Kakfa-Flink-Hive集成原理和实战代码

红太狼 2022-09-10 10:22 169阅读 0赞

大家好，我是土哥。

目前在某互联网大厂担任大数据算法工程师。

欢迎添加博主微信：`threeknowbigdata`，领取PDF版本

今天有位粉丝在群里发信息，问有没有`Flink DDL`的使用资料，表示自己是为刚入门的学习者。

![74286b61477a20ea33c8a9ad546484a8.png][]

为了让这位粉丝快速学会 `Flink DDL` 使用步骤，下面我将通过`Kafka - Flink -Hive`这个案例，讲解一下原理，并附上实战代码。

## 1、Flink-Hive理论 ##

### 1.1、Flink-Hive介绍 ###

在`Flink 1.11` 版本中，社区新增了一大功能是实时数仓，可以通过`kafka`,将`kafka sink`端的数据实时写入到`Hive`中。

为实现这个功能、`Flink1.11` 版本主要做了以下改变：

1.  将 FlieSystemStreaming Sink 重新修改，增加了分区提交和滚动策略机制。
2.  让HiveStreaming sink 重新使用文件系统流作为接收器。

可以通过Flink社区，查看`FLIP-85 Filesystem connector in Table`的设计思路。

### 1.2、Flink-Hive集成原理 ###

Flink与Hive集成原理图如下：

![bdd496f30f4db2290c3d96e4e5285704.png][]  
主要包含三部分内容：

1.  HiveDialect。`Flink1.1`新引入了`Hive`方言，所以在Flink SQL中可以编写HIve语法，即Hive Dialect。
2.  编写HIve SQL后，`FlinkSQL Planner` 会将`SQL`进行解析，验证，转换成逻辑计划，物理计划，最终变成`Jobgraph`。
3.  HiveCatalog。`HiveCatalog`作为`Flink` 和`Hive`的持久化介质，会将不同会话的`Flink`元数据存储到`Hive Metastore`中。

### 1.3、Flink-Hive版本支持 ###

Flink目前支持Hive的1.x,2.x,3.x，每个大的版本对于的Flink依赖如下：

![6c37dca8f3edd92b194d6352ca0ad91a.png][]

### 1.4、Flink SQL支持Hive语言 ###

Flink SQL支持两种SQL语言，分别为default和hive。

配置方式也包含两种，配置如下图所示：

1.  通过客户端配置。

![4d4920efda5d7d2f42c58bb54a04ef0c.png][]

1.  通过SQL配置。

![a0d6338bbac2307e35d594dad35b261a.png][]

## 2、kafka-Flink-Hive 集群配置 ##

需求：实时将kafka中的数据通过flink Sql 计算 存储到hive数据仓库中。

### 2.1集群部署 ###

配置信息如下：

1.  Hadoop: hadoop2.6.4
2.  Kafka: kafka\_2.11-2.2.0
3.  Flink: flink1.13.0
4.  Hive: hive-2.3.4-bin
5.  Zookeeper: zookeeper-3.4.5

![b755607d39b228fc5818b0a13f62e1fb.png][]

### 2.2 查询结果要求 ###

1.  希望Flink Sql 查询kafka输入的数据的表结构如下：

![7cd8d33335d980895bf13532859ed0d0.png][]

1.  希望FlinkSQL实时将kafka中的数据插入Hive 查询的结果根据分区查询如下：

![9a8354a18097f4598328cd803b9326d1.png][]

### 2.3 kafka启动命令 ###

1.  kafka启动

nohup ./kafka-server-start.sh ../config/server.properties &

1.  查看kafka Topic

./kafka-topics.sh --list --bootstrap-server 192.168.244.161:9092    //查看是否有需要用到的topic主题

1.  创建kafka Topic

kafka-topics.sh --create --bootstrap-server 192.168.244.161:9092 --topic test  --partitions 10 --replication-factor 1

