[大数据]使用spark 布隆过滤器的一个坑

object SparkAutoBloomFilterDemo {
	def main(args: Array[String]): Unit = {
		val spark = SparkSession.builder().enableHiveSupport().config(sparkConf).getOrCreate()
		import spark.implicits._
		//注意！id_不能为数字，否则无效！！！！！
//		val df = Seq(
//			("dsad11", "First Value3"),
//			("dsad12", "Second Value4")
//		).toDF("id_", "name")
//		val df2 = Seq(
//			("dsad11", "First Value31"),
//			("dsad12", "Second Value41"),
//			("dsad121", "Second Value4"),
//			("dsa3d11", "Second Value4")
//		).toDF("id_", "name")
		val df:DataFrame = getGenerDataframe(3,spark)
		val df2:DataFrame = df
		println("=====df")
		df.show

		println("=====df2")
		df2.show


		val bf = df.stat.bloomFilter("id_",df.count().toInt,0.01)
		//val bf = df.stat.bloomFilter("id",0,0.01)//如果是o的话会报错Expected insertions must be positive
//		val rightNum = rdd.map(x=>(x.toInt,bf.mightContainString(x)))
		println("=====dfmight")
		df2.where(!might_contain(bf)($"id_")).show
	}

	def might_contain(f: org.apache.spark.util.sketch.BloomFilter) = udf((x: String) =>
		if(x != null) f.mightContain(x) else false)
}

要记住布隆过滤器去重的那一列不能是数字