Pyspark 数据框 OrderBy 分区级别还是整体？

Posted 2023-03-31

【中文标题】Pyspark 数据框 OrderBy 分区级别还是整体？

【英文标题】：Pyspark dataframe OrderBy partition level or overall?

【发布时间】：2019-04-26 03:22:13

【问题描述】：

当我对 pyspark 数据框执行 orderBy 时，它是否会对所有分区中的数据（即整个结果）进行排序？还是分区级别的排序？ 如果是后者，那么任何人都可以建议如何跨数据执行 orderBy 吗？ 我在最后有一个订单

我当前的代码：

def extract_work(self, days_to_extract):

        source_folders = self.work_folder_provider.get_work_folders(s3_source_folder=self.work_source,
                                                                    warehouse_ids=self.warehouse_ids,
                                                                    days_to_extract=days_to_extract)
        source_df = self._load_from_s3(source_folders)

        # Partition and de-dupe the data-frame retaining latest
        source_df = self.data_frame_manager.partition_and_dedupe_data_frame(source_df,
                                                                            partition_columns=['binScannableId', 'warehouseId'],
                                                                            sort_key='cameraCaptureTimestampUtc',
                                                                            desc=True)
        # Filter out anything that does not qualify for virtual count.
        source_df = self._virtual_count_filter(source_df)

        history_folders = self.work_folder_provider.get_history_folders(s3_history_folder=self.history_source,
                                                                        days_to_extract=days_to_extract)
        history_df = self._load_from_s3(history_folders)

        # Filter out historical items
        if history_df:
            source_df = source_df.join(history_df, 'binScannableId', 'leftanti')
        else:
            self.logger.error("No History was found")

        # Sort by defectProbability
        source_df = source_df.orderBy(desc('defectProbability'))

        return source_df

def partition_and_dedupe_data_frame(data_frame, partition_columns, sort_key, desc): 
          if desc: 
            window = Window.partitionBy(partition_columns).orderBy(F.desc(sort_key)) 
          else: 
            window = Window.partitionBy(partition_columns).orderBy(F.asc(sort_key)) 

          data_frame = data_frame.withColumn('rank', F.rank().over(window)).filter(F.col('rank') == 1).drop('rank') 
          return data_frame

def _virtual_count_filter(self, source_df):
        df = self._create_data_frame()
        for key in self.virtual_count_thresholds.keys():
            temp_df = source_df.filter((source_df['expectedQuantity'] == key) & (source_df['defectProbability'] > self.virtual_count_thresholds[key]))
            df = df.union(temp_df)
        return df

当我执行 df.explain() 时，我得到以下信息-

Physical Plan == *Sort [defectProbability#2 DESC NULLS LAST], true, 0 +- Exchange rangepartitioning(defectProbability#2 DESC NULLS LAST, 25) +- *Project [expectedQuantity#0, cameraCaptureTimestampUtc#1, defectProbability#2, binScannableId#3, warehouseId#4, defectResult#5] +- *Filter ((isnotnull(rank#35) && (rank#35 = 1)) && (((((((expectedQuantity#0 = 0) && (defectProbability#2 > 0.99)) || ((expectedQuantity#0 = 1) && (defectProbability#2 > 0.98))) || ((expectedQuantity#0 = 2) && (defectProbability#2 > 0.99))) || ((expectedQuantity#0 = 3) && (defectProbability#2 > 0.99))) || ((expectedQuantity#0 = 4) && (defectProbability#2 > 0.99))) || ((expectedQuantity#0 = 5) && (defectProbability#2 > 0.99)))) +- Window [rank(cameraCaptureTimestampUtc#1) windowspecdefinition(binScannableId#3, warehouseId#4, cameraCaptureTimestampUtc#1 DESC NULLS LAST, ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS rank#35], [binScannableId#3, warehouseId#4], [cameraCaptureTimestampUtc#1 DESC NULLS LAST] +- *Sort [binScannableId#3 ASC NULLS FIRST, warehouseId#4 ASC NULLS FIRST, cameraCaptureTimestampUtc#1 DESC NULLS LAST], false, 0 +- Exchange hashpartitioning(binScannableId#3, warehouseId#4, 25) +- Union :- Scan ExistingRDD[expectedQuantity#0,cameraCaptureTimestampUtc#1,defectProbability#2,binScannableId#3,warehouseId#4,defectResult#5] +- *FileScan json [expectedQuantity#13,cameraCaptureTimestampUtc#14,defectProbability#15,binScannableId#16,warehouseId#17,defectResult#18] Batched: false, Format: JSON, Location: InMemoryFileIndex[s3://vbi-autocount-chunking-prod-nafulfillment2/TPA1/2019/04/25/12/vbi-ac-chunk..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<expectedQuantity:int,cameraCaptureTimestampUtc:string,defectProbability:double,binScannabl... 

【问题讨论】：

我想在我的代码中添加它，我将数据框划分为两列并删除重复项，然后再按第三列对数据框进行排序。我编写了一个测试来验证数据框中的项目是否已排序，而它们不是。一开始以为是测试有问题，现在好像还好

我从以下答案的评论中添加了您的代码。在您按某些列显式分区和数据排序的代码中，这只会导致分区级别排序。

其实我在上面的分区之后再做一个排序

请也添加这个问题（点击edit按钮）。

完成。感谢您的观看。

【参考方案1】：

orderBy() 是一个“wide transformation”，这意味着 Spark 需要触发一个“shuffle”和“stage splits（1 个分区到多个输出分区） )" 从而检索分布在集群中的所有分区拆分，以在此处执行orderBy()。

如果您查看解释计划，它有一个重新分区指示器，其中写入了默认的 200 个输出分区（spark.sql.shuffle.partitions 配置）执行后到磁盘。这告诉您在执行 Spark“action”时会发生“范围的转换”，也就是“shuffle”。

其他“广泛的转换”包括：distinct(), groupBy(), and join() => *sometimes*

from pyspark.sql.functions import desc
df = spark.range(10).orderBy(desc("id"))
df.show()
df.explain()

+---+
| id|
+---+
|  9|
|  8|
|  7|
|  6|
|  5|
|  4|
|  3|
|  2|
|  1|
|  0|
+---+

== Physical Plan ==
*(2) Sort [id#6L DESC NULLS LAST], true, 0
+- Exchange rangepartitioning(id#6L DESC NULLS LAST, 200)
   +- *(1) Range (0, 10, step=1, splits=8)

【讨论】：

感谢您的回复，我已将数据框明确划分为 2 列以删除重复项。然后我尝试按第三列排序。我看到数据没有在所有数据框中进行排序。您需要代码 sn-p 吗？

当然...请提供更多代码...您也可以使用.dropDuplicates() 删除重复行...不知道为什么要在这里分区

我有一个非常自定义的 drop 副本。看到这个问题***.com/questions/55660085/…