1.  启动kafka生产者 让批量传输数据

kafka-console-producer.sh --broker-list 192.168.244.161:9092 --topic test

1.  往kafka中批量传入的数据源

{ "user_id": "1", "order_amount":"124.5", "log_ts": "2020-08-24 10:20:15"}
    { "user_id": "2", "order_amount":"38.4", "log_ts": "2020-08-24 11:20:15"}
    { "user_id": "3", "order_amount":"176.9", "log_ts": "2020-08-25 13:20:15"}
    { "user_id": "4", "order_amount":"302", "log_ts": "2020-08-25 14:20:15"}
    { "user_id": "5", "order_amount":"124.5", "log_ts": "2020-08-26 14:26:15"}
    { "user_id": "6", "order_amount":"38.4", "log_ts": "2020-08-26 15:20:15"}
    { "user_id": "7", "order_amount":"176.9", "log_ts": "2020-08-27 16:20:15"}
    { "user_id": "8", "order_amount":"302", "log_ts": "2020-08-27 17:20:15"}
    { "user_id": "9", "order_amount":"124.5", "log_ts": "2020-08-24 10:20:15"}
    { "user_id": "10", "order_amount":"124.6", "log_ts": "2020-08-24 10:21:15"}
    { "user_id": "11", "order_amount":"124.7", "log_ts": "2020-08-24 10:22:15"}
    { "user_id": "12", "order_amount":"124.8", "log_ts": "2020-08-24 10:23:15"}
    { "user_id": "13", "order_amount":"124.9", "log_ts": "2020-08-24 10:24:15"}
    { "user_id": "14", "order_amount":"125.5", "log_ts": "2020-08-24 10:25:15"}
    { "user_id": "15", "order_amount":"126.5", "log_ts": "2020-08-24 10:26:15"}

### 2.4 Hive集成Flink ###

1.  hive安装 修改hive-env.sh

# Set HADOOP_HOME to point to a specific hadoop install directory
    HADOOP_HOME=/root/sd/hadoop-2.6.4
    
    # Hive Configuration Directory can be controlled by:
    export HIVE_CONF_DIR=/root/sd/apache-hive-2.3.4-bin/conf
    
    # Folder containing extra libraries required for hive compilation/execution can be controlled by:
    export HIVE_AUX_JARS_PATH=/root/sd/apache-hive-2.3.4-bin/lib

由于hive的文件本身就在hdfs中保存的，所以需要指定Hadoop\_Home的路径，同时指定配置文件路径和依赖包的路径。

1.  修改hive-site.xml文件

<property>
    <name>javax.jdo.option.ConnectionURL</name>
    <value>jdbc:mysql://192.168.244.161:3306/hive?createDatabaseIfNotExist=true</value>
    <description>JDBC connect string for a JDBC metastore</description>
    </property>
    
    <property>
    <name>javax.jdo.option.ConnectionDriverName</name>
    <value>com.mysql.jdbc.Driver</value>
    <description>Driver class name for a JDBC metastore</description>
    </property>
    
    <property>
    <name>javax.jdo.option.ConnectionUserName</name>
    <value>root</value>
    <description>username to use against metastore database</description>
    </property>
    
    <property>
    <name>javax.jdo.option.ConnectionPassword</name>
    <value>123456</value>
    <description>password to use against metastore database</description>
    </property>
    
    <property>
        <name>hive.metastore.uris</name>
        <value>thrift://hlink163:9083</value>
        <description>Thrift URI for the remote metastore. Used by metastore client to connect to remote metastore.</description>
    </property>
    
    <property>
        <name>datanucleus.schema.autoCreateAll</name>
        <value>true</value>
    </property>
    
    <property>
        <name>hive.server2.logging.operation.log.location</name>
        <value>/root/sd/apache-hive-2.3.4-bin/tmp/operation_logs</value>
        <description>Top level directory where operation logs are stored if logging functionality is enabled</description>
    </property>
    
    <property>
        <name>hive.exec.scratchdir</name>
        <value>/root/sd/apache-hive-2.3.4-bin/tmp/hive</value>
          <description>HDFS root scratch dir for Hive jobs which gets created with write all (733) permission. For each connecting user, an HDFS scratch dir: ${hive.exec.scratchdir}/<username> is created, with ${hive.scratch.dir.permission}.        </description>
      </property>
    
     <property>
        <name>hive.exec.local.scratchdir</name>
        <value>/root/sd/apache-hive-2.3.4-bin/tmp/hive/local</value>
        <description>Local scratch space for Hive jobs</description>
     </property>
    
     <property>
        <name>hive.downloaded.resources.dir</name>
        <value>/root/sd/apache-hive-2.3.4-bin/tmp/hive/resources</value>
        <description>Temporary local directory for added resources in the remote file system.</description>
     </property>

1.  添加Flink与Hadoop的依赖 在flink-conf.yaml 中添加hadoop依赖.

