无法读取json文件：使用java的Spark结构化流媒体

我有一个python脚本，它每分钟从纽约证券交易所获取一个新文件（单行）中的股票数据（如下所示）。它包含4种股票的数据——MSFT、ADBE、GOOGL和FB，如下json格式

[{"symbol": "MSFT", "timestamp": "2019-05-02 15:59:00", "priceData": {"open": "126.0800", "high": "126.1000", "low": "126.0500", "close": "126.0750", "volume": "57081"}}, {"symbol": "ADBE", "timestamp": "2019-05-02 15:59:00", "priceData": {"open": "279.2900", "high": "279.3400", "low": "279.2600", "close": "279.3050", "volume": "12711"}}, {"symbol": "GOOGL", "timestamp": "2019-05-02 15:59:00", "priceData": {"open": "1166.4100", "high": "1166.7400", "low": "1166.2900", "close": "1166.7400", "volume": "8803"}}, {"symbol": "FB", "timestamp": "2019-05-02 15:59:00", "priceData": {"open": "192.4200", "high": "192.5000", "low": "192.3600", "close": "192.4800", "volume": "33490"}}]

我正在尝试将此文件流读入Spark流数据帧。但是我不能为它定义合适的模式。到目前为止，调查了互联网并做了以下工作

import org.apache.log4j.Level;
import org.apache.log4j.Logger;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.streaming.StreamingQueryException;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructType;



public class Driver1 {

    public static void main(String args[]) throws InterruptedException, StreamingQueryException {


        SparkSession session = SparkSession.builder().appName("Spark_Streaming").master("local[2]").getOrCreate();
        Logger.getLogger("org").setLevel(Level.ERROR);


        StructType priceData = new StructType()
                .add("open", DataTypes.DoubleType)
                .add("high", DataTypes.DoubleType)
                .add("low", DataTypes.DoubleType)
                .add("close", DataTypes.DoubleType)
                .add("volume", DataTypes.LongType);

        StructType schema = new StructType()
                .add("symbol", DataTypes.StringType)
                .add("timestamp", DataTypes.StringType)
                .add("stock", priceData);


        Dataset<Row> rawData = session.readStream().format("json").schema(schema).json("/home/abhinavrawat/streamingData/data/*");
        rawData.printSchema();
        rawData.writeStream().format("console").start().awaitTermination();
        session.close();        

    }

我甚至尝试先将json字符串作为文本文件读取，然后应用模式（就像Kafka流媒体一样）

---

请帮我弄清楚

刚想好，记住以下两件事-

定义模式时，请确保名称和顺序字段与json文件中的字段完全相同

最初，仅对所有字段使用

StringType

，您可以应用转换将其更改回某些特定的数据类型

这对我来说很有用-

    StructType priceData = new StructType()
            .add("open", DataTypes.StringType)
            .add("high", DataTypes.StringType)
            .add("low", DataTypes.StringType)
            .add("close", DataTypes.StringType)
            .add("volume", DataTypes.StringType);

    StructType schema = new StructType()
            .add("symbol", DataTypes.StringType)
            .add("timestamp", DataTypes.StringType)
            .add("priceData", priceData);


    Dataset<Row> rawData = session.readStream().format("json").schema(schema).json("/home/abhinavrawat/streamingData/data/*");
    rawData.writeStream().format("console").start().awaitTermination();
    session.close();

现在，您可以使用

priceData.open

、

priceData.close

等方法展平priceData列

  Dataset<Row> rawData = session.readStream().format("text").load("/home/abhinavrawat/streamingData/data/*");
    Dataset<Row> raw2 = rawData.select(org.apache.spark.sql.functions.from_json(rawData.col("value"),schema)); 
raw2.writeStream().format("console").start().awaitTermination();

+--------------------+
|jsontostructs(value)|
+--------------------+
|                null|
|                null|
|                null|
|                null|
|                null|

    StructType priceData = new StructType()
            .add("open", DataTypes.StringType)
            .add("high", DataTypes.StringType)
            .add("low", DataTypes.StringType)
            .add("close", DataTypes.StringType)
            .add("volume", DataTypes.StringType);

    StructType schema = new StructType()
            .add("symbol", DataTypes.StringType)
            .add("timestamp", DataTypes.StringType)
            .add("priceData", priceData);


    Dataset<Row> rawData = session.readStream().format("json").schema(schema).json("/home/abhinavrawat/streamingData/data/*");
    rawData.writeStream().format("console").start().awaitTermination();
    session.close();

+------+-------------------+--------------------+
|symbol|          timestamp|           priceData|
+------+-------------------+--------------------+
|  MSFT|2019-05-02 15:59:00|[126.0800, 126.10...|
|  ADBE|2019-05-02 15:59:00|[279.2900, 279.34...|
| GOOGL|2019-05-02 15:59:00|[1166.4100, 1166....|
|    FB|2019-05-02 15:59:00|[192.4200, 192.50...|
|  MSFT|2019-05-02 15:59:00|[126.0800, 126.10...|
|  ADBE|2019-05-02 15:59:00|[279.2900, 279.34...|
| GOOGL|2019-05-02 15:59:00|[1166.4100, 1166....|
|    FB|2019-05-02 15:59:00|[192.4200, 192.50...|
|  MSFT|2019-05-02 15:59:00|[126.0800, 126.10...|
|  ADBE|2019-05-02 15:59:00|[279.2900, 279.34...|
| GOOGL|2019-05-02 15:59:00|[1166.4100, 1166....|
|    FB|2019-05-02 15:59:00|[192.4200, 192.50...|
|  MSFT|2019-05-02 15:59:00|[126.0800, 126.10...|
|  ADBE|2019-05-02 15:59:00|[279.2900, 279.34...|
| GOOGL|2019-05-02 15:59:00|[1166.4100, 1166....|
|    FB|2019-05-02 15:59:00|[192.4200, 192.50...|
|  MSFT|2019-05-02 15:59:00|[126.0800, 126.10...|
|  ADBE|2019-05-02 15:59:00|[279.2900, 279.34...|
| GOOGL|2019-05-02 15:59:00|[1166.4100, 1166....|
|    FB|2019-05-02 15:59:00|[192.4200, 192.50...|
+------+-------------------+--------------------+