如何在流式查询 (Java) 中使用 JSON 数组作为 Kafka 记录？

Question

我查看了很多示例以从 Kafa 主题中读取 JSON 数据。如果我从每个连接的主题中读取一条记录，我就能够成功地做到这一点，例如：

{"customer_id": "8d267162-1478-11ea-8d71-362b9e155667",
 "product": "Super widget",
 "price": 10,
 "bought_date": "2019-01-01"
}

下面的代码适用于上述用例：

package io.examle;

import org.apache.spark.sql.Column;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.functions;
import org.apache.spark.sql.streaming.StreamingQueryException;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.Metadata;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;

public class Whosebug {

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

        StructType schema = new StructType(new StructField[]{
                new StructField("customer_id", DataTypes.StringType, false, Metadata.empty()),  
                new StructField("product", DataTypes.StringType, false, Metadata.empty()),          
                new StructField("price", DataTypes.IntegerType, false, Metadata.empty()),               
                new StructField("bought_date", DataTypes.StringType, false, Metadata.empty()),
            });

        SparkSession spark = SparkSession
                .builder()
                .appName("SimpleExample")
                .getOrCreate();

        // Create a DataSet representing the stream of input lines from Kafka
        Dataset<Row> dataset = spark
                        .readStream()
                        .format("kafka")                
                        .option("kafka.bootstrap.servers", "localhost:9092")
                        .option("subscribe", "utilization")
                        .load()
                        .selectExpr("CAST(value AS STRING) as json");

        dataset.printSchema();

        Column col = new Column("json");

        Dataset<Row> customers = dataset.select(functions.from_json(col,schema).as("data")).select("data.*");           
        customers.printSchema();

        customers.writeStream()      
        .format("console")
        .start()
        .awaitTermination();

    }

}

但在我看来，上述方法效率低下，即连接到 Kafa 以获得每个连接的单个记录。因此，在我看来，传递下面形式的 JSON 数组会更有效率。因为每个 json 阵列可以储存许多 "records"。

[{
        "customer_id": "8d267162-1478-11ea-8d71-362b9e155667",
        "product": "Super widget",
        "price": 10,
        "bought_date": "2019-01-01"
    },
    {
        "customer_id": "498443a2-1479-11ea-8d71-362b9e155667",
        "product": "Food widget",
        "price": 4,
        "bought_date": "2019-01-01"
    } 
]

问题是我无法解压缩 JSON 数组并对其进行处理。以下代码失败：

package io.example;

import org.apache.spark.sql.Column;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.functions;
import org.apache.spark.sql.streaming.StreamingQueryException;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.Metadata;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;

public class Whosebug {

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

        StructType schema = new StructType(new StructField[]{
                new StructField("customer_id", DataTypes.StringType, false, Metadata.empty()),  
                new StructField("product", DataTypes.StringType, false, Metadata.empty()),          
                new StructField("price", DataTypes.IntegerType, false, Metadata.empty()),               
                new StructField("bought_date", DataTypes.StringType, false, Metadata.empty()),
            });

        SparkSession spark = SparkSession
                .builder()
                .appName("SimpleExample")
                .getOrCreate();

        // Create a DataSet representing the stream of input lines from Kafka
        Dataset<Row> dataset = spark
                        .readStream()
                        .format("kafka")                
                        .option("kafka.bootstrap.servers", "localhost:9092")
                        .option("subscribe", "utilization")
                        .load()
                        .selectExpr("CAST(value AS STRING) as json");

        dataset.printSchema();

        Column col = new Column("json");

        Dataset<Row> customers = dataset.select(functions.from_json(col,schema).as("data"));            


        Dataset<Row> data = customers.select(functions.explode_outer(functions.explode_outer(new Column("data"))));
        data.printSchema();

         data.writeStream()      
        .format("console")
        .start()
        .awaitTermination();
    }

}




Exception in thread "main" org.apache.spark.sql.AnalysisException: cannot resolve 'explode(`data`)' due to data type mismatch: input to function explode should be array or map type, not struct<customer_id:string,product:string,price:int,bought_date:string>;;

问题：

1) 如何正确编写代码以有效解压缩 JSON 数组？我怀疑我上面针对失败代码采用的方法是最好的，但我尝试遵循我看到的关于 functions.explode() 等的许多示例。

2) 如果失败的代码奇迹般地是一种正确的方法。如何将结构转换为数组或映射？

Answer 1

Spark 不会为每个连接提取一条记录。 Kafka API 将一次轮询一批记录。

就 Kafka 的最佳实践而言，多个事件应该拆分为多个对象，而不是塞入数组，除非它们确实需要关联，例如，您将有一个 "cart" 记录，其中包含一个列表"items" 订单

要使您的代码正常工作，您的模式必须是 ArrayType（不是结构或映射）。

StructType schema = new StructType(new StructField[]{
            new StructField("customer_id", DataTypes.StringType, false, Metadata.empty()),  
            new StructField("product", DataTypes.StringType, false, Metadata.empty()),          
            new StructField("price", DataTypes.IntegerType, false, Metadata.empty()),               
            new StructField("bought_date", DataTypes.StringType, false, Metadata.empty()),
        });

ArrayType arrSchema = new ArrayType(schema, false);

然后在使用from_json时使用数组架构。

Answer 2

为了完整起见，下面的代码使用上面的建议实现了预期的结果：

package io.example;


import org.apache.spark.sql.Column;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.functions;
import org.apache.spark.sql.streaming.StreamingQueryException;
import org.apache.spark.sql.types.ArrayType;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.Metadata;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;

public class Whosebug {

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

        StructType schema = new StructType(new StructField[]{
                new StructField("customer_id", DataTypes.StringType, false, Metadata.empty()),  
                new StructField("product", DataTypes.StringType, false, Metadata.empty()),          
                new StructField("price", DataTypes.IntegerType, false, Metadata.empty()),               
                new StructField("bought_date", DataTypes.StringType, false, Metadata.empty())
            });

        ArrayType  arrayType = new ArrayType(schema, false);

        SparkSession spark = SparkSession
                .builder()
                .appName("SimpleExample")
                .getOrCreate();

        // Create a DataSet representing the stream of input lines from Kafka
        Dataset<Row> dataset = spark
                        .readStream()
                        .format("kafka")                
                        .option("kafka.bootstrap.servers", "localhost:9092")
                        .option("subscribe", "utilization")
                        .load()
                        .selectExpr("CAST(value AS STRING) as json");

        dataset.printSchema();

        Column col = new Column("json");        
        Column data = functions.from_json(col,arrayType).as("data");    
        Column explode = functions.explode(data);
        Dataset<Row> customers = dataset.select(explode).select("col.*");
        customers.schema();

        customers.writeStream()      
        .format("console")
        .start()
        .awaitTermination();


    }

}


Batch: 77
-------------------------------------------
+-----------+-------------+-----+-----------+
|customer_id|      product|price|bought_date|
+-----------+-------------+-----+-----------+
|   d6315a00| Super widget|   10|2019-01-01 |
|   d6315cd0|  Food widget|    4| 2019-01-01|
|   d6315e2e|  Bike widget|   10| 2019-01-01|
|   d631614e|Garage widget|    4| 2019-01-01|
+-----------+-------------+-----+-----------+

如何在流式查询 (Java) 中使用 JSON 数组作为 Kafka 记录？

How to work with JSON array as Kafka records in streaming queries (Java)?

java

json

apache-kafka

apache-spark

spark-structured-streaming