java - 如何在java中处理来自Apache Spark Streaming的Json数据

Question

您好，我是 Apache Spark 新手。我正在学习的路上。 我已经为来自 kafka 主题的 json 数据编写了 Spark Streaming。下面是连续传输的 json 数据。 但现在我不知道如何处理这个 json 数据。我使用 DataSet 和 DataFrame 来处理 Json 数据，但遇到了一些错误。 请帮我举几个例子，我如何处理从流中传输的数据。

注意:我使用的是 Apache Spark 1.6.3 版本。

(null{"time":"2017/08/21 18:25:11","model":"20E84fb","speed":"20E84fb","cellId":"0605d822E84fb","course":"146.37E84fb","header":"ST600ALTE84fb","deviceId":206675884,"distance":"166E84fb","longitude":"-099.168493E84fb","latitude":"19.428616E84fb","payload":"ST600ALT+number+;206675884;20;376;20161005;16:26:59;0605d822;334;20;2ee5;63;+19.428616;-099.168493;000.213;146.37;6;1;166;12.21;000000;34;000887;4.4;1;0.00E84fb","date":"2017/08/21 18:25:11E84fb"})

代码:

package datapipeline;

import java.util.Arrays;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.regex.Pattern;

import org.apache.log4j.Level;
import org.apache.log4j.Logger;
import org.apache.spark.SparkConf;
import org.apache.spark.SparkContext;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.SQLContext;
import org.apache.spark.storage.StorageLevel;
import org.apache.spark.streaming.Duration;
import org.apache.spark.streaming.Time;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaPairReceiverInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;
import org.apache.spark.streaming.kafka.KafkaUtils;
import org.json.simple.JSONObject;
import org.json.simple.parser.JSONParser;
import org.onosproject.net.Device;

import scala.Tuple2;

public final class SparkConsumer {
    //private static SparkContext sc = new SparkContext();
    private static final Pattern SPACE = Pattern.compile(" ");


    private static void setLogLevels() {
        boolean log4jInitialized = Logger.getRootLogger().getAllAppenders().hasMoreElements();
        if (!log4jInitialized) {
            // We first log something to initialize Spark's default logging, then we override the
            // logging level.
            Logger.getLogger(SparkConsumer.class).info("Setting log level to [WARN] for streaming example." +
                    " To override add a custom log4j.properties to the classpath.");
            Logger.getRootLogger().setLevel(Level.WARN);
        }
    }

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

        String jars[]={"C:\\DeviceStreaming-1.0.0.jar"};


        setLogLevels();


        SparkConf sparkConf = new SparkConf().setAppName("CustomerKafkaConsumerThread")
                .set("spark.local.ip","localhost:9092")
                .setMaster("local[*]").setJars(jars);
        JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, new Duration(3000));
        JavaSparkContext ctx = JavaSparkContext.fromSparkContext(SparkContext.getOrCreate(sparkConf));

        SQLContext sqlContext = new SQLContext(ctx);

        Map<String, Integer> topicMap = new HashMap<>();

        topicMap.put("iot", 10);


        JavaPairReceiverInputDStream<String, String> messages = KafkaUtils.createStream(jssc,"localhost:2181","SparkConsumer", topicMap,StorageLevel.MEMORY_ONLY());
        messages.print();


        JavaDStream<String> json = messages.map(
                new Function<Tuple2<String, String>, String>() {
                    public String call(Tuple2<String, String> message) {

                        return message._2();
                    }
                }
            );

        json.foreachRDD(rdd -> {

            //DataFrame df = sqlContext.read().json(rdd);
            DataFrame df=sqlContext.createDataFrame(rdd, Device.class);
            df.registerTempTable("rdd");
            df.filter("cellId");
            /*DataFrame deviceFrame= sqlContext.sql("SELECT cellID FROM rdd where cellId=206675884");
            df.show();
            df.printSchema();

            List<String> list=  deviceFrame.javaRDD().map(row -> row.getString(0)).collect();*/

        });

        jssc.start();
        jssc.awaitTermination();
    }
}

Answer 1

您可以使用 get_json_object 函数从 JSON 中提取数据。

Extracts json object from a json string based on json path specified, and returns json string of the extracted json object. It will return null if the input json string is invalid.

尝试如下:

df.withCoulmn("已解析",..functions.from_json(new Column("raw_json))).printSchema

这应该为您提供它生成的架构，然后您可以根据需要执行操作。

编辑: 也许这不是 1.6.3 的最佳解决方案，也许您需要 UDF 对解析的对象执行操作

参见:https://spark.apache.org/docs/1.6.3/api/java/org/apache/spark/sql/functions.html#get_json_object(org.apache.spark.sql.Column,%20java.lang.String)