How can I sort in ascending/descending order a group of files based on their name with the following naming convention: myPath\numberTheFileInt.ext?
I would like to obtain something like the following:
myPath\1.csv
myPath\02.csv
...
myPath\21.csv
...
myPath\101.csv
Here is what I have at the moment:
myFiles = getFiles(myFilesDirectory).sortWith(_.getName < _.getName)
Despite the files being sorted in the directory, they are unsorted in myFiles.
I have in output:
myPath\1.csv
myPath\101.csv
myPath\02.csv
...
myPath\21.csv
I tried multiple things but it always throws an NoSuchElementException.
Has anyone already done this?
Comparing strings would yield an order based on unicode values of the strings being compared. What you need is to extract the file number and order based on that as an Integer.
import java.io.File
val extractor = "([\\d]+).csv$".r
val files = List(
"myPath/1.csv",
"myPath/101.csv",
"myPath/02.csv",
"myPath/21.csv",
"myPath/33.csv"
).map(new File(_))
val sorted = files.sortWith {(l, r) =>
val extractor(lFileNumber) = l.getName
val extractor(rFileNumber) = r.getName
lFileNumber.toInt < rFileNumber.toInt
}
sorted.foreach(println)
Results:
myPath/1.csv
myPath/02.csv
myPath/21.csv
myPath/33.csv
myPath/101.csv
UPDATE
An alternative as proposed by #dhg
val sorted = files.sortBy { f => f.getName match {
case extractor(n) => n.toInt
}}
A cleaner version of J.Romero's answer, using sortBy:
val Extractor = "([\\d]+)\\.csv".r
val sorted = files.map(_.getName).sortBy{ case Extractor(n) => n.toInt }
Related
i have this code :
case class DataText(name:String)
val dataModels = Seq(DataText("a.dm"),DataText("b.dm"),DataText("c.dm"),DataText("d.dm"),DataText("e.dm"),DataText("f.dm"))
val dataReports = Seq(DataText("a.d0"),DataText("b1.do"),DataText("c2.do"),DataText("d.do"),DataText("e3.do"),DataText("f5.do"))
how can i match dataModels and dataReports, when an item in dataModels split by "." like name.split(".").head can match to dataReports split by "." like name.split(".").head
The result could be:
Seq(DataText("a.dm"),DataText("d.dm"))
i have tried with map and an embedded filter but don't work.
I would transform dataReports into a Set of the target sub-elements for filtering via contains (which is a constant time O(1) operation):
val dataReportsSet = dataReports.map(_.name.split("\\.")(0)).toSet
dataModels.filter(dm => dataReportsSet.contains(dm.name.split("\\.")(0)))
// res1: Seq[DataText] = List(DataText(a.dm), DataText(d.dm))
I want to take headers (column name) from my csv file and the want to match with it my existing header.
I am using below code:
val cc = sparksession.read.csv(filepath).take(1)
Its giving me value like:
Array([id,name,salary])
and I have created one more static schema, which is giving me value like this:
val ss=Array("id","name","salary")
and then I'm trying to compare column name using if condition:
if(cc==ss){
println("matched")
} else{
println("not matched")
}
I guess due to [] and () mismatch its always going to else part is there any other way to compare these value without considering [] and ()?
First, for convenience, set the header option to true when reading the file:
val df = sparksession.read.option("header", true).csv(filepath)
Get the column names and define the expected column names:
val cc = df.columns
val ss = Array("id", "name", "salary")
To check if the two match (not considering the ordering):
if (cc.toSet == ss.toSet) {
println("matched")
} else {
println("not matched")
}
If the order is relevant, then the condition can be done as follows (you can't use Array here but Seq works):
cc.toSeq == ss.toSeq
or you a deep array comparison:
cc.deep == d.deep
First of all, I think you are trying to compare a Array[org.apache.spark.sql.Row] with an Array[String]. I believe you should change how you load the headers to something like: val cc = spark.read.format("csv").option("header", "true").load(fileName).columns.toArray.
Then you could compare using cc.deep == ss.deep.
