Lets say that I have a string template which contains N number of placeholders:
"{placeholder1}/{placeholder2}-{placeholder3}/{placeholder4}.{placeholder5}"
And let's say that I have a map:
"placeholder1" -> "aaa",
"placeholder2" -> "xxx",
"placeholder3" -> "yyy",
"placeholder4" -> "zzz",
"placeholder5" -> "bbb"
Given this map and placeholder string template, is it possible to replace the placeholder keys with the placeholder values? Or would this require using regex?
you can iterate over the data and apply to the template using String.replace, and keep iterating on new state.
Given,
scala> val template = "{placeholder1}/{placeholder2}-{placeholder3}/{placeholder4}.{placeholder5}"
template: String = {placeholder1}/{placeholder2}-{placeholder3}/{placeholder4}.{placeholder5}
scala> val data = Map("placeholder1" -> "aaa",
"placeholder2" -> "xxx",
"placeholder3" -> "yyy",
"placeholder4" -> "zzz",
"placeholder5" -> "bbb")
data: scala.collection.immutable.Map[String,String] = HashMap(placeholder5 -> bbb, placeholder1 -> aaa, placeholder3 -> yyy, placeholder2 -> xxx, placeholder4 -> zzz)
apply the template on data using foldLeft. I'm assuming {} indicates the template placeholder.
scala> data.foldLeft(template){ case (newState, kv) => newState.replace(s"{${kv._1}}", kv._2)}
res6: String = aaa/xxx-yyy/zzz.bbb
NOTE: kv above is each entry in Map, alternatively, you can deconstruct kv as (k, v).
scala> data.foldLeft(template){ case (newState, (k, v)) => newState.replace(s"{$k}", v)}
res7: String = aaa/xxx-yyy/zzz.bbb
Alternative solution:
Though .foldLeft is enough, you can write your own vanilla recursive that applies one entry at a time and keeps iterating until data is empty.
def format(template: String, data: Map[String, String]): String = {
if(data.isEmpty) template
else format(template.replace(s"{${data.head._1}}", data.head._2), data.tail)
}
val formatted = format(template, data) // aaa/xxx-yyy/zzz.bbb
why not use string interpolation
def replace(m: Map[String,String]) = s"${m("placeholder1")}/${m("placeholder2")}-...."
Related
The following method is supposed to count the number of occurrences of every char in a given string:
def countLetters(text: String): Map[Char, Int] = ???
For example, the input string "aabaabcab" should be mapped to
Map(a -> 5, b -> 3, c -> 1)
Here is a straightforward iterative approach:
def countLetters(text: String): Map[Char, Int] = {
val h = collection.mutable.HashMap.empty[Char, Int]
for (c <- text)
h(c) = h.getOrElse(c, 0) + 1
h.toMap
}
Is there any way to implement it without looping and explicitly allocating mutable hash maps?
Today finally did it! This is what worked for me:
text.foldLeft(Map[Char, Int]() withDefaultValue 0){(h, c) => h.updated(c, h(c)+1)}
Good luck on your next ones if you are reading this! ;)
I have a column of type array<array<struct<a: String, b: Int>>>.
I want a column of type struct<a: array<array<String>>, b: array<array<Int>>.
Ideally, this procedure should unnest all struct fields automatically (i.e. without me having to specify fields "a" and "b" manually), but anything that works would be extremely helpful here.
Example code that I have (I'm trying to turn ds into expected).
