Question

我有一个看起来像

的数据集（ds）

scala> ds.show()
+----+---+-----+----+-----+--------------+
|name|age|field|optr|value|          rule|
+----+---+-----+----+-----+--------------+
|   a| 75|  age|   <|   18|         Minor|
|   b| 10|  age|   <|   18|         Minor|
|   c| 30|  age|   <|   18|         Minor|
|   a| 75|  age|  >=|   18|         Major|
|   b| 10|  age|  >=|   18|         Major|
|   c| 30|  age|  >=|   18|         Major|
|   a| 75|  age|   >|   60|Senior Citizen|
|   b| 10|  age|   >|   60|Senior Citizen|
|   c| 30|  age|   >|   60|Senior Citizen|
+----+---+-----+----+-----+--------------+

现在我需要对此应用过滤器，以获取满足以下指定过滤条件的行。

对field列中的字段应用过滤器
要执行的操作在optr列中，并且
要比较的值在value列中。

示例：对于第一行-在age列上应用过滤器（此处所有字段值都是age，但可以不同），其中age是小于（< ）的值18 ，即false，为 age = 75 。

我不知道如何在Scala中指定此过滤条件。结果数据集应该看起来像

+----+---+-----+----+-----+--------------+
|name|age|field|optr|value|          rule|
+----+---+-----+----+-----+--------------+
|   b| 10|  age|   <|   18|         Minor|
|   a| 75|  age|  >=|   18|         Major|
|   c| 30|  age|  >=|   18|         Major|
|   a| 75|  age|   >|   60|Senior Citizen|
+----+---+-----+----+-----+--------------+

Answer 1

检查一下：

scala> val df = Seq(("a",75,"age","<",18,"Minor"),("b",10,"age","<",18,"Minor"),("c",30,"age","<",18,"Minor"),("a",75,"age",">=",18,"Major"),("b",10,"age",">=",18,"Major"),("c",30,"age",">=",18,"Major"),("a",75,"age",">",60,"Senior Citizen"),("b",10,"age",">",60,"Senior Citizen"),("c",30,"age",">",60,"Senior Citizen")).toDF("name","age","field","optr","value","rule")
df: org.apache.spark.sql.DataFrame = [name: string, age: int ... 4 more fields]

scala> df.show(false)
+----+---+-----+----+-----+--------------+
|name|age|field|optr|value|rule          |
+----+---+-----+----+-----+--------------+
|a   |75 |age  |<   |18   |Minor         |
|b   |10 |age  |<   |18   |Minor         |
|c   |30 |age  |<   |18   |Minor         |
|a   |75 |age  |>=  |18   |Major         |
|b   |10 |age  |>=  |18   |Major         |
|c   |30 |age  |>=  |18   |Major         |
|a   |75 |age  |>   |60   |Senior Citizen|
|b   |10 |age  |>   |60   |Senior Citizen|
|c   |30 |age  |>   |60   |Senior Citizen|
+----+---+-----+----+-----+--------------+

scala> val df2 = df.withColumn("condn", concat('field,'optr,'value))
df2: org.apache.spark.sql.DataFrame = [name: string, age: int ... 5 more fields]

scala> val condn_list=df2.groupBy().agg(collect_set('condn).as("condns")).as[(Seq[String])].first
condn_list: Seq[String] = List(age>60, age<18, age>=18)

scala>  val df_filters = condn_list.map{ x => df2.filter(s""" condn='${x}' and $x """) }
df_filters: Seq[org.apache.spark.sql.Dataset[org.apache.spark.sql.Row]] = List([name: string, age: int ... 5 more fields], [name: string, age: int ... 5 more fields], [name: string, age: int ... 5 more fields])

scala> df_filters(0).union(df_filters(1)).union(df_filters(2)).show(false)
+----+---+-----+----+-----+--------------+-------+
|name|age|field|optr|value|rule          |condn  |
+----+---+-----+----+-----+--------------+-------+
|b   |10 |age  |<   |18   |Minor         |age<18 |
|a   |75 |age  |>   |60   |Senior Citizen|age>60 |
|a   |75 |age  |>=  |18   |Major         |age>=18|
|c   |30 |age  |>=  |18   |Major         |age>=18|
+----+---+-----+----+-----+--------------+-------+


scala>

要获得工会，您可以做类似的事情

scala> var res = df_filters(0)
res: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [name: string, age: int ... 5 more fields]

scala> (1 until df_filters.length).map( x => { res = res.union(df_filters(x)) } )
res20: scala.collection.immutable.IndexedSeq[Unit] = Vector((), ())

scala> res.show(false)
+----+---+-----+----+-----+--------------+-------+
|name|age|field|optr|value|rule          |condn  |
+----+---+-----+----+-----+--------------+-------+
|b   |10 |age  |<   |18   |Minor         |age<18 |
|a   |75 |age  |>   |60   |Senior Citizen|age>60 |
|a   |75 |age  |>=  |18   |Major         |age>=18|
|c   |30 |age  |>=  |18   |Major         |age>=18|
+----+---+-----+----+-----+--------------+-------+


scala>

Answer 2

解决方法如下-

import org.apache.spark.sql.catalyst.encoders.RowEncoder
import org.apache.spark.sql.Row
import scala.collection.mutable

val encoder = RowEncoder(df.schema);
df.flatMap(row => {
    val result = new mutable.MutableList[Row];
    val ruleField = row.getAs[String]("field");
    val ruleValue = row.getAs[Int]("value");
    val ruleOptr = row.getAs[String]("optr");
    val rowField = row.getAs[Int](ruleField);
    val condition = {ruleOptr match{
        case "=" => rowField == ruleValue;
        case "<" => rowField < ruleValue;
        case "<=" => rowField <= ruleValue;
        case ">" => rowField > ruleValue;
        case ">=" => rowField >= ruleValue;
        case _ => false;
        }
    };
    if (condition){
        result+=row;
    };
    result;
})(encoder).show();

Spark：对Scala中的数据集进行动态过滤

2 个答案: