When I run the following on the spark-shell, I get a dataframe:
scala> val df = Seq(Array(1,2)).toDF("a")
scala> df.show(false)
+------+
|a |
+------+
|[1, 2]|
+------+
But when I run the following to create a dataframe with two columns:
scala> val df1 = Seq(Seq(Array(1,2)),"jf").toDF("a","b")
<console>:23: error: value toDF is not a member of Seq[Object]
val df1 = Seq(Seq(Array(1,2)),"jf").toDF("a","b")
I get the error:
Value toDF is not a member of Seq[Object].
How do I go about this? Is toDF only supported for sequences with primitive datatypes?
You need a Seq of Tuple for the toDF method to work:
val df1 = Seq((Array(1,2),"jf")).toDF("a","b")
// df1: org.apache.spark.sql.DataFrame = [a: array<int>, b: string]
df1.show
+------+---+
| a| b|
+------+---+
|[1, 2]| jf|
+------+---+
Add more tuples for more rows:
val df1 = Seq((Array(1,2),"jf"), (Array(2), "ab")).toDF("a","b")
// df1: org.apache.spark.sql.DataFrame = [a: array<int>, b: string]
df1.show
+------+---+
| a| b|
+------+---+
|[1, 2]| jf|
| [2]| ab|
+------+---+
Related
I want to perform a lookup on myMap. When col2 value is "0000" I want to update it with the value related to col1 key. Otherwise I want to keep the existing col2 value.
val myDF :
+-----+-----+
|col1 |col2 |
+-----+-----+
|1 |a |
|2 |0000 |
|3 |c |
|4 |0000 |
+-----+-----+
val myMap : Map[String, String] ("2" -> "b", "4" -> "d")
val broadcastMyMap = spark.sparkContext.broadcast(myMap)
def lookup = udf((key:String) => broadcastMyMap.value.get(key))
myDF.withColumn("col2", when ($"col2" === "0000", lookup($"col1")).otherwise($"col2"))
I've used the code above in spark-shell and it works fine but when I build the application jar and submit it to Spark using spark-submit it throws an error:
org.apache.spark.SparkException: Failed to execute user defined function(anonfun$5: (string) => string)
Caused by: java.lang.NullPointerException
Is there a way to perform the lookup without using UDF, which aren't the best option in terms of performance, or to fix the error?
I think I can't just use join because some values of myDF.col2 that have to be kept could be sobstituted in the operation.
your NullPointerException is NOT Valid.I proved with sample program like below.
its PERFECTLY WORKING FINE. you execute the below program.
package com.example
import org.apache.log4j.{Level, Logger}
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.expressions.UserDefinedFunction
object MapLookupDF {
Logger.getLogger("org").setLevel(Level.OFF)
def main(args: Array[String]) {
import org.apache.spark.sql.functions._
val spark = SparkSession.builder.
master("local[*]")
.appName("MapLookupDF")
.getOrCreate()
import spark.implicits._
val mydf = Seq((1, "a"), (2, "0000"), (3, "c"), (4, "0000")).toDF("col1", "col2")
mydf.show
val myMap: Map[String, String] = Map("2" -> "b", "4" -> "d")
println(myMap.toString)
val broadcastMyMap = spark.sparkContext.broadcast(myMap)
def lookup: UserDefinedFunction = udf((key: String) => {
println("getting the value for the key " + key)
broadcastMyMap.value.get(key)
}
)
val finaldf = mydf.withColumn("col2", when($"col2" === "0000", lookup($"col1")).otherwise($"col2"))
finaldf.show
}
}
Result :
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
+----+----+
|col1|col2|
+----+----+
| 1| a|
| 2|0000|
| 3| c|
| 4|0000|
+----+----+
Map(2 -> b, 4 -> d)
getting the value for the key 2
getting the value for the key 4
+----+----+
|col1|col2|
+----+----+
| 1| a|
| 2| b|
| 3| c|
| 4| d|
+----+----+
note: there wont be significant degradation for a small map broadcasted.
if you want to go with a dataframe you can go as convert map to dataframe
val df = myMap.toSeq.toDF("key", "val")
Map(2 -> b, 4 -> d) in dataframe format will be like
+----+----+
|key|val |
+----+----+
| 2| b|
| 4| d|
+----+----+
and then join like this
DIY...
I have a DataFrame with a column 'title_from' as below.
.
This colume contains a sentence and I want to transform this column into a Array[String]. I have tried something like this but it does not works.
val newDF = df.select("title_from").map(x => x.split("\\\s+")
How can I achieve this? How can I transform a datafram of strings into a dataframe of Array[string]? I want evry line of newDF to be an array of words from df.