case class Struct(foo: String, bar: Int)
case class Schema(structs: Vector[Vector[Struct]])
val ss = spark
import ss.implicits._
val ds = Seq(Schema(Vector(Vector(Struct("a", 1), Struct("b", 2)), Vector(Struct("c", 3))))).toDS
val expected = Seq(
(Vector(Vector("a", "b"), Vector("c")), Vector(Vector(1, 2), Vector(3)))
).toDF("foo", "bar")
The shortest solution is to use transform higher order function (introduced in Spark 2.4):
ds.selectExpr(
"transform(structs, xs -> transform(xs, x -> x.foo)) as foo",
"transform(structs, xs -> transform(xs, x -> x.bar)) as bar"
)
In older version you'll need either equivalent udf* or use typed map:
ds.as[Schema]
.map(x => (
x.structs.map(_.map(_.foo)),
x.structs.map(_.map(_.bar))
)).toDF("foo", "bar")
The former solution can be generalized:
import org.apache.spark.sql.types._
import org.apache.spark.sql.DataFrame
def expand(ds: DataFrame, col: String) = {
val fields = ds.schema(col).dataType match {
case ArrayType(ArrayType(s: StructType, _), _) => s.fieldNames
}
val exprs = fields.map {
field => expr(
s"transform(`$col`, xs -> transform(xs, x -> x.`$field`)) as `$field`"
)
}
ds.select(exprs: _*)
}
expand(ds.toDF, "structs")
The latter one probably not so much, unless you want to use Scala reflection (and that's a serious overkill).
* Something around these lines should do the trick:
import scala.reflect.runtime.universe.TypeTag
import org.apache.spark.sql.functions.udf
def extract[T : TypeTag](field: String) = udf(
(xs: Seq[Seq[Row]]) => xs.map(_.map(_.getAs[T](field)))
)
val extractString = extract[String] _
val extractInt = extract[Int] _
ds.select(
extractString("foo")($"structs").as("foo"),
extractInt("bar")($"structs").as("bar")
)
I have two strings
val a = "abc"
val b = "xyz"
I want to merge it and need output like below
axbycz
I added both strings to arraylist and then flatmap it
val c = listOf(a, b)
val d = c.flatMap {
it.toList()
}
but not getting the desired result
Use the zip function. It creates a list of pairs with "adjacent" letters. You can then use joinToString with a transformer to create your final result.
a.zip(b) // Returns the list [(a, x), (b, y), (c, z)]
.joinToString("") { (a, b) -> "$a$b" } // Joins the list back to a string with no separator
You can always use a simple loop, assuming both strings have the same size. That way You only allocate a StringBuilder and counter variable, without any lists, arrays or pairs:
val a = "abc"
val b = "xyz"
val sb = StringBuilder()
for(i in 0 until a.length){
sb.append(a[i]).append(b[i])
}
val d = sb.toString()
marstran's answer is really concise and Pawels answer is really fast. Using buildString you can have to best of both worlds:
buildString {
a.zip(b).forEach { (a, b) ->
append(a).append(b)
}
}
buildString creates a StringBuilder and offers it as receiver in the lambda. It returns the built string.
Try it out here: Kotlin Playground. Thanks to Pawel for creating the original benchmark.
I have a text variable which is an RDD of String in scala
val data = sc.parallelize(List("i am a good boy.Are you a good boy.","You are also working here.","I am posting here today.You are good."))
I have another variable in Scala Map(given below)
//list of words for which doc count needs to be found,initial doc count is 1
val dictionary = Map( """good""" -> 1,"""working""" -> 1,"""posting""" -> 1 ).
I want to do a document count of each of the dictionary terms and get the output in key value format
My output should be like below for the above data.
(good,2)
(working,1)
(posting,1)
What i have tried is
dictionary.map { case(k,v) => k -> k.r.findFirstIn(data.map(line => line.trim()).collect().mkString(",")).size}
I am getting counts as 1 for all the words.
Please help me in fixing the above line
Thanks in advance.
Why not use flatMap to create the dictionary and then you can query that.
val dictionary = data.flatMap {case line => line.split(" ")}.map {case word => (word, 1)}.reduceByKey(_+_)
If I collect this in the REPL I get the following result:
res9: Array[(String, Int)] = Array((here,1), (good.,1), (good,2), (here.,1), (You,1), (working,1), (today.You,1), (boy.Are,1), (are,2), (a,2), (posting,1), (i,1), (boy.,1), (also,1), (I,1), (am,2), (you,1))
Obviously you would need to do a better split than in my simple example.
First of all your dictionary should be a Set, because in general sense you need to map the Set of terms to the number of documents which contain them.
