Parquet中读写Parquet文件的操作是什么？

// Encoders for most common types are automatically provided by importing spark.implicits._import spark.implicits._

val peopleDF = spark.read.json("examples/src/main/resources/people.json")

// DataFrames can be saved as Parquet files, maintaining the schema informationpeopleDF.write.parquet("people.parquet")

// Read in the parquet file created above// Parquet files are self-describing so the schema is preserved// The result of loading a Parquet file is also a DataFrameval parquetFileDF = spark.read.parquet("people.parquet")

// Parquet files can also be used to create a temporary view and then used in SQL statementsparquetFileDF.createOrReplaceTempView("parquetFile")val namesDF = spark.sql("SELECT name FROM parquetFile WHERE age BETWEEN 13 AND 19")namesDF.map(attributes => "Name: " + attributes(0)).show()// +------------+// |       value|// +------------+// |Name: Justin|// +------------+