Flink 读取hive，写入hive

秒速五厘米 2022-01-29 08:37 324阅读 0赞

1，读取实现了，也是找的资料，核心就是实现了

HCatInputFormat  
HCatInputFormatBase

上面这两个类，底层也是 继承实现了 RichInputFormat：

public abstract class HCatInputFormatBase<T> extends RichInputFormat<T, HadoopInputSplit> implements ResultTypeQueryabl

百度下载这个jar，然后把类找出来

![20190521155658292.png][]  
` `

`依赖：（大概是这些）`

<dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-hadoop-fs</artifactId>
        <version>1.6.2</version>
    </dependency>
    
    <dependency>
        <groupId>com.jolbox</groupId>
        <artifactId>bonecp</artifactId>
        <version>0.8.0.RELEASE</version>
    </dependency>
    
    <dependency>
        <groupId>com.twitter</groupId>
        <artifactId>parquet-hive-bundle</artifactId>
        <version>1.6.0</version>
    </dependency>
    
    <dependency>
        <groupId>org.apache.hive</groupId>
        <artifactId>hive-exec</artifactId>
        <version>2.1.0</version>
    </dependency>
    
    
    <dependency>
        <groupId>org.apache.hive</groupId>
        <artifactId>hive-metastore</artifactId>
        <version>2.1.0</version>
    </dependency>
    
    
    <dependency>
        <groupId>org.apache.hive</groupId>
        <artifactId>hive-cli</artifactId>
        <version>2.1.0</version>
    </dependency>
    
    <dependency>
        <groupId>org.apache.hive</groupId>
        <artifactId>hive-common</artifactId>
        <version>2.1.0</version>
    </dependency>
    
    <dependency>
        <groupId>org.apache.hive</groupId>
        <artifactId>hive-service</artifactId>
        <version>2.1.0</version>
    </dependency>
    
    <dependency>
        <groupId>org.apache.hive</groupId>
        <artifactId>hive-shims</artifactId>
        <version>2.1.0</version>
    </dependency>
    
    <dependency>
        <groupId>org.apache.hive.hcatalog</groupId>
        <artifactId>hive-hcatalog-core</artifactId>
        <version>2.1.0</version>
    </dependency>
    
    <dependency>
        <groupId>org.apache.thrift</groupId>
        <artifactId>libfb303</artifactId>
        <version>0.9.3</version>
        <type>pom</type>
    </dependency>
    
    
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-hadoop-compatibility_2.11</artifactId>
        <version>1.6.2</version>
    
    </dependency>
    
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-shaded-hadoop2</artifactId>
        <version>1.6.2</version>
    </dependency>

package com.coder.flink.core.FlinkHive
    
    
    import org.apache.flink.api.scala.ExecutionEnvironment
    
    import org.apache.hadoop.conf.Configuration
    import org.apache.flink.api.scala._
    
    
    //读取hive的数据
    object ReadHive {
      def main(args: Array[String]): Unit = {
    
          val conf = new Configuration()
          conf.set("hive.metastore.local", "false")
    
          conf.set("hive.metastore.uris", "thrift://172.10.4.141:9083")
           //如果是高可用 就需要是nameserver
    //      conf.set("hive.metastore.uris", "thrift://172.10.4.142:9083")
    
          val env = ExecutionEnvironment.getExecutionEnvironment
    
          //todo 返回类型
          val dataset: DataSet[TamAlert] = env.createInput(new HCatInputFormat[TamAlert]("aijiami", "test", conf))
    
          dataset.first(10).print()
    //      env.execute("flink hive test")
    
    
      }
    
    }

2，写入正在研究，要继承实现 RichInputFormat，不过Flink 有一个![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMxODY2Nzkz_size_16_color_FFFFFF_t_70][]

具体不知道方法怎么实现。。。。。。等有时间再搞搞。

API地址：[https://hive.apache.org/javadocs/r2.1.1/api/org/apache/hadoop/hive/ql/io/HiveOutputFormat.html][https_hive.apache.org_javadocs_r2.1.1_api_org_apache_hadoop_hive_ql_io_HiveOutputFormat.html]

核心要实现接口：

[https://www.cnblogs.com/frankdeng/p/9256215.html][https_www.cnblogs.com_frankdeng_p_9256215.html] （参考）

[https://www.coder4.com/archives/4031][https_www.coder4.com_archives_4031]  （参考）

可是不知道输入的\[K,V\] 是什么格式......

