我正在练习有关Spark
的书中的一些示例。在这些示例中,我从.csv
个文件中读取了一些数据
val staticDataFrame = spark.read.format("csv")
.option("header", "true")
.option("inferSchema", "true")
.load("/data/retail-data/by-day/*.csv")
然后创建一个sql
视图
staticDataFrame.createOrReplaceTempView("retail_data")
val staticSchema = staticDataFrame.schema
然后运行查询
import org.apache.spark.sql.functions.{window, column, desc, col}
staticDataFrame
.selectExpr(
"CustomerId",
"(UnitPrice * Quantity) as total_cost",
"InvoiceDate")
.groupBy(
col("CustomerId"), window(col("InvoiceDate"), "1 day"))
.sum("total_cost")
.show(5)
我得到以下输出
+----------+--------------------+-----------------+
|CustomerId| window| sum(total_cost)|
+----------+--------------------+-----------------+
| 16057.0|[2011-12-05 00:00...| -37.6|
| 14126.0|[2011-11-29 00:00...|643.6300000000001|
| 13500.0|[2011-11-16 00:00...|497.9700000000001|
| 17160.0|[2011-11-08 00:00...|516.8499999999999|
| 15608.0|[2011-11-11 00:00...| 122.4|
+----------+--------------------+-----------------+
然后更改分区大小,然后再次运行相同的查询。但是我得到了不同的输出
scala> spark.conf.set("spark.sql.shuffle.partitions","5");
scala> staticDataFrame.
| selectExpr(
| "CustomerId",
| "(UnitPrice * Quantity) as total_cost",
| "InvoiceDate").
| groupBy(
| col("CustomerId"),window(col("InvoiceDate"),"1 day")).
| sum("total_cost").
| show(5)
+----------+--------------------+------------------+
|CustomerId| window| sum(total_cost)|
+----------+--------------------+------------------+
| 14075.0|[2011-12-05 00:00...|316.78000000000003|
| 18180.0|[2011-12-05 00:00...| 310.73|
| 15358.0|[2011-12-05 00:00...| 830.0600000000003|
| 15392.0|[2011-12-05 00:00...|304.40999999999997|
| 15290.0|[2011-12-05 00:00...|263.02000000000004|
+----------+--------------------+------------------+
only showing top 5 rows
是这种预期的行为。两种情况下的输出是否应该相同?
答案 0 :(得分:1)
您的数据框有多少条记录?没关系。
我相信它的行为符合预期,因为您仅显示5条记录,所以第二个查询在分区后返回了不同的数据集。
尝试对某列进行排序,并获得前5个结果,它应该在分区前后为您提供相同的结果。
谢谢