dataframe look up and optimization

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被撕碎了的回忆 2020-12-02 03:08

I am using spark-sql-2.4.3v with java. I have scenario below

val data = List(
  ("20", "score", "school",  14 ,12),
  ("21&         


        
2条回答
  •  时光取名叫无心
    2020-12-02 03:35

    If lookup data is of small size then you can create Map and broadcast it. broadcasted map can be easily used in udf as below-

    Load the test data provided

     val data = List(
          ("20", "score", "school",  14 ,12),
          ("21", "score", "school",  13 , 13),
          ("22", "rate", "school",  11 ,14),
          ("23", "score", "school",  11 ,14),
          ("24", "rate", "school",  12 ,12),
          ("25", "score", "school", 11 ,14)
        )
        val df = data.toDF("id", "code", "entity", "value1","value2")
        df.show
        /**
          * +---+-----+------+------+------+
          * | id| code|entity|value1|value2|
          * +---+-----+------+------+------+
          * | 20|score|school|    14|    12|
          * | 21|score|school|    13|    13|
          * | 22| rate|school|    11|    14|
          * | 23|score|school|    11|    14|
          * | 24| rate|school|    12|    12|
          * | 25|score|school|    11|    14|
          * +---+-----+------+------+------+
          */
    
        //this look up data populated from DB.
    
        val ll = List(
          ("aaaa", 11),
          ("aaa", 12),
          ("aa", 13),
          ("a", 14)
        )
        val codeValudeDf = ll.toDF( "code", "value")
        codeValudeDf.show
        /**
          * +----+-----+
          * |code|value|
          * +----+-----+
          * |aaaa|   11|
          * | aaa|   12|
          * |  aa|   13|
          * |   a|   14|
          * +----+-----+
          */
    

    broadcasted map can be easily used in udf as below-

    
        val lookUp = spark.sparkContext
          .broadcast(codeValudeDf.map{case Row(code: String, value: Integer) => value -> code}
          .collect().toMap)
    
        val look_up = udf((value: Integer) => lookUp.value.get(value))
    
        df.withColumn("value1",
          when($"code" === "score", look_up($"value1")).otherwise($"value1".cast("string")))
          .withColumn("value2",
            when($"code" === "score", look_up($"value2")).otherwise($"value2".cast("string")))
          .show(false)
        /**
          * +---+-----+------+------+------+
          * |id |code |entity|value1|value2|
          * +---+-----+------+------+------+
          * |20 |score|school|a     |aaa   |
          * |21 |score|school|aa    |aa    |
          * |22 |rate |school|11    |14    |
          * |23 |score|school|aaaa  |a     |
          * |24 |rate |school|12    |12    |
          * |25 |score|school|aaaa  |a     |
          * +---+-----+------+------+------+
          */
    
    
    

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