Hi
Please use explode, which is written to solve exactly your problem.
Consider below:
>>> s = ["ERN~58XXXXXX7~^EPN~5XXXXX551~|1000"]
>>> df = sc.parallelize(s).map(lambda t: t.split('|')).toDF(['phone','id'])
>>> df.registerTempTable("t")
>>> resDF = sqlContext.sql("select id,explode(phone) phones from (select
id, split(phone,'~') as phone from t) x")
>>> resDF.show(truncate=False)
+----+---------+
|id |phones |
+----+---------+
|1000|ERN |
|1000|58XXXXXX7|
|1000|^EPN |
|1000|5XXXXX551|
|1000| |
+----+---------+
HTH.....
On Tue, Jul 18, 2017 at 3:15 AM, nayan sharma <[email protected]>
wrote:
> Hi Pralabh,
>
> Thanks for your help.
>
> val xx = columnList.map(x => x->0).toMap
> val opMap = dataFrame.rdd.flatMap { row =>
> columnList.foldLeft(xx) { case (y, col) =>
> val s = row.getAs[String](col).split("\\^").length
> if (y(col) < s)
> y.updated(col, s)
> else
> y
> }.toList
> }
>
>
> val colMaxSizeMap = opMap.groupBy(x => x._1).map(x => x._2.toList.maxBy(x
> => x._2)).collect().toMap
> val x = dataFrame.rdd.map{x =>
> val op = columnList.flatMap{ y =>
> val op = x.getAs[String](y).split("\\^")
> op++List.fill(colMaxSizeMap(y)-op.size)("")
> }
> Row.fromSeq(op)
> }
>
> val structFieldList = columnList.flatMap{colName =>
> List.range(0,colMaxSizeMap(colName),1).map{ i =>
> StructField(s"$colName"+s"$i",StringType)
> }
> }
> val schema = StructType(structFieldList)
> val data1=spark.createDataFrame(x,schema)
>
> opMap
> res13: org.apache.spark.rdd.RDD[(String, Int)]
>
> But It is failing when opMap has null value.It is throwing java.lang.
> NullPointerException
> trying to figure out.
>
> val opMap1=opMap.filter(_._2 !="")
>
> tried doing this but it is also failing with same exception.
>
> Thanks,
> Nayan
>
>
>
>
> On 17-Jul-2017, at 4:54 PM, Pralabh Kumar <[email protected]> wrote:
>
> Hi Nayan
>
> Please find the solution of your problem which work on spark 2.
>
> val spark = SparkSession.builder().appName("practice").
> enableHiveSupport().getOrCreate()
> val sc = spark.sparkContext
> val sqlContext = spark.sqlContext
> import spark.implicits._
> val dataFrame = sc.parallelize(List("ERN~58XXXXXX7~^EPN~5XXXXX551~|C~
> MXXX~MSO~^CAxxE~~~~~~3XXX5"))
> .map(s=>s.split("\\|")).map(s=>(s(0),s(1)))
> .toDF("phone","contact")
> dataFrame.show()
> val newDataSet= dataFrame.rdd.map(data=>{
> val t1 = ArrayBuffer[String] ()
> for (i <- 0.to(1)) {
> val col = data.get(i).asInstanceOf[String]
> val dd= col.split("\\^").toSeq
> for(col<-dd){
> t1 +=(col)
> }
> }
> Row.fromSeq(t1.seq)
> })
>
> val firtRow = dataFrame.select("*").take(1)(0)
> dataFrame.schema.fieldNames
> var schema =""
>
> for ((colNames,idx) <- dataFrame.schema.fieldNames.zipWithIndex.view) {
> val data = firtRow(idx).asInstanceOf[String].split("\\^")
> var j = 0
> for(d<-data){
> schema = schema + colNames + j + ","
> j = j+1
> }
> }
> schema=schema.substring(0,schema.length-1)
> val sqlSchema = StructType(schema.split(",").map(s=>StructField(s,
> StringType,false)))
> sqlContext.createDataFrame(newDataSet,sqlSchema).show()
>
> Regards
> Pralabh Kumar
>
>
> On Mon, Jul 17, 2017 at 1:55 PM, nayan sharma <[email protected]>
> wrote:
>
>> If I have 2-3 values in a column then I can easily separate it and create
>> new columns with withColumn option.
>> but I am trying to achieve it in loop and dynamically generate the new
>> columns as many times the ^ has occurred in column values
>>
>> Can it be achieve in this way.
>>
>> On 17-Jul-2017, at 3:29 AM, ayan guha <[email protected]> wrote:
>>
>> You are looking for explode function.
>>
>> On Mon, 17 Jul 2017 at 4:25 am, nayan sharma <[email protected]>
>> wrote:
>>
>>> I’ve a Dataframe where in some columns there are multiple values, always
>>> separated by ^
>>>
>>> phone|contact|
>>> ERN~58XXXXXX7~^EPN~5XXXXX551~|C~MXXX~MSO~^CAxxE~~~~~~3XXX5|
>>>
>>> phone1|phone2|contact1|contact2|
>>> ERN~5XXXXXXX7|EPN~58XXXX91551~|C~MXXXH~MSO~|CAxxE~~~~~~3XXX5|
>>>
>>> How can this be achieved using loop as the separator between column
>>> values
>>> are not constant.
>>> data.withColumn("phone",split($"phone","\\^")).select($"phon
>>> e".getItem(0).as("phone1"),$"phone".getItem(1).as("phone2”))
>>> I though of doing this way but the problem is column are having 100+
>>> separator between the column values
>>>
>>>
>>>
>>> Thank you,
>>> Nayan
>>>
>> --
>> Best Regards,
>> Ayan Guha
>>
>>
>>
>
>
--
Best Regards,
Ayan Guha
