【问题标题】:Create a Spark dataframe by reading a Scala sequence having different datatypes通过读取具有不同数据类型的 Scala 序列来创建 Spark 数据帧
【发布时间】:2019-07-27 06:15:13
【问题描述】:

我想通过使用 Scala 读取 Seq 来创建 Spark 数据帧。 seq的数据类型有String、Dataframe、Long和Date类型。

我尝试应用以下方法,但出现了一些错误,可能不是处理问题的正确方法。

val Total_Record_Count = TotalRecordDF.count // geting count total number by reading a dataframe
val Rejected_Record_Count = rejectDF.count // geting count total number by reading a dataframe
val Batch_Run_ID = spark.range(1).select(unix_timestamp as "current_timestamp") 
case class JobRunDetails(Job_Name: String, Batch_Run_ID: DataFrame, Source_Entity_Name: String, Total_Record_Count: Long, Rejected_Record_Count: Long, Reject_Record_File_Path: String,Load_Date: String)
val inputSeq = Seq(JobRunDetails("HIT", Batch_Run_ID, "HIT", Total_Record_Count, Rejected_Record_Count, "blob.core.windows.net/feedlayer", Load_Date))

我试过了 val df = sc.parallelize(inputSeq).toDF() 但它抛出错误“java.lang.UnsupportedOperationException: No Encoder found for org.apache.spark.sql.DataFrame”

我只想通过读取序列来创建一个数据框。 任何帮助将不胜感激。 注意:- 我使用的是 Databricks Spark 2.3 版本。

【问题讨论】:

    标签: scala dataframe apache-spark azure-databricks


    【解决方案1】:

    通常我们使用 Java/Scala 原始类型创建案例类。还没有看到有人使用 DataFrame 作为成员元素之一创建案例类。

    如果我正确地满足了您的要求..这就是您要寻找的 -

    case class JobRunDetails(Job_Name: String, Batch_Run_ID: Int, Source_Entity_Name: String, Total_Record_Count: Long, Rejected_Record_Count: Long, Reject_Record_File_Path: String, Load_Date: String)
    //defined class JobRunDetails
    
    import spark.implicits._
        val Total_Record_Count = 100 //TotalRecordDF.count // geting count total number by reading a dataframe
        val Rejected_Record_Count = 200 //rejectDF.count // geting count total number by reading a dataframe
        val Batch_Run_ID = spark.range(1).select(unix_timestamp as "current_timestamp").take(1).head.get(0).toString().toInt
        val Load_Date = "2019-27-07"    
        val inputRDD: RDD[JobRunDetails] = spark.sparkContext.parallelize(Seq(JobRunDetails("HIT", Batch_Run_ID, "HIT", Total_Record_Count, Rejected_Record_Count, "blob.core.windows.net/feedlayer", Load_Date)))
    inputRDD.toDF().show
    
    /**
    import spark.implicits._
    Total_Record_Count: Int = 100
    Rejected_Record_Count: Int = 200
    Batch_Run_ID: Int = 1564224156
    Load_Date: String = 2019-27-07
    inputRDD: org.apache.spark.rdd.RDD[JobRunDetails] = ParallelCollectionRDD[3] at parallelize at command-330223868839989:6
    */
    
    +--------+------------+------------------+------------------+---------------------+-----------------------+----------+
    |Job_Name|Batch_Run_ID|Source_Entity_Name|Total_Record_Count|Rejected_Record_Count|Reject_Record_File_Path| Load_Date|
    +--------+------------+------------------+------------------+---------------------+-----------------------+----------+
    |     HIT|  1564224156|               HIT|               100|                  200|   blob.core.windows...|2019-27-07|
    +--------+------------+------------------+------------------+---------------------+-----------------------+----------+
    
    
    

    【讨论】:

    • 非常感谢 ValaravausBlack :)
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