I have some records in a List .
Now I want to create a new Map(Mutable Map) from that List with unique key for each record. I want to achieve this my reading a List and calling the higher order method called map in scala.
records.txt is my input file
100,Surender,2015-01-27
100,Surender,2015-01-30
101,Raja,2015-02-19
Expected Output :
Map(0-> 100,Surender,2015-01-27, 1 -> 100,Surender,2015-01-30,2 ->101,Raja,2015-02-19)
Scala Code :
object SampleObject{
def main(args:Array[String]) ={
val mutableMap = scala.collection.mutable.Map[Int,String]()
var i:Int =0
val myList=Source.fromFile("D:\\Scala_inputfiles\\records.txt").getLines().toList;
println(myList)
val resultList= myList.map { x =>
{
mutableMap(i) =x.toString()
i=i+1
}
}
println(mutableMap)
}
}
But I am getting output like below
Map(1 -> 101,Raja,2015-02-19)
I want to understand why it is keeping the last record alone .
Could some one help me?
val mm: Map[Int, String] = Source.fromFile(filename).getLines
.zipWithIndex
.map({ case (line, i) => i -> line })(collection.breakOut)
Here the (collection.breakOut) is to avoid the extra parse caused by toMap.
Consider
(for {
(line, i) <- Source.fromFile(filename).getLines.zipWithIndex
} yield i -> line).toMap
where we read each line, associate an index value starting from zero and create a map out of each association.
Related
I have a situation here
I have two strins
val keyMap = "anrodiApp,key1;iosApp,key2;xyz,key3"
val tentMap = "androidApp,tenant1; iosApp,tenant1; xyz,tenant2"
So what I want to add is to create a nested immutable nested map like this
tenant1 -> (andoidiApp -> key1, iosApp -> key2),
tenant2 -> (xyz -> key3)
So basically want to group by tenant and create a map of keyMap
Here is what I tried but is done using mutable map which I do want, is there a way to create this using immmutable map
case class TenantSetting() {
val requesterKeyMapping = new mutable.HashMap[String, String]()
}
val requesterKeyMapping = keyMap.split(";")
.map { keyValueList => keyValueList.split(',')
.filter(_.size==2)
.map(keyValuePair => (keyValuePair[0],keyValuePair[1]))
.toMap
}.flatten.toMap
val config = new mutable.HashMap[String, TenantSetting]
tentMap.split(";")
.map { keyValueList => keyValueList.split(',')
.filter(_.size==2)
.map { keyValuePair =>
val requester = keyValuePair[0]
val tenant = keyValuePair[1]
if (!config.contains(tenant)) config.put(tenant, new TenantSetting)
config.get(tenant).get.requesterKeyMapping.put(requester, requesterKeyMapping.get(requester).get)
}
}
The logic to break the strings into a map can be the same for both as it's the same syntax.
What you had for the first string was not quite right as the filter you were applying to each string from the split result and not on the array result itself. Which also showed in that you were using [] on keyValuePair which was of type String and not Array[String] as I think you were expecting. Also you needed a trim in there to cope with the spaces in the second string. You might want to also trim the key and value to avoid other whitespace issues.
Additionally in this case the combination of map and filter can be more succinctly done with collect as shown here:
How to convert an Array to a Tuple?
The use of the pattern with 2 elements ensures you filter out anything with length other than 2 as you wanted.
The iterator is to make the combination of map and collect more efficient by only requiring one iteration of the collection returned from the first split (see comments below).
With both strings turned into a map it just needs the right use of groupByto group the first map by the value of the second based on the same key to get what you wanted. Obviously this only works if the same key is always in the second map.
def toMap(str: String): Map[String, String] =
str
.split(";")
.iterator
.map(_.trim.split(','))
.collect { case Array(key, value) => (key.trim, value.trim) }
.toMap
val keyMap = toMap("androidApp,key1;iosApp,key2;xyz,key3")
val tentMap = toMap("androidApp,tenant1; iosApp,tenant1; xyz,tenant2")
val finalMap = keyMap.groupBy { case (k, _) => tentMap(k) }
Printing out finalMap gives:
Map(tenant2 -> Map(xyz -> key3), tenant1 -> Map(androidApp -> key1, iosApp -> key2))
Which is what you wanted.
