本地IDEA中使用Spark直連集羣上的Hive

背景

我用VMWare搭建了一個Hadoop集羣，Spark與Hive等組件都已經安裝完畢。現在我希望在我的開發機上使用IDEA連接到集羣上的Hive進行相關操作。

進行配置修改

修改Hive中的hive-site.xml

在hive-site.xml中找到這個配置，將改成如下形式

<property>
  <name>hive.metastore.uris</name>
  <value>thrift://master節點的ip地址:9083</value>
  <description>Thrift URI for the remote metastore. Used by metastore client to connect to remote metastore.</description>
</property>

在hive-site.xml中找到如下配置，將中設置爲false

<property>
  <name>hive.metastore.schema.verification</name>
  <value>false</value>
  <description>
      Enforce metastore schema version consistency.
      True: Verify that version information stored in is compatible with one from Hive jars.  Also disable automatic
            schema migration attempt. Users are required to manually migrate schema after Hive upgrade which ensures
            proper metastore schema migration. (Default)
      False: Warn if the version information stored in metastore doesn't match with one from in Hive jars.
  </description>
</property>

將相關文件拷貝

1. 複製hive-site.xml到spark目錄下的conf/中
2. 將hive文件夾jar下的mysql-connector-java-版本.jar拷貝到spark目錄下的jar/中

在集羣上啓動命令

在master節點的命令行中啓動

hive --service metastore
hive --service hiveserver2

以上2條命令可以在後臺運行，使用nohup即可

本地IDEA使用

示例代碼如下：

import ml.dmlc.xgboost4j.scala.spark.XGBoost
import org.apache.spark.ml.feature.{StringIndexer, VectorAssembler}
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types.{DoubleType, StringType, StructField, StructType}

object XgbPredict {
    def main(args: Array[String]): Unit = {
        val spark = SparkSession
          .builder()
          .master("spark://master:7077")
          .config("hive.metastore.uris", "thrift://172.16.74.128:9083")
          .config("hive.metastore.warehouse.dir", "hdfs://172.16.74.128:9000/user/hive/warehouse")
          .config("spark.sql.warehouse.dir", "hdfs://172.16.74.128:9000/user/hive/warehouse")
          .enableHiveSupport()
          .getOrCreate()

        spark.sql("show databases").show()
        println("Done!")
    }

}

pom.xml

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>org.wangt</groupId>
    <artifactId>SparkTest</artifactId>
    <version>1.0-SNAPSHOT</version>
    <properties>
        <spark.version>2.4.3</spark.version>
        <scala.version>2.11</scala.version>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs-client</artifactId>
            <version>2.8.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-client</artifactId>
            <version>2.2.0</version>
        </dependency>
        <dependency>
            <groupId>ml.dmlc</groupId>
            <artifactId>xgboost4j-spark</artifactId>
            <version>0.72</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-mllib_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-catalyst_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>com.google.guava</groupId>
            <artifactId>guava</artifactId>
            <version>14.0.1</version>
        </dependency>
        <dependency><!--數據庫驅動：Mysql-->
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>8.0.19</version>
        </dependency>
    </dependencies>

    <build>
        <plugins>
            <plugin>
                <groupId>org.scala-tools</groupId>
                <artifactId>maven-scala-plugin</artifactId>
                <version>2.15.2</version>
                <executions>
                    <execution>
                        <goals>
                            <goal>compile</goal>
                            <goal>testCompile</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>

            <plugin>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.6.0</version>
                <configuration>
                    <source>1.8</source>
                    <target>1.8</target>
                </configuration>
            </plugin>

            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-surefire-plugin</artifactId>
                <version>2.20</version>
            </plugin>
            <plugin>
                <artifactId>maven-assembly-plugin</artifactId>
                <configuration>
                    <descriptorRefs>
                        <descriptorRef>jar-with-dependencies</descriptorRef>
                    </descriptorRefs>
                </configuration>
            </plugin>
        </plugins>
    </build>
</project>