我有两个数据框，如下所示，我从MySQL表中读取逻辑DF
逻辑DF：
slNo | filterCondtion |
-----------------------
1 | age > 100 |
2 | age > 50 |
3 | age > 10 |
4 | age > 20 |
InputDF-从文件读取：
age | name |
------------------------
11 | suraj |
22 | surjeth |
33 | sam |
43 | ram |
我想从逻辑数据框中应用过滤器语句并添加这些过滤器的计数
结果输出：
slNo | filterCondtion | count |
------------------------------
1 | age > 100 | 10 |
2 | age > 50 | 2 |
3 | age > 10 | 5 |
4 | age > 20 | 6 |
-------------------------------
我尝试过的代码：
val LogicDF = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/testDB").option("driver", "com.mysql.jdbc.Driver").option("dbtable", "logic_table").option("user", "root").option("password", "password").load()
def filterCount(str: String): Long ={
val counte = inputDF.where(str).count()
counte
}
val filterCountUDF = udf[Long, String](filterCount)
LogicDF.withColumn("count",filterCountUDF(col("filterCondtion")))
错误跟踪：
Caused by: org.apache.spark.SparkException: Failed to execute user defined function($anonfun$1: (string) => bigint)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.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)
Caused by: java.lang.NullPointerException
at org.apache.spark.sql.Dataset.where(Dataset.scala:1525)
at filterCount(<console>:28)
at $anonfun$1.apply(<console>:25)
at $anonfun$1.apply(<console>:25)
... 21 more
任何其他选择也可以..！预先感谢。
答案 0 :(得分：0)
只要您的logicDF足够小，可以收集到驱动程序中，它就会起作用。
将您的逻辑收集为Array[(Int, String)]，如下所示：
val rules = logicDF.collect().map{ r: Row =>
val slNo = r.getAs[Int](0)
val condition = r.getAs[String](1)
(slNo, condition)
}
使用条件值构建一个新列，这些条件值将这些规则链接到when Column中。为此，请使用一些scala循环，例如：
val unused = when(lit(false), lit(false))
val filters: Column = rules.foldLeft(unused){
case (acc: Column, (slNo: Int, cond: String)) =>
acc.when(col("slNo") === slNo, expr(cond))
}
//You will get something like:
//when(col("slNo") === 1, expr("age > 10"))
//.when(col("slNo") === 2, expr("age > 20"))
//...
通过联接获取两个DataFrame的笛卡尔积，因此您可以将每个规则应用于数据中的每一行：
val joinDF = logicDF.join(inputDF, lit(true), "inner") //inner or whatever
使用前一个Column和条件过滤器进行过滤。
val withRulesDF = joinDF.filter(filters)
分组并计数：
val resultDF = withRulesDF
.groupBy("slNo", "filterCondtion")
.agg(count("*") as "count")
答案 1 :(得分：-3)
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