Flink实战（八十）：flink-sql使用（七）Flink SQL Clien读取Kafka数据流式写入Hive（用hive 管理kafka元数据）_flinksql读取kafka数据到hive

声明：本系列博客是根据SGG的视频整理而成，非常适合大家入门学习。

《2021年最新版大数据面试题全面开启更新》

版本说明：

1. Flink 1.11.2
2. Kafka 2.4.0
3. Hive 3.1.2
4. Hadoop 3.1.3

1 hive 

安装hive，使用mysql做为元数据存储

1.2 hive-site.xml 配置 （版本3.1.2）

<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
<configuration>
        <property>
            <name>javax.jdo.option.ConnectionURL</name>
            <value>jdbc:mysql://hadoop102:3306/metastore?createDatabaseIfNotExist=true</value>
            <description>JDBC connect string for a JDBC metastore</description>
        </property>

        <property>
            <name>javax.jdo.option.ConnectionDriverName</name>
            <value>com.mysql.cj.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.warehouse.dir</name>
         <value>/user/hive/warehouse</value>
         <description>location of default database for the warehouse</description>
    </property>

    <property>
        <name>hive.cli.print.header</name>
        <value>true</value>
    </property>

    <property>
        <name>hive.cli.print.current.db</name>
        <value>true</value>
    </property>

    <property>
        <name>hive.cli.print.current.db</name>
        <value>true</value>
    </property>

    <property>
        <name>hive.metastore.schema.verification</name>
        <value>false</value>
    </property>

    <property>
        <name>hive.server2.thrift.bind.host</name>
         <value>192.168.1.122</value>
    </property>

<property>
        <name>hive.metastore.event.db.notification.api.auth</name>
        <value>false</value>
 </property>


    <property>
        <name>datanucleus.schema.autoCreateAll</name>
        <value>true</value>
    </property>


    <property>
            <name>hive.metastore.uris</name>
            <value>thrift://localhost:9083</value> <!-- metastore 在的pc的ip-->
    </property>



</configuration>

2 flink（版本1.10.2） 

2.1 配置conf/sql-client-hive.yaml

################################################################################
#  Licensed to the Apache Software Foundation (ASF) under one
#  or more contributor license agreements.  See the NOTICE file
#  distributed with this work for additional information
#  regarding copyright ownership.  The ASF licenses this file
#  to you under the Apache License, Version 2.0 (the
#  "License"); you may not use this file except in compliance
#  with the License.  You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
# limitations under the License.
################################################################################


# This file defines the default environment for Flink's SQL Client.
# Defaults might be overwritten by a session specific environment.


# See the Table API & SQL documentation for details about supported properties.


#==============================================================================
# Tables
#==============================================================================

# Define tables here such as sources, sinks, views, or temporal tables.

#tables: [] # empty list
# A typical table source definition looks like:
# - name: ...
#   type: source-table
#   connector: ...
#   format: ...
#   schema: ...

# A typical view definition looks like:
# - name: ...
#   type: view
#   query: "SELECT ..."

# A typical temporal table definition looks like:
# - name: ...
#   type: temporal-table
#   history-table: ...
#   time-attribute: ...
#   primary-key: ...


#==============================================================================
# User-defined functions
#==============================================================================

# Define scalar, aggregate, or table functions here.

#functions: [] # empty list
# A typical function definition looks like:
# - name: ...
#   from: class
#   class: ...
#   constructor: ...


#==============================================================================
# Catalogs
#==============================================================================

# Define catalogs here.

catalogs: # empty list
# A typical catalog definition looks like:
  - name: myhive # 名字随意取
    type: hive 
    hive-conf-dir: /opt/module/hive/conf # hive-site.xml 所在的路径
#    default-database: ...

#==============================================================================
# Modules
#==============================================================================


# Define modules here.

#modules: # note the following modules will be of the order they are specified
#  - name: core
#    type: core

#==============================================================================
# Execution properties
#==============================================================================

