Spark UDF with varargs
Is it an only option to list all the arguments up to 22 as shown in documentation?
https://spark.apache.org/docs/1.5.0/api/scala/index.html#org.apache.spark.sql.UDFRegistration
Anyone figured out how to do something similar to this?
sc.udf.register("func", (s: String*) => s......
(writing custom concat function that skips nulls, had to 2 arguments at the time)
Thanks
scala apache-spark apache-spark-sql user-defined-functions
edited Jan 14 at 9:33
Community♦
11
asked Oct 15 '15 at 14:56
devopslifedevopslife
3361414
add a comment |
Is it an only option to list all the arguments up to 22 as shown in documentation?
https://spark.apache.org/docs/1.5.0/api/scala/index.html#org.apache.spark.sql.UDFRegistration
Anyone figured out how to do something similar to this?
sc.udf.register("func", (s: String*) => s......
(writing custom concat function that skips nulls, had to 2 arguments at the time)
Thanks
scala apache-spark apache-spark-sql user-defined-functions
edited Jan 14 at 9:33
Community♦
11
asked Oct 15 '15 at 14:56
devopslifedevopslife
3361414
add a comment |
Is it an only option to list all the arguments up to 22 as shown in documentation?
https://spark.apache.org/docs/1.5.0/api/scala/index.html#org.apache.spark.sql.UDFRegistration
Anyone figured out how to do something similar to this?
sc.udf.register("func", (s: String*) => s......
(writing custom concat function that skips nulls, had to 2 arguments at the time)
Thanks
scala apache-spark apache-spark-sql user-defined-functions
edited Jan 14 at 9:33
Community♦
11
asked Oct 15 '15 at 14:56
devopslifedevopslife
3361414
Is it an only option to list all the arguments up to 22 as shown in documentation?
https://spark.apache.org/docs/1.5.0/api/scala/index.html#org.apache.spark.sql.UDFRegistration
Anyone figured out how to do something similar to this?
sc.udf.register("func", (s: String*) => s......
(writing custom concat function that skips nulls, had to 2 arguments at the time)
Thanks
scala apache-spark apache-spark-sql user-defined-functions
scala apache-spark apache-spark-sql user-defined-functions
edited Jan 14 at 9:33
Community♦
11
asked Oct 15 '15 at 14:56
devopslifedevopslife
3361414
edited Jan 14 at 9:33
Community♦
11
asked Oct 15 '15 at 14:56
devopslifedevopslife
3361414
edited Jan 14 at 9:33
Community♦
11
edited Jan 14 at 9:33
Community♦
11
edited Jan 14 at 9:33
Community♦
11
11
asked Oct 15 '15 at 14:56
devopslifedevopslife
3361414
asked Oct 15 '15 at 14:56
devopslifedevopslife
3361414
asked Oct 15 '15 at 14:56
devopslifedevopslife
3361414
3361414
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
UDFs don't support varargs* but you can pass an arbitrary number of columns wrapped using an array function:
import org.apache.spark.sql.functions.{udf, array, lit}
val myConcatFunc = (xs: Seq[Any], sep: String) =>
xs.filter(_ != null).mkString(sep)
val myConcat = udf(myConcatFunc)
An example usage:
val df = sc.parallelize(Seq(
(null, "a", "b", "c"), ("d", null, null, "e")
)).toDF("x1", "x2", "x3", "x4")
val cols = array($"x1", $"x2", $"x3", $"x4")
val sep = lit("-")
df.select(myConcat(cols, sep).alias("concatenated")).show
// +------------+
// |concatenated|
// +------------+
// | a-b-c|
// | d-e|
// +------------+
With raw SQL:
df.registerTempTable("df")
sqlContext.udf.register("myConcat", myConcatFunc)
sqlContext.sql(
"SELECT myConcat(array(x1, x2, x4), '.') AS concatenated FROM df"
).show
// +------------+
// |concatenated|
// +------------+
// | a.c|
// | d.e|
// +------------+
A slightly more complicated approach is not use UDF at all and compose SQL expressions with something roughly like this:
import org.apache.spark.sql.functions._
import org.apache.spark.sql.Column
def myConcatExpr(sep: String, cols: Column*) = regexp_replace(concat(
cols.foldLeft(lit(""))(
(acc, c) => when(c.isNotNull, concat(acc, c, lit(sep))).otherwise(acc)
)
), s"($sep)?$$", "")
df.select(
myConcatExpr("-", $"x1", $"x2", $"x3", $"x4").alias("concatenated")
).show
// +------------+
// |concatenated|
// +------------+
// | a-b-c|
// | d-e|
// +------------+
but I doubt it is worth the effort unless you work with PySpark.
