firstLinesRDDs = fileNames.map(sc.hadoopFile(_,classOf[TextInputFormat],classOf[LongWritable],classOf[Text]).flatMap {
case (k, v) => if (k.get == 0) Seq(v.toString) else Seq.empty[String]
})
// firstLinesRDDs is a list of RDD[String]. Based on this code, each RDD
// should consist in a single String value. We collect them using RDD#collect:
val firstLines = firstLinesRDDs.map(_.collect)
However, this approach suffers from a flaw which prevent us to benefit from any advantage Spark can provide.
When we apply the operation in map to filenames, we are not working with an RDD, hence the file names are processed sequentially on the driver (the process which hosts your Spark session) and not part of a parallelizable Spark job. This is equivalent to doing what you wrote in your second block of code, one file name at a time.
To address the problem, what can we do? A good thing to keep in mind when working with Spark is to try to push the declaration of the RDDs as early as possible in our code. Why? Because this allows Spark to parallelize and optimize the work we want to do. Your example could be a textbook illustration of this concept, though an additional complexity here is added by the requirement to manipulate files.
In our present case, we can benefit from the fact that hadoopFile accepts comma-separated files in input. Therefore, instead of sequentially creating RDDs for every file, we create one RDD for all of them:
val firstLinesRDD = sc.hadoopFile(fileNames.mkString(","), classOf[TextInputFormat],classOf[LongWritable],classOf[Text]).flatMap {
case (k, v) => if (k.get == 0) Seq(v.toString) else Seq.empty[String]
}
And we retrieve our first lines with a single collect:
val firstLines = firstLinesRDD.collect
I have a loop which generates rows in each iteration. My goal is to create a dataframe, with a given schema, that contents just those rows. I have in mind a set of steps to follow, but I am not able to add a new Row to a List[Row] in each loop iteration
I am trying the following approach:
var listOfRows = List[Row]()
val dfToExtractValues: DataFrame = ???
dfToExtractValues.foreach { x =>
//Not really important how to generate here the variables
//So to simplify all the rows will have the same values
var col1 = "firstCol"
var col2 = "secondCol"
var col3 = "thirdCol"
val newRow = RowFactory.create(col1,col2,col3)
//This step I am not able to do
//listOfRows += newRow -> Just for strings
//listOfRows.add(newRow) -> This add doesnt exist, it is a addString
//listOfRows.aggregate(1)(newRow) -> This is not how aggreage works...
}
val rdd = sc.makeRDD[RDD](listOfRows)
val dfWithNewRows = sqlContext.createDataFrame(rdd, myOriginalDF.schema)
Can someone tell me what am I doing wrong, or what could I change in my approach to generate a dataframe from the rows I'm generating?
Maybe there is a better way to collect the Rows instead of List[Row]. But then I need to convert that other type of collection into a dataframe.
Can someone tell me what am I doing wrong
Closures:
First of all it looks like you skipped over Understanding Closures in the Programming Guide. Any attempt to modify variables passed with closure is futile. All you can do is modify a copy and changes won't be reflected globally.
Variable doesn't make object mutable:
Following
var listOfRows = List[Row]()
creates a variable. Assigned List is as immutable as it was. If it wasn't in the Spark context you could create a new List and reassign:
listOfRows = newRow :: listOfRows
Note that we perpend not append - you don't want to append to the list in a loop.
Variables with immutable objects are useful, when you want to share data (it is common pattern in Akka for example), but don't have many applications in Spark.
Keep things distributed:
Finally never fetch data to the driver just to distribute it again. You should also avoid unnecessary conversions between RDDs and DataFrames. It is best to use DataFrame operators all the way:
dfToExtractValues.select(...)
but if you need something more complex map:
import org.apache.spark.sql.catalyst.encoders.RowEncoder
dfToExtractValues.map(x => ...)(RowEncoder(schema))
I have a CSV file that represent a map[String,Int], then I am reading the file as follows:
def convI2N (vkey:Int):String={
val in = new Scanner("dictionaryNV.csv")
loop.breakable{
while (in.hasNext) {
val nodekey = in.next(',')
val value = in.next('\n')
if (value == vkey.toString){
n=nodekey
loop.break()}
}}
in.close
n
}
the function give the String given the Int. The problem here is that I must browse the whole file, and the file is to big, then the procedure is too slow. Someone tell me that this is O(n) complexity time, and recomend me to pass to O(log n). I suppose that the function map.getOrElse is O(log n).
Someone can help me to find a way to get a best performance of this code?
As additional comment, the dictionaryNV file is sorted by the Int values
maybe I can divide the file by lines, or set of lines. The CSV has like 167000 Tuples [String,Int]
or in another way how you make some kind of binary search through the csv in scala?
If you are calling confI2N function many times then definitely the job will be slow because each time you have to scan the big file. So if the function is called many times then it is recommended to store them in temporary variable as properties or hashmap or collection of tuple2 and change the other code that is eating the memory.
You can try following way which should be faster than scanner way
Assuming that your csv file is comma separated as
key1,value1
key2,value2
Using Source.fromFile can be your solution as
def convI2N (vkey:Int):String={
var n = "not found"
val filtered = Source.fromFile("<your path to dictionaryNV.csv>")
.getLines()
.map(line => line.split(","))
.filter(sline => sline(0).equalsIgnoreCase(vkey.toString))
for(str <- filtered){
n = str(0)
}
n
}
I have RDD and I want to loop over it. I do like this:
pointsMap.foreach({ p =>
val pointsWithCoordinatesWithDistance = pointsMap.leftOuterJoin(xCoordinatesWithDistance)
pointsWithCoordinatesWithDistance.foreach(println)
println("---")
})
However, NullPointerException is occuring:
java.lang.NullPointerException
at org.apache.spark.rdd.RDD.<init>(RDD.scala:125)
at org.apache.spark.rdd.CoGroupedRDD.<init>(CoGroupedRDD.scala:69)
at org.apache.spark.rdd.PairRDDFunctions.cogroup(PairRDDFunctions.scala:651)
at org.apache.spark.rdd.PairRDDFunctions.leftOuterJoin(PairRDDFunctions.scala:483)
at org.apache.spark.rdd.PairRDDFunctions.leftOuterJoin(PairRDDFunctions.scala:555)
...
Both pointsMap and xCoordinatesWithDistance are initialized before foreach and contain elements. Not inside foreach loop leftOuterJoin also works. For the full version of my code please see https://github.com/timasjov/spark-learning/blob/master/src/DBSCAN.scala
Don't use a RDD in a function of some RDD operator. You need to use proper RDD operators when you want to manipulate more than one RDDs together, such as join.