Question

我有CSV数据：

"id","price"
"1","79.07"
"2","91.27"
"3","85.6"

使用SparkSession进行阅读：

def readToDs(resource: String, schema: StructType): Dataset = {
    sparkSession.read
      .option("header", "true")
      .schema(schema)
      .csv(resource)
      .as[ItemPrice]
}

案例分类：

case class ItemPrice(id: Long, price: BigDecimal)

打印数据集：

def main(args: Array[String]): Unit = {
    val prices: Dataset = 
        readToDs("src/main/resources/app/data.csv", Encoders.product[ItemPrice].schema);
    prices.show();
}

输出：

+----------+--------------------+
|        id|               price|
+----------+--------------------+
|         1|79.07000000000000...|
|         2|91.27000000000000...|
|         3|85.60000000000000...|
+----------+--------------------+

所需的输出：

+----------+--------+
|        id|   price|
+----------+--------+
|         1|   79.07|
|         2|   91.27|
|         3|   85.6 |
+----------+--------+

我已经知道的选项：

使用硬编码的列顺序和数据类型手动定义架构，例如：

def defineSchema(): StructType =
    StructType(
      Seq(StructField("id", LongType, nullable = false)) :+
        StructField("price", DecimalType(3, 2), nullable = false)
    )

并像这样使用它：

val prices: Dataset = readToDs("src/main/resources/app/data.csv", defineSchema);

如何在不手动定义所有结构的情况下设置精度(3,2)？

Answer 1

假设您的csv为

scala> val df = Seq(("1","79.07","89.04"),("2","91.27","1.02"),("3","85.6","10.01")).toDF("item","price1","price2")
df: org.apache.spark.sql.DataFrame = [item: string, price1: string ... 1 more field]

scala> df.printSchema
root
 |-- item: string (nullable = true)
 |-- price1: string (nullable = true)
 |-- price2: string (nullable = true)

您可以像下面一样投射

scala> val df2 = df.withColumn("price1",'price1.cast(DecimalType(4,2)))
df2: org.apache.spark.sql.DataFrame = [item: string, price1: decimal(4,2) ... 1 more field]

scala> df2.printSchema
root
 |-- item: string (nullable = true)
 |-- price1: decimal(4,2) (nullable = true)
 |-- price2: string (nullable = true)


scala>

现在，如果您知道csv ..中带有数组的小数列列表，则可以像下面这样动态地进行

scala> import org.apache.spark.sql.types._
import org.apache.spark.sql.types._

scala> val decimal_cols = Array("price1","price2")
decimal_cols: Array[String] = Array(price1, price2)

scala> val df3 = decimal_cols.foldLeft(df){ (acc,r) => acc.withColumn(r,col(r).cast(DecimalType(4,2))) }
df3: org.apache.spark.sql.DataFrame = [item: string, price1: decimal(4,2) ... 1 more field]

scala> df3.show
+----+------+------+
|item|price1|price2|
+----+------+------+
|   1| 79.07| 89.04|
|   2| 91.27|  1.02|
|   3| 85.60| 10.01|
+----+------+------+


scala> df3.printSchema
root
 |-- item: string (nullable = true)
 |-- price1: decimal(4,2) (nullable = true)
 |-- price2: decimal(4,2) (nullable = true)


scala>

有帮助吗？。

UPDATE1：

使用inferSchema读取csv文件，然后将所有双精度字段动态转换为DecimalType（4,2）。

val df = spark.read.format("csv").option("header","true").option("inferSchema","true").load("in/items.csv")
df.show
df.printSchema()
val decimal_cols = df.schema.filter( x=> x.dataType.toString == "DoubleType" ).map(x=>x.name)
// or df.schema.filter( x=> x.dataType==DoubleType )
val df3 = decimal_cols.foldLeft(df){ (acc,r) => acc.withColumn(r,col(r).cast(DecimalType(4,2))) }
df3.printSchema()
df3.show()

