Counting filtered items on dataframe SPARK

I have the following dataframe :df

In some point I need to filter out items base on timestamps(milliseconds). However it is important to me to save how much records werefiltered(In case it is too many I want to fail the job) Naively I can do:

======Lots of calculations on df ======
val df_filtered = df.filter($"ts" >= startDay && $"ts"  <= endDay)
val filtered_count = df.count - df_filtered.count

However it feels like complete overkill since SPARK will perform the whole execution tree, 3 times (filter and 2 counts). This task in Hadoop MapReduce is really easy since I can maintain counter for each row filtered. Is there more efficient way, I could only find accumulators but I can't connect it to filter.

A suggested approach was to cache df before the filter however I would prefer this option as last resort due to DF size.

Spark 1.6.0 code:

import org.apache.spark.sql.SQLContext
import org.apache.spark.{SparkConf, SparkContext}

object Main {

  val conf = new SparkConf().setAppName("myapp").setMaster("local[*]")
  val sc = new SparkContext(conf)
  val sqlContext = new SQLContext(sc)

  case class xxx(a: Int, b: Int)

  def main(args: Array[String]): Unit = {

    val df = sqlContext.createDataFrame(sc.parallelize(Seq(xxx(1, 1), xxx(2, 2), xxx(3,3))))

    val acc = sc.accumulator[Long](0)

    val filteredRdd = df.rdd.filter(r => {
      if (r.getAs[Int]("a") > 2) {
        true
      } else {
        acc.add(1)
        false
      }
    })

    val filteredRddDf = sqlContext.createDataFrame(filteredRdd, df.schema)

    filteredRddDf.show()

    println(acc.value)
  }
}

Spark 2.x.x code:

import org.apache.spark.sql.SparkSession

object Main {

  val ss = SparkSession.builder().master("local[*]").getOrCreate()
  val sc = ss.sparkContext

  case class xxx(a: Int, b: Int)

  def main(args: Array[String]): Unit = {

    val df = ss.createDataFrame(sc.parallelize(Seq(xxx(1, 1), xxx(2, 2), xxx(3,3))))

    val acc = sc.longAccumulator

    val filteredDf = df.filter(r => {
      if (r.getAs[Int]("a") > 2) {
        true
      } else {
        acc.add(1)
        false
      }
    }).toDF()


    filteredDf.show()

    println(acc.value)

  }
}