![2c0f2fe94c498ebec44f28f8c96885eb.png][]

### 2.5 Hive集群启动 ###

1.  启动hive服务器

hive --service metastore    //端口号9083

可以使用命令查询一下，看是否启动成功

netstat -ntpl | grep 9083netstat -ntpl | grep 9083

### 2.6 Flink集群启动 ###

1.  启动Flink SQL(在bin目录下)

./sql-client.sh embedded -d ../conf/sql-client-defaults.yaml

1.  在flink sql 下查看hive的catalogs

show catalogs

结果如下：  
![e8f5d346a250c5b59d0174e7c2d8aeb4.png][]

1.  使用myhive catalog

use catalog myhive; 
    show tables;

## 3、kafka-Flink-Hive DDL ##

### 3.1、创建flink读取kafka的表（source） ###

# 指定使用flink sql默认的语言
    SET table.sql-dialect=default;
    CREATE TABLE log_kafka (
      user_id STRING,
      order_amount DOUBLE,
      log_ts TIMESTAMP(3),
      WATERMARK FOR log_ts AS log_ts - INTERVAL '5' SECOND
    ) WITH (
       'connector' = 'kafka',
       'topic' = 'test',
       'properties.bootstrap.servers' = '192.168.244.161:9092',
       'scan.startup.mode' = 'earliest-offset',
       'format' = 'json',
       'json.ignore-parse-errors' = 'true', 
       'json.fail-on-missing-field' = 'false',
       'properties.group.id' = 'flink1'
    );

kafka消费的启动模式有  
‘earliest-offset’，  
‘latest-offset’，  
‘group-offsets’，  
‘timestamp’，  
'specific-offsets’等

### 3.2、创建flink写入hive表（sink） ###

SET table.sql-dialect=hive;
    CREATE TABLE log_hive (
      user_id STRING,
      order_amount DOUBLE
    ) PARTITIONED BY (dt STRING, hr STRING) STORED AS parquet TBLPROPERTIES (
      'partition.time-extractor.timestamp-pattern'='$dt $hr:00:00',
      'sink.partition-commit.trigger'='partition-time',
      'sink.partition-commit.delay'='1min',
      'sink.semantic' = 'exactly-once',
      'sink.rolling-policy.file-size'='128MB',
      'sink.rolling-policy.rollover-interval' ='1min',
      'sink.rolling-policy.check-interval'='1min',
      'sink.partition-commit.policy.kind'='metastore,success-file'
    );

配置解释：

1.  `'sink.partition-commit.trigger'='partition-time'`,  
    – 使用partition中抽取时间，加上watermark决定partiton commit的时机
2.  `'partition.time-extractor.timestamp-pattern'=’dt hour:00:00’`,  
    – 配置hour级别的partition时间抽取策略，这个例子中dt字段是yyyy-MM-dd格式的天，hour是0-23的小时，timestamp-pattern定义了如何从这两个partition字段推出完整的timestamp
3.  `'sink.partition-commit.delay'='1 h'`,  
    – 配置dalay为小时级，当 watermark > partition时间 + 1小时，会commit这个partition
4.  `'sink.partition-commit.policy.kind’='metastore,success-file'`  
    – partitiion commit的策略是：先更新metastore(addPartition)，再写SUCCESS文件

### 3.3、将数据插入hive中 ###

INSERT INTO TABLE log_hive SELECT user_id, order_amount,DATE_FORMAT(log_ts, 'yyyy-MM-dd'), DATE_FORMAT(log_ts, 'HH') FROM log_kafka;

### 3.4、查询结果 ###

-- batch sql, select with partition pruning
    SELECT * FROM hive_table WHERE dt='2020-08-25' and hr='16';

![fefe31a4172356725c38083542244481.png][]

## 4、kafka-Flink-Hive Table API编写 ##

### 4.1 pom.xml配置 ###

<?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>com.flink</groupId>
        <artifactId>flinkhive</artifactId>
        <packaging>jar</packaging>
        <version>1.0-SNAPSHOT</version>
        <properties>
            <scala.bin.version>2.11</scala.bin.version>
            <flink.version>1.11.1</flink.version>
            <hadoop.version>2.6.4</hadoop.version>
            <hive.version>2.3.4</hive.version>
        </properties>
    
        <dependencies>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-scala_${scala.bin.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-java_${scala.binary.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_${scala.bin.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-common</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-api-scala-bridge_${scala.bin.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-table-planner-blink_${scala.bin.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-hive_${scala.bin.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-sql-connector-kafka_${scala.bin.version}</artifactId>
            <version>${flink.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-json</artifactId>
            <version>${flink.version}</version>
        </dependency>
            <dependency>
                <groupId>org.apache.hadoop</groupId>
                <artifactId>hadoop-common</artifactId>
                <version>${hadoop.version}</version>
            </dependency>
    
            <dependency>
                <groupId>org.apache.hadoop</groupId>
                <artifactId>hadoop-hdfs</artifactId>
                <version>${hadoop.version}</version>
            </dependency>
    
