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I have scenario to capture some data (not all) from an existing RDD and then pass it to other Scala class for actual operations. Lets see with example data(empnum, empname, emplocation, empsal) in a text file.
11,John,Paris,1000
12,Daniel,UK,3000
first step, I create an RDD with RDD[String] by below code,
val empRDD = spark
.sparkContext
.textFile("empInfo.txt")
So, my requirement is to create another RDD with empnum, empname, emplocation (again with RDD[String]).
For that I have tried below code hence I am getting RDD[String, String, String].
val empReqRDD = empRDD
.map(a=> a.split(","))
.map(x=> (x(0), x(1), x(2)))
I have tried with Slice also, it gives me RDD[Array(String)].
My required RDD should be of RDD[String] to pass to required Scala class to do some operations.
The expected output should be,
11,John,Paris
12,Daniel,UK
Can anyone help me how to achieve?
I would try this
val empReqRDD = empRDD
.map(a=> a.split(","))
.map(x=> (x(0), x(1), x(2)))
val rddString = empReqRDD.map({case(id,name,city) => "%s,%s,%s".format(id,name,city)})
In your initial implementation, the second map is putting the array elements into a 3-tuple, hence the RDD[(String, String, String)].
One way to accomplish your objective is to change the second map to construct a string like so:
empRDD
.map(a=> a.split(","))
.map(x => s"${x(0)},${x(1)},${x(2)}")
Alternatively, and a bit more concise, you could do it by taking the first 3 elements of the array and using the mkString method:
empRDD.map(_.split(',').take(3).mkString(","))
Probably overkill for this use-case, but you could also use a regex to extract the values:
val r = "([^,]*),([^,]*),([^,]*).*".r
empRDD.map { case r(id, name, city) => s"$id,$name,$city" }
I have the following Scala value:
val values: List[Iterable[Any]] = Traces().evaluate(features).toList
and I want to convert it to a DataFrame.
When I try the following:
sqlContext.createDataFrame(values)
I got this error:
error: overloaded method value createDataFrame with alternatives:
[A <: Product](data: Seq[A])(implicit evidence$2: reflect.runtime.universe.TypeTag[A])org.apache.spark.sql.DataFrame
[A <: Product](rdd: org.apache.spark.rdd.RDD[A])(implicit evidence$1: reflect.runtime.universe.TypeTag[A])org.apache.spark.sql.DataFrame
cannot be applied to (List[Iterable[Any]])
sqlContext.createDataFrame(values)
Why?
Thats what spark implicits object is for. It allows you to convert your common scala collection types into DataFrame / DataSet / RDD.
Here is an example with Spark 2.0 but it exists in older versions too
import org.apache.spark.sql.SparkSession
val values = List(1,2,3,4,5)
val spark = SparkSession.builder().master("local").getOrCreate()
import spark.implicits._
val df = values.toDF()
Edit: Just realised you were after 2d list. Here is something I tried on spark-shell. I converted a 2d List to List of Tuples and used implicit conversion to DataFrame:
val values = List(List("1", "One") ,List("2", "Two") ,List("3", "Three"),List("4","4")).map(x =>(x(0), x(1)))
import spark.implicits._
val df = values.toDF
Edit2: The original question by MTT was How to create spark dataframe from a scala list for a 2d list for which this is a correct answer. The original question is https://stackoverflow.com/revisions/38063195/1
The question was later changed to match an accepted answer. Adding this edit so that if someone else looking for something similar to the original question can find it.
As zero323 mentioned, we need to first convert List[Iterable[Any]] to List[Row] and then put rows in RDD and prepare schema for the spark data frame.
To convert List[Iterable[Any]] to List[Row], we can say
val rows = values.map{x => Row(x:_*)}
and then having schema like schema, we can make RDD
val rdd = sparkContext.makeRDD[RDD](rows)
and finally create a spark data frame
val df = sqlContext.createDataFrame(rdd, schema)
Simplest approach:
val newList = yourList.map(Tuple1(_))
val df = spark.createDataFrame(newList).toDF("stuff")
In Spark 2 we can use DataSet by just converting list to DS by toDS API
val ds = list.flatMap(_.split(",")).toDS() // Records split by comma
or
val ds = list.toDS()
This more convenient than rdd or df
The most concise way I've found:
val df = spark.createDataFrame(List("A", "B", "C").map(Tuple1(_)))
I'm trying to implement k-means method using scala.
