[英]How to convert a simple DataFrame to a DataSet Spark Scala with case class?
[英]How to convert a dataframe to dataset in Apache Spark in Scala?
我需要將我的數據幀轉換為數據集,並使用以下代碼:
val final_df = Dataframe.withColumn(
"features",
toVec4(
// casting into Timestamp to parse the string, and then into Int
$"time_stamp_0".cast(TimestampType).cast(IntegerType),
$"count",
$"sender_ip_1",
$"receiver_ip_2"
)
).withColumn("label", (Dataframe("count"))).select("features", "label")
final_df.show()
val trainingTest = final_df.randomSplit(Array(0.3, 0.7))
val TrainingDF = trainingTest(0)
val TestingDF=trainingTest(1)
TrainingDF.show()
TestingDF.show()
///lets create our liner regression
val lir= new LinearRegression()
.setRegParam(0.3)
.setElasticNetParam(0.8)
.setMaxIter(100)
.setTol(1E-6)
case class df_ds(features:Vector, label:Integer)
org.apache.spark.sql.catalyst.encoders.OuterScopes.addOuterScope(this)
val Training_ds = TrainingDF.as[df_ds]
我的問題是, 我收到以下錯誤:
Error:(96, 36) Unable to find encoder for type stored in a Dataset. Primitive types (Int, String, etc) and Product types (case classes) are supported by importing spark.implicits._ Support for serializing other types will be added in future releases.
val Training_ds = TrainingDF.as[df_ds]
似乎數據框中的值的數量與我的類中的值的數量不同。 但是我在TrainingDF數據幀上使用了case class df_ds(features:Vector, label:Integer)
,因為它有一個特征向量和一個整數標簽。 這是TrainingDF數據幀:
+--------------------+-----+
| features|label|
+--------------------+-----+
|[1.497325796E9,19...| 19|
|[1.497325796E9,19...| 19|
|[1.497325796E9,19...| 19|
|[1.497325796E9,19...| 19|
|[1.497325796E9,19...| 19|
|[1.497325796E9,19...| 19|
|[1.497325796E9,19...| 19|
|[1.497325796E9,19...| 19|
|[1.497325796E9,19...| 19|
|[1.497325796E9,19...| 19|
|[1.497325796E9,19...| 19|
|[1.497325796E9,19...| 19|
|[1.497325796E9,19...| 19|
|[1.497325796E9,19...| 19|
|[1.497325796E9,19...| 19|
|[1.497325796E9,19...| 19|
|[1.497325796E9,19...| 19|
|[1.497325796E9,19...| 19|
|[1.497325796E9,19...| 19|
|[1.497325796E9,10...| 10|
+--------------------+-----+
這是我原來的final_df數據幀:
+------------+-----------+-------------+-----+
|time_stamp_0|sender_ip_1|receiver_ip_2|count|
+------------+-----------+-------------+-----+
| 05:49:56| 10.0.0.2| 10.0.0.3| 19|
| 05:49:56| 10.0.0.2| 10.0.0.3| 19|
| 05:49:56| 10.0.0.2| 10.0.0.3| 19|
| 05:49:56| 10.0.0.2| 10.0.0.3| 19|
| 05:49:56| 10.0.0.2| 10.0.0.3| 19|
| 05:49:56| 10.0.0.2| 10.0.0.3| 19|
| 05:49:56| 10.0.0.2| 10.0.0.3| 19|
| 05:49:56| 10.0.0.2| 10.0.0.3| 19|
| 05:49:56| 10.0.0.2| 10.0.0.3| 19|
| 05:49:56| 10.0.0.2| 10.0.0.3| 19|
| 05:49:56| 10.0.0.2| 10.0.0.3| 19|
| 05:49:56| 10.0.0.2| 10.0.0.3| 19|
| 05:49:56| 10.0.0.2| 10.0.0.3| 19|
| 05:49:56| 10.0.0.2| 10.0.0.3| 19|
| 05:49:56| 10.0.0.2| 10.0.0.3| 19|
| 05:49:56| 10.0.0.2| 10.0.0.3| 19|
| 05:49:56| 10.0.0.2| 10.0.0.3| 19|
| 05:49:56| 10.0.0.2| 10.0.0.3| 19|
| 05:49:56| 10.0.0.2| 10.0.0.3| 19|
| 05:49:56| 10.0.0.3| 10.0.0.2| 10|
+------------+-----------+-------------+-----+
但是我得到了上面提到的錯誤! 有誰能夠幫助我? 提前致謝。
您正在閱讀的錯誤消息是一個非常好的指針。
當您將DataFrame
轉換為Dataset
您必須擁有適當的Encoder
,以存儲在DataFrame
行中的任何內容。
類似原始類型( Int
s, String
s等)和case classes
編碼器只需導入SparkSession
的implicits,如下所示:
case class MyData(intField: Int, boolField: Boolean) // e.g.
val spark: SparkSession = ???
val df: DataFrame = ???
import spark.implicits._
val ds: Dataset[MyData] = df.as[MyData]
如果不工作,要么是因為你想投的類型DataFrame
是不支持。 在這種情況下,你會寫自己的Encoder
:您可能會發現更多關於它的信息在這里看到一個例子(該Encoder
用於java.time.LocalDateTime
) 這里 。
Spark 1.6.0
case class MyCase(id: Int, name: String)
val encoder = org.apache.spark.sql.catalyst.encoders.ExpressionEncoder[MyCase]
val dataframe = …
val dataset = dataframe.as(encoder)
Spark 2.0或以上
case class MyCase(id: Int, name: String)
val encoder = org.apache.spark.sql.Encoders.product[MyCase]
val dataframe = …
val dataset = dataframe.as(encoder)
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