Spark java：使用给定模式创建新数据集

Question

我有这段代码在 scala 中运行良好：

val schema = StructType(Array(
        StructField("field1", StringType, true),
        StructField("field2", TimestampType, true),
        StructField("field3", DoubleType, true),
        StructField("field4", StringType, true),
        StructField("field5", StringType, true)
    ))

val df = spark.read
    // some options
    .schema(schema)
    .load(myEndpoint)

我想在 Java 中做类似的事情。所以我的代码如下：

final StructType schema = new StructType(new StructField[] {
     new StructField("field1",  new StringType(), true,new Metadata()),
     new StructField("field2", new TimestampType(), true,new Metadata()),
     new StructField("field3", new StringType(), true,new Metadata()),
     new StructField("field4", new StringType(), true,new Metadata()),
     new StructField("field5", new StringType(), true,new Metadata())
});

Dataset<Row> df = spark.read()
    // some options
    .schema(schema)
    .load(myEndpoint);

但这给了我以下错误：

Exception in thread "main" scala.MatchError: org.apache.spark.sql.types.StringType@37c5b8e8 (of class org.apache.spark.sql.types.StringType)

我的架构似乎没有问题，所以我真的不知道问题出在哪里。

spark.read().load(myEndpoint).printSchema();
root
 |-- field5: string (nullable = true)
 |-- field2: timestamp (nullable = true)
 |-- field1: string (nullable = true)
 |-- field4: string (nullable = true)
 |-- field3: string (nullable = true)

schema.printTreeString();
root
 |-- field1: string (nullable = true)
 |-- field2: timestamp (nullable = true)
 |-- field3: string (nullable = true)
 |-- field4: string (nullable = true)
 |-- field5: string (nullable = true)

编辑：

这是一个数据样本：

spark.read().load(myEndpoint).show(false);
+---------------------------------------------------------------+-------------------+-------------+--------------+---------+
|field5                                                         |field2             |field1       |field4        |field3   |
+---------------------------------------------------------------+-------------------+-------------+--------------+---------+
|{"fieldA":"AAA","fieldB":"BBB","fieldC":"CCC","fieldD":"DDD"}  |2018-01-20 16:54:50|SOME_VALUE   |SOME_VALUE    |0.0      |
|{"fieldA":"AAA","fieldB":"BBB","fieldC":"CCC","fieldD":"DDD"}  |2018-01-20 16:58:50|SOME_VALUE   |SOME_VALUE    |50.0     |
|{"fieldA":"AAA","fieldB":"BBB","fieldC":"CCC","fieldD":"DDD"}  |2018-01-20 17:00:50|SOME_VALUE   |SOME_VALUE    |20.0     |
|{"fieldA":"AAA","fieldB":"BBB","fieldC":"CCC","fieldD":"DDD"}  |2018-01-20 18:04:50|SOME_VALUE   |SOME_VALUE    |10.0     |
 ...
+---------------------------------------------------------------+-------------------+-------------+--------------+---------+

Answer 1

使用 Datatypes 类中的静态方法和字段，而不是构造函数在 Spark 2.3.1 中为我工作：

    StructType schema = DataTypes.createStructType(new StructField[] {
            DataTypes.createStructField("field1",  DataTypes.StringType, true),
            DataTypes.createStructField("field2", DataTypes.TimestampType, true),
            DataTypes.createStructField("field3", DataTypes.StringType, true),
            DataTypes.createStructField("field4", DataTypes.StringType, true),
            DataTypes.createStructField("field5", DataTypes.StringType, true)
    });