离开了业务谈技术都是耍流氓。我们来聊聊基于业务的痛点，衍生出来的多种数据同步方案。
1、多个库的表合并到一张表。不同的业务线或者微服务在不同的数据库里开发，但是此时有些报表需要将多个库的类似的数据合并后做查询统计。或者，某些历史原因，类似刚开始的商业模式不清晰，导致一些业务线分分合合。或者某些边缘业务逐步融合到了主业务。早起的数据是分开的，业务运营也是分开，后来又合并成了一个大块业务。
2、某个数据需要写到多个存储中。业务数据需要写入到多个中间件或者存储中，比如业务的数据存储再MySQL的数据中，后来为了方便检索需要写入到ES，或者为了缓存需要写入到redis，或者是Mysql分表的数据合并写入到Doris中。
3、数据仓库的场景。比如将表里的数据实时写入到DWS数据仓库的宽表中。
4、应急场景。如果不采专用CDC的方案，那么要达到实时查询的效果，只能在BFF层的代码调用多个中心层的查询API，然后再BFF层做各种聚合，运算。这种方式开发效率低下，万一有的中心层没有提供合适的查询API，临时开发的话，会让开发进度不可控。
总之，不管是数据多写、还是多表合并、还是建立数据仓库，都属于数据同步任务。
这种任务放在业务代码里做，是不可持续的。你要尽量让业务系统解耦，专注于做业务的事情，这种数据同步的任务应该交给专门的系统来做。如果在业务系统中增加额外的数据同步功能，同时为了提高数据同步的可用性，就需要写许多数据同步的代码和容错的代码（效率问题、并发问题、数据一致性问题、集群问题等等），这会让业务系统不堪重负，到后期业务系统几乎会达到不可维护的地步。
基于以上问题，本场数据同步的主角FlinkCDC就登场了，FlinkCDC是专门为数据同步（同步+计算）而生。通过CDC工具，可以将数据同步任务从业务系统中解耦出来，同时还可以将一份变动的数据，写入到多个存储中。这种方式不但让业务系统解耦，而且可以让数据同步任务更加健壮，方便后续的维护。
CDC 是变更数据捕获（Change Data Capture）技术的缩写，它可以将源数据库（Source）的增量变动记录，同步到一个或多个数据目的（Sink）。在同步过程中，还可以对数据进行一定的处理，例如过滤、关联、分组、统计等。
目前专业做数据库事件接受和解析的中间件是Debezium，如果是捕获Mysql，还有Canal。
Debezium官方https://debezium.io/
Debezium官方定义：Debezium is an open source distributed platform for change data capture. Start it up, point it at your databases, and your Apps can start responding to all of the inserts, updates, and deletes that other apps commit to your databases. Debezium is durable and fast, so your apps can respond quickly and never miss an event, even when things go wrong。翻译过来则是：Debezium 是一个用于变更数据捕获的开源分布式平台。 启动它，将其指向您的数据库，您的应用程序就可以开始响应其他应用程序提交给您的数据库的所有插入、更新和删除操作。 Debezium 耐用且快速，因此即使出现问题，您的应用程序也可以快速响应并且不会错过任何事件。
CDC的原理是，当数据源表发生变动时，会通过附加在表上的触发器或者 binlog 等途径，将操作记录下来。下游可以通过数据库底层的协议，订阅并消费这些事件，然后对数据库变动记录做重放，从而实现同步。这种方式的优点是实时性高，可以精确捕捉上游的各种变动。
官网地址：https://ververica.Github.io/flink-cdc-connectors/
官方定义：This project provides a set of source connectors for Apache Flink® directly ingesting changes coming from different databases using Change Data Capture(CDC)。根据FlinkCDC官方给出的定义，FlinkCDC提供一组源数据的连接器，使用变更数据捕获的方式，直接吸收来自不同数据库的变更数据。
1、FlinkCDC 提供了对 Debezium 连接器的封装和集成，简化了配置和使用的过程，并提供了更高级的 API 和功能，例如数据格式转换、事件时间处理等。Flink CDC 使用 Debezium 连接器作为底层的实现，将其与 Flink 的数据处理能力结合起来。通过配置和使用 Flink CDC，您可以轻松地将数据库中的变化数据流转化为 Flink 的 DataStream 或 Table，并进行实时的数据处理、转换和分析。
2、Flink的DataStream和SQL比较成熟和易用
3、Flink支持状态后端（State Backends），允许存储海量的数据状态
4、Flink有更好的生态，更多的Source和Sink的支持
数据合并流向：
数据多写流向：
网上有数据同步的多种技术方案的比较，我只挑选我实践过的2种做个比较，Canal和FlinkCDC。
通过下图，我们可以看到Canal处理数据的链路比FlinkCDC更长，数据链路一旦变长意味着，出错的可能性更高。
我在实践Canal的过程中，监听到Kafka之后，通过一个Springboot项目的微服务项目去监听Kafka处理业务逻辑，这种负责度更高，内部数据关联啥的也是调用Dubbo API，我不建议你也使用这种方法。当然啦，这是我没遇到Flink之前的方案，嘻嘻。当然还用过更差的方案，定时任务扫描，再写入别的库，哈哈。
Mysql单次提交多条数据的时候，Canal拿到的数据是1条数据，FlinkCDC拿到的是多条数据。FlinkCDC的这种方式更便于处理。
canal数据格式：