I'm pretty new to Scala and while working I found the need to map some data found within a log file. The log file follows this format (values changed from original):
1343,37284.ab1-tbd,283
1344,37284.ab1-tbd,284
1345,37284.ab1-tbd,0
1346,28374.ab1-tbd,107
1347,28374.ab1-tbd,0
...
The first number is not important, but the number portion of the second field and the third field are what need to be mapped. I need the map to have keys that correspond to the number portion of the second field that map to a list of every 3rd field that follows it. That was a bad explanation, so as an example here is what I would need after parsing the above log:
{
37284 => { 283, 284, 0 }
28374 => { 107, 0 }
}
The solution I came up with is this:
val data = for (line <- Source fromFile "path/to/log" getLines) yield line.split(',')
val ls = data.toList
val keys = ls.map(_(1).split('.')(0).toInt)
val vals = ls.map(_(2).toInt)
val keys2vals = for {
(k, v) <- (keys zip vals).groupBy(_._1)
list = v.map(_._2)
} yield (k, list)
Is there a more idiomatic way to do this in Scala? This seems kinda awkward and convoluted to me. (When explaining, please assume little to no background knowledge of langauge features, etc.) Also, if later down the line I wanted to exclude the number zero from the mappings, how would I do so?
EDIT:
In addition, how would I similarly turn the data into the form:
{
{ 37284, { 283 ,284, 0 } }
{ 28374, { 107, 0 } }
}
i.e. a List[(Int, List[Int])]? (This form is for use with apache-spark's indexed rdds)
How about:
val assocList = for {
line <- Source.fromFile("path/to/log").getLines
Array(_, snd, thd) = line.split(',')
} yield (snd.split('.')(0).toInt, thd.toInt)
assocList.toList.groupBy(_._1).mapValues(_.map(_._2))
If you want a List[(Int, List[Int])], add .toList.
I might be tempted to write it in fewer lines (arguably clearer too) like this:
val l = List((1343,"37284.ab1-tbd",283),
(1344,"37284.ab1-tbd",284),
(1345,"37284.ab1-tbd",0),
(1346,"28374.ab1-tbd",107),
(1347,"28374.ab1-tbd",0))
// drop the unused data
val m = l.map(a => a._2.split('.')(0).toInt -> a._3)
// transform to Map of key -> matchedValues
m.groupBy(_._1) mapValues (_ map (_._2))
gives:
m: List[(Int, Int)] = List((37284,283), (37284,284), (37284,0), (28374,107), (28374,0))
res0: scala.collection.immutable.Map[Int,List[Int]] = Map(37284 -> List(283, 284, 0), 28374 -> List(107, 0))
"Also, if later down the line I wanted to exclude the number zero from the mappings, how would I do so?" - You could filter the initial list:
val m = l.filter(_._3 != 0).map(a => a._2.split('.')(0) -> a._3)
To convert to List[(Int, List[Int])] you just need to call .toList on the resulting Map.
val lines = io.Source.fromFile("path/to/log").getLines.toList
lines.map{x=>
val Array(_,second,_,fourth) = x.split("[,.]")
(second,fourth)
}.groupBy(_._1)
.mapValues(_.map(_._2))
Say we have an array of one dimensional javascript objects contained in a file Array.json for which the key schema isn't known, that is the keys aren't known until the file is read.
Then we wish to output a CSV file with a header or first entry which is a comma delimited set of keys from all of the objects.
Each next line of the file should contain the comma separated values which correspond to each key from the file.
Array.json
[
abc:123,
xy:"yz",
s12:13,
],
...
[
abc:1
s:133,
]
A valid output:
abc,xy,s12,s
123,yz,13,
1,,,133
I'm teaching myself 'functional style' programming but I'm thinking that this problem doesn't lend itself well to a functional solution.
I believe that this problem requires some state to be kept for the output header and that subsequently each line depends on that header.
I'm looking to solve the problem in a single pass. My goals are efficiency for a large data set, minimal traversals, and if possible, parallelizability. If this isn't possible then can you give a proof or reasoning to explain why?
EDIT: Is there a way to solve the problem like this functionally?:
Say you pass through the array once, in some particular order. Then
from the start the header set looks like abc,xy,s12 for the first
object. With CSV entry 123,yz,13 . Then on the next object we add an
additional key to the header set so abc,xy,s12,s would be the header
and the CSV entry would be 1,,,133 . In the end we wouldn't need to
pass through the data set a second time. We could just append extra
commas to the result set. This is one way we could approach a single
pass....
