如何从年月日分区列列表中提取最新/最近的分区

问题描述：

我在 spark sql 中使用了显示分区，它给了我以下内容:

I have used show partitions in spark sql which gives me the following:

year=2019/month=1/day=21
year=2019/month=1/day=22
year=2019/month=1/day=23
year=2019/month=1/day=24
year=2019/month=1/day=25
year=2019/month=1/day=26
year=2019/month=2/day=27

我需要提取最新的分区
我需要分别指定年、月和日，以便我可以在另一个数据框中将其用作变量.即:

part_year=2019
part_month=1
part_day=29

我用过:

val overwrite2 = overwrite.select(col("partition",8,8) as year

我从中得到

2019/month

为了删除它，我使用另一个数据框，其中我使用 regex_replace 将月份替换为空白，以便创建另一个数据框.

For removing this I use another dataframe where I use regex_replace to replace month with blank so another dataframe is created.

这反过来又会产生大量开销.我想要的是在一个数据帧中完成所有这些步骤，这样我就可以得到结果数据帧:

This is in turn creating a lot of overhead. What I want is for all these steps to be done in one dataframe so I can get the resultant dataframe as:

part_year=2019
part_month=2
part_day=27

选择最新的分区.

答

问题:如何从年月日列表中提取最新/最近的分区分区列

Question : How to extract latest/recent partition from the list of year month day partition columns

1) 我需要提取最新的分区.

1) I need to extract latest partition.

2) 我需要分别指定年、月和日，以便我可以在另一个数据框作为变量.

2) I need to the year, month and day separately so I can use it in another dataframe as variables.

由于最终目标是获取最新/最近的分区...您可以使用 joda api DateTime 通过使用 isAfter 进行排序来获取最新的分区，如下例所示.

Since final goal is to get latest/recent partition... You can use joda api DateTime by sorting with isAfter to get latest partition like given as below example.

在 spark.sql(s"show Partitions $yourtablename") 之后你会得到一个数据框 collect，因为它的小数据没有问题.

After spark.sql(s"show Partitions $yourtablename") you will get a dataframe collect that since its small data no issue.

一旦你收集了数据帧分区，你就会得到一个这样的数组

once you collect the dataframe partitions you will get an array like this

       val x = Array(
    "year=2019/month=1/day=21",
    "year=2019/month=1/day=22",
    "year=2019/month=1/day=23",
    "year=2019/month=1/day=24",
    "year=2019/month=1/day=25",
    "year=2019/month=1/day=26",
    "year=2019/month=2/day=27"
  )
  val finalPartitions = listKeys()

  import org.joda.time.DateTime

  def listKeys(): Seq[Map[String, DateTime]] = {
    val keys: Seq[DateTime] = x.map(row => {
      println(s" Identified Key: ${row.toString()}")
      DateTime.parse(row.replaceAll("/", "")
        .replaceAll("year=", "")
        .replaceAll("month=", "-")
        .replaceAll("day=", "-")
      )
    })
      .toSeq
    println(keys)
    println(s"Fetched ${keys.size} ")
    val myPartitions: Seq[Map[String, DateTime]] = keys.map(key => Map("businessdate" -> key))

    myPartitions
  }
  val mapWithMostRecentBusinessDate = finalPartitions.sortWith(
    (a, b) => a("businessdate").isAfter(b("businessdate"))
  ).head

  println(mapWithMostRecentBusinessDate)
  val latest: Option[DateTime] = mapWithMostRecentBusinessDate.get("businessdate")
  val year = latest.get.getYear();
  val month = latest.get.getMonthOfYear();
  val day = latest.get.getDayOfMonth();
  println("latest year "+ year + "  latest month " + month + "  latest day  " + day)

最终结果:即您最近的日期是 2019-02-27 现在基于此您可以以优化的方式查询 hive 数据.

Final result : i.e. your most recent date is 2019-02-27 now based on this you can query hive data in an optimized way.

 Identified Key: year=2019/month=1/day=22
 Identified Key: year=2019/month=1/day=23
 Identified Key: year=2019/month=1/day=24
 Identified Key: year=2019/month=1/day=25
 Identified Key: year=2019/month=1/day=26
 Identified Key: year=2019/month=2/day=27
WrappedArray(2019-01-21T00:00:00.000-06:00, 2019-01-22T00:00:00.000-06:00, 2019-01-23T00:00:00.000-06:00, 2019-01-24T00:00:00.000-06:00, 2019-01-25T00:00:00.000-06:00, 2019-01-26T00:00:00.000-06:00, 2019-02-27T00:00:00.000-06:00)
Fetched 7 
Map(businessdate -> 2019-02-27T00:00:00.000-06:00)
latest year 2019  latest month 2  latest day  27

如何从年月日分区列列表中提取最新/最近的分区

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