大数据技术-FlinkCDC学习笔记-Toy模板网

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Flink-CDC简介

官网地址 https://ververica.github.io/flink-cdc-connectors/master/

CDC 是 Change Data Capture(变更数据获取)的简称。核心思想是，监测并捕获数据库的变动（包括数据或数据表的插入、更新以及删除等），将这些变更按发生的顺序完整记录下来，写入到消息中间件中以供其他服务进行订阅及消费。

CDC主要分为基于查询和基于Binlog两种方式

	基于查询的CDC	基于Binlog的CDC
开源产品	Sqoop、Kafka JDBC Source	Canal、MAxwell、Debezium
执行模式	Batch批处理	Streaming流处理
是否可以捕获所有数据变化	否	是
延迟性	高延迟	低延迟
是否增加数据库压力	是	否

MySQL开启binlog日志文件

修改 /etc/my.cnf

log-bin=mysql-bin
binlog_format=row
binlog-do-db=bigdata-realtime

大数据技术-FlinkCDC学习笔记

DataStream方式应用

引入依赖

<dependencies>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-java</artifactId>
        <version>1.12.0</version>
    </dependency>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-streaming-java_2.12</artifactId>
        <version>1.12.0</version>
    </dependency>
    <dependency>
        <groupId>org.apache.flink</groupId>
        <artifactId>flink-clients_2.12</artifactId>
        <version>1.12.0</version>
    </dependency>
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoop-client</artifactId>
        <version>3.1.3</version>
    </dependency>
    <dependency>
        <groupId>mysql</groupId>
        <artifactId>mysql-connector-java</artifactId>
        <version>5.1.49</version>
    </dependency>
    <dependency>
        <groupId>com.alibaba.ververica</groupId>
        <artifactId>flink-connector-mysql-cdc</artifactId>
        <version>1.2.0</version>
    </dependency>
    <dependency>
        <groupId>com.alibaba</groupId>
        <artifactId>fastjson</artifactId>
        <version>1.2.75</version>
    </dependency>
</dependencies>

打包方式：全量包

<build>
    <plugins>
        <plugin>
            <groupId>org.apache.maven.plugins</groupId>
            <artifactId>maven-assembly-plugin</artifactId>
            <version>3.0.0</version>
            <configuration>
                <descriptorRefs>
                    <descriptorRef>jar-with-dependencies</descriptorRef>
                </descriptorRefs>
            </configuration>
            <executions>
                <execution>
                    <id>make-assembly</id>
                    <phase>package</phase>
                    <goals>
                        <goal>single</goal>
                    </goals>
                </execution>
            </executions>
        </plugin>
    </plugins>
</build>

监控数据库变化

public static void main(String[] args) throws Exception {
    // 1. 获取执行环境
    StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    env.setParallelism(1);

    // Flink-cdc将读取binlog的位置信息以状态的方式保存在CK,如果想要做到断电续传，需要从CheckPoint或者SavePoint启动程序

    // 开启CK,每隔5秒钟做一次CK,CK超时时间10s,最大并发数2,CK之间间隔1,访问HDFS的用户名root,重启策略延时2秒重启3次
    env.enableCheckpointing(5000L);
    env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
    env.getCheckpointConfig().enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
    env.setRestartStrategy(RestartStrategies.fixedDelayRestart(3, 2000L));
    env.setStateBackend(new FsStateBackend("hdfs://hadoop102:8020/gmall-210325/ck"));
    System.setProperty("HADOOP_USER_NAME", "root");
    env.getCheckpointConfig().setCheckpointTimeout(10000L);
    env.getCheckpointConfig().setMaxConcurrentCheckpoints(2);
    env.getCheckpointConfig().setMinPauseBetweenCheckpoints(1L);

