在 Spark 2.0.0 中使用 stat.bloomFilter 筛选另一个数据帧

Question

我有两个大数据帧[a]，其中一个包含由id [b]标识的所有事件，一个ID列表。我想使用 Spark 2.0.0 中的 stat.bloomFilter 实现根据 [b] 中的 id 过滤 [a]

但是，我在数据集 API 中没有看到任何将布隆过滤器连接到数据框的操作 [a]

val in1 = spark.sparkContext.parallelize(List(0, 1, 2, 3, 4, 5))
val df1 = in1.map(x => (x, x+1, x+2)).toDF("c1", "c2", "c3")
val in2 = spark.sparkContext.parallelize(List(0, 1, 2))
val df2 = in2.map(x => (x)).toDF("c1")
val expectedNumItems: Long = 1000
val fpp: Double = 0.005
val sbf = df.stat.bloomFilter($"c1", expectedNumItems, fpp)
val sbf2 = df2.stat.bloomFilter($"c1", expectedNumItems, fpp)

根据 df2 中的值过滤"df1"的最佳方法是什么？

谢谢！

Answer 1

您可以使用 UDF：

def might_contain(f: org.apache.spark.util.sketch.BloomFilter) = udf((x: Int) => 
  if(x != null) f.mightContain(x) else false)
df1.where(might_contain(sbf2)($"C1"))

Answer 2

我想我找到了正确的方法来做到这一点，但仍然希望有指针看看是否有更好的方法来管理它。

这是我的解决方案 -

val in1 = spark.sparkContext.parallelize(List(0, 1, 2, 3, 4, 5))
val d1 = in1.map(x => (x, x+1, x+2)).toDF("c1", "c2", "c3")
val in2 = spark.sparkContext.parallelize(List(0, 1, 2))
val d2 = in2.map(x => (x)).toDF("c1")
val s2 = d2.stat.bloomFilter($"c1", expectedNumItems, fpp)
val a = spark.sparkContext.broadcast(s2)
val x = d1.rdd.filter(x => a.value.mightContain(x(0)))
case class newType(c1: Int, c2: Int, c3: Int) extends Serializable
val xDF = x.map(y => newType(y(0).toString.toInt, y(1).toString.toInt, y(2).toString.toInt)).toDF()
scala> d1.show(10)
+---+---+---+
| c1| c2| c3|
+---+---+---+
|  0|  1|  2|
|  1|  2|  3|
|  2|  3|  4|
|  3|  4|  5|
|  4|  5|  6|
|  5|  6|  7|
+---+---+---+
scala> d2.show(10)
+---+
| c1|
+---+
|  0|
|  1|
|  2|
+---+
scala> xDF.show(10)
+---+---+---+
| c1| c2| c3|
+---+---+---+
|  0|  1|  2|
|  1|  2|  3|
|  2|  3|  4|
+---+---+---+

Answer 3

我构建了一个包装 https://stackoverflow.com/a/41989703/6723616 的隐式类欢迎评论！

/**
  * Copyright 2017 Yahoo, Inc.
  * Zlib license: https://www.zlib.net/zlib_license.html
  */
package me.klotz.spark.utils
import org.apache.spark.sql.functions._
import org.apache.spark.sql.Dataset
import org.apache.spark.sql.Row
import org.apache.spark.util.sketch.BloomFilter
import org.apache.spark.SparkContext
object BloomFilterEnhancedJoin {
  // not parameterized for field typel; assumes string
  /**
    *  Like .join(bigDF, smallDF, but accelerated with a Bloom filter.
    *  You pass in a size estimate of the bigDF, and a ratio of acceptable false positives out of the expected result set size.
    *  ratio=1 is a good start; that will result in about 50% false positives in the big-small join, so the filter accepts
    *  about as many as it passes, rather than rejecting almost all.  Pass in a size estimate of the big dataframe
    *  to avoid enumerating it.  The small DataFrame gets enumerated anyway.
    *  
    *  Example use:
    *  <code>
    *  import me.klotz.spark.utils.BloomFilterEnhancedJoin._
    *  val (dups_joined, bloomFilterBroadcast) = df_big.joinBloom(1024L*1024L*1024L, dups, 10.0, "id")
    *  dups_joined.write.format("orc").save("dups")
    *  bloomFilterBroadcast.unpersist
    *  <code>
    */
  implicit class BloomFilterEnhancedJoiner(bigdf:Dataset[Row]) {
    /**
      * You should call bloomFilterBroadcast.unpersist after
      */
    def joinBloom(bigDFCountEstimate:Long, smallDF: Dataset[Row], ratio:Double, field:String) = {
      val sc = smallDF.sparkSession.sparkContext
      val smallDFCount = smallDF.count
      val fpr = smallDFCount.toDouble / bigDFCountEstimate.toDouble / ratio
      println(s"fpr=${fpr} = smallDFCount=${smallDFCount} / bigDFCountEstimate=${bigDFCountEstimate} / ratio=${ratio}")
      val bloomFilterBroadcast = sc.broadcast((smallDF.stat.bloomFilter(field, smallDFCount, fpr)))
      val mightContain = udf((x: String) => if (x != null) bloomFilterBroadcast.value.mightContainString(x) else false)
      (bigdf.filter(mightContain(col(field))).join(smallDF, field), bloomFilterBroadcast)
    }
  }
}