Spark消费kafkaf的数据,解析数据并将数据存入到Hive中

import kafka.serializer.StringDecoder;
import net.icsoc.bigdata.common.config.PropertiesLoad;
import org.apache.hadoop.fs.LocalFileSystem;
import org.apache.hadoop.hdfs.DistributedFileSystem;
import org.apache.spark.SparkConf;
import org.apache.spark.SparkContext;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.StorageLevels;
import org.apache.spark.api.java.function.VoidFunction;

import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.hive.HiveContext;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
import org.apache.spark.streaming.Duration;
import org.apache.spark.streaming.api.java.JavaPairInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;
import org.apache.spark.streaming.kafka.KafkaUtils;

import java.util.*;



/***
 * Spark Stream 拉取kafka的消息
 */
public class LoadDataBySparkStream {
    private static HiveContext hiveContxt;
    private  static SparkConf sparkConf;
    private static StructType schema;
    private static JavaSparkContext sparkContext;
    private static Map params;
    private static  Set topics;
    private static JavaStreamingContext jsc;
    private static JavaPairInputDStream stream;
    public static void main(String[] args) {
        //最好的方式是从参数传入
        PropertiesLoad.environment = args[0];
        //组装Spark启动需要的配置的参数
        createSparkConf();
        //组装spark全局的SparkContext
        createSparkContext();
        //创建hiveContxt用于向hive写入数据
        createHiveContext(sparkContext);
        //创建StreamContext
        createJavaStreamContext();
        //创建消费kafka生成Dstream
        createDStream();
        //创建解析输入JSON数据的格式
        createOutputSchema();
        //数据清洗以及数据存入hive
        etl(stream);
        try {
            jsc.start();
            jsc.awaitTermination();
        }catch (Exception e) {
            System.out.println("Stream Context Exception!");
        }finally {
            jsc.stop();
        }
    }

    /**
     * 创建DStream对象,用于从kafka中读取,b并且返回DStream
     */
    private static void createDStream(){
        createParams();
        stream = KafkaUtils.createDirectStream(jsc,String.class,String.class,
                StringDecoder.class,StringDecoder.class,params,topics);
    }

    /**
     * 使用SparkConxt对象,创建SparkStreamContx对象用于SparkStream任务调度
     */
    private static void  createJavaStreamContext(){
        jsc = new JavaStreamingContext(sparkContext,new Duration(PropertiesLoad.getConfig().getInt("stream.duration.time")));
    }

    /**
     * 组装kafkaUtils工具创建DStream所需要的配置文件
     */
    private static void createParams(){
        params = new HashMap<>();
        params.put("bootstrap.servers",PropertiesLoad.getConfig().getString("bootstrap.servers"));
        topics = new HashSet<>();
        topics.add(PropertiesLoad.getConfig().getString("stream.kafka.topics"));
    }

    /**
     * 创建SparkContext全局对象
     */
    private static void createSparkContext(){
        sparkContext  = new JavaSparkContext(sparkConf);
    }

    /**
     * 创建SparkConf配置文件对象
     */
    private static void createSparkConf(){
    sparkConf = new SparkConf()
            .set("fs.hdfs.impl", DistributedFileSystem.class.getName())
            .set("fs.file.impl", LocalFileSystem.class.getName())
            .set("spark.sql.warehouse.dir", PropertiesLoad.getConfig().getString("spark.sql.warehouse.dir"))
            .set("dfs.client.use.datanode.hostname", "true")
            .set("fs.defaultFS", PropertiesLoad.getConfig().getString("fs.defaultFS"))
            .set("ffs.default.name", PropertiesLoad.getConfig().getString("fs.default.name"))
            .set("hive.server2.thrift.bind.host", PropertiesLoad.getConfig().getString("hive.server2.thrift.bind.host"))
            .set("hive.server2.webui.host", PropertiesLoad.getConfig().getString("hive.server2.webui.host"))
            .set("javax.jdo.option.ConnectionURL", PropertiesLoad.getConfig().getString("javax.jdo.option.ConnectionURL"))
            .set("hive.metastore.uris", PropertiesLoad.getConfig().getString("hive.metastore.uris"))
            .set("mapred.job.tracker", PropertiesLoad.getConfig().getString("mapred.job.tracker"))
            .set("dfs.support.append", "true")
            .set("dfs.client.block.write.replace-datanode-on-failure.policy", "NEVER")
            .set("dfs.client.block.write.replace-datanode-on-failure.enable", "true").setAppName("load-data-to-hive")
            .setMaster("local[1]");
    }

