In spark iterate through each column and find the max length

Question

I am new to spark scala and I have following situation as below I have a table "TEST_TABLE" on cluster(can be hive table) I am converting that to dataframe as:

scala> val testDF = spark.sql("select * from TEST_TABLE limit 10")

Now the DF can be viewed as

scala> testDF.show()

COL1|COL2|COL3  
----------------
abc|abcd|abcdef 
a|BCBDFG|qddfde 
MN|1234B678|sd

I want an output like below

COLUMN_NAME|MAX_LENGTH
       COL1|3
       COL2|8
       COL3|6

Is this feasible to do so in spark scala?

Answer 1

Plain and simple:

import org.apache.spark.sql.functions._

val df = spark.table("TEST_TABLE")
df.select(df.columns.map(c => max(length(col(c)))): _*)

Answer 2

You can try in the following way:

import org.apache.spark.sql.functions.{length, max}
import spark.implicits._

val df = Seq(("abc","abcd","abcdef"),
          ("a","BCBDFG","qddfde"),
          ("MN","1234B678","sd"),
          (null,"","sd")).toDF("COL1","COL2","COL3")
df.cache()
val output = df.columns.map(c => (c, df.agg(max(length(df(s"$c")))).as[Int].first())).toSeq.toDF("COLUMN_NAME", "MAX_LENGTH")
        +-----------+----------+
        |COLUMN_NAME|MAX_LENGTH|
        +-----------+----------+
        |       COL1|         3|
        |       COL2|         8|
        |       COL3|         6|
        +-----------+----------+

I think it's good idea to cache input dataframe df to make the computation faster.

Answer 3

Here is one more way to get the report of column names in vertical

scala> val df = Seq(("abc","abcd","abcdef"),("a","BCBDFG","qddfde"),("MN","1234B678","sd")).toDF("COL1","COL2","COL3")
df: org.apache.spark.sql.DataFrame = [COL1: string, COL2: string ... 1 more field]

scala> df.show(false)
+----+--------+------+
|COL1|COL2    |COL3  |
+----+--------+------+
|abc |abcd    |abcdef|
|a   |BCBDFG  |qddfde|
|MN  |1234B678|sd    |
+----+--------+------+

scala> val columns = df.columns
columns: Array[String] = Array(COL1, COL2, COL3)

scala> val df2 = columns.foldLeft(df) { (acc,x) => acc.withColumn(x,length(col(x))) }
df2: org.apache.spark.sql.DataFrame = [COL1: int, COL2: int ... 1 more field]

scala> df2.select( columns.map(x => max(col(x))):_* ).show(false)
+---------+---------+---------+
|max(COL1)|max(COL2)|max(COL3)|
+---------+---------+---------+
|3        |8        |6        |
+---------+---------+---------+


scala> df3.flatMap( r => { (0 until r.length).map( i => (columns(i),r.getInt(i)) ) } ).show(false)
+----+---+
|_1  |_2 |
+----+---+
|COL1|3  |
|COL2|8  |
|COL3|6  |
+----+---+


scala>

To get the results into Scala collections, say Map()

scala> val result = df3.flatMap( r => { (0 until r.length).map( i => (columns(i),r.getInt(i)) ) } ).as[(String,Int)].collect.toMap
result: scala.collection.immutable.Map[String,Int] = Map(COL1 -> 3, COL2 -> 8, COL3 -> 6)

scala> result
res47: scala.collection.immutable.Map[String,Int] = Map(COL1 -> 3, COL2 -> 8, COL3 -> 6)

scala>