* If you pass a function using varargs it will be stripped from all the syntactic sugar and resulting UDF will expect an ArrayType. For example:
def f(s: String*) = s.mkString
udf(f _)
will be of type:
UserDefinedFunction(<function1>,StringType,List(ArrayType(StringType,true)))
answered Oct 15 '15 at 15:07
zero323zero323
166k40484576
Hi, Is there any way to get column name while concatenating...
– Kal
Aug 23 '16 at 12:27
No, unless you pass column names explicitly as literals.
– zero323
Aug 23 '16 at 13:31
Hey thanks, can you please share the syntax for the same
– Kal
Aug 23 '16 at 14:23
2
@Kalpesharray(df.columns.map(c => struct(lit(c), col(c)): _*)->udf(xs: Seq[Row] => ???).
– zero323
Aug 23 '16 at 14:32
Pay attention to write array and not Array when calling the function
– Ameba Spugnosa
Nov 2 '16 at 9:16
add a comment |
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1 Answer
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UDFs don't support varargs* but you can pass an arbitrary number of columns wrapped using an array function:
import org.apache.spark.sql.functions.{udf, array, lit}
val myConcatFunc = (xs: Seq[Any], sep: String) =>
xs.filter(_ != null).mkString(sep)
val myConcat = udf(myConcatFunc)
An example usage:
val df = sc.parallelize(Seq(
(null, "a", "b", "c"), ("d", null, null, "e")
)).toDF("x1", "x2", "x3", "x4")
val cols = array($"x1", $"x2", $"x3", $"x4")
val sep = lit("-")
df.select(myConcat(cols, sep).alias("concatenated")).show
// +------------+
// |concatenated|
// +------------+
// | a-b-c|
// | d-e|
// +------------+
With raw SQL:
df.registerTempTable("df")
sqlContext.udf.register("myConcat", myConcatFunc)
sqlContext.sql(
"SELECT myConcat(array(x1, x2, x4), '.') AS concatenated FROM df"
).show
// +------------+
// |concatenated|
// +------------+
// | a.c|
// | d.e|
// +------------+
A slightly more complicated approach is not use UDF at all and compose SQL expressions with something roughly like this:
import org.apache.spark.sql.functions._
import org.apache.spark.sql.Column
def myConcatExpr(sep: String, cols: Column*) = regexp_replace(concat(
cols.foldLeft(lit(""))(
(acc, c) => when(c.isNotNull, concat(acc, c, lit(sep))).otherwise(acc)
)
), s"($sep)?$$", "")
df.select(
myConcatExpr("-", $"x1", $"x2", $"x3", $"x4").alias("concatenated")
).show
// +------------+
// |concatenated|
// +------------+
// | a-b-c|
// | d-e|
// +------------+
but I doubt it is worth the effort unless you work with PySpark.
* If you pass a function using varargs it will be stripped from all the syntactic sugar and resulting UDF will expect an ArrayType. For example:
def f(s: String*) = s.mkString
udf(f _)
will be of type:
UserDefinedFunction(<function1>,StringType,List(ArrayType(StringType,true)))
answered Oct 15 '15 at 15:07
zero323zero323
166k40484576
Hi, Is there any way to get column name while concatenating...
– Kal
Aug 23 '16 at 12:27
No, unless you pass column names explicitly as literals.
– zero323
Aug 23 '16 at 13:31
Hey thanks, can you please share the syntax for the same
– Kal
Aug 23 '16 at 14:23
2
@Kalpesharray(df.columns.map(c => struct(lit(c), col(c)): _*)->udf(xs: Seq[Row] => ???).
