In spark iterate through each column and find the max length

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清歌不尽
清歌不尽 2021-01-15 20:28

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:

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  • 2021-01-15 21:03

    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)))): _*)
    
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  • 2021-01-15 21:08

    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>
    
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  • 2021-01-15 21:29

    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.

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