Below code worked for me.
val cc= spark.read.csv("filepath").take(1)(0).toString
The above code gave output as String:[id,name,salary].
created one one stating schema as
val ss="[id,name,salary]"
then wrote the if else Conditions.
Is there a way to simplify this scala code into a for comprehension?
val selectedNames = names filter {setOfNames}
val selectedPersons = persons filter {p => seletectedNames contains p.name}
Here I'm assuming that persons have a name attribute.
Edit
Of course the value names is obtained as
val names = persons map _.name
How about
val selectedPersons = persons filter { person => setOfNames contains person.name }
I'm not sure this is much of a simplification. It's just doing the same thing via a for comprehension as requested.
val selectedPersons = for {
p <- persons
if setOfNames(p.name)
} yield p
So I have a large dataset that is a sample of a stackoverflow userbase. One line from this dataset is as follows:
<row Id="42" Reputation="11849" CreationDate="2008-08-01T13:00:11.640" DisplayName="Coincoin" LastAccessDate="2014-01-18T20:32:32.443" WebsiteUrl="" Location="Montreal, Canada" AboutMe="A guy with the attention span of a dead goldfish who has been having a blast in the industry for more than 10 years.
Mostly specialized in game and graphics programming, from custom software 3D renderers to accelerated hardware pipeline programming." Views="648" UpVotes="337" DownVotes="40" Age="35" AccountId="33" />
I would like to extract the number from reputation, in this case it is "11849" and the number from age, in this example it is "35" I would like to have them as floats.
The file is located in a HDFS so it comes in the format RDD
val linesWithAge = lines.filter(line => line.contains("Age=")) //This is filtering data which doesnt have age
val repSplit = linesWithAge.flatMap(line => line.split("\"")) //Here I am trying to split the data where there is a "
so when I split it with quotation marks the reputation is in index 3 and age in index 23 but how do I assign these to a map or a variable so I can use them as floats.
Also I need it to do this for every line on the RDD.
EDIT:
val linesWithAge = lines.filter(line => line.contains("Age=")) //transformations from the original input data
val repSplit = linesWithAge.flatMap(line => line.split("\""))
val withIndex = repSplit.zipWithIndex
val indexKey = withIndex.map{case (k,v) => (v,k)}
val b = indexKey.lookup(3)
println(b)
So if added an index to the array and now I've successfully managed to assign it to a variable but I can only do it to one item in the RDD, does anyone know how I could do it to all items?
What we want to do is to transform each element in the original dataset (represented as an RDD) into a tuple containing (Reputation, Age) as numeric values.
One possible approach is to transform each element of the RDD using String operations in order to extract the values of the elements "Age" and "Reputation", like this:
// define a function to extract the value of an element, given the name
def findElement(src: Array[String], name:String):Option[String] = {
for {
entry <- src.find(_.startsWith(name))
value <- entry.split("\"").lift(1)
} yield value
}
We then use that function to extract the interesting values from every record:
val reputationByAge = lines.flatMap{line =>
val elements = line.split(" ")
for {
age <- findElement(elements, "Age")
rep <- findElement(elements, "Reputation")
} yield (rep.toInt, age.toInt)
}
Note how we don't need to filter on "Age" before doing this. If we process a record that does not have "Age" or "Reputation", findElement will return None. Henceforth the result of the for-comprehension will be None and the record will be flattened by the flatMap operation.
A better way to approach this problem is by realizing that we are dealing with structured XML data. Scala provides built-in support for XML, so we can do this:
import scala.xml.XML
import scala.xml.XML._
// help function to map Strings to Option where empty strings become None
def emptyStrToNone(str:String):Option[String] = if (str.isEmpty) None else Some(str)
val xmlReputationByAge = lines.flatMap{line =>
val record = XML.loadString(line)
for {
rep <- emptyStrToNone((record \ "#Reputation").text)
age <- emptyStrToNone((record \ "#Age").text)
} yield (rep.toInt, age.toInt)
}
This method relies on the structure of the XML record to extract the right attributes. As before, we use the combination of Option values and flatMap to remove records that do not contain all the information we require.
