简介
Flink CDC：
解决了传统数据库实时同步的痛点， 该技术抛弃了其他第三方组件（例如Kafka等），能够实时读取Mysql master节点全量和增量数据，能够捕获所有数据的变化，同时它完全与业务解耦，运维也及其简单。具体介绍请参考：flink-cdc-connectors。
Apache Doris：
它是一个现代化的MPP分析型数据库产品。仅需亚秒级响应时间即可获得查询结果，有效地支持实时数据分析。Apache Doris的分布式架构非常简洁，易于运维，并且可以支持10PB以上的超大数据集。
Apache Doris可以满足多种数据分析需求，例如固定历史报表，实时数据分析，交互式数据分析和探索式数据分析等。令您的数据分析工作更加简单高效！目前Apache Doris已成为Apache顶级项目，成为许多一线大厂实时数据分析数据仓库的不二选择。具体请参考：Apache Doris 。
技术选型
本次采用Flink-CDC和flink-doris-connector技术利用FLink DataStream的方式实现Mysql全量或增量数据同步至分析性数据仓库Doris中。
版本选择：
Flink | Flink-doris-connector | Flink-CDC | Doris |
1.11.x | 1.11.6-2.12-xx | 1.0.0/1.1.0 | 0.13.+ |
1.12.x | 1.12.7-2.12-xx | 1.2.0/1.3.0 | 0.13.+ |
1.13.x | 1.13.5-2.12-xx | 1.4.0 / 2.0.* / 2.1.* / 2.2.* | 0.13.+ |
1.14.x | 1.14.4-2.12-xx | 2.2.* | 0.13.+ |
注：Flink-doris-connector版本号解读：例如1.13.5-2.12-1.0.1 表示flink 版本 1.13.5，scala 版本 2.12，connector 版本 1.0.1。
具体实现
本次代码构建选择的版本号为：
Flink | Flink-doris-connector | Flink-CDC | Doris |
1.13.6 | 1.0.3 | 2.2.0 | 0.15.13 |
导入依赖：
<!--flink cdc -->
<dependency>
<groupId>com.ververica</groupId>
<artifactId>flink-connector-mysql-cdc</artifactId>
<version>2.2.0</version>
</dependency>
<!-- flink -->
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-java</artifactId>
<version>1.13.6</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-clients_2.12</artifactId>
<version>1.13.6</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-streaming-java_2.12</artifactId>
<version>1.13.6</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-connector-jdbc_2.12</artifactId>
<version>1.13.6</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.flink</groupId>
<artifactId>flink-table-planner-blink_2.12</artifactId>
<version>1.13.6</version>
<scope>provided</scope>
</dependency>
<!-- flink doris connector -->
<dependency>
<groupId>org.apache.doris</groupId>
<artifactId>flink-doris-connector-1.13_2.12</artifactId>
<version>1.0.3</version>
</dependency>代码实现：
public static void main(String[] args) throws Exception {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
// enable checkpoint
env.enableCheckpointing(3000);
MySqlSource<String> mySqlSource = MySqlSource.<String>builder()
.hostname("127.0.0.1")
.port(3306)
.databaseList("test") // set captured database
.tableList("test.test_cdc") // set captured table
.username("bigdata")
.password("123456")
.deserializer(new JsonDebeziumDeserializationSchema()).build();
Properties pro = new Properties();
pro.setProperty("format", "json");
pro.setProperty("strip_outer_array", "true");
DataStreamSource<String> dataStreamSource = env.fromSource(mySqlSource, WatermarkStrategy.noWatermarks(), "mysql source");
dataStreamSource.setParallelism(1).print();
/**
* flink cdc同步数据时的数据几种数据格式：
* insert :{"before":null,"after":{"id":30,"name":"wangsan","age":27,"address":"北京"},"source":{"version":"1.5.4.Final","connector":"mysql","name":"mysql_binlog_source","ts_ms":1652844463000,"snapshot":"false","db":"test","sequence":null,"table":"test_cdc","server_id":1,"gtid":null,"file":"mysql-bin.000001","pos":10525,"row":0,"thread":null,"query":null},"op":"c","ts_ms":1652844463895,"transaction":null}
* update :{"before":{"id":30,"name":"wangsan","age":27,"address":"北京"},"after":{"id":30,"name":"wangsan","age":27,"address":"上海"},"source":{"version":"1.5.4.Final","connector":"mysql","name":"mysql_binlog_source","ts_ms":1652844511000,"snapshot":"false","db":"test","sequence":null,"table":"test_cdc","server_id":1,"gtid":null,"file":"mysql-bin.000001","pos":10812,"row":0,"thread":null,"query":null},"op":"u","ts_ms":1652844511617,"transaction":null}
* delete :{"before":{"id":25,"name":"wanger","age":26,"address":"西安"},"after":null,"source":{"version":"1.5.4.Final","connector":"mysql","name":"mysql_binlog_source","ts_ms":1652844336000,"snapshot":"false","db":"test","sequence":null,"table":"test_cdc","server_id":1,"gtid":null,"file":"mysql-bin.000001","pos":10239,"row":0,"thread":null,"query":null},"op":"d","ts_ms":1652844336733,"transaction":null}
*
* doris:
* 因为doris是不能根据非主键字段做删除操作的，所以当mysql中有删除操作时，这边边采用逻辑删除的方式，将删出字段标记为已删除
* 后期在做数据分析时，可以将这部分数据过滤掉即可。
* is_delete：逻辑删除标志符 0表示正常 1表示删除
* updateStamp：数据的落库时间
*/
env.fromSource(mySqlSource, WatermarkStrategy.noWatermarks(), "mysql source").map(line -> {
JSONObject lineObj = JSONObject.parseObject(line);
//如果是insert或者update操作，则取after中的数据，将是否删除设置为0
JSONObject data;
if ("d".equals(lineObj.getString("op"))) {
//如果是delete操作，则取before中的数据，将其设置为1
data = JSONObject.parseObject(lineObj.getString("before"));
data.put("is_delete", 1);
data.put("updateStamp", new Date().toLocaleString());
} else {
data = JSONObject.parseObject(lineObj.getString("after"));
data.put("is_delete", 0);
data.put("updateStamp", new Date().toLocaleString());
}
return data.toJSONString();
}).addSink(DorisSink.sink(DorisExecutionOptions.builder()
.setBatchSize(3)
.setBatchIntervalMs(10L)
.setMaxRetries(3)
.setStreamLoadProp(pro)
.setEnableDelete(true)
.build(),
DorisOptions.builder()
.setFenodes("127.0.0.1:8030")
.setTableIdentifier("test_lit.test_cdc_sink")
.setUsername("root")
.setPassword("").build()));
env.execute("Print MySQL Snapshot + Binlog");
}总结：
通过Flink CDC和flink doris connector 技术，我们可以很简单的将Mysql中增量或全量数据同步至Doris中，链路短，时效性高，简化了传统数据同步的方式，便于维护。
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