Thanks for any help!
You can use the withColumn function.
import org.apache.spark.sql.functions._
val newDF = df.withColumn("split_title_from", split(col("title_from"), "\\s+"))
.select("split_title_from")
Can you try following to get the list of all authors
scala> val df = Seq((1,"a1,a2,a3"), (2,"a1,a4,a10")).toDF("id","author")
df: org.apache.spark.sql.DataFrame = [id: int, author: string]
scala> df.show()
+---+---------+
| id| author|
+---+---------+
| 1| a1,a2,a3|
| 2|a1,a4,a10|
+---+---------+
scala> df.select("author").show
+---------+
| author|
+---------+
| a1,a2,a3|
|a1,a4,a10|
+---------+
scala> df.select("author").flatMap( row => { row.get(0).toString().split(",")}).show()
+-----+
|value|
+-----+
| a1|
| a2|
| a3|
| a1|
| a4|
| a10|
+-----+
I have a column named root and need to filter dataframe based on the different values of a root column.
Suppose I have a values in root are parent,child or sub-child and I want to apply these filters dynamically through a variable.
val x = ("parent,child,sub-child").split(",")
x.map(eachvalue <- {
var df1 = df.filter(col("root").contains(eachvalue))
}
But when I am doing it, it always overwriting the DF1 instead, I want to apply all the 3 filters and get the result.
May be in future I may extend the list to any number of filter values and the code should work.
Thanks,
Bab
You should apply the subsequent filters to the result of the previous filter, not on df:
val x = ("parent,child,sub-child").split(",")
var df1 = df
x.map(eachvalue <- {
df1 = df1.filter(col("root").contains(eachvalue))
}
df1 after the map operation will have all filters applied to it.
Let's see an example with spark shell. Hope it helps you.
scala> import spark.implicits._
import spark.implicits._
scala> val df0 =
spark.sparkContext.parallelize(List(1,2,1,3,3,2,1)).toDF("number")
df0: org.apache.spark.sql.DataFrame = [number: int]
scala> val list = List(1,2,3)
list: List[Int] = List(1, 2, 3)
scala> val dfFiltered = for (number <- list) yield { df0.filter($"number" === number)}
dfFiltered: List[org.apache.spark.sql.Dataset[org.apache.spark.sql.Row]] = List([number: int], [number: int], [number: int])
scala> dfFiltered(0).show
+------+
|number|
+------+
| 1|
| 1|
| 1|
+------+
scala> dfFiltered(1).show
+------+
|number|
+------+
| 2|
| 2|
+------+
scala> dfFiltered(2).show
+------+
|number|
+------+
| 3|
| 3|
+------+
AFAIK isin can be used in this case below is the example.
import spark.implicits._
val colorStringArr = "red,yellow,blue".split(",")
val colorDF =
List(
"red",
"yellow",
"purple"
).toDF("color")
// to derive a column using a list
colorDF.withColumn(
"is_primary_color",
col("color").isin(colorStringArr: _*)
).show()
println( "if you don't want derived column and directly want to filter using a list with isin then .. ")
colorDF.filter(col("color").isin(colorStringArr: _*)).show
Result :
+------+----------------+
| color|is_primary_color|
+------+----------------+
| red| true|
|yellow| true|
|purple| false|
+------+----------------+
if you don't want derived column and directly want to filter using a list with isin then ....
+------+
| color|
+------+
| red|
|yellow|
+------+
One more way using array_contains and swapping the arguments.
scala> val x = ("parent,child,sub-child").split(",")
x: Array[String] = Array(parent, child, sub-child)
scala> val df = Seq(("parent"),("grand-parent"),("child"),("sub-child"),("cousin")).toDF("root")
df: org.apache.spark.sql.DataFrame = [root: string]
scala> df.show
+------------+
| root|
+------------+
| parent|
|grand-parent|
| child|
| sub-child|
| cousin|
+------------+
scala> df.withColumn("check", array_contains(lit(x),'root)).show
+------------+-----+
| root|check|
+------------+-----+
| parent| true|
|grand-parent|false|
| child| true|
| sub-child| true|
| cousin|false|
+------------+-----+
scala>
Here are my two cents
val filters = List(1,2,3)
val data = List(5,1,2,1,3,3,2,1,4)
val colName = "number"
val df = spark.
sparkContext.
parallelize(data).
toDF(colName).
