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，格式如下json
[{"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取到Spark Streaming数据帧中。但是我无法为其定义适当的架构。到目前为止，我们已经研究了互联网
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();
}
}
我得到的输出是这个-
root
|-- symbol: string (nullable = true)
|-- timestamp: string (nullable = true)
|-- stock: struct (nullable = true)
| |-- open: double (nullable = true)
| |-- high: double (nullable = true)
| |-- low: double (nullable = true)
| |-- close: double (nullable = true)
| |-- volume: long (nullable = true)
-------------------------------------------
Batch: 0
-------------------------------------------
+------+-------------------+-----+
|symbol| timestamp|stock|
+------+-------------------+-----+
| MSFT|2019-05-02 15:59:00| null|
| ADBE|2019-05-02 15:59:00| null|
| GOOGL|2019-05-02 15:59:00| null|
| FB|2019-05-02 15:59:00| null|
| MSFT|2019-05-02 15:59:00| null|
| ADBE|2019-05-02 15:59:00| null|
| GOOGL|2019-05-02 15:59:00| null|
| FB|2019-05-02 15:59:00| null|
| MSFT|2019-05-02 15:59:00| null|
| ADBE|2019-05-02 15:59:00| null|
| GOOGL|2019-05-02 15:59:00| null|
| FB|2019-05-02 15:59:00| null|
| MSFT|2019-05-02 15:59:00| null|
| ADBE|2019-05-02 15:59:00| null|
| GOOGL|2019-05-02 15:59:00| null|
| FB|2019-05-02 15:59:00| null|
| MSFT|2019-05-02 15:59:00| null|
| ADBE|2019-05-02 15:59:00| null|
| GOOGL|2019-05-02 15:59:00| null|
| FB|2019-05-02 15:59:00| null|
+------+-------------------+-----+
我什至尝试先将json字符串读取为文本文件，然后再应用架构（就像通过Kafka-Streaming完成）...
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();
获取下面的输出，在这种情况下，rawData数据帧作为字符串fromat中的json数据
+--------------------+
|jsontostructs(value)|
+--------------------+
| null|
| null|
| null|
| null|
| null|
请帮我弄清楚。
答案 0 :(得分：1)
弄清楚了，请牢记以下两点-
在定义架构时，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();
查看输出-
+------+-------------------+--------------------+
|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...|
+------+-------------------+--------------------+
您现在可以使用priceData.open，priceData.close等来对priceData列进行展平。