            <dependency>
                <groupId>org.apache.hadoop</groupId>
                <artifactId>hadoop-yarn-client</artifactId>
                <version>${hadoop.version}</version>
            </dependency>
    
            <dependency>
                <groupId>org.apache.hadoop</groupId>
                <artifactId>hadoop-mapreduce-client-core</artifactId>
                <version>${hadoop.version}</version>
            </dependency>
        <dependency>
            <groupId>org.apache.hive</groupId>
            <artifactId>hive-exec</artifactId>
            <version>${hive.version}</version>
        </dependency>
        </dependencies>
    </project>

### 4.2 代码存放路径截图 ###

![b8ed0628b13b93804647d02f8b802cb4.png][]

Scala版本代码:

import java.time.Duration
    
    import org.apache.flink.streaming.api.{ CheckpointingMode, TimeCharacteristic}
    import org.apache.flink.streaming.api.environment.ExecutionCheckpointingOptions
    import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment
    import org.apache.flink.table.api.{ EnvironmentSettings, SqlDialect}
    import org.apache.flink.table.api.bridge.scala.StreamTableEnvironment
    import org.apache.flink.table.catalog.hive.HiveCatalog
    
    object KafkaToHive { 
      def main(args: Array[String]): Unit = { 
        val streamEnv = StreamExecutionEnvironment.getExecutionEnvironment
        streamEnv.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
        streamEnv.setParallelism(3)
    
        val tableEnvSettings = EnvironmentSettings.newInstance()
          .useBlinkPlanner()
          .inStreamingMode()
          .build()
        val tableEnv = StreamTableEnvironment.create(streamEnv, tableEnvSettings)
        tableEnv.getConfig.getConfiguration.set(ExecutionCheckpointingOptions.CHECKPOINTING_MODE, CheckpointingMode.EXACTLY_ONCE)
        tableEnv.getConfig.getConfiguration.set(ExecutionCheckpointingOptions.CHECKPOINTING_INTERVAL, Duration.ofSeconds(20))
    
    
        val catalogName = "my_catalog"
        val catalog = new HiveCatalog(
          catalogName,              // catalog name
          "default",                // default database
          "./src/main/resources",  // Hive config (hive-site.xml) directory
          "2.3.4"                   // Hive version
        )
        tableEnv.registerCatalog(catalogName, catalog)
        tableEnv.useCatalog(catalogName)
    
        tableEnv.executeSql("CREATE DATABASE IF NOT EXISTS stream_tmp")
        tableEnv.executeSql("DROP TABLE IF EXISTS stream_tmp.log_kafka")
    
        tableEnv.executeSql(
          """ |CREATE TABLE stream_tmp.log_kafka ( | user_id STRING, | order_amount DOUBLE, | log_ts TIMESTAMP(3), | WATERMARK FOR log_ts AS log_ts - INTERVAL '5' SECOND |) WITH ( | 'connector' = 'kafka', | 'topic' = 'test', | 'properties.bootstrap.servers' = 'hlink163:9092', | 'properties.group.id' = 'flink1', | 'scan.startup.mode' = 'earliest-offset', | 'format' = 'json', | 'json.fail-on-missing-field' = 'false', | 'json.ignore-parse-errors' = 'true' |) """.stripMargin
        )
    
    
        tableEnv.getConfig.setSqlDialect(SqlDialect.HIVE)
    
        tableEnv.executeSql("CREATE DATABASE IF NOT EXISTS hive_tmp")
        tableEnv.executeSql("DROP TABLE IF EXISTS hive_tmp.log_hive")
    
        tableEnv.executeSql(
          """ |CREATE TABLE hive_tmp.log_hive ( | user_id STRING, | order_amount DOUBLE |) PARTITIONED BY ( | dt STRING, | hr STRING |) STORED AS PARQUET |TBLPROPERTIES ( | 'sink.partition-commit.trigger' = 'partition-time', | 'sink.partition-commit.delay' = '1 min', | 'format' = 'json', | 'sink.partition-commit.policy.kind' = 'metastore,success-file', | 'partition.time-extractor.timestamp-pattern'='$dt $hr:00:00' |) """.stripMargin
        )
        tableEnv.getConfig.setSqlDialect(SqlDialect.DEFAULT)
        tableEnv.executeSql(
          """ |INSERT INTO hive_tmp.log_hive |SELECT | user_id, | order_amount, | DATE_FORMAT(log_ts, 'yyyy-MM-dd'), DATE_FORMAT(log_ts, 'HH') | FROM stream_tmp.log_kafka """.stripMargin
        )
    
