I am using spark-sql-2.4.3v with java. I have scenario below
val data = List(
("20", "score", "school", 14 ,12),
("21&
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-
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 |
* +---+-----+------+------+------+
*/