– zero323
Aug 23 '16 at 14:32
Pay attention to write array and not Array when calling the function
– Ameba Spugnosa
Nov 2 '16 at 9:16
add a comment |
UDFs don't support varargs* but you can pass an arbitrary number of columns wrapped using an array function:
import org.apache.spark.sql.functions.{udf, array, lit}
val myConcatFunc = (xs: Seq[Any], sep: String) =>
xs.filter(_ != null).mkString(sep)
val myConcat = udf(myConcatFunc)
An example usage:
val df = sc.parallelize(Seq(
(null, "a", "b", "c"), ("d", null, null, "e")
)).toDF("x1", "x2", "x3", "x4")
val cols = array($"x1", $"x2", $"x3", $"x4")
val sep = lit("-")
df.select(myConcat(cols, sep).alias("concatenated")).show
// +------------+
// |concatenated|
// +------------+
// | a-b-c|
// | d-e|
// +------------+
With raw SQL:
df.registerTempTable("df")
sqlContext.udf.register("myConcat", myConcatFunc)
sqlContext.sql(
"SELECT myConcat(array(x1, x2, x4), '.') AS concatenated FROM df"
).show
// +------------+
// |concatenated|
// +------------+
// | a.c|
// | d.e|
// +------------+
A slightly more complicated approach is not use UDF at all and compose SQL expressions with something roughly like this:
import org.apache.spark.sql.functions._
import org.apache.spark.sql.Column
def myConcatExpr(sep: String, cols: Column*) = regexp_replace(concat(
cols.foldLeft(lit(""))(
(acc, c) => when(c.isNotNull, concat(acc, c, lit(sep))).otherwise(acc)
)
), s"($sep)?$$", "")
df.select(
myConcatExpr("-", $"x1", $"x2", $"x3", $"x4").alias("concatenated")
).show
// +------------+
// |concatenated|
// +------------+
// | a-b-c|
// | d-e|
// +------------+
but I doubt it is worth the effort unless you work with PySpark.
* If you pass a function using varargs it will be stripped from all the syntactic sugar and resulting UDF will expect an ArrayType. For example:
def f(s: String*) = s.mkString
udf(f _)
will be of type:
UserDefinedFunction(<function1>,StringType,List(ArrayType(StringType,true)))
answered Oct 15 '15 at 15:07
zero323zero323
166k40484576
Hi, Is there any way to get column name while concatenating...
– Kal
Aug 23 '16 at 12:27
No, unless you pass column names explicitly as literals.
– zero323
Aug 23 '16 at 13:31
Hey thanks, can you please share the syntax for the same
– Kal
Aug 23 '16 at 14:23
2
@Kalpesharray(df.columns.map(c => struct(lit(c), col(c)): _*)->udf(xs: Seq[Row] => ???).
– zero323
Aug 23 '16 at 14:32
Pay attention to write array and not Array when calling the function
– Ameba Spugnosa
Nov 2 '16 at 9:16
add a comment |
UDFs don't support varargs* but you can pass an arbitrary number of columns wrapped using an array function:
import org.apache.spark.sql.functions.{udf, array, lit}
val myConcatFunc = (xs: Seq[Any], sep: String) =>
xs.filter(_ != null).mkString(sep)
val myConcat = udf(myConcatFunc)
An example usage:
val df = sc.parallelize(Seq(
(null, "a", "b", "c"), ("d", null, null, "e")
)).toDF("x1", "x2", "x3", "x4")
val cols = array($"x1", $"x2", $"x3", $"x4")
val sep = lit("-")
df.select(myConcat(cols, sep).alias("concatenated")).show
// +------------+
// |concatenated|
// +------------+
// | a-b-c|
// | d-e|
// +------------+
With raw SQL:
df.registerTempTable("df")
sqlContext.udf.register("myConcat", myConcatFunc)
sqlContext.sql(
"SELECT myConcat(array(x1, x2, x4), '.') AS concatenated FROM df"
).show
// +------------+
// |concatenated|
// +------------+
// | a.c|
// | d.e|
// +------------+
A slightly more complicated approach is not use UDF at all and compose SQL expressions with something roughly like this:
import org.apache.spark.sql.functions._
import org.apache.spark.sql.Column
def myConcatExpr(sep: String, cols: Column*) = regexp_replace(concat(
cols.foldLeft(lit(""))(
(acc, c) => when(c.isNotNull, concat(acc, c, lit(sep))).otherwise(acc)
)
), s"($sep)?$$", "")
df.select(
myConcatExpr("-", $"x1", $"x2", $"x3", $"x4").alias("concatenated")
).show
// +------------+
// |concatenated|
// +------------+
// | a-b-c|
// | d-e|
// +------------+
but I doubt it is worth the effort unless you work with PySpark.
