1.代码

// 让框架推断字段类型
val dfInfer: DataFrame = spark.read.option("inferSchema","true").csv("data/stu.csv")
//字段名 重设
val df2: DataFrame = dfInfer.toDF("id","name","age","city","score")
df2.printSchema()
df2.show()

1.1 来源

/**

• Loads an Dataset[String] storing CSV rows and returns the result as a DataFrame.
• If the schema is not specified using schema function and inferSchema option is enabled,
• this function goes through the input once to determine the input schema.
• If the schema is not specified using schema function and inferSchema option is disabled,
• it determines the columns as string types and it reads only the first line to determine the
• names and the number of fields.
• @param csvDataset input Dataset with one CSV row per record
• @since 2.2.0
  */
  

1.2 不建议使用

因为该方法进行推断需要进行全局扫描，比较慢，可以用自定义Schema来进行代替

1.3 自定义Schema代码样例

val schema = new StructType(Array(
  StructField("id",DataTypes.IntegerType),
  StructField("name",DataTypes.StringType),
  StructField("age",DataTypes.IntegerType),
  StructField("city",DataTypes.StringType),
  StructField("score",DataTypes.DoubleType)
))

val df3 = spark.read.schema(schema).csv("data/stu.csv")