getItem with argument is column name
My column col1 is an Array.
I know that col1.getItem(2) allows you to access the second argument of the column. Is there a function to access with argument as column col1.getItem(col2) ?
I can create a UDF but I would have to specify which type the array is (and it can be multiple type) so a generic way would be better and welcome !
The UDF I use:
def retrieveByIndexSingle[T : ClassTag](value:Seq[T] ,index:Int,offset:Int=0):T = value(index + offset)
def retrieveByIndexSingleDUDF = udf((value:Seq[Double] ,index:Int) => {
retrieveByIndexSingle[Double](value, index)
})
def retrieveByIndexSingleSUDF = udf((value:Seq[String] ,index:Int) => {
retrieveByIndexSingle[String](value, index)
})
scala apache-spark user-defined-functions
asked Nov 19 '18 at 23:21
Guillaume GGuillaume G
768714
add a comment |
My column col1 is an Array.
I know that col1.getItem(2) allows you to access the second argument of the column. Is there a function to access with argument as column col1.getItem(col2) ?
I can create a UDF but I would have to specify which type the array is (and it can be multiple type) so a generic way would be better and welcome !
The UDF I use:
def retrieveByIndexSingle[T : ClassTag](value:Seq[T] ,index:Int,offset:Int=0):T = value(index + offset)
def retrieveByIndexSingleDUDF = udf((value:Seq[Double] ,index:Int) => {
retrieveByIndexSingle[Double](value, index)
})
def retrieveByIndexSingleSUDF = udf((value:Seq[String] ,index:Int) => {
retrieveByIndexSingle[String](value, index)
})
scala apache-spark user-defined-functions
asked Nov 19 '18 at 23:21
Guillaume GGuillaume G
768714
add a comment |
My column col1 is an Array.
I know that col1.getItem(2) allows you to access the second argument of the column. Is there a function to access with argument as column col1.getItem(col2) ?
I can create a UDF but I would have to specify which type the array is (and it can be multiple type) so a generic way would be better and welcome !
The UDF I use:
def retrieveByIndexSingle[T : ClassTag](value:Seq[T] ,index:Int,offset:Int=0):T = value(index + offset)
def retrieveByIndexSingleDUDF = udf((value:Seq[Double] ,index:Int) => {
retrieveByIndexSingle[Double](value, index)
})
def retrieveByIndexSingleSUDF = udf((value:Seq[String] ,index:Int) => {
retrieveByIndexSingle[String](value, index)
})
scala apache-spark user-defined-functions
asked Nov 19 '18 at 23:21
Guillaume GGuillaume G
768714
My column col1 is an Array.
I know that col1.getItem(2) allows you to access the second argument of the column. Is there a function to access with argument as column col1.getItem(col2) ?
I can create a UDF but I would have to specify which type the array is (and it can be multiple type) so a generic way would be better and welcome !
The UDF I use:
def retrieveByIndexSingle[T : ClassTag](value:Seq[T] ,index:Int,offset:Int=0):T = value(index + offset)
def retrieveByIndexSingleDUDF = udf((value:Seq[Double] ,index:Int) => {
retrieveByIndexSingle[Double](value, index)
})
def retrieveByIndexSingleSUDF = udf((value:Seq[String] ,index:Int) => {
retrieveByIndexSingle[String](value, index)
})
scala apache-spark user-defined-functions
scala apache-spark user-defined-functions
asked Nov 19 '18 at 23:21
Guillaume GGuillaume G
768714
asked Nov 19 '18 at 23:21
Guillaume GGuillaume G
768714
asked Nov 19 '18 at 23:21
Guillaume GGuillaume G
768714
asked Nov 19 '18 at 23:21
Guillaume GGuillaume G
768714
asked Nov 19 '18 at 23:21
Guillaume GGuillaume G
768714
768714
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
It is possible to use SQL expression for example with expr:
import org.apache.spark.sql.functions.expr
val df = Seq(
(Seq("a", "b", "c"), 0), (Seq("d", "e", "f"), 2)
).toDF("col1", "col2")
df.withColumn("col3", expr("col1[col2]")).show
+---------+----+----+
| col1|col2|col3|
+---------+----+----+
|[a, b, c]| 0| a|
|[d, e, f]| 2| f|
+---------+----+----+
or, in Spark 2.4 or later, element_at function:
import org.apache.spark.sql.functions.element_at
df.withColumn("col3", element_at($"col1", $"col2" + 1)).show
+---------+----+----+
| col1|col2|col3|
+---------+----+----+
|[a, b, c]| 0| a|
|[d, e, f]| 2| f|
+---------+----+----+
Please note that at the moment (Spark 2.4) there is inconsistency between these two methods:
- SQL
indexing is 0-based.
element_atindexing is 1-based.
answered Nov 20 '18 at 1:05
user10465355user10465355
1,8732417
thanks. The 1-based notation is terrible ...
