debezium 监听 MySQL ,并用flink消费初体验

环境准备

• MySQL（开启binlog）
• Kafka(使用内嵌式debezium则不需要)
• debezium连接器

官网参考   https://debezium.io/documentation/reference/1.3/tutorial.html

在  Kafka 环境下安装 debezium 连接器

把 从官网下载的mysql 连接器 上传到Kafka 服务器上并解压，我的解压路径为 /opt/kafka/plugins/debezium-connector-mysql 

然后在 /opt/kafka/config/connect-distribute.properties 中编辑 

　note: 因为是分布式环境，所以配置connect-distribute.properties 

##
# 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 contains some of the configurations for the Kafka Connect distributed worker. This file is intended
# to be used with the examples, and some settings may differ from those used in a production system, especially
# the `bootstrap.servers` and those specifying replication factors.

# A list of host/port pairs to use for establishing the initial connection to the Kafka cluster.
bootstrap.servers=kafka1:9092,kafka2:9093,kafka3:9094

# unique name for the cluster, used in forming the Connect cluster group. Note that this must not conflict with consumer group IDs
group.id=connect-cluster

# The converters specify the format of data in Kafka and how to translate it into Connect data. Every Connect user will
# need to configure these based on the format they want their data in when loaded from or stored into Kafka
key.converter=org.apache.kafka.connect.json.JsonConverter
value.converter=org.apache.kafka.connect.json.JsonConverter
# Converter-specific settings can be passed in by prefixing the Converter's setting with the converter we want to apply
# it to
key.converter.schemas.enable=true
# 这个配置开启之后会附带schema 信息
value.converter.schemas.enable=true

# Topic to use for storing offsets. This topic should have many partitions and be replicated and compacted.
# Kafka Connect will attempt to create the topic automatically when needed, but you can always manually create
# the topic before starting Kafka Connect if a specific topic configuration is needed.
# Most users will want to use the built-in default replication factor of 3 or in some cases even specify a larger value.
# Since this means there must be at least as many brokers as the maximum replication factor used, we'd like to be able
# to run this example on a single-broker cluster and so here we instead set the replication factor to 1.
offset.storage.topic=connect-offsets
offset.storage.replication.factor=1
#offset.storage.partitions=25

# Topic to use for storing connector and task configurations; note that this should be a single partition, highly replicated,
# and compacted topic. Kafka Connect will attempt to create the topic automatically when needed, but you can always manually create
# the topic before starting Kafka Connect if a specific topic configuration is needed.
# Most users will want to use the built-in default replication factor of 3 or in some cases even specify a larger value.
# Since this means there must be at least as many brokers as the maximum replication factor used, we'd like to be able
# to run this example on a single-broker cluster and so here we instead set the replication factor to 1.
config.storage.topic=connect-configs
config.storage.replication.factor=1

# Topic to use for storing statuses. This topic can have multiple partitions and should be replicated and compacted.
# Kafka Connect will attempt to create the topic automatically when needed, but you can always manually create
# the topic before starting Kafka Connect if a specific topic configuration is needed.
# Most users will want to use the built-in default replication factor of 3 or in some cases even specify a larger value.
# Since this means there must be at least as many brokers as the maximum replication factor used, we'd like to be able
# to run this example on a single-broker cluster and so here we instead set the replication factor to 1.
status.storage.topic=connect-status
status.storage.replication.factor=1
#status.storage.partitions=5

# Flush much faster than normal, which is useful for testing/debugging
offset.flush.interval.ms=10000

# These are provided to inform the user about the presence of the REST host and port configs 
# Hostname & Port for the REST API to listen on. If this is set, it will bind to the interface used to listen to requests.
#rest.host.name=
#rest.port=8083

# The Hostname & Port that will be given out to other workers to connect to i.e. URLs that are routable from other servers.
#rest.advertised.host.name=
#rest.advertised.port=

# Set to a list of filesystem paths separated by commas (,) to enable class loading isolation for plugins
# (connectors, converters, transformations). The list should consist of top level directories that include 
# any combination of: 
# a) directories immediately containing jars with plugins and their dependencies
# b) uber-jars with plugins and their dependencies
# c) directories immediately containing the package directory structure of classes of plugins and their dependencies
# Examples: 
# plugin.path=/usr/local/share/java,/usr/local/share/kafka/plugins,/opt/connectors,
plugin.path=/opt/kafka/plugins

重点配置 plugin.path ，同时也要注意，路径为连接器解压路径的父级目录

开启Kafka connect 

just a shell

bin/connect-distributed.sh config/connect-distributed.properties

 Kafka connect 的具体使用方式得去官网看，但总体来说就是通过 发送post 请求来搞的，启动后先测试下是否启动成功

curl -H "Accept:application/json" kafka1:8083/

注册Mysql 监听器

这里直接从官网抄个demo下来，改改参数

curl -i -X POST -H "Accept:application/json" -H "Content-Type:application/json" kafka1:8083/connectors/ -d '{ "name": "connector_demo", "config": { "connector.class": "io.debezium.connector.mysql.MySqlConnector", "tasks.max": "1", "database.hostname": "host.docker.internal", "database.port": "3306", "database.user": "root", "database.password": "123", "database.server.id": "184054", "database.server.name": "dbserver1", "database.include.list": "sensor_offset", "database.history.kafka.bootstrap.servers": "kafka1:9092", "database.history.kafka.topic": "dbhistory.sensor_offset" } }'

具体配置参考官网： https://debezium.io/documentation/reference/1.3/connectors/mysql.html#configure-the-mysql-connector_debezium

然后 你就可以看到你的Kafka里多了几个topic 

配置flink-connecter

老规矩，抄官网： https://ci.apache.org/projects/flink/flink-docs-release-1.11/zh/dev/table/connectors/formats/debezium.html

 利用table api 直接来操作

@Test
    public void testDebezium() throws Exception {
        tableEnvironment.getConfig().setSqlDialect(SqlDialect.DEFAULT);
        tableEnvironment.executeSql("CREATE TABLE offset_manager (
" +
                "  groupid STRING,
" +
                "  topic STRING,
" +
                "  `partition` int,
" +
                "  untiloffset int
" +
                ") WITH (
" +
                " 'connector' = 'kafka',
" +
                " 'topic' = 'dbserver1.sensor_offset.offset_manager',
" +
                " 'properties.bootstrap.servers' = 'kafka1:9092',
" +
                " 'properties.group.id' = 'testGroup',
" +
                " 'format' = 'debezium-json'
" +
                ")");

        Table offset_manager = tableEnvironment.from("offset_manager");
        tableEnvironment.toRetractStream(offset_manager, Row.class).print();
        env.execute();
    }