    /**
     * 创建SparkSql中的HiveContx对象
     * @param sparkContext
     */
    private static void createHiveContext(JavaSparkContext sparkContext){
        hiveContxt = new HiveContext(sparkContext);
    }

    /**
     * 对DStream流中的每天数据进行遍历
     * @param stream
     */

    private static void etl(JavaPairInputDStream stream){
     stream.foreachRDD(new VoidFunction>() {
         @Override
         public void call(JavaPairRDD tuple) throws Exception {
             if (tuple.isEmpty())
                 return;
            Dataset rowDataset = hiveContxt.jsonRDD(tuple.values(),schema);
            writeToHive(rowDataset);
         }
     });
    }

    /**
     *
     * 创建解析JSON数据的schema结构
     *
     */
    private static void createOutputSchema(){
        List outputFields = new ArrayList<>();
        outputFields.add(DataTypes.createStructField("vccId",DataTypes.StringType,true));
        outputFields.add(DataTypes.createStructField("clientName",DataTypes.StringType,true));
        outputFields.add(DataTypes.createStructField("clientPhone",DataTypes.StringType,true));
        outputFields.add(DataTypes.createStructField("ticketNo",DataTypes.StringType,true));
        outputFields.add(DataTypes.createStructField("ticketStatus",DataTypes.StringType,true));
        outputFields.add(DataTypes.createStructField("ticketSource",DataTypes.StringType,true));
        outputFields.add(DataTypes.createStructField("ticketSourceOrder",DataTypes.StringType,true));
        outputFields.add(DataTypes.createStructField("ticketPriority",DataTypes.StringType,true));
        outputFields.add(DataTypes.createStructField("ticketPriorityOrder",DataTypes.IntegerType,true));
        outputFields.add(DataTypes.createStructField("flowName",DataTypes.StringType,true));
        outputFields.add(DataTypes.createStructField("flowId",DataTypes.IntegerType,true));
        outputFields.add(DataTypes.createStructField("ticketTypes",DataTypes.StringType,true));
        outputFields.add(DataTypes.createStructField("currentNodeName",DataTypes.StringType,true));
        outputFields.add(DataTypes.createStructField("createUserName",DataTypes.StringType,true));
        outputFields.add(DataTypes.createStructField("currentUserName",DataTypes.StringType,true));
        outputFields.add(DataTypes.createStructField("participants",DataTypes.createArrayType(DataTypes.StringType),true));
        outputFields.add(DataTypes.createStructField("doneUserName",DataTypes.StringType,true));
        outputFields.add(DataTypes.createStructField("remindNums",DataTypes.IntegerType,true));
        outputFields.add(DataTypes.createStructField("createTime",DataTypes.StringType,true));
        outputFields.add(DataTypes.createStructField("updateTime",DataTypes.StringType,true));
        outputFields.add(DataTypes.createStructField("lastHandleTime",DataTypes.StringType,true));
        outputFields.add(DataTypes.createStructField("lastSubmitTime",DataTypes.StringType,true));
        outputFields.add(DataTypes.createStructField("doneTime",DataTypes.StringType,true));
        outputFields.add(DataTypes.createStructField("sendBackTime",DataTypes.StringType,true));
        outputFields.add(DataTypes.createStructField("transferTime",DataTypes.StringType,true));
        outputFields.add(DataTypes.createStructField("nodeAssignTime",DataTypes.createArrayType(DataTypes.StringType),true));
        outputFields.add(DataTypes.createStructField("nodeStartTime",DataTypes.createArrayType(DataTypes.StringType),true));