So your data should look like:
scala> val docs = List("i am a good boy.Are you a good boy.","You are also working here.","I am posting here today.You are good.")
docs: List[String] = List(i am a good boy.Are you a good boy., You are also working here., I am posting here today.You are good.)
Your dictionary should look like:
scala> val dictionary = Set("good", "working", "posting")
dictionary: scala.collection.immutable.Set[String] = Set(good, working, posting)
Then you have to implement your transformation, for the simplest logic of the contains function it might look like:
scala> dictionary.map(k => k -> docs.count(_.contains(k))) toMap
res4: scala.collection.immutable.Map[String,Int] = Map(good -> 2, working -> 1, posting -> 1)
For better solution I'd recommend you to implement specific function for your requirements
(String, String) => Boolean
to determine the presence of the term in the document:
scala> def foo(doc: String, term: String): Boolean = doc.contains(term)
foo: (doc: String, term: String)Boolean
Then final solution will look like:
scala> dictionary.map(k => k -> docs.count(d => foo(d, k))) toMap
res3: scala.collection.immutable.Map[String,Int] = Map(good -> 2, working -> 1, posting -> 1)
The last thing you have to do is to calculate the result map using SparkContext. First of all you have to define what data you want to have parallelised. Let's assume we want to parallelize the collection of the documents, then solution might be like following:
val docsRDD = sc.parallelize(List(
"i am a good boy.Are you a good boy.",
"You are also working here.",
"I am posting here today.You are good."
))
docsRDD.mapPartitions(_.map(doc => dictionary.collect {
case term if doc.contains(term) => term -> 1
})).map(_.toMap) reduce { case (m1, m2) => merge(m1, m2) }
def merge(m1: Map[String, Int], m2: Map[String, Int]) =
m1 ++ m2 map { case (k, v) => k -> (v + m1.getOrElse(k, 0)) }
I'm fairly new to Scala, but I'm doing my exercises now.
I have a string like "A>Augsburg;B>Berlin". What I want at the end is a map
val mymap = Map("A"->"Augsburg", "B"->"Berlin")
What I did is:
val st = locations.split(";").map(dynamicListExtract _)
with the function
private def dynamicListExtract(input: String) = {
if (input contains ">") {
val split = input split ">"
Some(split(0), split(1)) // return key , value
} else {
None
}
}
Now I have an Array[Option[(String, String)
How do I elegantly convert this into a Map[String, String]
Can anybody help?
Thanks
Just change your map call to flatMap:
scala> sPairs.split(";").flatMap(dynamicListExtract _)
res1: Array[(java.lang.String, java.lang.String)] = Array((A,Augsburg), (B,Berlin))
scala> Map(sPairs.split(";").flatMap(dynamicListExtract _): _*)
res2: scala.collection.immutable.Map[java.lang.String,java.lang.String] = Map((A,Augsburg), (B,Berlin))
For comparison:
scala> Map("A" -> "Augsburg", "B" -> "Berlin")
res3: scala.collection.immutable.Map[java.lang.String,java.lang.String] = Map((A,Augsburg), (B,Berlin))
In 2.8, you can do this:
val locations = "A>Augsburg;B>Berlin"
val result = locations.split(";").map(_ split ">") collect { case Array(k, v) => (k, v) } toMap
collect is like map but also filters values that aren't defined in the partial function. toMap will create a Map from a Traversable as long as it's a Traversable[(K, V)].
It's also worth seeing Randall's solution in for-comprehension form, which might be clearer, or at least give you a better idea of what flatMap is doing.
Map.empty ++ (for(possiblePair<-sPairs.split(";"); pair<-dynamicListExtract(possiblePair)) yield pair)
A simple solution (not handling error cases):
val str = "A>Aus;B>Ber"
var map = Map[String,String]()
str.split(";").map(_.split(">")).foreach(a=>map += a(0) -> a(1))
but Ben Lings' is better.
val str= "A>Augsburg;B>Berlin"
Map(str.split(";").map(_ split ">").map(s => (s(0),s(1))):_*)
--or--
str.split(";").map(_ split ">").foldLeft(Map[String,String]())((m,s) => m + (s(0) -> s(1)))