Spark：在Scala中对数据框进行动态过滤聚合

Question

I have a dataframe like

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)

I want to remove some rows with 'condition' column. But I am in trouble.

I tried with below test code. But it does not seem to work properly.

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();

And This is error.

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)

I want to keep rows that match the value of the condition column. This is the desired result.

+------+--------+---------+------------+----------------------------------------+
|    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)|
+------+--------+---------+------------+----------------------------------------+

Please help me if you have a good idea. Thanks.

Answer 1

In the above case, Spark is trying to convert the String value to Boolean. It is not evaluating the expression itself.
And expression evaluation has to be done by the user using an external library or custom code.
The closest(Not the exact scenario though) I could come up is
How to evaluate a math expression given in string form? .

Answer 2

Here is one way using scala reflection API withing a UDF function. The udf handles both cases for int and string values:

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)

Cases for evalExpressionUDF :

• int: replace expression with the actual int value then execute string code with mkToolBox
• string: enclose string value into "" then replace expression with the double quoted string and execute the string code

Output:

+-----+-------------------------------------+----+ 
|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: I know that the performance of the above solution may be bad since it invokes reflection although I am not aware of an alternative.