代码 | Spark读取mongoDB数据写入Hive普通表和分区表

版本：
spark 2.2.0
hive 1.1.0
scala 2.11.8
hadoop-2.6.0-cdh5.7.0
jdk 1.8
MongoDB 3.6.4

一原始数据及Hive表

MongoDB数据格式

{
    "_id" : ObjectId("5af65d86222b639e0c2212f3"),
    "id" : "1",
    "name" : "lisi",
    "age" : "18",
    "deptno" : "01"
}

Hive普通表

create table mg_hive_test(
id string,
name string,
age string,
deptno string
)row format delimited fields terminated by '\t';

Hive分区表

create table  mg_hive_external(
id string,
name string,
age string
)
partitioned by (deptno string)
row format delimited fields terminated by '\t';

二 IDEA+Maven+Java
依赖

<dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-sql_2.11</artifactId>
      <version>${spark.version}</version>
    </dependency>
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-hive_2.11</artifactId>
      <version>${spark.version}</version>
    </dependency>
    <dependency>
      <groupId>org.mongodb</groupId>
      <artifactId>mongo-java-driver</artifactId>
      <version>3.6.3</version>
    </dependency>
    <dependency>
      <groupId>org.mongodb.spark</groupId>
      <artifactId>mongo-spark-connector_2.11</artifactId>
      <version>2.2.2</version>
    </dependency>

代码

package com.huawei.mongo;/*
 * @Author: Create by Achun
 *@Time: 2018/6/2 21:00
 *
 */

import com.mongodb.spark.MongoSpark;

import org.apache.spark.SparkConf;
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.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
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.bson.Document;

import java.io.File;
import java.util.ArrayList;
import java.util.List;

public class sparkreadmgtohive {
    public static void main(String[] args) {
        //spark 2.x
        String warehouseLocation = new File("spark-warehouse").getAbsolutePath();
        SparkSession spark = SparkSession.builder()
                .master("local[2]")
                .appName("SparkReadMgToHive")
                .config("spark.sql.warehouse.dir", warehouseLocation)
                .config("spark.mongodb.input.uri", "mongodb://127.0.0.1:27017/test.mgtest")
                .enableHiveSupport()
                .getOrCreate();
        JavaSparkContext sc = new JavaSparkContext(spark.sparkContext());

        //spark 1.x
//        JavaSparkContext sc = new JavaSparkContext(conf);
//        sc.addJar("/Users/mac/zhangchun/jar/mongo-spark-connector_2.11-2.2.2.jar");
//        sc.addJar("/Users/mac/zhangchun/jar/mongo-java-driver-3.6.3.jar");
//        SparkConf conf = new SparkConf().setMaster("local[2]").setAppName("SparkReadMgToHive");
//        conf.set("spark.mongodb.input.uri", "mongodb://127.0.0.1:27017/test.mgtest");
//        conf.set("spark. serializer","org.apache.spark.serializer.KryoSerialzier");
//        HiveContext sqlContext = new HiveContext(sc);
//        //create df from mongo
//        Dataset<Row> df = MongoSpark.read(sqlContext).load().toDF();
//        df.select("id","name","name").show();

        String querysql= "select id,name,age,deptno,DateTime,Job from mgtable b";
        String opType ="P";

        SQLUtils sqlUtils = new SQLUtils();
        List<String> column = sqlUtils.getColumns(querysql);

        //create rdd from mongo
        JavaRDD<Document> rdd = MongoSpark.load(sc);
        //将Document转成Object
        JavaRDD<Object> Ordd = rdd.map(new Function<Document, Object>() {
            public Object call(Document document){
                List list = new ArrayList();
                for (int i = 0; i < column.size(); i++) {
                    list.add(String.valueOf(document.get(column.get(i))));
                }
                return list;

//                return list.toString().replace("[","").replace("]","");
            }
        });
        System.out.println(Ordd.first());
        //通过编程方式将RDD转成DF
        List ls= new ArrayList();
        for (int i = 0; i < column.size(); i++) {
            ls.add(column.get(i));
        }
        String schemaString = ls.toString().replace("[","").replace("]","").replace(" ","");
        System.out.println(schemaString);

