scala - 填补时间序列Spark的空白



apache-spark apache-spark-sql (1)

处理时间序列数据时遇到问题。 由于电源故障,数据集中缺少某些时间戳。 我需要通过添加行来填补这些空白,然后我可以插入缺失的值。

输入数据:

periodstart                usage
---------------------------------
2015-09-11 02:15           23000   
2015-09-11 03:15           23344   
2015-09-11 03:30           23283  
2015-09-11 03:45           23786   
2015-09-11 04:00           25039

通缉输出:

periodstart                usage
---------------------------------
2015-09-11 02:15           23000   
2015-09-11 02:30           0   
2015-09-11 02:45           0   
2015-09-11 03:00           0   
2015-09-11 03:15           23344   
2015-09-11 03:30           23283   
2015-09-11 03:45           23786   
2015-09-11 04:00           25039  

现在我已在数据集foreach函数中使用while循环修复此问题。 问题是我必须首先将数据集收集到驱动程序才能执行while循环。 所以这不是Spark的正确方法。

有人能给我一个更好的解决方案吗?

这是我的代码:

MissingMeasurementsDS.collect().foreach(row => {
  // empty list for new generated measurements
  val output = ListBuffer.empty[Measurement]
  // Missing measurements
  val missingMeasurements = row.getAs[Int]("missingmeasurements")
  val lastTimestamp = row.getAs[Timestamp]("previousperiodstart")
  //Generate missing timestamps
  var i = 1
  while (i <= missingMeasurements) {
    //Increment timestamp with 15 minutes (900000 milliseconds)
    val newTimestamp = lastTimestamp.getTime + (900000 * i)
    output += Measurement(new Timestamp(newTimestamp), 0))
    i += 1
  }
  //Join interpolated measurements with correct measurements
  completeMeasurementsDS.join(output.toDS())
})
completeMeasurementsDS.show()
println("OutputDF count = " + completeMeasurementsDS.count())

如果输入 DataFrame 具有以下结构:

root
 |-- periodstart: timestamp (nullable = true)
 |-- usage: long (nullable = true)

斯卡拉

确定最小/最大:

val (minp, maxp) = df
  .select(min($"periodstart").cast("bigint"), max($"periodstart".cast("bigint")))
  .as[(Long, Long)]
  .first

设置步骤,例如15分钟:

val step: Long = 15 * 60

生成参考范围:

val reference = spark
  .range((minp / step) * step, ((maxp / step) + 1) * step, step)
  .select($"id".cast("timestamp").alias("periodstart"))

加入并填补空白:

reference.join(df, Seq("periodstart"), "leftouter").na.fill(0, Seq("usage"))

蟒蛇

同样在PySpark中:

from pyspark.sql.functions import col, min as min_, max as max_

step = 15 * 60

minp, maxp = df.select(
    min_("periodstart").cast("long"), max_("periodstart").cast("long")
).first()

reference = spark.range(
    (minp / step) * step, ((maxp / step) + 1) * step, step
).select(col("id").cast("timestamp").alias("periodstart"))

reference.join(df, ["periodstart"], "leftouter")




time-series