好消息是 Flink 1.9支持了Hive读写接口

不过我们可以用Hive Jdbc的方式去读写hive，可能就是性能会比较慢：

需要导入

<dependency>
        <groupId>org.apache.hive</groupId>
        <artifactId>hive-jdbc</artifactId>
        <version>2.1.0</version>
    </dependency>

参考：[https://www.cnblogs.com/guotianqi/p/8057740.html][https_www.cnblogs.com_guotianqi_p_8057740.html]

package com.coder.flink.core.FlinkHive;
    
    import java.sql.Connection;
    import java.sql.DriverManager;
    import java.sql.ResultSet;
    import java.sql.Statement;
    
    import java.sql.*;
    
    public class FlinkReadHive {
        public static void main(String[] args) throws ClassNotFoundException, SQLException {
    
            Class.forName("org.apache.hive.jdbc.HiveDriver");
            Connection con = DriverManager.getConnection("jdbc:hive2://172.10.4.143:10000/aijiami","hive","hive");
            Statement st = con.createStatement();
            ResultSet rs = st.executeQuery("SELECT * from ods_scenes_detail_new limit 10");
            while (rs.next()){
                System.out.println(rs.getString(1) + "," + rs.getString(2));
            }
            rs.close();
            st.close();
            con.close();
    
    
        }
    }

通过load方式写入hive：

[https://blog.csdn.net/haohaixingyun/article/details/57090626][https_blog.csdn.net_haohaixingyun_article_details_57090626]  （参考）

public class HiveApp \{  
       
    private static String driver = "org.apache.hive.jdbc.HiveDriver";  
    private static String url = "jdbc:hive2://Master:10000/default";  
    private static String user = "root"; //一般情况下可以使用匿名的方式，在这里使用了root是因为整个Hive的所有安装等操作都是root  
    private static String password = "";  
   
    public static void main(String\[\] args) \{  
        ResultSet res = null;  
           
        try \{  
            /\*\*  
             \* 第一步：把JDBC驱动通过反射的方式加载进来  
             \*/  
            Class.forName(driver);  
               
            /\*\*  
             \* 第二步：通过JDBC建立和Hive的连接器，默认端口是10000，默认用户名和密码都为空  
             \*/  
            Connection conn = DriverManager.getConnection(url, user, password);   
               
            /\*\*  
             \* 第三步：创建Statement句柄，基于该句柄进行SQL的各种操作；  
             \*/  
            Statement stmt = conn.createStatement();  
               
            /\*\*  
             \* 接下来就是SQL的各种操作；  
             \* 第4.1步骤：建表Table,如果已经存在的话就要首先删除；  
             \*/  
            String tableName = "testHiveDriverTable";  
            stmt.execute("drop table if exists " + tableName );  
              
               
            stmt.execute("create table " + tableName + " (id int, name string)" + "ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t' LINES TERMINATED BY '\\n'");  
            /\*\*  
             \*  第4.2步骤：查询建立的Table；  
             \*/  
            String sql = "show tables '" + tableName + "'";  
            System.out.println("Running: " + sql);  
            res = stmt.executeQuery(sql);  
            if (res.next()) \{  
              System.out.println(res.getString(1));  
            \}  
            /\*\*  
             \*  第4.3步骤：查询建立的Table的schema；  
             \*/  
            sql = "describe " + tableName;  
            System.out.println("Running: " + sql);  
            res = stmt.executeQuery(sql);  
            while (res.next()) \{  
              System.out.println(res.getString(1) + "\\t" + res.getString(2));  
            \}  
            
