如何针对Spark DataFrame并行化/分发查询/计数？-Java 学习之路

我有一个 DataFrame ，我必须应用一系列过滤查询 . 例如，我按如下方式加载 DataFrame .

val df = spark.read.parquet("hdfs://box/some-parquet")

然后，我有一堆“任意”过滤器，如下所示 .

C0 = 'true'和C1 = 'false'
C0 = 'false'和C3 = 'true'
等等......

我通常使用util方法动态获取这些过滤器 .

val filters: List[String] = getFilters()

我所做的只是将这些过滤器应用于 DataFrame 以获取计数 . 例如 .

val counts = filters.map(filter => {
 df.where(filter).count
})

我注意到在映射过滤器时不是并行/分布式操作 . 如果我将过滤器粘贴到RDD / DataFrame中，这种方法也不会起作用，因为我随后将执行嵌套数据帧操作（正如我在SO上读到的那样，Spark中不允许这样做） . 类似下面的内容给出了NullPointerException（NPE） .

val df = spark.read.parquet("hdfs://box/some-parquet")
val filterRDD = spark.sparkContext.parallelize(List("C0='false'", "C1='true'"))
val counts = filterRDD.map(df.filter(_).count).collect

Caused by: java.lang.NullPointerException
  at org.apache.spark.sql.Dataset.filter(Dataset.scala:1127)
  at $anonfun$1.apply(:27)
  at $anonfun$1.apply(:27)
  at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
  at scala.collection.Iterator$class.foreach(Iterator.scala:893)
  at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
  at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
  at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
  at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
  at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
  at scala.collection.AbstractIterator.to(Iterator.scala:1336)
  at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
  at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1336)
  at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
  at scala.collection.AbstractIterator.toArray(Iterator.scala:1336)
  at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:912)
  at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:912)
  at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1899)
  at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1899)
  at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
  at org.apache.spark.scheduler.Task.run(Task.scala:86)
  at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
  at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
  at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
  at java.lang.Thread.run(Thread.java:745)

有没有办法在Spark中 DataFrame 上并行/分配计数过滤器？顺便说一句，我在Spark v2.0.2上 .

1 回答

通过这样做，唯一可预期的增益（可能非常大）将仅在输入数据上传递一次 .

我会这样做（程序化解决方案，但可能是等效的SQL）：

将过滤器转换为返回1或0的UDF
为每个UDFS添加一列
Group By / sum your datas .

示例火花会话看起来像：

scala> val data = spark.createDataFrame(Seq("A", "BB", "CCC").map(Tuple1.apply)).withColumnRenamed("_1", "input")

data: org.apache.spark.sql.DataFrame = [input: string]

scala> data.show
+-----+
|input|
+-----+
|    A|
|   BB|
|  CCC|
+-----+

scala> val containsBFilter = udf((input: String) => if(input.contains("B")) 1 else 0)
containsBFilter: org.apache.spark.sql.expressions.UserDefinedFunction = UserDefinedFunction(<function1>,IntegerType,Some(List(StringType)))

scala> val lengthFilter = udf((input: String) => if (input.length < 3) 1 else 0)
lengthFilter: org.apache.spark.sql.expressions.UserDefinedFunction = UserDefinedFunction(<function1>,IntegerType,Some(List(StringType)))

scala> data.withColumn("inputLength", lengthFilter($"input")).withColumn("containsB", containsBFilter($"input")).select(sum($"inputLength"), sum($"containsB")).show

+----------------+--------------+
|sum(inputLength)|sum(containsB)|
+----------------+--------------+
|               2|             1|
+----------------+--------------+

回复于 2024-04-28T02:18:05+08:00

如何针对Spark DataFrame并行化/分发查询/计数？

1 回答

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