How to extract values from key value map, spark dataframe
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I have a column with map, where the key and value changes. I am trying to extract the value and create a new column.
Input
---------------+
|symbols |
+---------------+
|[3pea -> 3PEA] |
|[barello -> BA]|
| |
| |
+---------------+
Expected output
---------------+
|symbols |
+---------------+
|3PEA |
|BA |
| |
| |
+---------------+
Here is what I tried so far using an UDF
def map_value=udf((inputMap:Map[String,String])=> {inputMap.map(x=>x._2)
})
but this is giving me
java.lang.UnsupportedOperationException: Schema for type scala.collection.immutable.Iterable[String] is not supported
apache-spark-sql
asked Nov 23 '18 at 18:59
MasterbuilderMasterbuilder
128111
add a comment |
0
I have a column with map, where the key and value changes. I am trying to extract the value and create a new column.
Input
---------------+
|symbols |
+---------------+
|[3pea -> 3PEA] |
|[barello -> BA]|
| |
| |
+---------------+
Expected output
---------------+
|symbols |
+---------------+
|3PEA |
|BA |
| |
| |
+---------------+
Here is what I tried so far using an UDF
def map_value=udf((inputMap:Map[String,String])=> {inputMap.map(x=>x._2)
})
but this is giving me
java.lang.UnsupportedOperationException: Schema for type scala.collection.immutable.Iterable[String] is not supported
apache-spark-sql
asked Nov 23 '18 at 18:59
MasterbuilderMasterbuilder
128111
Can you show the def / structure of the DF ?
– thebluephantom
Nov 23 '18 at 19:45
add a comment |
0
0
0
I have a column with map, where the key and value changes. I am trying to extract the value and create a new column.
Input
---------------+
|symbols |
+---------------+
|[3pea -> 3PEA] |
|[barello -> BA]|
| |
| |
+---------------+
Expected output
---------------+
|symbols |
+---------------+
|3PEA |
|BA |
| |
| |
+---------------+
Here is what I tried so far using an UDF
def map_value=udf((inputMap:Map[String,String])=> {inputMap.map(x=>x._2)
})
but this is giving me
java.lang.UnsupportedOperationException: Schema for type scala.collection.immutable.Iterable[String] is not supported
apache-spark-sql
asked Nov 23 '18 at 18:59
MasterbuilderMasterbuilder
128111
I have a column with map, where the key and value changes. I am trying to extract the value and create a new column.
Input
---------------+
|symbols |
+---------------+
|[3pea -> 3PEA] |
|[barello -> BA]|
| |
| |
+---------------+
Expected output
---------------+
|symbols |
+---------------+
|3PEA |
|BA |
| |
| |
+---------------+
Here is what I tried so far using an UDF
def map_value=udf((inputMap:Map[String,String])=> {inputMap.map(x=>x._2)
})
but this is giving me
java.lang.UnsupportedOperationException: Schema for type scala.collection.immutable.Iterable[String] is not supported
apache-spark-sql
apache-spark-sql
asked Nov 23 '18 at 18:59
MasterbuilderMasterbuilder
128111
asked Nov 23 '18 at 18:59
MasterbuilderMasterbuilder
128111
asked Nov 23 '18 at 18:59
MasterbuilderMasterbuilder
128111
asked Nov 23 '18 at 18:59
MasterbuilderMasterbuilder
128111
asked Nov 23 '18 at 18:59
MasterbuilderMasterbuilder
128111
128111
Can you show the def / structure of the DF ?
– thebluephantom
Nov 23 '18 at 19:45
add a comment |
Can you show the def / structure of the DF ?
– thebluephantom
Nov 23 '18 at 19:45
Can you show the def / structure of the DF ?
– thebluephantom
Nov 23 '18 at 19:45
Can you show the def / structure of the DF ?
– thebluephantom
Nov 23 '18 at 19:45
add a comment |
1 Answer
1
active
oldest
votes
0
import org.apache.spark.sql.functions._
import spark.implicits._
val m = Seq(Array("A -> abc"), Array("B -> 0.11856755943424617"), Array("C -> kqcams"))
val df = m.toDF("map_data")
df.show
// Simulate your data I think.
val df2 = df.withColumn("xxx", split(concat_ws("",$"map_data"), "-> ")).select($"xxx".getItem(1).as("map_val")).drop("xxx")
df2.show(false)
results in:
+--------------------+
| map_data|
+--------------------+
| [A -> abc]|
|[B -> 0.118567559...|
| [C -> kqcams]|
+--------------------+
+-------------------+
|map_val |
+-------------------+
|abc |
|0.11856755943424617|
|kqcams |
+-------------------+
answered Nov 23 '18 at 20:36
thebluephantomthebluephantom
3,35641033
I think your answer is correct, I ended up using an udf like this def map_value=udf((inputMap:Map[String,String])=> {inputMap.map(x=>x._2).toArray.mkString })
– Masterbuilder
Nov 24 '18 at 15:29
Also fine. Cheers
– thebluephantom
Nov 24 '18 at 15:55
Thats why I inquired how is your input.