            /\*\*  
             \*  第4.4步骤：加载数据进入Hive中的Table；  
             \*/  
            String filepath = "/root/Documents/data/sql/testHiveDriver.txt";  
            sql = "load data local inpath '" + filepath + "' into table " + tableName;  
            System.out.println("Running: " + sql);  
            stmt.execute(sql);  
            
            /\*\*  
             \*  第4.5步骤：查询进入Hive中的Table的数据；  
             \*/  
            sql = "select \* from " + tableName;  
            System.out.println("Running: " + sql);  
            res = stmt.executeQuery(sql);  
            while (res.next()) \{  
              System.out.println(String.valueOf(res.getInt(1)) + "\\t" + res.getString(2));  
            \}  
            
            /\*\*  
             \*  第4.6步骤：Hive中的对Table进行统计操作；  
             \*/  
            sql = "select count(1) from " + tableName;   //在执行select count(\*) 时候会生成mapreduce 操作  ，那么需要启动资源管理器 yarn  ： start-yarn.sh   
            System.out.println("Running: " + sql);  
            res = stmt.executeQuery(sql);  
             
            while (res.next()) \{  
              System.out.println("Total lines :" + res.getString(1));  
            \}      
               
        \} catch (Exception e) \{  
            // TODO Auto-generated catch block  
            e.printStackTrace();  
        \}     
           
           
   
    \}  
   
\}

写入HDFS的简单案例：

package com.coder.flink.core.test_demo
    
    import org.apache.flink.api.scala.{DataSet, ExecutionEnvironment, _}
    import org.apache.flink.core.fs.FileSystem.WriteMode
    
    object WriteToHDFS {
      def main(args: Array[String]): Unit = {
        val env = ExecutionEnvironment.getExecutionEnvironment
        //2.定义数据 stu(age,name,height)
        val stu: DataSet[(Int, String, String)] = env.fromElements(
          (19, "zhangsan","aaaa"),
          (1449, "zhangsan","aaaa"),
          (33, "zhangsan","aaaa"),
          (22, "zhangsan","aaaa")
        )
    
        //todo 输出到本地
        stu.setParallelism(1).writeAsText("file:///C:/Users/Administrator/Desktop/Flink代码/测试数据/test001.txt",
          WriteMode.OVERWRITE)
        env.execute()
    
    
        //todo 写入到hdfs，文本文档,路径不存在则自动创建路径。
        stu.setParallelism(1).writeAsText("hdfs:///output/flink/datasink/test001.txt",
          WriteMode.OVERWRITE)
        env.execute()
    
        //todo 写入到hdfs，CSV文档
        //3.1读取csv文件
        val inPath = "hdfs:///input/flink/sales.csv"
        case class Sales(transactionId: String, customerId: Int, itemId: Int, amountPaid: Double)
        val ds2 = env.readCsvFile[Sales](
          filePath = inPath,
          lineDelimiter = "\n",
          fieldDelimiter = ",",
          lenient = false,
          ignoreFirstLine = true,
          includedFields = Array(0, 1, 2, 3),
          pojoFields = Array("transactionId", "customerId", "itemId", "amountPaid")
        )
        //3.2将CSV文档写入到hdfs
        val outPath = "hdfs:///output/flink/datasink/sales.csv"
        ds2.setParallelism(1).writeAsCsv(filePath = outPath, rowDelimiter = "\n",fieldDelimiter = "|", WriteMode.OVERWRITE)
    
        env.execute()
      }
    }

`================更新 最新的Flink 1.9已经支持了hive的读取======================`地址：

[https://ci.apache.org/projects/flink/flink-docs-release-1.9/dev/table/hive/read\_write\_hive.html][https_ci.apache.org_projects_flink_flink-docs-release-1.9_dev_table_hive_read_write_hive.html]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMxODY2Nzkz_size_16_color_FFFFFF_t_70 1][]![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMxODY2Nzkz_size_16_color_FFFFFF_t_70 2][]

