Question

我有一个类似的数据框

scala> testDf.show()
+------+--------+---------+------------+----------------------------------------+
|    id|    item|    value|  value_name|                               condition|
+------+--------+---------+------------+----------------------------------------+
|    11|    3210|        0|         OFF|                                value==0|
|    12|    3210|        1|         OFF|                                value==0|
|    13|    3210|        0|         OFF|                                value==0|
|    14|    3210|        0|         OFF|                                value==0|
|    15|    3210|        1|         OFF|                                value==0|
|    16|    5440|        5|          ON|                     value>0 && value<10|
|    17|    5440|        0|          ON|                     value>0 && value<10|
|    18|    5440|        6|          ON|                     value>0 && value<10|
|    19|    5440|        7|          ON|                     value>0 && value<10|
|    20|    5440|        0|          ON|                     value>0 && value<10|
|    21|    7780|        A|        TYPE|   Set("A","B").contains(value.toString)|
|    22|    7780|        A|        TYPE|   Set("A","B").contains(value.toString)|
|    23|    7780|        A|        TYPE|   Set("A","B").contains(value.toString)|
|    24|    7780|        C|        TYPE|   Set("A","B").contains(value.toString)|
|    25|    7780|        C|        TYPE|   Set("A","B").contains(value.toString)|
+------+--------+---------+------------+----------------------------------------+

scala> testDf.printSchema
root
 |-- id: string (nullable = true)
 |-- item: string (nullable = true)
 |-- value: string (nullable = true)
 |-- value_name: string (nullable = true)
 |-- condition: string (nullable = true)

我想删除一些带有“条件”列的行。但我有麻烦。

我尝试了以下测试代码。但这似乎无法正常工作。

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

val encoder = RowEncoder(testDf.schema);

testDf.flatMap(row => {
  val result = new mutable.MutableList[Row];
  val setting_value = row.getAs[String]("setting_value").toInt
  val condition = row.getAs[String]("condition").toBoolean
  if (condition){
      result+=row;
  };
  result;
})(encoder).show();

这是错误。

19/05/30 02:04:31 ERROR TaskSetManager: Task 0 in stage 267.0 failed 4 times; aborting job
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 267.0 failed 4 times, most recent failure: Lost task 0.3 in stage 267.0 (TID 3763, .compute.internal, executor 1): java.lang.IllegalArgumentException: For input string: "setting_value==0"
        at scala.collection.immutable.StringLike$class.parseBoolean(StringLike.scala:291)
        at scala.collection.immutable.StringLike$class.toBoolean(StringLike.scala:261)
        at scala.collection.immutable.StringOps.toBoolean(StringOps.scala:29)
        at $anonfun$1.apply(<console>:40)
        at $anonfun$1.apply(<console>:37)
        at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:435)
        at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:441)
        at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
        at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage3.processNext(Unknown Source)
        at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
        at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
        at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
        at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
        at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
        at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
        at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
        at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
        at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
        at org.apache.spark.scheduler.Task.run(Task.scala:121)
        at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:402)
        at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
        at java.lang.Thread.run(Thread.java:748)

我想保留与条件列的值匹配的行。这是理想的结果。

+------+--------+---------+------------+----------------------------------------+
|    id|    item|    value|  value_name|                               condition|
+------+--------+---------+------------+----------------------------------------+
|    11|    3210|        0|         OFF|                                value==0|
|    13|    3210|        0|         OFF|                                value==0|
|    14|    3210|        0|         OFF|                                value==0|
|    16|    5440|        5|          ON|                     value>0 && value<10|
|    18|    5440|        6|          ON|                     value>0 && value<10|
|    19|    5440|        7|          ON|                     value>0 && value<10|
|    21|    7780|        A|        TYPE|   Set("A","B").contains(value.toString)|
|    22|    7780|        A|        TYPE|   Set("A","B").contains(value.toString)|
|    23|    7780|        A|        TYPE|   Set("A","B").contains(value.toString)|
+------+--------+---------+------------+----------------------------------------+

如果您有个好主意，请帮助我。谢谢。

Answer 1

在上述情况下，Spark尝试将String值转换为Boolean。它不在评估表达式本身。
用户必须使用外部库或自定义代码来完成表达式评估。
我能想到的最接近的（虽然不是确切的情况）是
How to evaluate a math expression given in string form?。

Answer 2

这是结合使用scala reflection API和UDF函数的一种方法。 udf处理int和字符串值的两种情况：

import scala.reflect.runtime.currentMirror
import scala.tools.reflect.ToolBox

val tb = currentMirror.mkToolBox()

val df = Seq(("0","value==0"),
("1", "value==0"),
("6", """value>0 && value<10"""),
("7", """value>0 && value<10"""),
("0", """value>0 && value<10"""),
("A", """Set("A","B").contains(value.toString)"""),
("C", """Set("A","B").contains(value.toString)""")).toDF("value", "condition")

def isAllDigits(x: String) = x.forall(Character.isDigit)

val evalExpressionUDF = udf((value: String, expr: String) => {
  val result =  isAllDigits(value) match {
    case true => tb.eval(tb.parse(expr.replace("value", s"""${value.toInt}""")))
    case false => tb.eval(tb.parse(expr.replace("value", s""""${value}"""")))
  }

  result.asInstanceOf[Boolean]
})

df.withColumn("eval", evalExpressionUDF($"value", $"condition"))
  .where($"eval" === true)
  .show(false)

evalExpressionUDF的情况：

int：用实际的int值替换表达式，然后用mkToolBox执行字符串代码
字符串：将字符串值括在""中，然后将表达式替换为双引号字符串并执行字符串代码

输出：

+-----+-------------------------------------+----+ 
|value|                           condition |eval| 
+-----+-------------------------------------+----+ 
|0    |value==0                             |true| 
|6    |value>0 && value<10                  |true| 
|7    |value>0 && value<10                  |true| 
|A    |Set("A","B").contains(value.toString)|true| 
+-----+-------------------------------------+----+

PS：我知道上述解决方案的性能可能很差，因为尽管我不知道有什么替代方案，但它会引起反射。

Spark：在Scala中对数据框进行动态过滤聚合

2 个答案: