我尝试过扩展上面链接的spark文档中的示例代码，我的假设似乎是正确的。见下表：

// This is used to implicitly convert an RDD to a DataFrame.
scala> import spark.implicits._
import spark.implicits._

// Create a simple DataFrame, store into a partition directory
scala> val squaresDF = spark.sparkContext.makeRDD(1 to 5).map(i => (i, i * i)).toDF("value", "square")
squaresDF: org.apache.spark.sql.DataFrame = [value: int, square: int]

scala> squaresDF.write.parquet("test_data/test_table/key=1")

// Create another DataFrame in a new partition directory,
// adding a new column and dropping an existing column
scala> val cubesDF = spark.sparkContext.makeRDD(6 to 10).map(i => (i, i * i * i)).toDF("value", "cube")
scala> cubesDF: org.apache.spark.sql.DataFrame = [value: int, cube: int]

scala> cubesDF.write.parquet("test_data/test_table/key=2")

// Read the partitioned table

scala> val mergedDF = spark.read.option("mergeSchema", "true").parquet("test_data/test_table")
mergedDF: org.apache.spark.sql.DataFrame = [value: int, square: int ... 2 more fields]

scala> mergedDF.printSchema()
root
 |-- value: integer (nullable = true)
 |-- square: integer (nullable = true)
 |-- cube: integer (nullable = true)
 |-- key: integer (nullable = true)

// Read without mergeSchema option
scala> val naiveDF = spark.read.parquet("test_data/test_table")
naiveDF: org.apache.spark.sql.DataFrame = [value: int, square: int ... 1 more field]

// Note that cube column is missing.
scala> naiveDF.printSchema()
root
 |-- value: integer (nullable = true)
 |-- square: integer (nullable = true)
 |-- key: integer (nullable = true)

// Take the schema from the mergedDF above and use it to read the same table with an explicit schema, but without the "mergeSchema" option.
scala> val explicitSchemaDF = spark.read.schema(mergedDF.schema).parquet("test_data/test_table")
explicitSchemaDF: org.apache.spark.sql.DataFrame = [value: int, square: int ... 2 more fields]

// Spark was able to use the correct schema despite not using the "mergeSchema" option
scala> explicitSchemaDF.printSchema()
root
 |-- value: integer (nullable = true)
 |-- square: integer (nullable = true)
 |-- cube: integer (nullable = true)
 |-- key: integer (nullable = true)

// Data is as expected.
scala> explicitSchemaDF.show()
+-----+------+----+---+
|value|square|cube|key|
+-----+------+----+---+
|    3|     9|null|  1|
|    4|    16|null|  1|
|    5|    25|null|  1|
|    8|  null| 512|  2|
|    9|  null| 729|  2|
|   10|  null|1000|  2|
|    1|     1|null|  1|
|    2|     4|null|  1|
|    6|  null| 216|  2|
|    7|  null| 343|  2|
+-----+------+----+---+

如您所见，在使用显式模式读取数据时，spark似乎正确地为Parquet分区中缺少的任何列提供了null值。
这让我感到相当自信，我可以用“不，在这种情况下没有必要使用mergeschema选项”来回答我的问题，但我仍然想知道是否有什么需要注意的注意事项。如有任何其他人的帮助，我们将不胜感激。