– Guillaume G
Nov 20 '18 at 2:46
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
It is possible to use SQL expression for example with expr:
import org.apache.spark.sql.functions.expr
val df = Seq(
(Seq("a", "b", "c"), 0), (Seq("d", "e", "f"), 2)
).toDF("col1", "col2")
df.withColumn("col3", expr("col1[col2]")).show
+---------+----+----+
| col1|col2|col3|
+---------+----+----+
|[a, b, c]| 0| a|
|[d, e, f]| 2| f|
+---------+----+----+
or, in Spark 2.4 or later, element_at function:
import org.apache.spark.sql.functions.element_at
df.withColumn("col3", element_at($"col1", $"col2" + 1)).show
+---------+----+----+
| col1|col2|col3|
+---------+----+----+
|[a, b, c]| 0| a|
|[d, e, f]| 2| f|
+---------+----+----+
Please note that at the moment (Spark 2.4) there is inconsistency between these two methods:
- SQL
indexing is 0-based.
element_atindexing is 1-based.
answered Nov 20 '18 at 1:05
user10465355user10465355
1,8732417
thanks. The 1-based notation is terrible ...
– Guillaume G
Nov 20 '18 at 2:46
add a comment |
It is possible to use SQL expression for example with expr:
import org.apache.spark.sql.functions.expr
val df = Seq(
(Seq("a", "b", "c"), 0), (Seq("d", "e", "f"), 2)
).toDF("col1", "col2")
df.withColumn("col3", expr("col1[col2]")).show
+---------+----+----+
| col1|col2|col3|
+---------+----+----+
|[a, b, c]| 0| a|
|[d, e, f]| 2| f|
+---------+----+----+
or, in Spark 2.4 or later, element_at function:
import org.apache.spark.sql.functions.element_at
df.withColumn("col3", element_at($"col1", $"col2" + 1)).show
+---------+----+----+
| col1|col2|col3|
+---------+----+----+
|[a, b, c]| 0| a|
|[d, e, f]| 2| f|
+---------+----+----+
Please note that at the moment (Spark 2.4) there is inconsistency between these two methods:
- SQL
indexing is 0-based.
element_atindexing is 1-based.
answered Nov 20 '18 at 1:05
user10465355user10465355
1,8732417
thanks. The 1-based notation is terrible ...
– Guillaume G
Nov 20 '18 at 2:46
add a comment |
It is possible to use SQL expression for example with expr:
import org.apache.spark.sql.functions.expr
val df = Seq(
(Seq("a", "b", "c"), 0), (Seq("d", "e", "f"), 2)
).toDF("col1", "col2")
df.withColumn("col3", expr("col1[col2]")).show
+---------+----+----+
| col1|col2|col3|
+---------+----+----+
|[a, b, c]| 0| a|
|[d, e, f]| 2| f|
+---------+----+----+
or, in Spark 2.4 or later, element_at function:
import org.apache.spark.sql.functions.element_at
df.withColumn("col3", element_at($"col1", $"col2" + 1)).show
+---------+----+----+
| col1|col2|col3|
+---------+----+----+
|[a, b, c]| 0| a|
|[d, e, f]| 2| f|
+---------+----+----+
Please note that at the moment (Spark 2.4) there is inconsistency between these two methods:
- SQL
indexing is 0-based.
element_atindexing is 1-based.
answered Nov 20 '18 at 1:05
user10465355user10465355
1,8732417
It is possible to use SQL expression for example with expr:
import org.apache.spark.sql.functions.expr
val df = Seq(
(Seq("a", "b", "c"), 0), (Seq("d", "e", "f"), 2)
).toDF("col1", "col2")
df.withColumn("col3", expr("col1[col2]")).show
+---------+----+----+
| col1|col2|col3|
+---------+----+----+
|[a, b, c]| 0| a|
|[d, e, f]| 2| f|
+---------+----+----+
or, in Spark 2.4 or later, element_at function:
import org.apache.spark.sql.functions.element_at
df.withColumn("col3", element_at($"col1", $"col2" + 1)).show
+---------+----+----+
| col1|col2|col3|
+---------+----+----+
|[a, b, c]| 0| a|
|[d, e, f]| 2| f|
+---------+----+----+
Please note that at the moment (Spark 2.4) there is inconsistency between these two methods:
- SQL
indexing is 0-based.
element_atindexing is 1-based.
answered Nov 20 '18 at 1:05
user10465355user10465355
1,8732417
edited Nov 20 '18 at 1:10
answered Nov 20 '18 at 1:05
user10465355user10465355
1,8732417
answered Nov 20 '18 at 1:05
user10465355user10465355
1,8732417
answered Nov 20 '18 at 1:05
user10465355user10465355
1,8732417
1,8732417
thanks. The 1-based notation is terrible ...
– Guillaume G
Nov 20 '18 at 2:46
add a comment |
thanks. The 1-based notation is terrible ...
– Guillaume G
Nov 20 '18 at 2:46
thanks. The 1-based notation is terrible ...
– Guillaume G
Nov 20 '18 at 2:46
thanks. The 1-based notation is terrible ...
– Guillaume G
Nov 20 '18 at 2:46
add a comment |
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