– thebluephantom
Nov 24 '18 at 16:00
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
0
import org.apache.spark.sql.functions._
import spark.implicits._
val m = Seq(Array("A -> abc"), Array("B -> 0.11856755943424617"), Array("C -> kqcams"))
val df = m.toDF("map_data")
df.show
// Simulate your data I think.
val df2 = df.withColumn("xxx", split(concat_ws("",$"map_data"), "-> ")).select($"xxx".getItem(1).as("map_val")).drop("xxx")
df2.show(false)
results in:
+--------------------+
| map_data|
+--------------------+
| [A -> abc]|
|[B -> 0.118567559...|
| [C -> kqcams]|
+--------------------+
+-------------------+
|map_val |
+-------------------+
|abc |
|0.11856755943424617|
|kqcams |
+-------------------+
answered Nov 23 '18 at 20:36
thebluephantomthebluephantom
3,35641033
I think your answer is correct, I ended up using an udf like this def map_value=udf((inputMap:Map[String,String])=> {inputMap.map(x=>x._2).toArray.mkString })
– Masterbuilder
Nov 24 '18 at 15:29
Also fine. Cheers
– thebluephantom
Nov 24 '18 at 15:55
Thats why I inquired how is your input.
– thebluephantom
Nov 24 '18 at 16:00
add a comment |
0
import org.apache.spark.sql.functions._
import spark.implicits._
val m = Seq(Array("A -> abc"), Array("B -> 0.11856755943424617"), Array("C -> kqcams"))
val df = m.toDF("map_data")
df.show
// Simulate your data I think.
val df2 = df.withColumn("xxx", split(concat_ws("",$"map_data"), "-> ")).select($"xxx".getItem(1).as("map_val")).drop("xxx")
df2.show(false)
results in:
+--------------------+
| map_data|
+--------------------+
| [A -> abc]|
|[B -> 0.118567559...|
| [C -> kqcams]|
+--------------------+
+-------------------+
|map_val |
+-------------------+
|abc |
|0.11856755943424617|
|kqcams |
+-------------------+
answered Nov 23 '18 at 20:36
thebluephantomthebluephantom
3,35641033
I think your answer is correct, I ended up using an udf like this def map_value=udf((inputMap:Map[String,String])=> {inputMap.map(x=>x._2).toArray.mkString })
– Masterbuilder
Nov 24 '18 at 15:29
Also fine. Cheers
– thebluephantom
Nov 24 '18 at 15:55
Thats why I inquired how is your input.
– thebluephantom
Nov 24 '18 at 16:00
add a comment |
0
0
0
import org.apache.spark.sql.functions._
import spark.implicits._
val m = Seq(Array("A -> abc"), Array("B -> 0.11856755943424617"), Array("C -> kqcams"))
val df = m.toDF("map_data")
df.show
// Simulate your data I think.
val df2 = df.withColumn("xxx", split(concat_ws("",$"map_data"), "-> ")).select($"xxx".getItem(1).as("map_val")).drop("xxx")
df2.show(false)
results in:
+--------------------+
| map_data|
+--------------------+
| [A -> abc]|
|[B -> 0.118567559...|
| [C -> kqcams]|
+--------------------+
+-------------------+
|map_val |
+-------------------+
|abc |
|0.11856755943424617|
|kqcams |
+-------------------+
answered Nov 23 '18 at 20:36
thebluephantomthebluephantom
3,35641033
import org.apache.spark.sql.functions._
import spark.implicits._
val m = Seq(Array("A -> abc"), Array("B -> 0.11856755943424617"), Array("C -> kqcams"))
val df = m.toDF("map_data")
df.show
// Simulate your data I think.
val df2 = df.withColumn("xxx", split(concat_ws("",$"map_data"), "-> ")).select($"xxx".getItem(1).as("map_val")).drop("xxx")
df2.show(false)
results in:
+--------------------+
| map_data|
+--------------------+
| [A -> abc]|
|[B -> 0.118567559...|
| [C -> kqcams]|
+--------------------+
+-------------------+
|map_val |
+-------------------+
|abc |
|0.11856755943424617|
|kqcams |
+-------------------+
answered Nov 23 '18 at 20:36
thebluephantomthebluephantom
3,35641033
answered Nov 23 '18 at 20:36
thebluephantomthebluephantom
3,35641033
answered Nov 23 '18 at 20:36
thebluephantomthebluephantom
3,35641033
answered Nov 23 '18 at 20:36
thebluephantomthebluephantom
3,35641033
3,35641033
I think your answer is correct, I ended up using an udf like this def map_value=udf((inputMap:Map[String,String])=> {inputMap.map(x=>x._2).toArray.mkString })
– Masterbuilder
Nov 24 '18 at 15:29
Also fine. Cheers
– thebluephantom
Nov 24 '18 at 15:55
Thats why I inquired how is your input.
– thebluephantom
Nov 24 '18 at 16:00
add a comment |
I think your answer is correct, I ended up using an udf like this def map_value=udf((inputMap:Map[String,String])=> {inputMap.map(x=>x._2).toArray.mkString })
– Masterbuilder
Nov 24 '18 at 15:29
Also fine. Cheers
– thebluephantom
Nov 24 '18 at 15:55
Thats why I inquired how is your input.
– thebluephantom
Nov 24 '18 at 16:00
I think your answer is correct, I ended up using an udf like this def map_value=udf((inputMap:Map[String,String])=> {inputMap.map(x=>x._2).toArray.mkString })
– Masterbuilder
Nov 24 '18 at 15:29
I think your answer is correct, I ended up using an udf like this def map_value=udf((inputMap:Map[String,String])=> {inputMap.map(x=>x._2).toArray.mkString })
– Masterbuilder
Nov 24 '18 at 15:29
Also fine. Cheers
– thebluephantom
Nov 24 '18 at 15:55
Also fine. Cheers
– thebluephantom
Nov 24 '18 at 15:55
Thats why I inquired how is your input.
– thebluephantom
Nov 24 '18 at 16:00
Thats why I inquired how is your input.
– thebluephantom
Nov 24 '18 at 16:00
add a comment |
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Can you show the def / structure of the DF ?
– thebluephantom
Nov 23 '18 at 19:45