scala - 将 Spark DataFrame 模式转换为新模式

Question

我有多个从不同来源读取的 spark 作业，它们具有不同的架构，但它们非常接近，我想要做的是将它们全部写入同一个 Redshift 表，因此我需要统一所有 DataFrame 架构，什么是最好的方法吗？

假设第一个输入数据的架构如下:

  val schema1 = StructType(Seq(
    StructField("date", DateType),
    StructField("campaign_id", StringType),
    StructField("campaign_name", StringType),
    StructField("platform", StringType),
    StructField("country", StringType),
    StructField("views", DoubleType),
    StructField("installs", DoubleType),
    StructField("spend", DoubleType)
  ))

seconf inout 源的架构如下:

  val schema2 = StructType(Seq(
    StructField("date", DateType),
    StructField("creator_id", StringType),
    StructField("creator_name", StringType),
    StructField("platform", StringType),
    StructField("views", DoubleType),
    StructField("installs", DoubleType),
    StructField("spend", DoubleType),
    StructField("ecpm", DoubleType)
  ))

表架构(预期统一数据帧):

  val finalSchema = StructType(Seq(
    StructField("date", DateType),
    StructField("account_name", StringType),
    StructField("adset_id", StringType),
    StructField("adset_name", StringType),
    StructField("campaign_id", StringType),
    StructField("campaign_name", StringType),
    StructField("pub_id", StringType),
    StructField("pub_name", StringType),
    StructField("creative_id", StringType),
    StructField("creative_name", StringType),
    StructField("platform", StringType),
    StructField("install_source", StringType),
    StructField("views", IntegerType),
    StructField("clicks", IntegerType),
    StructField("installs", IntegerType),
    StructField("cost", DoubleType)
  ))

正如您在最终模式中看到的那样，我有一些列可能不在输入模式中，因此它应该为空，一些列名称也应该重命名。还有一些列如 ecpm应该掉线。

Answer 1

添加 index columns到 dataframes和 join它们基于 index所以会有一对一的映射。之后 select只有您想要的 columns来自 joined dataframe .

如果您有两个 dataframes像下面

// df1.show
+-----+---+
| name|age|
+-----+---+
|Alice| 25|
|  Bob| 29|
|  Tom| 26|
+-----+---+

//df2.show
+--------+-------+
|    city|country|
+--------+-------+
|   Delhi|  India|
|New York|    USA|
|  London|     UK|
+--------+-------+

现在添加 index columns并获得一对一映射

import org.apache.spark.sql.functions._

val df1Index=df1.withColumn("index1",monotonicallyIncreasingId)

val df2Index=df2.withColumn("index2",monotonicallyIncreasingId)

val joinedDf=df1Index.join(df2Index,df1Index("index1")===df2Index("index2"))

//joinedDf

+-----+---+------+--------+-------+------+
| name|age|index1|    city|country|index2|
+-----+---+------+--------+-------+------+
|Alice| 25|     0|   Delhi|  India|     0|
|  Bob| 29|     1|New York|    USA|     1|
|  Tom| 26|     2|  London|     UK|     2|
+-----+---+------+--------+-------+------+

现在您可以编写如下查询

val queryList=List(col("name"),col("age"),col("country"))
joinedDf.select(queryList:_*).show

//Output df
+-----+---+-------+
| name|age|country|
+-----+---+-------+
|Alice| 25|  India|
|  Bob| 29|    USA|
|  Tom| 26|     UK|
+-----+---+-------+