Apache Spark Dataset API - 不接受模式StructType

Question

我有以下类使用Spark数据API加载无头CSV文件。

我遇到的问题是我不能让SparkSession接受应该定义每列的模式StructType。 生成的Dataframe是String类型的未命名列

public class CsvReader implements java.io.Serializable {

public CsvReader(StructType builder) {
        this.builder = builder;
    }
private StructType builder;

SparkConf conf = new SparkConf().setAppName("csvParquet").setMaster("local");
// create Spark Context
SparkContext context = new SparkContext(conf);
// create spark Session
SparkSession sparkSession = new SparkSession(context);

Dataset<Row> df = sparkSession
        .read()
        .format("com.databricks.spark.csv")
        .option("header", false)
        //.option("inferSchema", true)
        .schema(builder)
        .load("/Users/Chris/Desktop/Meter_Geocode_Data.csv"); //TODO: CMD line arg

public void printSchema() {
    System.out.println(builder.length());
    df.printSchema();
}

public void printData() {
    df.show();
}

public void printMeters() {
    df.select("meter").show();
}

public void printMeterCountByGeocode_result() {
    df.groupBy("geocode_result").count().show();
}

public Dataset getDataframe() {
            return df;
 }

}

产生的数据帧架构是：

root
 |-- _c0: string (nullable = true)
 |-- _c1: string (nullable = true)
 |-- _c2: string (nullable = true)
 |-- _c3: string (nullable = true)
 |-- _c4: string (nullable = true)
 |-- _c5: string (nullable = true)
 |-- _c6: string (nullable = true)
 |-- _c7: string (nullable = true)
 |-- _c8: string (nullable = true)
 |-- _c9: string (nullable = true)
 |-- _c10: string (nullable = true)
 |-- _c11: string (nullable = true)
 |-- _c12: string (nullable = true)
 |-- _c13: string (nullable = true)

调试器显示正确定义了'builder'StrucType：

0 = {StructField@4904} "StructField(geocode_result,DoubleType,false)"
1 = {StructField@4905} "StructField(meter,StringType,false)"
2 = {StructField@4906} "StructField(orig_easting,StringType,false)"
3 = {StructField@4907} "StructField(orig_northing,StringType,false)"
4 = {StructField@4908} "StructField(temetra_easting,StringType,false)"
5 = {StructField@4909} "StructField(temetra_northing,StringType,false)"
6 = {StructField@4910} "StructField(orig_address,StringType,false)"
7 = {StructField@4911} "StructField(orig_postcode,StringType,false)"
8 = {StructField@4912} "StructField(postcode_easting,StringType,false)"
9 = {StructField@4913} "StructField(postcode_northing,StringType,false)"
10 = {StructField@4914} "StructField(distance_calc_method,StringType,false)"
11 = {StructField@4915} "StructField(distance,StringType,false)"
12 = {StructField@4916} "StructField(geocoded_address,StringType,false)"
13 = {StructField@4917} "StructField(geocoded_postcode,StringType,false)"

我究竟做错了什么？ 任何帮助都非常感谢！

Answer 1

定义变量Dataset<Row> df并移动代码块以读取getDataframe()方法中的CSV文件，如下所示。

private Dataset<Row> df = null;

public Dataset getDataframe() {
    df = sparkSession
        .read()
        .format("com.databricks.spark.csv")
        .option("header", false)
        //.option("inferSchema", true)
        .schema(builder)
        .load("src/main/java/resources/test.csv"); //TODO: CMD line arg
        return df;
}

现在你可以像下面这样调用它。

    CsvReader cr = new CsvReader(schema);
    Dataset df = cr.getDataframe();
    cr.printSchema();

我建议你重新设计你的课程。 一种选择是你可以将df作为参数传递给其他方法。 如果您使用的是Spark 2.0，则不需要SparkConf。 请参阅文档以创建SparkSession。

Answer 2

如果要通过构建器初始化它，则应将df放在构造函数中。或者可以将其放在成员函数中。