注意hive版本跟依赖：

[https://ci.apache.org/projects/flink/flink-docs-release-1.9/dev/table/hive/\#depedencies][https_ci.apache.org_projects_flink_flink-docs-release-1.9_dev_table_hive_depedencies]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMxODY2Nzkz_size_16_color_FFFFFF_t_70 3][]

最新的Flink 1.11.0版本读写hive文章已经发了：

# Flink1.11 streaming写入hive踩坑 #

[https://blog.csdn.net/qq\_31866793/article/details/107341511][https_blog.csdn.net_qq_31866793_article_details_107341511]

# Flink sql之hive catalog踩雷记 #

[https://blog.csdn.net/qq\_31866793/article/details/106426057][https_blog.csdn.net_qq_31866793_article_details_106426057]

# Flink1.11 读取kafka数据写入hive #

[https://blog.csdn.net/qq\_31866793/article/details/107206019][https_blog.csdn.net_qq_31866793_article_details_107206019]

# Flink1.11读取hive遇到的各种依赖问题 #

[https://blog.csdn.net/qq\_31866793/article/details/107321041][https_blog.csdn.net_qq_31866793_article_details_107321041]

[20190521155658292.png]: /images/20220129/a680b7dbe3364543920086cf85b0c62e.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMxODY2Nzkz_size_16_color_FFFFFF_t_70]: /images/20220129/31d663a5ac7b41e2b81391da2564a5af.png
[https_hive.apache.org_javadocs_r2.1.1_api_org_apache_hadoop_hive_ql_io_HiveOutputFormat.html]: https://hive.apache.org/javadocs/r2.1.1/api/org/apache/hadoop/hive/ql/io/HiveOutputFormat.html
[https_www.cnblogs.com_frankdeng_p_9256215.html]: https://www.cnblogs.com/frankdeng/p/9256215.html
[https_www.coder4.com_archives_4031]: https://www.coder4.com/archives/4031
[https_www.cnblogs.com_guotianqi_p_8057740.html]: https://www.cnblogs.com/guotianqi/p/8057740.html
[https_blog.csdn.net_haohaixingyun_article_details_57090626]: https://blog.csdn.net/haohaixingyun/article/details/57090626
[https_ci.apache.org_projects_flink_flink-docs-release-1.9_dev_table_hive_read_write_hive.html]: https://ci.apache.org/projects/flink/flink-docs-release-1.9/dev/table/hive/read_write_hive.html
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMxODY2Nzkz_size_16_color_FFFFFF_t_70 1]: https://img-blog.csdnimg.cn/2019082712291680.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMxODY2Nzkz,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMxODY2Nzkz_size_16_color_FFFFFF_t_70 2]: /images/20220129/7cf1d36eb26943d59ba29f6ac0dc2c66.png
[https_ci.apache.org_projects_flink_flink-docs-release-1.9_dev_table_hive_depedencies]: https://ci.apache.org/projects/flink/flink-docs-release-1.9/dev/table/hive/#depedencies
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMxODY2Nzkz_size_16_color_FFFFFF_t_70 3]: /images/20220129/c4585eb0bf8b4f63b62baa3ab5508ad4.png
[https_blog.csdn.net_qq_31866793_article_details_107341511]: https://blog.csdn.net/qq_31866793/article/details/107341511
[https_blog.csdn.net_qq_31866793_article_details_106426057]: https://blog.csdn.net/qq_31866793/article/details/106426057
[https_blog.csdn.net_qq_31866793_article_details_107206019]: https://blog.csdn.net/qq_31866793/article/details/107206019
[https_blog.csdn.net_qq_31866793_article_details_107321041]: https://blog.csdn.net/qq_31866793/article/details/107321041