{"data":[{"id":"G00002","name":"潞城市小蚂蚁家政保洁有限公司","province_id":"32","province":"江苏省","city_id":"3201","city":"南京市","district_id":"320114","district":"雨花台区","address":"科创城23222333444","logo_url":"http://bm-oss.oss-cn-hangzhou.aliyuncs.com/jfe-app3.0/baba/goods/icon_112.png","slogan":"欢迎来到","credit_code":"2343243","master_name":"江鑫","master_idcard":"532524199911304246","power_group_id":"9996","opt_time":"2021-10-19 17:41:57","add_user_id":"132","add_user_name":"测试","add_time":"2020-07-27 13:59:34","emAIl":"123456@qq.com","master_wechat":null,"service_phone":"13232323232","max_shop_num":"5","pay_mode":null,"business_license":"https://guard.bm001.com/kacloud/null/image/MXVSeb7Pv4r1f1346237770562140.jpg","idcard_front":"https://guard.bm001.com/kacloud/null/image/13uty9yyvrlvWb346237808202139.png","idcard_back":"https://guard.bm001.com/kacloud/null/image/5uA7IblI6r1zoW346237778042125.png","cloud_shop_state":"0","expiration_time":null,"version_id":null,"company_type":"0","company_property":"1","main_sell":null,"introduction":null,"contact_name":null,"contact_phone":null,"certification_name":"潞城市小蚂蚁家政保洁有限公司","is_test":null,"login_account":null,"delete_at":"0","self_invitation_code":"IN6501"}],"database":"cloud_test","es":1669010586000,"id":8150,"isDdl":false,"mysqlType":{"id":"varchar(32)","name":"varchar(64)","province_id":"int(6)","province":"varchar(32)","city_id":"int(6)","city":"varchar(32)","district_id":"int(6)","district":"varchar(64)","address":"varchar(128)","logo_url":"varchar(500)","slogan":"varchar(255)","credit_code":"varchar(18)","master_name":"varchar(16)","master_idcard":"varchar(18)","power_group_id":"bigint(20)","opt_time":"datetime","add_user_id":"varchar(32)","add_user_name":"varchar(32)","add_time":"datetime","email":"varchar(255)","master_wechat":"varchar(255)","service_phone":"varchar(32)","max_shop_num":"int(11)","pay_mode":"int(1)","business_license":"varchar(128)","idcard_front":"varchar(128)","idcard_back":"varchar(128)","cloud_shop_state":"int(1)","expiration_time":"datetime","version_id":"bigint(20)","company_type":"tinyint(2)","company_property":"int(2)","main_sell":"varchar(200)","introduction":"varchar(512)","contact_name":"varchar(32)","contact_phone":"varchar(11)","certification_name":"varchar(64)","is_test":"int(1)","login_account":"varchar(50)","delete_at":"bigint(14)","self_invitation_code":"char(6)"},"old":[{"address":"科创城23222333"}],"pkNames":["id"],"sql":"","sqlType":{"id":12,"name":12,"province_id":4,"province":12,"city_id":4,"city":12,"district_id":4,"district":12,"address":12,"logo_url":12,"slogan":12,"credit_code":12,"master_name":12,"master_idcard":12,"power_group_id":-5,"opt_time":93,"add_user_id":12,"add_user_name":12,"add_time":93,"email":12,"master_wechat":12,"service_phone":12,"max_shop_num":4,"pay_mode":4,"business_license":12,"idcard_front":12,"idcard_back":12,"cloud_shop_state":4,"expiration_time":93,"version_id":-5,"company_type":-6,"company_property":4,"main_sell":12,"introduction":12,"contact_name":12,"contact_phone":12,"certification_name":12,"is_test":4,"login_account":12,"delete_at":-5,"self_invitation_code":1},"table":"uc_company","ts":1669010468134,"type":"UPDATE"}FlinkCDC数据格式：