        outputFields.add(DataTypes.createStructField("nodeCreateTime",DataTypes.createArrayType(DataTypes.StringType),true));
        outputFields.add(DataTypes.createStructField("nodeExpireTime",DataTypes.createArrayType(DataTypes.StringType),true));
        outputFields.add(DataTypes.createStructField("nodeWaitSecs",DataTypes.createArrayType(DataTypes.StringType),true));
        outputFields.add(DataTypes.createStructField("nodeHandleSecs",DataTypes.createArrayType(DataTypes.StringType),true));
        outputFields.add(DataTypes.createStructField("evaluteSendTime",DataTypes.StringType,true));
        outputFields.add(DataTypes.createStructField("evaluteEndTime",DataTypes.StringType,true));
        outputFields.add(DataTypes.createStructField("evaluteStar",DataTypes.StringType,true));
        outputFields.add(DataTypes.createStructField("evaluteTags",DataTypes.createArrayType(DataTypes.StringType),true));
        outputFields.add(DataTypes.createStructField("totalSecs",DataTypes.IntegerType,true));
        outputFields.add(DataTypes.createStructField("isFirstDone",DataTypes.IntegerType,true));
        outputFields.add(DataTypes.createStructField("nodeForm",DataTypes.createMapType(DataTypes.StringType,DataTypes.StringType),true));
        outputFields.add(DataTypes.createStructField("nodeDynamicField",DataTypes.createMapType(DataTypes.StringType,DataTypes.StringType),true));
        schema = DataTypes.createStructType(outputFields);
    }
    /**
     * 利用Hql语句的insert into table select from table 将解析的DataSet通过HiveContext存储入hive中
     * @param row
     */
    private static void writeToHive(Dataset row){
        row.createOrReplaceTempView("tempview");
        String sql = "insert into " +PropertiesLoad.getConfig().getString("hive.table.ticket") +
        " PARTITION(year_month_day='" + org.apache.tools.ant.util.DateUtils.format(new Date(), "yyyy-MM-dd") + "') "
                + "select vccId as vcc_id, clientName as client_name, clientPhone as client_phone, ticketNo as ticket_no, ticketStatus as ticket_statuts , " +
                        "ticketSource as ticket_source ,ticketSourceOrder as ticket_source_order, ticketPriority as ticket_priority ,ticketPriorityOrder as ticket_priority_order , flowName flow_name, flowId as flow_id, ticketTypes as ticket_types  , currentNodeName as current_node_name , createUserName as create_user_name , currentUserName as current_user_name , participants , doneUserName as done_user_name, remindNums as remind_nums, createTime as create_time ,updateTime as update_time, lastHandleTime as laste_handle_time , lastSubmitTime as last_submit_time , doneTime as done_time, sendBackTime as send_back_time , transferTime  as transfer_time, nodeAssignTime as node_assign_time , nodeStartTime as node_start_time , nodeCreateTime as node_create_time , nodeExpireTime as node_expire_time, nodeWaitSecs as node_secs , nodeHandleSecs as node_handle_secs , evaluteSendTime as evalute_send_time , evaluteEndTime as evalute_end_time, evaluteStar as evalute_star , evaluteTags as evalute_tags , totalSecs as total_secs , isFirstDone as is_first_secs , nodeForm as node_form , nodeDynamicField as node_dynamic_fie from tempview";
        long start = System.currentTimeMillis();
        hiveContxt.sql(sql);
        long end = System.currentTimeMillis();
        System.out.println("insert into hive Cost Time   "+(end -start)+"      ones time size is    "+ row.count());
    }
}

转：https://blog.csdn.net/u012164361/article/details/79742201