I created a RDD something like that
val df = sc.parallelize(data).groupByKey().collect().map((chunk)=> {
sc.parallelize(chunk._2.toSeq).toDF()
})
val examples = df.map(dataframe =>{
dataframe.selectExpr(
"avg(time) as avg_time",
"variance(size) as var_size",
"variance(time) as var_time",
"count(size) as examples"
).rdd
})
val rdd_final=examples.reduce(_ union _)
val kmeans= new KMeans()
val model = kmeans.run(rdd_final)
With this code I obtain an error
type mismatch;
[error] found : org.apache.spark.rdd.RDD[org.apache.spark.sql.Row]
[error] required:org.apache.spark.rdd.RDD[org.apache.spark.mllib.linalg.Vector]
So I tried to cast doing:
val rdd_final_Vector = rdd_final.map{x:Row => x.getAs[org.apache.spark.mllib.linalg.Vector](0)}
val model = kmeans.run(rdd_final_Vector)
But then I obtain an error:
java.lang.ClassCastException: java.lang.Double cannot be cast to org.apache.spark.mllib.linalg.Vector
So I'm looking for a way to do that cast, but I can't find any method.
Any idea?
Best regards
At least a couple of issues here:
No you really can not cast a Row to a Vector: a Row is a collection of potentially disparate types understood by Spark SQL. A Vector is not a native spark sql type
There seems to be a mismatch between the content of your SQL statement and what you are attempting to achieve with KMeans: the SQL is performing aggregations. But KMeans expects a series of individual data points in the form a Vector (which encapsulates an Array[Double]) . So then - why are you supplying sum's and average's to a KMeans operation?
Addressing just #1 here: you will need to do something along the lines of:
val doubVals = <rows rdd>.map{ row => row.getDouble("colname") }
val vector = Vectors.toDense{ doubVals.collect}
Then you have a properly encapsulated Array[Double] (within a Vector) that can be supplied to Kmeans.
I'm practicing on doing sorts in the Spark shell. I have an rdd with about 10 columns/variables. I want to sort the whole rdd on the values of column 7.
rdd
org.apache.spark.rdd.RDD[Array[String]] = ...
From what I gather the way to do that is by using sortByKey, which in turn only works on pairs. So I mapped it so I'd have a pair consisting of column7 (string values) and the full original rdd (array of strings)
rdd2 = rdd.map(c => (c(7),c))
rdd2: org.apache.spark.rdd.RDD[(String, Array[String])] = ...
I then apply sortByKey, still no problem...
rdd3 = rdd2.sortByKey()
rdd3: org.apache.spark.rdd.RDD[(String, Array[String])] = ...
But now how do I split off, collect and save that sorted original rdd from rdd3 (Array[String])? Whenever I try a split on rdd3 it gives me an error:
val rdd4 = rdd3.map(_.split(',')(2))
<console>:33: error: value split is not a member of (String, Array[String])
What am I doing wrong here? Are there other, better ways to sort an rdd on one of its columns?
what you did with rdd2 = rdd.map(c => (c(7),c)) is to map it to a tuple.
rdd2: org.apache.spark.rdd.RDD[(String, Array[String])]
exactly as it says :).
now if you want to split the record you need to get it from this tuple.
you can map again, taking only the second part of the tuple (which is the array of Array[String]...) like so : rdd3.map(_._2)
but i would strongly suggest to use try rdd.sortBy(_(7)) or something of this sort. this way you do not need to bother yourself with tuple and such.
if you want to sort the rdd using the 7th string in the array, you can just do it directly by
rdd.sortBy(_(6)) // array starts at 0 not 1
or
rdd.sortBy(arr => arr(6))
That will save you all the hassle of doing multiple transformations. The reason why rdd.sortBy(_._7) or rdd.sortBy(x => x._7) won't work is because that's not how you access an element inside an Array. To access the 7th element of an array, say arr, you should do arr(6).
