How to update column of spark dataframe based on the values of previous record - scala

Question

I have three columns in df
Col1,col2,col3
X,x1,x2
Z,z1,z2
Y,
X,x3,x4
P,p1,p2
Q,q1,q2
Y
I want to do the following
when col1=x,store the value of col2 and col3
and assign those column values to next row when col1=y
expected output
X,x1,x2
Z,z1,z2
Y,x1,x2
X,x3,x4
P,p1,p2
Q,q1,q2
Y,x3,x4
Any help would be appreciated
Note:-spark 1.6

Answer 1

Here's one approach using Window function with steps as follows:
Add row-identifying column (not needed if there is already one) and combine non-key columns (presumably many of them) into one
Create tmp1 with conditional nulls and tmp2 using last/rowsBetween Window function to back-fill with the last non-null value
Create newcols conditionally from cols and tmp2
Expand newcols back to individual columns using foldLeft
Note that this solution uses Window function without partitioning, thus may not work for large dataset.
val df = Seq(
("X", "x1", "x2"),
("Z", "z1", "z2"),
("Y", "", ""),
("X", "x3", "x4"),
("P", "p1", "p2"),
("Q", "q1", "q2"),
("Y", "", "")
).toDF("col1", "col2", "col3")
import org.apache.spark.sql.functions._
import org.apache.spark.sql.expressions.Window
val colList = df.columns.filter(_ != "col1")
val df2 = df.select($"col1", monotonically_increasing_id.as("id"),
struct(colList.map(col): _*).as("cols")
)
val df3 = df2.
withColumn( "tmp1", when($"col1" === "X", $"cols") ).
withColumn( "tmp2", last("tmp1", ignoreNulls = true).over(
Window.orderBy("id").rowsBetween(Window.unboundedPreceding, 0)
) )
df3.show
// +----+---+-------+-------+-------+
// |col1| id| cols| tmp1| tmp2|
// +----+---+-------+-------+-------+
// | X| 0|[x1,x2]|[x1,x2]|[x1,x2]|
// | Z| 1|[z1,z2]| null|[x1,x2]|
// | Y| 2| [,]| null|[x1,x2]|
// | X| 3|[x3,x4]|[x3,x4]|[x3,x4]|
// | P| 4|[p1,p2]| null|[x3,x4]|
// | Q| 5|[q1,q2]| null|[x3,x4]|
// | Y| 6| [,]| null|[x3,x4]|
// +----+---+-------+-------+-------+
val df4 = df3.withColumn( "newcols",
when($"col1" === "Y", $"tmp2").otherwise($"cols")
).select($"col1", $"newcols")
df4.show
// +----+-------+
// |col1|newcols|
// +----+-------+
// | X|[x1,x2]|
// | Z|[z1,z2]|
// | Y|[x1,x2]|
// | X|[x3,x4]|
// | P|[p1,p2]|
// | Q|[q1,q2]|
// | Y|[x3,x4]|
// +----+-------+
val dfResult = colList.foldLeft( df4 )(
(accDF, c) => accDF.withColumn(c, df4(s"newcols.$c"))
).drop($"newcols")
dfResult.show
// +----+----+----+
// |col1|col2|col3|
// +----+----+----+
// | X| x1| x2|
// | Z| z1| z2|
// | Y| x1| x2|
// | X| x3| x4|
// | P| p1| p2|
// | Q| q1| q2|
// | Y| x3| x4|
// +----+----+----+
[UPDATE]
For Spark 1.x, last(colName, ignoreNulls) isn't available in the DataFrame API. A work-around is to revert to use Spark SQL which supports ignore-null in its last() method:
df2.
withColumn( "tmp1", when($"col1" === "X", $"cols") ).
createOrReplaceTempView("df2table")
// might need to use registerTempTable("df2table") instead
val df3 = spark.sqlContext.sql("""
select col1, id, cols, tmp1, last(tmp1, true) over (
order by id rows between unbounded preceding and current row
) as tmp2
from df2table
""")

Answer 2

Yes, there is a lag function that requires ordering
import org.apache.spark.sql.expressions.Window.orderBy
import org.apache.spark.sql.functions.{coalesce, lag}
case class Temp(a: String, b: Option[String], c: Option[String])
val input = ss.createDataFrame(
Seq(
Temp("A", Some("a1"), Some("a2")),
Temp("D", Some("d1"), Some("d2")),
Temp("B", Some("b1"), Some("b2")),
Temp("E", None, None),
Temp("C", None, None)
))
+---+----+----+
| a| b| c|
+---+----+----+
| A| a1| a2|
| D| d1| d2|
| B| b1| b2|
| E|null|null|
| C|null|null|
+---+----+----+
val order = orderBy($"a")
input
.withColumn("b", coalesce($"b", lag($"b", 1).over(order)))
.withColumn("c", coalesce($"c", lag($"c", 1).over(order)))
.show()
+---+---+---+
| a| b| c|
+---+---+---+
| A| a1| a2|
| B| b1| b2|
| C| b1| b2|
| D| d1| d2|
| E| d1| d2|
+---+---+---+