Are there functional tools ( functions ) designed to solve problems like this, and what should I be considering? [ By functional tools I mean Monads,FlatMap, Filters, etc. ] . Alternatively, should I be considering things like Futures ?
Currently I've been trying to approach this using Java8, but am open to solutions from Scala, etc. Ideally I would be able to determine if Java8s' functional approach can solve the problem since that's the language I'm currently working in.
Since the csv output will change with every new line of input, you must hold that in memory before writing it out. If you consider creating an output text format from an internal representation of a csv file another "pass" over the data (the internal representation of the csv is practically a Map[String,List[String]] which you must traverse to convert it to text) then it's not possible to do this in a single pass.
If, however, this is acceptable, then you can use a Stream to read a single item from your json file, merge that into the csv file, and do this until the stream is empty.
Assuming, that the internal representation of the csv file is
trait CsvFile {
def merge(line: Map[String, String]): CsvFile
}
And you can represent a single item as
trait Item {
def asMap: Map[String, String]
}
You can implement it using foldLeft:
def toCsv(items: Stream[Item]): CsvFile =
items.foldLeft(CsvFile(Map()))((csv, item) => csv.merge(item.asMap))
or use recursion to get the same result
#tailrec def toCsv(items: Stream[Item], prevCsv: CsvFile): CsvFile =
items match {
case Stream.Empty => prevCsv
case item #:: rest =>
val newCsv = prevCsv.merge(item.asMap)
toCsv(rest, newCsv)
}
Note: Of course you don't have to create types for CsvFile or Item, you can use Map[String,List[String]] and Map[String,String] respectively
UPDATE:
As more detail was requested for the CsvFile trait/class, here's an example implementation:
case class CsvFile(lines: Map[String, List[String]], rowCount: Int = 0) {
def merge(line: Map[String, String]): CsvFile = {
val orig = lines.withDefaultValue(List.fill(rowCount)(""))
val current = line.withDefaultValue("")
val newLines = (lines.keySet ++ line.keySet) map {
k => (k, orig(k) :+ current(k))
}
CsvFile(newLines.toMap, rowCount + 1)
}
}
This could be one approach:
val arr = Array(Map("abc" -> 123, "xy" -> "yz", "s12" -> 13), Map("abc" -> 1, "s" -> 133))
val keys = arr.flatMap(_.keys).distinct // get the distinct keys for header
arr.map(x => keys.map(y => x.getOrElse(y,""))) // get an array of rows
Its completely OK to have state in functional programming. But having mutable state or mutating state is not allowed in functional programming.
Functional programming advocates creating new changed state instead of mutating the state in place.
So, its Ok to read and access state created in the program until and unless you are mutating or side effecting.
Coming to the point.
val list = List(List("abc" -> "123", "xy" -> "yz"), List("abc" -> "1"))
list.map { inner => inner.map { case (k, v) => k}}.flatten
list.map { inner => inner.map { case (k, v) => v}}.flatten
REPL
scala> val list = List(List("abc" -> "123", "xy" -> "yz"), List("abc" -> "1"))
list: List[List[(String, String)]] = List(List((abc,123), (xy,yz)), List((abc,1)))
scala> list.map { inner => inner.map { case (k, v) => k}}.flatten
res1: List[String] = List(abc, xy, abc)
scala> list.map { inner => inner.map { case (k, v) => v}}.flatten
res2: List[String] = List(123, yz, 1)
or use flatMap instead of map and flatten
val list = List(List("abc" -> "123", "xy" -> "yz"), List("abc" -> "1"))
list.flatMap { inner => inner.map { case (k, v) => k}}
list.flatMap { inner => inner.map { case (k, v) => v}}
In functional programming, mutable state is not allowed. But immutable states/values are fine.
Assuming that you have read your json file in to a value input:List[Map[String,String]], the codes below will solve your problem:
val input = List(Map("abc"->"123", "xy"->"yz" , "s12"->"13"), Map("abc"->"1", "s"->"33"))
val keys = input.map(_.keys).flatten.toSet
val keyvalues = input.map(kvs => keys.map(k => (k->kvs.getOrElse(k,""))).toMap)
val values = keyvalues.map(_.values)
val result = keys.mkString(",") + "\n" + values.map(_.mkString(",")).mkString("\n")
This post is essentially about how to build joint and marginal histograms from a (String, String) RDD. I posted the code that I eventually used below as the answer.