        List<StructField> fields = new ArrayList<StructField>();
        for (String fieldName : schemaString.split(",")) {
            StructField field = DataTypes.createStructField(fieldName, DataTypes.StringType, true);
            fields.add(field);
        }
        StructType schema = DataTypes.createStructType(fields);

        JavaRDD<Row> rowRDD = Ordd.map((Function<Object, Row>) record -> {
            List fileds = (List) record;
//            String[] attributes = record.toString().split(",");
            return RowFactory.create(fileds.toArray());
        });

        Dataset<Row> df = spark.createDataFrame(rowRDD,schema);

        //将DF写入到Hive中
        //选择Hive数据库
        spark.sql("use datalake");
        //注册临时表
        df.registerTempTable("mgtable");

        if ("O".equals(opType.trim())) {
            System.out.println("数据插入到Hive ordinary table");
            Long t1 = System.currentTimeMillis();
            spark.sql("insert into mgtohive_2 " + querysql + " " + "where b.id not in (select id from mgtohive_2)");
            Long t2 = System.currentTimeMillis();
            System.out.println("共耗时：" + (t2 - t1) / 60000 + "分钟");
        }else if ("P".equals(opType.trim())) {

        System.out.println("数据插入到Hive  dynamic partition table");
        Long t3 = System.currentTimeMillis();
        //必须设置以下参数 否则报错
        spark.sql("set hive.exec.dynamic.partition.mode=nonstrict");
        //depton为分区字段   select语句最后一个字段必须是deptno
        spark.sql("insert into mg_hive_external partition(deptno) select id,name,age,deptno from mgtable b where b.id not in (select id from mg_hive_external)");
        Long t4 = System.currentTimeMillis();
        System.out.println("共耗时："+(t4 -t3)/60000+ "分钟");
        }
        spark.stop();
    }

}

工具类

package com.huawei.mongo;/*
 * @Author: Create by Achun
 *@Time: 2018/6/3 23:20
 *
 */

import java.util.ArrayList;
import java.util.List;

public class SQLUtils {

    public List<String> getColumns(String querysql){
        List<String> column = new ArrayList<String>();
        String tmp = querysql.substring(querysql.indexOf("select") + 6,
                querysql.indexOf("from")).trim();
        if (tmp.indexOf("*") == -1){
            String cols[] = tmp.split(",");
            for (String c:cols){
                column.add(c);
            }
        }
        return column;
    }

    public String getTBname(String querysql){
        String tmp = querysql.substring(querysql.indexOf("from")+4).trim();
        int sx = tmp.indexOf(" ");
        if(sx == -1){
            return tmp;
        }else {
            return tmp.substring(0,sx);
        }
    }

}

三错误解决办法
1 IDEA会获取不到Hive的数据库和表，将hive-site.xml放入resources文件中。并且将resources设置成配置文件(设置成功文件夹是蓝色否则是灰色)
file–>Project Structure–>Modules–>Source

2 上面错误处理完后如果报JDO类型的错误，那么检查HIVE_HOME/lib下时候否mysql驱动，如果确定有，那么就是IDEA获取不到。解决方法如下：

将mysql驱动拷贝到jdk1.8.0_171.jdk/Contents/Home/jre/lib/ext路径下(jdk/jre/lib/ext)

在IDEA项目External Libraries下的<1.8>里面添加mysql驱动

四注意点
由于将MongoDB数据表注册成了临时表和Hive表进行了关联，所以要将MongoDB中的id字段设置成索引字段，否则性能会很慢。
MongoDB设置索引方法：

db.getCollection('mgtest').ensureIndex({"id" : "1"}),{"background":true}

查看索引：

db.getCollection('mgtest').getIndexes()

MongoSpark网址：https://docs.mongodb.com/spark-connector/current/java-api/

本文转自若泽大数据：https://mp.weixin.qq.com/s/7uQG-g8oilqJebynTS6Bkg

代码 | Spark读取mongoDB数据写入Hive普通表和分区表

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