# Properties that change the fundamental execution behavior of a table program.

execution:
  # select the implementation responsible for planning table programs
  # possible values are 'blink' (used by default) or 'old'
  planner: blink
  # 'batch' or 'streaming' execution
  type: streaming
  # allow 'event-time' or only 'processing-time' in sources
  time-characteristic: event-time
  # interval in ms for emitting periodic watermarks
  periodic-watermarks-interval: 200
  # 'changelog' or 'table' presentation of results
  result-mode: table
  # maximum number of maintained rows in 'table' presentation of results
  max-table-result-rows: 1000000
  # parallelism of the program
  parallelism: 1
  # maximum parallelism
  max-parallelism: 128
  # minimum idle state retention in ms
  min-idle-state-retention: 0
  # maximum idle state retention in ms
  max-idle-state-retention: 0
  # current catalog ('default_catalog' by default)
  current-catalog: myhive
  # current database of the current catalog (default database of the catalog by default)
  current-database: hive
  # controls how table programs are restarted in case of a failures
  restart-strategy:
    # strategy type
    # possible values are "fixed-delay", "failure-rate", "none", or "fallback" (default)
    type: fallback

#==============================================================================
# Configuration options
#==============================================================================

# Configuration options for adjusting and tuning table programs.

# A full list of options and their default values can be found
# on the dedicated "Configuration" web page.

# A configuration can look like:
# configuration:
#   table.exec.spill-compression.enabled: true
#   table.exec.spill-compression.block-size: 128kb
#   table.optimizer.join-reorder-enabled: true

#==============================================================================
# Deployment properties
#==============================================================================

# Properties that describe the cluster to which table programs are submitted to.

deployment:
  # general cluster communication timeout in ms
  response-timeout: 5000
  # (optional) address from cluster to gateway
  gateway-address: ""
  # (optional) port from cluster to gateway
  gateway-port: 0

2.2 配置jar包

 

/flink-1.10.2
   /lib

       // Flink's Hive connector.Contains flink-hadoop-compatibility and flink-orc jars
       flink-connector-hive_2.11-1.10.2.jar

       // Hadoop dependencies
       // You can pick a pre-built Hadoop uber jar provided by Flink, alternatively
       // you can use your own hadoop jars. Either way, make sure it's compatible with your Hadoop
       // cluster and the Hive version you're using.
       flink-shaded-hadoop-2-uber-2.7.5-8.0.jar

       // Hive dependencies
       hive-exec-2.3.4.jar
       hive-metastore-3.1.2.jar
　　　　libfb303-0.9.3.jar

       // kafka dependencies
        flink-sql-connector-kafka_2.11-1.11.2.jar

后三个JAR包都是Hive自带的，可以在${HIVE_HOME}/lib目录下找到。前两个可以通过阿里云Maven搜索GAV找到并手动下载（groupId都是org.apache.flink）。

注意：要将lib包分发到集群中其他flink机器上

3 启动

3.1 启动hadoop集群

省略。。。

3.2 启动Hive meatastore

hive --service metastore &

3.3 启动Flink  

$FLINK_HOME/bin/start-cluster.sh

3.4 启动 Flink SQL Client

atguigu@hadoop102:/opt/module/flink$ bin/sql-client.sh embedded -d conf/sql-client-hive.yaml -l lib/

3.5 在Flink SQL Client中创建Hive表，指定数据源为Kafka

CREATE TABLE student(
  id INT,
  name STRING,
  password STRING,
  age INT,
  ts BIGINT,
  eventTime AS TO_TIMESTAMP(FROM_UNIXTIME(ts / 1000, 'yyyy-MM-dd HH:mm:ss')), -- 事件时间
  WATERMARK FOR eventTime AS eventTime - INTERVAL '10' SECOND -- 水印
) WITH (
  'connector.type' = 'kafka',
  'connector.version' = 'universal', -- 指定Kafka连接器版本，不能为2.4.0，必须为universal，否则会报错
  'connector.topic' = 'student', -- 指定消费的topic
  'connector.startup-mode' = 'latest-offset', -- 指定起始offset位置
  'connector.properties.zookeeper.connect' = 'hadoop000:2181',
  'connector.properties.bootstrap.servers' = 'hadooop000:9092',
  'connector.properties.group.id' = 'student_1',
  'format.type' = 'json',
  'format.derive-schema' = 'true', -- 由表schema自动推导解析JSON
  'update-mode' = 'append'
);

3.6 启动Kafka，发送数据

$KAFKA_HOME/bin/kafka-console-producer.sh --broker-list hadoop000:9092 --topic student

{"id":12, "name":"kevin", "password":"wong", "age":22, "ts":1603769073}

3.7 通过Flink SQL Client查询表中的数据

select * from student

 

 参考：https://blog.csdn.net/hll19950830/article/details/109308055

错误参考：

java.lang.ClassNotFoundException: org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer

https://blog.csdn.net/qq_31866793/article/details/107487858