filter(
r => filters.contains(r.getAs[Int](colName))
)
df.show()
which results in
+------+
|number|
+------+
| 1|
| 2|
| 1|
| 3|
| 3|
| 2|
| 1|
+------+
I am trying to aggregate multitple columns after a pivot in Scala Spark 2.0.1:
scala> val df = List((1, 2, 3, None), (1, 3, 4, Some(1))).toDF("a", "b", "c", "d")
df: org.apache.spark.sql.DataFrame = [a: int, b: int ... 2 more fields]
scala> df.show
+---+---+---+----+
| a| b| c| d|
+---+---+---+----+
| 1| 2| 3|null|
| 1| 3| 4| 1|
+---+---+---+----+
scala> val pivoted = df.groupBy("a").pivot("b").agg(max("c"), max("d"))
pivoted: org.apache.spark.sql.DataFrame = [a: int, 2_max(`c`): int ... 3 more fields]
scala> pivoted.show
+---+----------+----------+----------+----------+
| a|2_max(`c`)|2_max(`d`)|3_max(`c`)|3_max(`d`)|
+---+----------+----------+----------+----------+
| 1| 3| null| 4| 1|
+---+----------+----------+----------+----------+
I am unable to select or rename those columns so far:
scala> pivoted.select("3_max(`d`)")
org.apache.spark.sql.AnalysisException: syntax error in attribute name: 3_max(`d`);
scala> pivoted.select("`3_max(`d`)`")
org.apache.spark.sql.AnalysisException: syntax error in attribute name: `3_max(`d`)`;
scala> pivoted.select("`3_max(d)`")
org.apache.spark.sql.AnalysisException: cannot resolve '`3_max(d)`' given input columns: [2_max(`c`), 3_max(`d`), a, 2_max(`d`), 3_max(`c`)];
There must be a simple trick here, any ideas? Thanks.
Seems like a bug, the back ticks caused the problem. One fix here would be to remove the back ticks from the column names:
val pivotedNewName = pivoted.columns.foldLeft(pivoted)((df, col) =>
df.withColumnRenamed(col, col.replace("`", "")))
Now you can select by column names as normal:
pivotedNewName.select("2_max(c)").show
+--------+
|2_max(c)|
+--------+
| 3|
+--------+
test.csv
name,key1,key2
A,1,2
B,1,3
C,4,3
I want to change this data like this (as dataset or rdd)
whatIwant.csv
name,key,newkeyname
A,1,KEYA
A,2,KEYB
B,1,KEYA
B,3,KEYB
C,4,KEYA
C,3,KEYB
I loaded data with read method.
val df = spark.read
.option("header", true)
.option("charset", "euc-kr")
.csv(csvFilePath)
I can load each dataset like (name, key1) or (name, key2), and union them by union, but want to do this in single spark session.
Any idea of this?
Those are not working.
val df2 = df.select( df("TAG_NO"), df.map { x => (x.getAs[String]("MK_VNDRNM"), x.getAs[String]("WK_ORD_DT")) })
val df2 = df.select( df("TAG_NO"), Seq(df("TAG_NO"), df("WK_ORD_DT")))
This can be accomplished with explode and a udf:
scala> val df = Seq(("A", 1, 2), ("B", 1, 3), ("C", 4, 3)).toDF("name", "key1", "key2")
df: org.apache.spark.sql.DataFrame = [name: string, key1: int ... 1 more field]
scala> df.show
+----+----+----+
|name|key1|key2|
+----+----+----+
| A| 1| 2|
| B| 1| 3|
| C| 4| 3|
+----+----+----+
scala> val explodeUDF = udf((v1: String, v2: String) => Vector((v1, "Key1"), (v2, "Key2")))
explodeUDF: org.apache.spark.sql.expressions.UserDefinedFunction = UserDefinedFunction(<function2>,ArrayType(StructType(StructField(_1,StringType,true), StructField(_2,StringType,true)),true),Some(List(StringType, StringType)))
scala> df = df.withColumn("TMP", explode(explodeUDF($"key1", $"key2"))).drop("key1", "key2")
df: org.apache.spark.sql.DataFrame = [name: string, TMP: struct<_1: string, _2: string>]
scala> df = df.withColumn("key", $"TMP".apply("_1")).withColumn("new key name", $"TMP".apply("_2"))
df: org.apache.spark.sql.DataFrame = [name: string, TMP: struct<_1: string, _2: string> ... 2 more fields]
scala> df = df.drop("TMP")
df: org.apache.spark.sql.DataFrame = [name: string, key: string ... 1 more field]
scala> df.show
+----+---+------------+
|name|key|new key name|
+----+---+------------+
| A| 1| Key1|
| A| 2| Key2|
| B| 1| Key1|
| B| 3| Key2|
| C| 4| Key1|
| C| 3| Key2|
+----+---+------------+