Using stat.bloomFilter in Spark 2.0.0 to filter another dataframe - scala

Question

I have two large dataframes [a] one which has all events identified by an id [b] a list of ids. I want to filter [a] based on the ids in [b] using the stat.bloomFilter implementation in spark 2.0.0
However I don't see any operations in the dataset API to join the bloom filter to the data frame [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)
What is the best way to filter 'df1' based on values in df2?
Thanks!

Answer 1

You can use an 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

I think I found the correct way to do this, but would still like pointers to see if there are better ways to manage this.
Here's my solution -
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

I built an implicit class that wraps https://stackoverflow.com/a/41989703/6723616
Comments welcome!
/**
* 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)
}
}
}