    // 2. 通过FlinkCDC构建SourceFunction并读取数据
    DebeziumSourceFunction<String> sourceFunction = MySQLSource.<String>builder()
            .hostname("hadoop102")
            .port(3306)
            .username("root")
            .password("root")
            .databaseList("gmall-210325-flink")
            .tableList("gmall-210325-flink.base_trademark") // 如果不添加该参数，则消费指定数据库中所有表的数据，如果指定，指定方式为db.table，必须带上库名
            .deserializer(new StringDebeziumDeserializationSchema())
            .startupOptions(StartupOptions.initial())
            .build();
    DataStreamSource<String> streamSource = env.addSource(sourceFunction);
    // 3. 打印数据
    streamSource.print();
    // 4. 启动任务
    env.execute("FlinkCDC");
}

启动任务

bin/flink run -m hadoop102:8081 -c com.atguigu.FlinkCDC ./gmall-flink-cdc.jar

大数据技术-FlinkCDC学习笔记

保存savepoint

# bin/flink savepoint 任务id hdfs地址
bin/flink savepoint eaebb93839f0c66014b34a9bf21b4cfa hdfs://hadoop102:8020/gmall-210325/sv

从savepoint处启动

bin/flink run -m hadoop102:8081 -s hdfs://hadoop102:8020/gmall-210325/sv/savepoint-eaebb9-0759b72fba40 -c com.atguigu.FlinkCDC ./gmall-flink-cdc.jar

大数据技术-FlinkCDC学习笔记

FlinkSQL方式应用

引入依赖

<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-table-planner-blink_2.12</artifactId>
    <version>1.12.0</version>
</dependency>

监控数据库变化

public static void main(String[] args) throws Exception {
    // 获取执行环境
    StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    env.setParallelism(1);
    StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
    // DDL方式建表
    tableEnv.executeSql("CREATE TABLE mysql_binlog ( " +
            " id STRING NOT NULL, " +
            " tm_name STRING, " +
            " logo_url STRING " +
            ") WITH ( " +
            " 'connector' = 'mysql-cdc', " +
            " 'hostname' = 'hadoop102', " +
            " 'port' = '3306', " +
            " 'username' = 'root', " +
            " 'password' = 'root', " +
            " 'database-name' = 'gmall-210325-flink', " +
            " 'table-name' = 'base_trademark' " +
            ")");
    // 查询数据
    Table table = tableEnv.sqlQuery("select * from mysql_binlog");
    // 将动态表转换为流
    DataStream<Tuple2<Boolean, Row>> retractStream = tableEnv.toRetractStream(table, Row.class);
    retractStream.print();
    // 启动任务
    env.execute("FlinkCDCWithSQL");
}

自定义反序列化器

public class CustomerDeserialization implements DebeziumDeserializationSchema<String> {

    // 封装的数据格式 json
    /*
        {
            "database":"",
            "tableName":"",
            "type":"",
            "before":"json",
            "after":"json",
        }
     */
    @Override
    public void deserialize(SourceRecord sourceRecord, Collector<String> collector) throws Exception {
        // 创建JSON对线用于存储最终数据
        JSONObject result = new JSONObject();
        // 获取库名和表名
        String topic = sourceRecord.topic();
        String[] fields = topic.split("\\.");
        String database = fields[1];
        String tableName = fields[2];

        Struct value = (Struct) sourceRecord.value();
        // 获取before数据
        Struct before = value.getStruct("before");
        JSONObject beforeJson = new JSONObject();
        if (before != null) {
            Schema beforeSchema = before.schema();
            List<Field> beforeFields = beforeSchema.fields();
            for (int i = 0; i < beforeFields.size(); i++) {
                Field field = beforeFields.get(i);
                Object beforeValue = before.get(field);
                beforeJson.put(field.name(), beforeValue);
            }
        }
        // 获取after数据
        Struct after = value.getStruct("after");
        JSONObject afterJson = new JSONObject();
        if (after != null) {
            Schema afterSchema = after.schema();
            List<Field> afterFields = afterSchema.fields();
            for (Field field : afterFields) {
                Object afterValue = after.get(field);
                afterJson.put(field.name(), afterValue);
            }
        }
        // 获取操作类型
        Envelope.Operation operation = Envelope.operationFor(sourceRecord);
        String type = operation.toString().toLowerCase();
        if("create".equals(type)){
            type="insert";
        }
        // 将字段写入JSON对线
        result.put("database", database);
        result.put("tableName", tableName);
        result.put("before", beforeJson);
        result.put("after", afterJson);
        result.put("type", type);
        // 输出数据
        collector.collect(result.toJSONString());
    }