      }
    }

java版本代码:

import org.apache.flink.streaming.api.CheckpointingMode;
    import org.apache.flink.streaming.api.TimeCharacteristic;
    import org.apache.flink.streaming.api.environment.ExecutionCheckpointingOptions;
    import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
    import org.apache.flink.table.api.EnvironmentSettings;
    import org.apache.flink.table.api.SqlDialect;
    import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
    import org.apache.flink.table.catalog.hive.HiveCatalog;
    
    import java.time.Duration;
    
    /** * 类描述： * * @ClassName KafkaToHive * @Description: * @Author: lyz * @Date: 2021/9/6 下午9:50 */
    public class KafkaToHive { 
        public static void main(String[] args) { 
            StreamExecutionEnvironment senv = StreamExecutionEnvironment.getExecutionEnvironment();
            senv.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
            senv.setParallelism(3);
            EnvironmentSettings tableEnvSettings = EnvironmentSettings.newInstance().useBlinkPlanner().inStreamingMode()
                    .build();
    
            StreamTableEnvironment tableEnv = StreamTableEnvironment.create(senv, tableEnvSettings);
    
            //
            tableEnv.getConfig().getConfiguration().set(ExecutionCheckpointingOptions.CHECKPOINTING_MODE, CheckpointingMode.EXACTLY_ONCE);
    
            tableEnv.getConfig().getConfiguration().set(ExecutionCheckpointingOptions.CHECKPOINTING_INTERVAL, Duration.ofSeconds(20));
    
            String catalogName = "my_catalog";
    
            HiveCatalog catalog = new HiveCatalog(
                    catalogName,              // catalog name
                    "default",                // default database
                    "./src/main/resources",  // Hive config (hive-site.xml) directory
                    "2.3.4"                   // Hive version
            );
    
            tableEnv.registerCatalog(catalogName, catalog);
            tableEnv.useCatalog(catalogName);
    
            tableEnv.executeSql("CREATE DATABASE IF NOT EXISTS stream_tmp");
            tableEnv.executeSql("DROP TABLE IF EXISTS stream_tmp.log_kafka");
    
            tableEnv.executeSql("create table stream_tmp.log_kafka(" +
                                    "user_id String,\n" +
                                    "order_amount Double,\n" +
                                    "log_ts Timestamp(3),\n" +
                                    "WATERMARK FOR log_ts AS log_ts -INTERVAL '5' SECOND" +
                            ")WITH(" +
                                    " 'connector' = 'kafka',\n" +
                                    "'topic' = 'test',\n" +
                                    " 'properties.bootstrap.servers' = 'hlink163:9092',\n" +
                                    "'properties.group.id' = 'flink1',\n" +
                                    "'scan.startup.mode' = 'earliest-offset',\n" +
                                    "'format' = 'json',\n" +
                                    "'json.fail-on-missing-field' = 'false',\n" +
                                    "'json.ignore-parse-errors' = 'true'" +
                            ")");
            tableEnv.getConfig().setSqlDialect(SqlDialect.HIVE);
    
            tableEnv.executeSql("CREATE DATABASE IF NOT EXISTS hive_tmp");
            tableEnv.executeSql("DROP TABLE IF EXISTS hive_tmp.log_hive");
    
            tableEnv.executeSql(" CREATE TABLE hive_tmp.log_hive (\n" +
                    " user_id STRING,\n" +
                    " order_amount DOUBLE\n" +
                    " ) PARTITIONED BY (\n" +
                    " dt STRING,\n" +
                    " hr STRING\n" +
                    " ) STORED AS PARQUET\n" +
                    " TBLPROPERTIES (\n" +
                    " 'sink.partition-commit.trigger' = 'partition-time',\n" +
                    " 'sink.partition-commit.delay' = '1 min',\n" +
                    " 'format' = 'json',\n" +
                    " 'sink.partition-commit.policy.kind' = 'metastore,success-file',\n" +
                    " 'partition.time-extractor.timestamp-pattern'='$dt $hr:00:00'" +
                    " )");
            tableEnv.getConfig().setSqlDialect(SqlDialect.DEFAULT);
            tableEnv.executeSql("" +
                    " INSERT INTO hive_tmp.log_hive\n" +
                    " SELECT\n" +
                    " user_id,\n" +
                    " order_amount,\n" +
                    " DATE_FORMAT(log_ts, 'yyyy-MM-dd'), DATE_FORMAT(log_ts, 'HH')\n" +
                    " FROM stream_tmp.log_kafka");
        }
    }

以上就是全部的讲解内容，觉得好的，点赞、在看走起！

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