I'm writing a method to parse schema and want to filter the resulting StructType with a list of column names. Which is a subset of StructField names of the original schema.
As a result, if a flag isFilteringReq = true, I want to return a StructType containing only StructFields with the names from the specialColumnNames, in the same order. If the flag is false, then return an original StructType.
val specialColumnNames = Seq("metric_1", "metric_2", "metric_3")
First I'm getting an original schema with pattern-matching.
val customSchema: StructType = schemaType match {
case "type_1" => getType1chema()
case "type_2" => getType2chema()
}
There are two problems:
1 - I wasn't able to apply .filter() directly to the customSchema right after the curly brace. And geting a Cannot resolve symbol filter. So I wrote a separate method makeCustomSchema. But I don't need a separate object. Is there a more elegant way to apply filtering in this case?
2 - I could filter the originalStruct but only with a single hardcoded column name. How should I pass the specialColumnNames to contains()?
def makeCustomSchema(originalStruct: StructType, isFilteringReq: Boolean, updColumns: Seq[String]) = if (isFilteringReq) {
originalStruct.filter(s => s.name.contains("metric_1"))
} else {
originalStruct
}
val newSchema = makeCustomSchema(customSchema, isFilteringReq, specialColumnNames)
Instead of passing a Seq, pass a Set and you can filter if the field is in the set or not.
Also, I wouldn't use a flag, instead, you could pass an empty Set when there's no filtering, or use Option[Set[String]].
Anyway, you could also use the copy method that comes for free with case classes.
Something like this should work.
def makeCustomSchema(originalStruct: StructType, updColumns:Set[String]): StructType = {
updColumns match {
case s if s.isEmpty => originalStruct
case _ => originalStruct.copy(
fields = originalStruct.fields.filter(
f => updColumns.contains(f.name)))
}
}
Usually you don't need to build structs like this, have you tried using the drop() method in DataFrame/DataSet ?
Related
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.
I have a dataframe - converted dtypes to map.
val dfTypesMap:Map[String,String]] = df.dtypes.toMap
Output:
(PRODUCT_ID,StringType)
(PRODUCT_ID_BSTP_MAP,MapType(StringType,IntegerType,false))
(PRODUCT_ID_CAT_MAP,MapType(StringType,StringType,true))
(PRODUCT_ID_FETR_MAP_END_FR,ArrayType(StringType,true))
When I use type [String] hardcoding in row.getAS[String], there is no compilation error.
df.foreach(row => {
val prdValue = row.getAs[String]("PRODUCT_ID")
})
I want to iterate above map dfTypesMap and get corresponding value type. Is there any way to convert dt column types to general types like below?
StringType --> String
MapType(StringType,IntegerType,false) ---> Map[String,Int]
MapType(StringType,StringType,true) ---> Map[String,String]
ArrayType(StringType,true) ---> List[String]
As mentioned, Datasets make it easier to work with types.
Dataset is basically a collection of strongly-typed JVM objects.
You can map your data to case classes like so
case class Foo(PRODUCT_ID: String, PRODUCT_NAME: String)
val ds: Dataset[Foo] = df.as[Foo]
Then you can safely operate on your typed objects. In your case you could do
ds.foreach(foo => {
val prdValue = foo.PRODUCT_ID
})
For more on Datasets, check out
https://spark.apache.org/docs/latest/sql-programming-guide.html#creating-datasets
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]
}
I need help to iterate this piece of code written in Spark-Scala with DataFrame. I'm new on Scala, so I apologize if my question may seem trivial.
The function is very simple: Given a dataframe, the function casts the column if there is a pattern matching, otherwise select all field.
/* Load sources */
val df = sqlContext.sql("select id_vehicle, id_size, id_country, id_time from " + working_database + carPark);
val df2 = df.select(
df.columns.map {
case id_vehicle # "id_vehicle" => df(id_vehicle).cast("Int").as(id_vehicle)
case other => df(other)
}: _*
)
This function, with pattern matching, works perfectly!
Now I have a question: Is there any way to "iterate" this? In practice I need a function that given a dataframe, an Array[String] of column (column_1, column_2, ...) and another Array[String] of type (int, double, float, ...), return to me the same dataframe with the right cast at right position.
I need help :)
//Your supplied code fits nicely into this function
def castOnce(df: DataFrame, colName: String, typeName: String): DataFrame = {
val colsCasted = df.columns.map{
case colName => df(colName).cast(typeName).as(colName)
case other => df(other)
}
df.select(colsCasted:_ *)
}
def castMany(df: DataFrame, colNames: Array[String], typeNames: Array[String]): DataFrame = {
assert(colNames.length == typeNames.length, "The lengths are different")
val colsWithTypes: Array[(String, String)] = colNames.zip(typeNames)
colsWithTypes.foldLeft(df)((cAndType, newDf) => castOnce(newDf, cAndType._1, cAndType._2))
}
When you have a function that you just need to apply many times to the same thing a fold is often what you want.
The above code zips the two arrays together to combine them into one.
It then iterates through this list applying your function each time to the dataframe and then applying the next pair to the resultant dataframe etc.
Based on your edit I filled in the function above. I don't have a compiler so I'm not 100% sure its correct. Having written it out I am also left questioning my original approach. Below is a better way I believe but I am leaving the previous one for reference.
def(df: DataFrame, colNames: Array[String], typeNames: Array[String]): DataFrame = {
assert(colNames.length == typeNames.length, "The lengths are different")
val nameToType: Map[String, String] = colNames.zip(typeNames).toMap
val newCols= df.columns.map{dfCol =>
nameToType.get(dfCol).map{newType =>
df(dfCol).cast(newType).as(dfCol)
}.getOrElse(df(dfCol))
}
df.select(newCols:_ *)
}
The above code creates a map of column name to the desired type.
Then foreach column in the dataframe it looks the type up in the Map.
If the type exists we cast the column to that new type. If the column does not exist in the Map then we default to the column from the DataFrame directly.
We then select these columns from the DataFrame
I'm very new to Scala and trying to use it as an interface to Spark. I'm running into a problem making a generic CSV to DataFrame function. For example, I've got a CSV with about 50 fields, the first of which are task, name, and id. I can get the following to work:
val reader = new CSVReader(new StringReader(txt))
reader.readAll().map(_ match {
case Array(task, name, id, _*) => Row(task, name, id)
case unexpectedArrayForm =>
throw new RuntimeException("Record did not have correct number of fields: "+ unexpectedArrayForm.mkString(","))
})
However, I'd rather not have to hard code the number of fields needed to create a spark Row. I tried this:
val reader = new CSVReader(new StringReader(txt))
reader.readAll().map(_ match {
case Array(args # _*) => Row(args)
case unexpectedArrayForm =>
throw new RuntimeException("Record did not have correct number of fields: "+ unexpectedArrayForm.mkString(","))
})
But it just creates a Row object with a single element. How can I make it expand the args in Row(args) so that if I have an array of N elements I'll get a Row with N elements?
Change your input to be variable length by adding _*:
Row(args:_*)
This is what Row accepts per its apply signature.
In fact, you don't even need to do anything other than pass this in to the Row as it is already of the right sequence type.
reader.readAll().map(Row(_:_*))
This should do the trick:
val reader = new CSVReader(new StringReader(txt))
reader.readAll().map(_ match {
case a: Array[String] => Row(a:_*)
case unexpectedArrayForm =>
throw new RuntimeException("Record did not have correct number of fields: "+ unexpectedArrayForm.mkString(","))
})
Edited to correct omission of Array type