python - Pyspark 数据框 OrderBy 分区级别还是整体？

Question

当我在 pyspark 数据帧上执行 orderBy 时，它是否会对所有分区中的数据(即整个结果)进行排序？或者是在分区级别进行排序？ 如果是后者，那么有人可以建议如何跨数据执行 orderBy 吗？ 我在最后有一个 orderBy

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，我得到以下结果 -

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... 

Answer 1

orderBy() 是一个“宽范围转换”，这意味着 Spark 需要触发“洗牌”和“阶段拆分” (1 个分区到多个输出分区)”，因此检索分布在集群中的所有分区分割，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，其中包含默认的 200 个输出分区(spark.sql.shuffle.partitions 配置)，这些分区被写入执行后到磁盘。这告诉您，当执行 Spark“操作”时，将会发生“宽转换”，又名“随机播放”。

其他“广泛转换”包括:distinct()、groupBy() 和 join() => *有时*

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)