Spark：scala中数据集的动态过滤器

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得满足下面指定的过滤条件的那些行。

• 在field列中的字段上应用过滤器
• 要执行的操作是在optr列，和
• 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（此处所有字段值均为年龄，但可以不同），其中age小于（<）值18，即false，年龄= 75。 我不知道如何在scala中指定此过滤条件。生成的数据集应如下所示

+----+---+-----+----+-----+--------------+
|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

看一下这个：

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>

为了得到工会，你可以做点什么

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

解决方案如下 -

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();