To test this, i did the following:
val rdd = sc.parallelize(Array(Array("ard", "bas", "wer"), Array("csg", "dip", "hwd"), Array("asg", "qtw", "hasd")))
// I want to sort it using the 3rd String
val sorted_rdd = rdd.sortBy(_(2))
Here's the result:
Array(Array("ard", "bas", "wer"), Array("csg", "dip", "hwd"), Array("asg", "qtw", "hasd"))
just do this:
val rdd4 = rdd3.map(_._2)
I thought you don't familiar with Scala,
So, below should help you understand more,
rdd3.map(kv => {
println(kv._1) // This represent String
println(kv._2) // This represent Array[String]
})
I am learning how to use Apache Spark and I am trying to get the average temperature from each hour from a data set. The data set that I am trying to use is from weather information stored in a csv. I am having trouble finding how to first read in the csv file and then calculating the average temperature for each hour.
From the spark documentation I am using the example Scala line to read in a file.
val textFile = sc.textFile("README.md")
I have given the link for the data file below. I am using the file called JCMB_2014.csv as it is the latest one with all months covered.
Weather Data
Edit:
The code I have tried so far is:
class SimpleCSVHeader(header:Array[String]) extends Serializable {
val index = header.zipWithIndex.toMap
def apply(array:Array[String], key:String):String = array(index(key))
}
val csv = sc.textFile("JCMB_2014.csv")
val data = csv.map(line => line.split(",").map(elem => elem.trim))
val header = new SimpleCSVHeader(data.take(1)(0)) // we build our header
val header = new SimpleCSVHeader(data.take(1)(0))
val rows = data.filter(line => header(line,"date-time") != "date-time")
val users = rows.map(row => header(row,"date-time")
val usersByHits = rows.map(row => header(row,"date-time") -> header(row,"surface temperature (C)").toInt)
Here is sample code for calculating averages on hourly basis
Step1:Read file, Filter header,extract time and temp columns
scala> val hourlyTemps = lines.map(line=>line.split(",")).filter(entries=>(!"time".equals(entries(3)))).map(entries=>(entries(3).toInt/60,(entries(8).toFloat,1)))
scala> hourlyTemps.take(1)
res25: Array[(Int, (Float, Int))] = Array((9,(10.23,1)))
(time/60) discards minutes and keeps only hours
Step2:Aggregate temperatures and no of occurrences
scala> val aggregateTemps=hourlyTemps.reduceByKey((a,b)=>(a._1+b._1,a._2+b._2))
scala> aggreateTemps.take(1)
res26: Array[(Int, (Double, Int))] = Array((34,(8565.25,620)))
Step2:Calculate Averages using total and no of occurrences
Find the final result below.
val avgTemps=aggregateTemps.map(tuple=>(tuple._1,tuple._2._1/tuple._2._2))
scala> avgTemps.collect
res28: Array[(Int, Float)] = Array((34,13.814922), (4,11.743354), (16,14.227251), (22,15.770312), (28,15.5324545), (30,15.167026), (14,13.177828), (32,14.659948), (36,12.865237), (0,11.994799), (24,15.662579), (40,12.040322), (6,11.398838), (8,11.141323), (12,12.004652), (38,12.329914), (18,15.020147), (20,15.358524), (26,15.631921), (10,11.192643), (2,11.848178), (13,12.616284), (19,15.198371), (39,12.107664), (15,13.706351), (21,15.612191), (25,15.627121), (29,15.432097), (11,11.541124), (35,13.317129), (27,15.602408), (33,14.220147), (37,12.644306), (23,15.83412), (1,11.872819), (17,14.595772), (3,11.78971), (7,11.248139), (9,11.049844), (31,14.901464), (5,11.59693))
You may want to provide Structure definition of your CSV file and convert your RDD to DataFrame, like described in the documentation. Dataframes provide a whole set of useful predefined statistic functions as well as the possibility to write some simple custom functions. You then will be able to compute the average with:
dataFrame.groupBy(<your columns here>).agg(avg(<column to compute average>)