I have an RDD that contains a set of tuples of type (String,String) and since they aren't unique I want to get a look at how many times each String, String combination occurs so I use countByValue like so
val PairCount = Pairs.countByValue().toSeq
which gives me a tuple as output like this ((String,String),Long) where long is the number of times that the (String, String) tuple appeared
These Strings can be repeated in different combinations and I essentially want to run word count on this PairCount variable so I tried something like this to start:
PairCount.map(x => (x._1._1, x._2))
But the output the this spits out is String1->1, String2->1, String3->1, etc.
How do I output a key value pair from a map job in this case where the key is going to be one of the String values from the inner tuple, and the value is going to be the Long value from the outter tuple?
Update:
#vitalii gets me almost there. the answer gets me to a Seq[(String,Long)], but what I really need is to turn that into a map so that I can run reduceByKey it afterwards. when I run
PairCount.flatMap{case((x,y),n) => Seq[x->n]}.toMap
for each unique x I get x->1
for example the above line of code generates mom->1 dad->1 even if the tuples out of the flatMap included (mom,30) (dad,59) (mom,2) (dad,14) in which case I would expect toMap to provide mom->30, dad->59 mom->2 dad->14. However, I'm new to scala so I might be misinterpreting the functionality.
how can I get the Tuple2 sequence converted to a map so that I can reduce on the map keys?
If I correctly understand question, you need flatMap:
val pairCountRDD = pairs.countByValue() // RDD[((String, String), Int)]
val res : RDD[(String, Int)] = pairCountRDD.flatMap { case ((s1, s2), n) =>
Seq(s1 -> n, s2 -> n)
}
Update: I didn't quiet understand what your final goal is, but here's a few more examples that may help you, btw code above is incorrect, I have missed the fact that countByValue returns map, and not RDD:
val pairs = sc.parallelize(
List(
"mom"-> "dad", "dad" -> "granny", "foo" -> "bar", "foo" -> "baz", "foo" -> "foo"
)
)
// don't use countByValue, if pairs is large you will run out of memmory
val pairCountRDD = pairs.map(x => (x, 1)).reduceByKey(_ + _)
val wordCount = pairs.flatMap { case (a,b) => Seq(a -> 1, b ->1)}.reduceByKey(_ + _)
wordCount.take(10)
// count in how many pairs each word occur, keys and values:
val wordPairCount = pairs.flatMap { case (a,b) =>
if (a == b) {
Seq(a->1)
} else {
Seq(a -> 1, b ->1)
}
}.reduceByKey(_ + _)
wordPairCount.take(10)
to get the histograms for the (String,String) RDD I used this code.
val Hist_X = histogram.map(x => (x._1-> 1.0)).reduceByKey(_+_).collect().toMap
val Hist_Y = histogram.map(x => (x._2-> 1.0)).reduceByKey(_+_).collect().toMap
val Hist_XY = histogram.map(x => (x-> 1.0)).reduceByKey(_+_)
where histogram was the (String,String) RDD
I am trying to create a map after getting result for each items in the list. Here is what I tried so far:
val sourceList: List[(Int, Int)] = ....
val resultMap: Map[Int, Int] = for(srcItem <- sourceList) {
val result: Int = someFunction(srcItem._1)
Map(srcItem._1 -> result)
}
But I am getting type mismatch error in IntelliJ and I am definitely not writing proper syntax here. I don't think I can use yield as I don't want List of Map. What is correct way to create Map using for loop. Any suggestion?
The simplest way is to create the map out of a list of tuples:
val resultMap = sourceList.map(item => (item._1, someFunction(item._1))).toMap
Or, in the monadic way:
val listOfTuples = for {
(value, _) <- sourceList
} yield (value, someFunction(value))
val resultMap = listOfTuples.toMap
Alternatively, if you want to avoid the creation of listOfTuples you can make the transformation a lazy one by calling .view on sourceList and then call toMap:
val resultMap = sourceList.view
.map(item => (item._1, someFunction(item._1)))
.toMap
Finally, if you really want to avoid generating extra objects you can use a mutable Map instead and append the keys and values to it using += or .put