{"before":{"id":"PF1784570096901248","pay_order_no":null,"out_no":"J1784570080435328","title":"充值办卡","from_user_id":"PG11111","from_account_id":"1286009802396288","user_id":"BO1707796995184000","account_id":"1707895210106496","amount":13400,"profit_state":1,"profit_time":1686758315000,"refund_state":0,"refund_time":null,"add_time":1686758315000,"remark":"充值办卡","acct_circle":"PG11111","user_type":92,"from_user_type":90,"company_id":"PG11111","profit_mode":1,"type":2,"parent_id":null,"oc_profit_id":"1784570096901248","keep_account_from_user_id":null,"keep_account_from_bm_user_id":null,"keep_account_user_id":null,"keep_account_bm_user_id":null,"biz_company_id":"PG11111"},"after":{"id":"PF1784570096901248","pay_order_no":null,"out_no":"J1784570080435328","title":"充值办卡","from_user_id":"PG11111","from_account_id":"1286009802396288","user_id":"BO1707796995184000","account_id":"1707895210106496","amount":13400,"profit_state":1,"profit_time":1686758315000,"refund_state":0,"refund_time":null,"add_time":1686758315000,"remark":"充值办卡1","acct_circle":"PG11111","user_type":92,"from_user_type":90,"company_id":"PG11111","profit_mode":1,"type":2,"parent_id":null,"oc_profit_id":"1784570096901248","keep_account_from_user_id":null,"keep_account_from_bm_user_id":null,"keep_account_user_id":null,"keep_account_bm_user_id":null,"biz_company_id":"PG11111"},"source":{"version":"1.6.4.Final","connector":"mysql","name":"mysql_binlog_source","ts_ms":1686734882000,"snapshot":"false","db":"cloud_test","sequence":null,"table":"acct_profit","server_id":1,"gtid":null,"file":"mysql-bin.000514","pos":650576218,"row":0,"thread":null,"query":null},"op":"u","ts_ms":1686734882689,"transaction":null}FlinkCDC同步数据，有两种方式，一种是FlinkSQL的方式，一种是Flink DataStream和Table API的方式。为了方便管理，这两种方式我都写在代码里。
1、准备好Flink集群。FlinkCDC也是以任务的形式提交到Flink集群去执行的。可以按照Flink官网进行下载安装：https://nightlies.apache.org/flink/flink-docs-release-1.15/zh/docs/try-flink/local_installation/
2、开启Mysql的binlog。这一步自行解决。
为了方便管理，FlinkSQL方式也是用JAVA代码写
1、创建database
tEnv.executeSql("CREATE DATABASE IF NOT EXISTS cloud_test");tEnv.executeSql("CREATE DATABASE IF NOT EXISTS league_test");
2、创建source表
注意类型是'connector' = 'mysql-cdc'。
tEnv.executeSql("CREATE TABLE league_test.oc_settle_profit (n" +" id STRING,n" +" show_profit_id STRING,n" +" order_no STRING,n" +" from_user_id STRING,n" +" from_user_type INT,n" +" user_id STRING,n" +" user_type INT,n" +" rate INT,n" +" amount INT,n" +" type INT,n" +" add_time TIMESTAMP,n" +" state INT,n" +" expect_profit_time TIMESTAMP,n" +" profit_time TIMESTAMP,n" +" profit_mode INT,n" +" opt_code STRING,n" +" opt_name STRING,n" +" acct_circle STRING,n" +" process_state INT,n" +" parent_id STRING,n" +" keep_account_from_user_id STRING,n" +" keep_account_from_bm_user_id STRING,n" +" keep_account_user_id STRING,n" +" keep_account_bm_user_id STRING,n" +" biz_type INT,n" +" remark STRING,n" +" contribute_user_id STRING,n" +" relation_brand_owner_id STRING,n" +" PRIMARY KEY (id) NOT ENFORCEDn" +") WITH (n" +" 'connector' = 'mysql-cdc',n" +" 'hostname' = '10.20.1.11',n" +" 'port' = '3306',n" +" 'username' = 'root',n" +" 'password' = '123456',n" +" 'database-name' = 'league_test',n" +" 'table-name' = 'oc_settle_profit',n" +" 'scan.incremental.snapshot.enabled' = 'false'n" +")");