First, you need a function which extracts the value for a given key of your line (getValueForKeyAs[T]), then do:
val rdd = linesWithAge.map(line => (getValueForKeyAs[Float](line,"Age"), getValueForKeyAs[Float](line,"Reputation")))
This should give you an rdd of type RDD[(Float,Float)]
getValueForKeyAs could be implemented like this:
def getValueForKeyAs[A](line:String, key:String) : A = {
val res = line.split(key+"=")
if(res.size==1) throw new RuntimeException(s"no value for key $key")
val value = res(1).split("\"")(1)
return value.asInstanceOf[A]
}
When conducting research, I find it somewhat difficult to delete all the subsets in Spark RDD.
The data structure is RDD[(key,set)]. For example, it could be:
RDD[ ("peter",Set(1,2,3)), ("mike",Set(1,3)), ("jack",Set(5)) ]
Since the set of mike (Set(1,3)) is a subset of peter's (Set(1,2,3)), I want to delete "mike", which will end up with
RDD[ ("peter",Set(1,2,3)), ("jack",Set(5)) ]
It is easy to implement in python locally with two "for" loop operation. But when I want to extend to cloud with scala and spark, it is not that easy to find a good solution.
Thanks
I doubt we can escape to comparing each element to each other (the equivalent of a double loop in a non-distributed algorithm). The subset operation between sets is not reflexive, meaning that we need to compare is "alice" subsetof "bob" and is "bob" subsetof "alice".
To do this using the Spark API, we can resort to multiplying the data with itself using a cartesian product and verifying each entry of the resulting matrix:
val data = Seq(("peter",Set(1,2,3)), ("mike",Set(1,3)), ("anne", Set(7)),("jack",Set(5,4,1)), ("lizza", Set(5,1)), ("bart", Set(5,4)), ("maggie", Set(5)))
// expected result from this dataset = peter, olga, anne, jack
val userSet = sparkContext.parallelize(data)
val prod = userSet.cartesian(userSet)
val subsetMembers = prod.collect{case ((name1, set1), (name2,set2)) if (name1 != name2) && (set2.subsetOf(set1)) && (set1 -- set2).nonEmpty => (name2, set2) }
val superset = userSet.subtract(subsetMembers)
// lets see the results:
superset.collect()
// Array[(String, scala.collection.immutable.Set[Int])] = Array((olga,Set(1, 2, 3)), (peter,Set(1, 2, 3)), (anne,Set(7)), (jack,Set(5, 4, 1)))
This can be achieved by using RDD.fold function.
In this case the output required is a "List" (ItemList) of superset items. For this the input should also be converted to "List" (RDD of ItemList)
import org.apache.spark.rdd.RDD
// type alias for convinience
type Item = Tuple2[String, Set[Int]]
type ItemList = List[Item]
// Source RDD
val lst:RDD[Item] = sc.parallelize( List( ("peter",Set(1,2,3)), ("mike",Set(1,3)), ("jack",Set(5)) ) )
// Convert each element as a List. This is needed for using fold function on RDD
// since the data-type of the parameters are the same as output parameter
// data-type for fold function
val listOflst:RDD[ItemList] = lst.map(x => List(x))
// for each element in second ItemList
// - Check if it is not subset of any element in first ItemList and add first
// - Remove the subset of newly added elements
def combiner(first:ItemList, second:ItemList) : ItemList = {
def helper(lst: ItemList, i:Item) : ItemList = {
val isSubset: Boolean = lst.exists( x=> i._2.subsetOf(x._2))
if( isSubset) lst else i :: lst.filterNot( x => x._2.subsetOf(i._2))
}
second.foldLeft(first)(helper)
}
listOflst.fold(List())(combiner)
You can use filter after a map.
You can build like a map that will return a value for what you want to delete. First build a function:
def filter_mike(line):
if line[1] != Set(1,3):
return line
else:
return None
Then you can filter now like this:
your_rdd.map(filter_mike).filter(lambda x: x != None)
This will work