    @Override
    public TypeInformation<String> getProducedType() {
        return BasicTypeInfo.STRING_TYPE_INFO;
    }
}

使用自定义序列化器

public static void main(String[] args) throws Exception {
    // 1. 获取执行环境
    StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    env.setParallelism(1);

    // Flink-cdc将读取binlog的位置信息以状态的方式保存在CK,如果想要做到断电续传，需要从CheckPoint或者SavePoint启动程序

    // 开启CK,每隔5秒钟做一次CK,CK超时时间10s,最大并发数2,CK之间间隔1,访问HDFS的用户名root,重启策略延时2秒重启3次
    env.enableCheckpointing(5000L);
    env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
    env.getCheckpointConfig().enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION);
    env.setRestartStrategy(RestartStrategies.fixedDelayRestart(3, 2000L));
    env.setStateBackend(new FsStateBackend("hdfs://hadoop102:8020/gmall-210325/ck"));
    System.setProperty("HADOOP_USER_NAME", "root");
    env.getCheckpointConfig().setCheckpointTimeout(10000L);
    env.getCheckpointConfig().setMaxConcurrentCheckpoints(2);
    env.getCheckpointConfig().setMinPauseBetweenCheckpoints(1L);

    // 2. 通过FlinkCDC构建SourceFunction并读取数据
    DebeziumSourceFunction<String> sourceFunction = MySQLSource.<String>builder()
            .hostname("hadoop102")
            .port(3306)
            .username("root")
            .password("root")
            .databaseList("gmall-210325-flink")
            .tableList("gmall-210325-flink.base_trademark") // 如果不添加该参数，则消费指定数据库中所有表的数据，如果指定，指定方式为db.table，必须带上库名
            .deserializer(new CustomerDeserialization())
            .startupOptions(StartupOptions.initial())
            .build();
    DataStreamSource<String> streamSource = env.addSource(sourceFunction);
    // 3. 打印数据
    streamSource.print();
    // 4. 启动任务
    env.execute("FjavalinkCDCWithCustomerDeserialization");
}

Flink-SQLClient方式应用

同步Kafka

所需依赖

flink-sql-connector-kafka_2.11-1.13.6.jar

客户端设置

# 设置CheckPoint时间
set execution.checkpointing.interval = 3s;
# 设置方便查询
set sql-client.execution.result-mode=tableau;

创建FlinkCDC表

CREATE TABLE cdc_source (
id bigint,
tm_name STRING,
logo_url STRING,
PRIMARY KEY (id) NOT ENFORCED
) WITH (
'connector' = 'mysql-cdc',
'hostname' = 'bigdata',
'port' = '3306',
'username' = 'root',
'password' = 'root',
'database-name' = 'mall',
'table-name' = 'base_trademark'
);

创建Kafka表

对于upsert-kafka，新增则是配置的json格式数据，删除则是null

CREATE TABLE kafka_sink (
`id` BIGINT,
`name` STRING,
`pwd` STRING,
PRIMARY KEY (`id`) NOT ENFORCED
) WITH (
'connector' = 'upsert-kafka',
'topic' = 'test1',
'properties.bootstrap.servers' = 'bigdata:9092',
'key.format' = 'json',
'value.format' = 'json'
);

对于kafka，则是debezium格式

CREATE TABLE kafka_sink (
`id` BIGINT,
`tm_name` STRING,
`logo_url` STRING
) WITH (
'connector' = 'kafka',
'topic' = 'test1',
'properties.bootstrap.servers' = '192.168.80.100:9092',
'properties.group.id' = 'test1',
'scan.startup.mode' = 'earliest-offset',
'format' = 'debezium-json',
'debezium-json.ignore-parse-errors'='true'
);

插入数据

insert into kafka_sink select * from cdc_source;

同步MySQL

所需依赖

flink-sql-connector-mysql-cdc-2.2.1.jar

客户端设置

# 设置CheckPoint时间
set execution.checkpointing.interval = 3s;
# 设置方便查询
set sql-client.execution.result-mode=tableau;

CDC表

CREATE TABLE db1 (
id INT,
name STRING,
pwd STRING,
PRIMARY KEY(id) NOT ENFORCED
) WITH (
'connector' = 'mysql-cdc',
'hostname' = 'bigdata',
'port' = '3306',
'username' = 'root',
'password' = 'root',
'database-name' = 'db1',
'table-name' = 'user');

同步Oracle

官网参考地址

cdc-oracle依赖官网下载地址：https://repo1.maven.org/maven2/com/ververica/flink-sql-connector-oracle-cdc/

使用docker安装oracle：https://blog.csdn.net/qq_36039236/article/details/124224500?spm=1001.2014.3001.5502

cdc-oracle官网地址：https://ververica.github.io/flink-cdc-connectors/master/content/connectors/oracle-cdc.html

所需依赖

flink-sql-connector-oracle-cdc-2.2.1.jar

Oracle日志支持准备

# 修改STUDENT_INFO表让其支持增量日志，这句先在Oracle里创建user表再执行
ALTER DATABASE ADD SUPPLEMENTAL LOG DATA;
ALTER TABLE CZS.STUDENT_INFO ADD SUPPLEMENTAL LOG DATA (ALL) COLUMNS;

客户端设置

# 设置CheckPoint时间
set execution.checkpointing.interval = 3s;
# 设置方便查询
set sql-client.execution.result-mode=tableau;

创建Oracle表

create table student_info 
( 
sid number(10) constraint pk_sid primary key, 
sname varchar2(10), 
sex varchar2(2)
);

CDC表

CREATE TABLE test (
SID INT NOT NULL,
SNAME STRING,
SEX STRING,
PRIMARY KEY(SID) NOT ENFORCED
) WITH (
'connector' = 'oracle-cdc',
'hostname' = 'bigdata',
'port' = '1521',
'username' = 'czs',
'password' = 'czs',
'database-name' = 'helowin',
'schema-name' = 'czs',
'table-name' = 'student_info',
# 或略表名大小写
'debezium.database.tablename.case.insensitive'='false'
);

同步PG

官网参考地址

postgres-cdc官网地址：https://ververica.github.io/flink-cdc-connectors/master/content/connectors/postgres-cdc.html

所需依赖

flink-sql-connector-postgres-cdc-2.2.1.jar

环境准备

dockers启动pg

# 拉取镜像
docker pull postgres

# 创建容器
docker run --name postgres -d -p 5432:5432 -v /mydata/postgresql/data:/var/lib/postgresql/data/ -e POSTGRES_DB=postgres -e POSTGRES_USER=postgres -e POSTGRES_PASSWORD=postgres postgres

# 进入pg
docker exec -it postgres psql -U postgres -d postgres

# 进入psql
psql -U postgres -d postgres

所需权限

-- pg新建用户
CREATE USER mtcs WITH PASSWORD 'mtcs';

-- 给用户复制流权限
ALTER USER mtcs REPLICATION;

-- 给用户登录数据库权限
grant CONNECT ON DATABASE postgres to mtcs;

-- 把当前库public下所有表查询权限赋给用户
GRANT SELECT ON ALL TABLES IN SCHEMA public TO mtcs;

-- 设置发布为true
update pg_publication set puballtables=true where pubname is not null;

-- 把所有表进行发布
CREATE PUBLICATION dbz_publication FOR ALL TABLES;

-- 查询哪些表已经发布
select * from pg_publication_tables;

创建postgres-cdc

CREATE TABLE postgres (
id INT,
name STRING,
pwd STRING,
PRIMARY KEY (id) NOT ENFORCED
) WITH (
'connector' = 'postgres-cdc',
'hostname' = 'bigdata',
'port' = '5432',
'username' = 'czs',
'password' = 'czs',
'database-name' = 'hive',
'schema-name' = 'public',
'table-name' = 'user',
'debezium.plugin.name' = 'pgoutput'
);

同步MongoDB

mongodb快速入门：https://www.runoob.com/mongodb/

cdc官网说明地址：https://ververica.github.io/flink-cdc-connectors/master/content/connectors/mongodb-cdc.html

基于docker创建mongodb副本集：https://blog.csdn.net/weixin_44975592/article/details/125112407

cdc-mongo依赖官网下载地址：https://repo1.maven.org/maven2/com/ververica/flink-sql-connector-mongodb-cdc/

所需依赖

flink-sql-connector-mongodb-cdc-2.2.1.jar

环境准备

docker启动mongodb

# docker启动mongo
# -p 主机端口号：docker端口号
# –n me mongodb名称
# -v 主机路径：docker路径
# -replSet rs(集群名称，1主2从便是一个集群)
docker run -p 27017:27017 --name mongo -v /root/data/db:/data/db -d mongo --replSet rs

# 进入mongo
docker exec -it mongo mongo

# 添加从节点并初始化,这是初始化，直接初始化可以直接将当前节点变为主节点，然后可以在通过rs.add()添加，但是要注意，添加成功后，查看时会看到展示的是一个数组，数组中是一个节点一个集合，集合中有元素，元素有_id，这个_id的值必须是顺序下来，且主节点应该是0，是不是必须并不清楚。
# 看到"ok":1为成功，0为失败，如果你为0,看看上面有无操作出错
rs.initiate()

# 十秒左右，按回车，可看到变为主节点
rs: primary>

# 查看状态
rs.status()


# 创建admin用户 需要use admin在admin数据库下创建
db.createUser({ user:'admin',pwd:'admin',roles:[ { role:'userAdminAnyDatabase', db: 'admin'},"readWriteAnyDatabase"]});

# 使用admin认证
db.auth('admin', 'admin')

# czs用户是官网说明的cdc同步权限，需要use admin在admin数据库下创建
db.createUser({
  user: "czs",
  pwd: "czs",
  roles: [
    { role: "read", db: "admin" }, 
    { role: "readAnyDatabase", db: "admin" }
  ]
});

创建CDC-MongoDB表

# 创建数据库
use runoob

# mongo中插入数据
db.runoob.insert({"name":"czs"})

# 查看所有数据库
show dbs

# mongo中查询数据
db.runoob.find()

# 创建mongo-flinkcdc表
CREATE TABLE runoob (
  _id STRING,
  name STRING,
  PRIMARY KEY(_id) NOT ENFORCED
) WITH (
  'connector' = 'mongodb-cdc',
  'hosts' = 'bigdata:27017',
  'username' = 'czs',
  'password' = 'czs',
  'database' = 'runoob'
);

# 验证cdc同步
select * from runoob;

操作Hive

所需依赖

flink-sql-connector-hive-3.1.2_2.11-1.13.6.jar

flink-shaded-hadoop3-uber-blink-3.3.0.jar、flink-shaded-hadoop-2-uber-2.8.3-10.0.jar（只需要一个即可）

使用Orc格式的Hive表

orc表：https://blog.csdn.net/qq_35260875/article/details/109196553
官网hive依赖地址：https://mvnrepository.com/artifact/org.apache.flink/flink-sql-connector-hive-3.1.2_2.11/1.13.6
官网flink hive依赖说明：https://nightlies.apache.org/flink/flink-docs-release-1.15/docs/connectors/table/hive/overview/

set sql-client.execution.result-mode=tableau;

# 创建目录
CREATE CATALOG myhive WITH (
    'type' = 'hive',
    'default-database' = 'default',
    'hive-conf-dir' = '/opt/module/hive-3.1.2/conf'
);

# 创建orc格式的hive表
create table test(
     a     int,
     b     int,
     c     int,
     d     int
)
row format delimited fields terminated by ',' 
stored as orc tblproperties ("orc.compress"="NONE");

# 写入数据
insert into test values(1,2,3,4);

写入Hive的分区数据

set sql-client.execution.result-mode=tableau;

SET table.sql-dialect=hive;

# 创建目录
CREATE CATALOG myhive WITH (
    'type' = 'hive',
    'default-database' = 'default',
    'hive-conf-dir' = '/opt/module/hive-3.1.2/conf'
);

CREATE TABLE hive_table (
  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'='1 h',   
  'sink.partition-commit.policy.kind'='metastore,success-file'  
);

SET table.sql-dialect=default;

insert into hive_table values('czs1',1.0,'part1','hr1');

同步Hive维表


CREATE CATALOG myhive WITH ( 'type' = 'hive', 'default-database' = 'default', 'hive-conf-dir' = '/opt/module/hive-3.1.2/conf' );

SET table.sql-dialect=hive;
CREATE EXTERNAL TABLE hive_table_test (
  obuid STRING,
  ck_time STRING
) PARTITIONED BY (dt STRING) 
 STORED AS ORC 
 LOCATION  'hdfs://bigdata:8020/datas/hive_table_test'
  TBLPROPERTIES (
  'partition.time-extractor.timestamp-pattern'='$dt $hr:00:00',   
  'sink.partition-commit.trigger'='partition-time',  
  'sink.partition-commit.delay'='1 h',   
  'sink.partition-commit.policy.kind'='metastore,success-file'  
);

# 查看表信息
describe formatted hive_table_test;
describe hive_table_test;
show partitions hive_table_test;


SET table.sql-dialect=default;
create table kafka_bus_yiqi_rt_ck
(  
obuid               string   ,  
ck_time             string   ,  
proc_time as proctime()
)
WITH(
'connector.type' = 'kafka',  
'connector.topic' = 'kafka_bus',  
'connector.properties.zookeeper.connect' = 'localhost:2181',
'connector.properties.bootstrap.servers' = 'localhost:9092',   
'connector.properties.group.id' = 'testGroup',  
'connector.version'='universal',  
'format.type' = 'json',   
'connector.startup-mode'='earliest-offset' 
);

炸裂函数使用

json格式如下

{
    "id":1238123899121,
    "name":"asdlkjasjkdla998y1122",
    "date":"1990-10-14",
    "obj":{
        "time1":"12:12:43Z",
        "str":"sfasfafs",
        "lg":2324342345
    },
    "arr":[
        {
            "f1":"f1str11",
            "f2":134
        },
        {
            "f1":"f1str22",
            "f2":555
        }
    ],
    "time":"12:12:43Z",
    "timestamp":"1990-10-14T12:12:43Z",
    "map":{
        "flink":123
    },
    "mapinmap":{
        "inner_map":{
            "key":234
        }
    }
}

创建flink表

CREATE TABLE json_source (
    id            BIGINT,
    name          STRING,
    `date`        DATE,
    obj           ROW<time1 TIME,str STRING,lg BIGINT>,
    arr           ARRAY<ROW<f1 STRING,f2 INT>>,
    `time`        TIME,
    `timestamp`   TIMESTAMP(3),
    `map`         MAP<STRING,BIGINT>,
    mapinmap      MAP<STRING,MAP<STRING,INT>>,
    proctime as PROCTIME()
 ) WITH (
    'connector.type' = 'kafka',  
    'connector.topic' = 'test',  
    'connector.properties.zookeeper.connect' = 'localhost:2181',
    'connector.properties.bootstrap.servers' = 'localhost:9092',   
    'connector.properties.group.id' = 'testGroup',  
    'connector.version'='universal',  
    'format.type' = 'json',   
    'connector.startup-mode'='latest-offset' 
);

插入验证

insert into json_source select 111 as id,'name' as name,Row(CURRENT_TIME,'ss',123) as obj,Array[Row('f',1),Row('s',2)] as arr,Map['k1','v1','k2','v2'] as `map`,Map['inner_map',Map['k','v']] as mapinmap;

查询验证文章来源地址https://www.toymoban.com/news/detail-404194.html

select id,name,f1,f2 from json_source, UNNEST(arr) AS t (f1,f2);

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大数据技术-FlinkCDC学习笔记

Flink-CDC简介

MySQL开启binlog日志文件

DataStream方式应用

FlinkSQL方式应用

自定义反序列化器

Flink-SQLClient方式应用

同步Kafka

同步MySQL

同步Oracle

同步PG

同步MongoDB

操作Hive

同步Hive维表

炸裂函数使用

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