* If you pass a function using varargs it will be stripped from all the syntactic sugar and resulting UDF will expect an ArrayType. For example:
def f(s: String*) = s.mkString
udf(f _)
will be of type:
UserDefinedFunction(<function1>,StringType,List(ArrayType(StringType,true)))
answered Oct 15 '15 at 15:07
zero323zero323
166k40484576
UDFs don't support varargs* but you can pass an arbitrary number of columns wrapped using an array function:
import org.apache.spark.sql.functions.{udf, array, lit}
val myConcatFunc = (xs: Seq[Any], sep: String) =>
xs.filter(_ != null).mkString(sep)
val myConcat = udf(myConcatFunc)
An example usage:
val df = sc.parallelize(Seq(
(null, "a", "b", "c"), ("d", null, null, "e")
)).toDF("x1", "x2", "x3", "x4")
val cols = array($"x1", $"x2", $"x3", $"x4")
val sep = lit("-")
df.select(myConcat(cols, sep).alias("concatenated")).show
// +------------+
// |concatenated|
// +------------+
// | a-b-c|
// | d-e|
// +------------+
With raw SQL:
df.registerTempTable("df")
sqlContext.udf.register("myConcat", myConcatFunc)
sqlContext.sql(
"SELECT myConcat(array(x1, x2, x4), '.') AS concatenated FROM df"
).show
// +------------+
// |concatenated|
// +------------+
// | a.c|
// | d.e|
// +------------+
A slightly more complicated approach is not use UDF at all and compose SQL expressions with something roughly like this:
import org.apache.spark.sql.functions._
import org.apache.spark.sql.Column
def myConcatExpr(sep: String, cols: Column*) = regexp_replace(concat(
cols.foldLeft(lit(""))(
(acc, c) => when(c.isNotNull, concat(acc, c, lit(sep))).otherwise(acc)
)
), s"($sep)?$$", "")
df.select(
myConcatExpr("-", $"x1", $"x2", $"x3", $"x4").alias("concatenated")
).show
// +------------+
// |concatenated|
// +------------+
// | a-b-c|
// | d-e|
// +------------+
but I doubt it is worth the effort unless you work with PySpark.
* If you pass a function using varargs it will be stripped from all the syntactic sugar and resulting UDF will expect an ArrayType. For example:
def f(s: String*) = s.mkString
udf(f _)
will be of type:
UserDefinedFunction(<function1>,StringType,List(ArrayType(StringType,true)))
answered Oct 15 '15 at 15:07
zero323zero323
166k40484576
edited Apr 29 '16 at 19:38
answered Oct 15 '15 at 15:07
zero323zero323
166k40484576
answered Oct 15 '15 at 15:07
zero323zero323
166k40484576
answered Oct 15 '15 at 15:07
zero323zero323
166k40484576
166k40484576
Hi, Is there any way to get column name while concatenating...
– Kal
Aug 23 '16 at 12:27
No, unless you pass column names explicitly as literals.
– zero323
Aug 23 '16 at 13:31
Hey thanks, can you please share the syntax for the same
– Kal
Aug 23 '16 at 14:23
2
@Kalpesharray(df.columns.map(c => struct(lit(c), col(c)): _*)->udf(xs: Seq[Row] => ???).
– zero323
Aug 23 '16 at 14:32
Pay attention to write array and not Array when calling the function
– Ameba Spugnosa
Nov 2 '16 at 9:16
add a comment |
Hi, Is there any way to get column name while concatenating...
– Kal
Aug 23 '16 at 12:27
No, unless you pass column names explicitly as literals.
– zero323
Aug 23 '16 at 13:31
Hey thanks, can you please share the syntax for the same
– Kal
Aug 23 '16 at 14:23
2
@Kalpesharray(df.columns.map(c => struct(lit(c), col(c)): _*)->udf(xs: Seq[Row] => ???).
– zero323
Aug 23 '16 at 14:32
Pay attention to write array and not Array when calling the function
– Ameba Spugnosa
Nov 2 '16 at 9:16
Hi, Is there any way to get column name while concatenating...
– Kal
Aug 23 '16 at 12:27
Hi, Is there any way to get column name while concatenating...
– Kal
Aug 23 '16 at 12:27
No, unless you pass column names explicitly as literals.
– zero323
Aug 23 '16 at 13:31
No, unless you pass column names explicitly as literals.
– zero323
Aug 23 '16 at 13:31
Hey thanks, can you please share the syntax for the same
– Kal
Aug 23 '16 at 14:23
Hey thanks, can you please share the syntax for the same
– Kal
Aug 23 '16 at 14:23
2
2
@Kalpesh
array(df.columns.map(c => struct(lit(c), col(c)): _*) -> udf(xs: Seq[Row] => ???).– zero323
Aug 23 '16 at 14:32
@Kalpesh
array(df.columns.map(c => struct(lit(c), col(c)): _*) -> udf(xs: Seq[Row] => ???).– zero323
Aug 23 '16 at 14:32
Pay attention to write array and not Array when calling the function
– Ameba Spugnosa
Nov 2 '16 at 9:16
Pay attention to write array and not Array when calling the function
– Ameba Spugnosa
Nov 2 '16 at 9:16
add a comment |
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