结果：

+-----+------+------+
|items|price1|price2|
+-----+------+------+
|    1| 79.07| 89.04|
|    2| 91.27|  1.02|
|    3|  85.6| 10.01|
+-----+------+------+

root
 |-- items: integer (nullable = true)
 |-- price1: double (nullable = true)
 |-- price2: double (nullable = true)

root
 |-- items: integer (nullable = true)
 |-- price1: decimal(4,2) (nullable = true)
 |-- price2: decimal(4,2) (nullable = true)

+-----+------+------+
|items|price1|price2|
+-----+------+------+
|    1| 79.07| 89.04|
|    2| 91.27|  1.02|
|    3| 85.60| 10.01|
+-----+------+------+

Answer 2

一个选项是为输入模式定义转换器：

def defineDecimalType(schema: StructType): StructType = {
    new StructType(
      schema.map {
        case StructField(name, dataType, nullable, metadata) =>
          if (dataType.isInstanceOf[DecimalType])
            // Pay attention to max precision in the source data
            StructField(name, new DecimalType(20, 2), nullable, metadata)
          else 
            StructField(name, dataType, nullable, metadata)
      }.toArray
    )
} 

def main(args: Array[String]): Unit = {
    val prices: Dataset = 
        readToDs("src/main/resources/app/data.csv", defineDecimalType(Encoders.product[ItemPrice].schema));
    prices.show();
}

此方法的缺点是此映射应用于每个列，并且如果您的ID不能满足精确度要求（假设ID = 10000至DecimalType(3, 2)），则您会得到一个例外：

原因：java.lang.IllegalArgumentException：要求失败：十进制精度4超过最大精度3 scala.Predef $ .require（Predef.scala：224）在 org.apache.spark.sql.types.Decimal.set（Decimal.scala：113）在 org.apache.spark.sql.types.Decimal $ .apply（Decimal.scala：426）在 org.apache.spark.sql.execution.datasources.csv.CSVTypeCast $ .castTo（CSVInferSchema.scala：273）在 org.apache.spark.sql.execution.datasources.csv.CSVRelation $$ anonfun $ csvParser $ 3.apply（CSVRelation.scala：125）在 org.apache.spark.sql.execution.datasources.csv.CSVRelation $$ anonfun $ csvParser $ 3.apply（CSVRelation.scala：94）在 org.apache.spark.sql.execution.datasources.csv.CSVFileFormat $$ anonfun $ buildReader $ 1 $$ anonfun $ apply $ 2.apply（CSVFileFormat.scala：167）在 org.apache.spark.sql.execution.datasources.csv.CSVFileFormat $$ anonfun $ buildReader $ 1 $$ anonfun $ apply $ 2.apply（CSVFileFormat.scala：166）

这就是为什么保持精度高于源数据中最大小数的重要性：

if (dataType.isInstanceOf[DecimalType])
    StructField(name, new DecimalType(20, 2), nullable, metadata)

Answer 3

我尝试使用2个不同的CSV文件加载示例数据，并且工作正常，结果与以下代码所预期的一样。我在Windows上使用Spark 2.3.1。

//read with double quotes
val df1 = spark.read
.format("csv")
.option("header","true")
.option("inferSchema","true")
.option("nullValue","")
.option("mode","failfast")
.option("path","D:/bitbuket/spark-examples/53667822/string.csv")
.load()

df1.show
/*
scala> df1.show
+---+-----+
| id|price|
+---+-----+
|  1|79.07|
|  2|91.27|
|  3| 85.6|
+---+-----+
*/

//read with without quotes
val df2 = spark.read
.format("csv")
.option("header","true")
.option("inferSchema","true")
.option("nullValue","")
.option("mode","failfast")
.option("path","D:/bitbuket/spark-examples/53667822/int-double.csv")
.load()

df2.show

/*
scala> df2.show
+---+-----+
| id|price|
+---+-----+
|  1|79.07|
|  2|91.27|
|  3| 85.6|
+---+-----+
*/

Spark Dataset案例类编码器的十进制精度

3 个答案: