scala中两个数据框的模式比较

问题描述

我正在尝试编写一些测试用例来验证源 (.csv) 文件和目标(配置单元表)之间的数据.验证之一是表的结构验证.

I am trying to write some test cases to validate the data between source (.csv) file and target (hive table). One of the validation is the Structure validation of the table.

我已将 .csv 数据(使用定义的架构)加载到一个数据框中，并将 hive 表数据提取到另一个数据框中.
当我现在尝试比较两个数据帧的架构时，它返回 false.不知道为什么.请问对此有什么想法吗?

I have load the .csv data (using a defined schema) into one dataframe and extracted the hive table data into another dataframe.
When I now try to compare the schema of the two dataframes, it returns false. Not sure why. Any idea on this please?

源数据帧架构:

scala> res39.printSchema
root
 |-- datetime: timestamp (nullable = true)
 |-- load_datetime: timestamp (nullable = true)
 |-- source_bank: string (nullable = true)
 |-- emp_name: string (nullable = true)
 |-- header_row_count: integer (nullable = true)
 |-- emp_hours: double (nullable = true)

目标数据帧架构:

scala> targetRawData.printSchema
root
 |-- datetime: timestamp (nullable = true)
 |-- load_datetime: timestamp (nullable = true)
 |-- source_bank: string (nullable = true)
 |-- emp_name: string (nullable = true)
 |-- header_row_count: integer (nullable = true)
 |-- emp_hours: double (nullable = true)

当我比较时，它返回false:

When I compare, it returns false:

scala> res39.schema == targetRawData.schema
res47: Boolean = false

两个数据框中的数据如下所示:

Data in the two dataframes is shown below:

scala> res39.show
+-------------------+-------------------+-----------+--------+----------------+---------+
|           datetime|      load_datetime|source_bank|emp_name|header_row_count|emp_hours|
+-------------------+-------------------+-----------+--------+----------------+---------+
|2017-01-01 01:02:03|2017-01-01 01:02:03|        RBS| Naveen |             100|    15.23|
|2017-03-15 01:02:03|2017-03-15 01:02:03|        RBS| Naveen |             100|   115.78|
|2015-04-02 23:24:25|2015-04-02 23:24:25|        RBS|   Arun |             200|     2.09|
|2010-05-28 12:13:14|2010-05-28 12:13:14|        RBS|   Arun |             100|    30.98|
|2018-06-04 10:11:12|2018-06-04 10:11:12|        XZX|   Arun |             400|     12.0|
+-------------------+-------------------+-----------+--------+----------------+---------+


scala> targetRawData.show
+-------------------+-------------------+-----------+--------+----------------+---------+
|           datetime|      load_datetime|source_bank|emp_name|header_row_count|emp_hours|
+-------------------+-------------------+-----------+--------+----------------+---------+
|2017-01-01 01:02:03|2017-01-01 01:02:03|        RBS|  Naveen|             100|    15.23|
|2017-03-15 01:02:03|2017-03-15 01:02:03|        RBS|  Naveen|             100|   115.78|
|2015-04-02 23:25:25|2015-04-02 23:25:25|        RBS|    Arun|             200|     2.09|
|2010-05-28 12:13:14|2010-05-28 12:13:14|        RBS|    Arun|             100|    30.98|
+-------------------+-------------------+-----------+--------+----------------+---------+

完整代码如下:

//import org.apache.spark
import org.apache.spark.sql.hive._
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import org.apache.spark.SparkConf
import org.apache.spark.sql.functions.{to_date, to_timestamp}
import org.apache.spark.sql._
import org.apache.spark.sql.types._
import org.apache.spark.sql.SparkSession
import java.sql.Timestamp
import java.text.SimpleDateFormat
import java.text._
import java.util.Date
import scala.util._
import org.apache.spark.sql.hive.HiveContext

  //val conf = new SparkConf().setAppName("Simple Application")
  //val sc = new SparkContext(conf)
  val hc = new HiveContext(sc)
  val spark: SparkSession = SparkSession.builder().appName("Simple Application").config("spark.master", "local").getOrCreate()

   // set source and target location
    val sourceDataLocation = "hdfs://localhost:9000/source.txt"
    val targetTableName = "TableA"

    // Extract source data
    println("Extracting SAS source data from csv file location " + sourceDataLocation);
    val sqlContext = new org.apache.spark.sql.SQLContext(sc)
    val sourceRawCsvData = sc.textFile(sourceDataLocation)

    println("Extracting target data from hive table " + targetTableName)
    val targetRawData = hc.sql("Select datetime,load_datetime,trim(source_bank) as source_bank,trim(emp_name) as emp_name,header_row_count, emp_hours from " + targetTableName)


    // Add the test cases here
    // Test 2 - Validate the Structure
       val headerColumns = sourceRawCsvData.first().split(",").to[List]
       val schema = TableASchema(headerColumns)

       val data = sourceRawCsvData.mapPartitionsWithIndex((index, element) => if (index == 0) element.drop(1) else element)
       .map(_.split(",").toList)
       .map(row)

       val dataFrame = spark.createDataFrame(data,schema)
       val sourceDataFrame = dataFrame.toDF(dataFrame.columns map(_.toLowerCase): _*)
       data.collect
       data.getClass
    // Test 3 - Validate the data
    // Test 4 - Calculate the average and variance of Int or Dec columns
    // Test 5 - Test 5

  def UpdateResult(tableName: String, returnCode: Int, description: String){
    val insertString = "INSERT INTO TestResult VALUES('" + tableName + "', " + returnCode + ",'" + description + "')"
    val a = hc.sql(insertString)

    }


  def TableASchema(columnName: List[String]): StructType = {
    StructType(
      Seq(
        StructField(name = "datetime", dataType = TimestampType, nullable = true),
        StructField(name = "load_datetime", dataType = TimestampType, nullable = true),
        StructField(name = "source_bank", dataType = StringType, nullable = true),
        StructField(name = "emp_name", dataType = StringType, nullable = true),
        StructField(name = "header_row_count", dataType = IntegerType, nullable = true),
        StructField(name = "emp_hours", dataType = DoubleType, nullable = true)
        )
    )
  }

  def row(line: List[String]): Row = {
       Row(convertToTimestamp(line(0).trim), convertToTimestamp(line(1).trim), line(2).trim, line(3).trim, line(4).toInt, line(5).toDouble)
    }


  def convertToTimestamp(s: String) : Timestamp = s match {
     case "" => null
     case _ => {
        val format = new SimpleDateFormat("ddMMMyyyy:HH:mm:ss")
        Try(new Timestamp(format.parse(s).getTime)) match {
        case Success(t) => t
        case Failure(_) => null
      }
    }
  }

  }

推荐答案

基于 @Derek Kaknes 的回答，这是我为比较 schema 提出的解决方案s，只关心列名称、数据类型和；可空性并且对元数据

Based on @Derek Kaknes's answer, here's the solution I came up with for comparing schemas, being concerned only about column name, datatype & nullability and indifferent to metadata

// Extract relevant information: name (key), type & nullability (values) of columns
def getCleanedSchema(df: DataFrame): Map[String, (DataType, Boolean)] = {
    df.schema.map { (structField: StructField) =>
      structField.name.toLowerCase -> (structField.dataType, structField.nullable)
    }.toMap
  }

// Compare relevant information
def getSchemaDifference(schema1: Map[String, (DataType, Boolean)],
                        schema2: Map[String, (DataType, Boolean)]
                       ): Map[String, (Option[(DataType, Boolean)], Option[(DataType, Boolean)])] = {
  (schema1.keys ++ schema2.keys).
    map(_.toLowerCase).
    toList.distinct.
    flatMap { (columnName: String) =>
      val schema1FieldOpt: Option[(DataType, Boolean)] = schema1.get(columnName)
      val schema2FieldOpt: Option[(DataType, Boolean)] = schema2.get(columnName)

      if (schema1FieldOpt == schema2FieldOpt) None
      else Some(columnName -> (schema1FieldOpt, schema2FieldOpt))
    }.toMap
}

• getCleanedSchema 方法提取感兴趣的信息 - 列 datatype &nullability 并将列名的 map 返回到 tuple

  • getCleanedSchema method extracts information of interest - column datatype & nullability and returns a map of column name to tuple

    getSchemaDifference 方法返回一个 map 只包含那些在两个模式中不同的列.如果两个模式之一中不存在列，则其对应的属性将为 None

    getSchemaDifference method returns a map containing only those columns that differ in the two schemas. If a column is absent in one of the two schemas, then it's corresponding properties would be None

    这篇关于scala中两个数据框的模式比较的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！