3、创建sink表
注意类型是'connector' = 'jdbc'。
tEnv.executeSql("CREATE TABLE cloud_test.dws_profit_record_hdj_flink (n" +" id STRING,n" +" show_profit_id STRING,n" +" order_no STRING,n" +" from_user_id STRING,n" +" from_user_type INT,n" +" user_id STRING,n" +" user_type INT,n" +" amount INT,n" +" profit_time TIMESTAMP,n" +" state INT,n" +" acct_circle STRING,n" +" biz_type INT,n" +" contribute_user_id STRING,n" +" relation_brand_owner_id STRING,n" +" remark STRING,n" +" add_time TIMESTAMP,n" +" PRIMARY KEY (id) NOT ENFORCEDn" +") WITH (n" +" 'connector' = 'jdbc',n" +" 'url' = 'jdbc:mysql://10.20.1.11:3306/cloud_test',n" +" 'username' = 'root',n" +" 'password' = 'root12345',n" +" 'table-name' = 'dws_profit_record_hdj_flink'n" +")");
4、执行insert。
如果需要多表关联的，可以注册多个'connector' = 'jdbc'的源表，然后这里编写类似insert into select join这样代码
tEnv.executeSql("INSERT INTO cloud_test.dws_profit_record_hdj_flink (id, show_profit_id, order_no, from_user_id, from_user_type, user_id,n" +" user_type, amount, profit_time, state, acct_circle, biz_type,n" +" contribute_user_id, relation_brand_owner_id, remark, add_time)n" +"select f.id,n" +" f.show_profit_id,n" +" f.order_no,n" +" f.from_user_id,n" +" f.from_user_type,n" +" f.user_id,n" +" f.user_type,n" +" f.amount,n" +" f.profit_time,n" +" f.state,n" +" f.acct_circle,n" +" f.biz_type,n" +" f.contribute_user_id,n" +" f.relation_brand_owner_id,n" +" f.remark,n" +" f.add_timen" +"from league_test.oc_settle_profit fn" +"where f.id is not nulln" +" and f.biz_type is not nulln" +" and f.biz_type = 9");
FlinkSQL方式结束，此时只要source表有变动，那么会自动监听到数据，自动插入到新的表中。
个人觉得这种方式虽说有些繁琐，但是灵活度更好，可以用Java代码处理很多逻辑，比SQL更灵活些。
1、监听source
MySqlSource<String> mySqlSource = MySqlSource.<String>builder().hostname(MYSQL_HOST).port(MYSQL_PORT).databaseList(SYNC_DB) // set captured database.tableList(String.join(",", SYNC_TABLES)) // set captured table.username(MYSQL_USER).password(MYSQL_PASSWD).deserializer(new JsonDebeziumDeserializationSchema()) // converts SourceRecord to JSON String.build();StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();env.setParallelism(3);env.enableCheckpointing(5000);DataStreamSource<String> cdcSource = env.fromSource(mySqlSource, WatermarkStrategy.noWatermarks(), "CDC Source" + LeagueOcSettleProfit2DwsHdjProfitRecordAPI.class.getName());
2、清洗数据（过滤、转换等等）
此处逻辑比较自定义，文中是过滤掉了不相关的表，然后过滤掉了删除数据的log。
过滤掉不相关的表。
private static SingleOutputStreamOperator<String> filterTableData(DataStreamSource<String> source, String table) {return source.filter(new FilterFunction<String>() {@Overridepublic boolean filter(String row) throws Exception {try {JSONObject rowJson = JSON.parseobject(row);JSONObject source = rowJson.getJSONObject("source");String tbl = source.getString("table");return table.equals(tbl);} catch (Exception ex) {ex.printStackTrace();return false;}}});}
过滤掉删除数据的log
private static SingleOutputStreamOperator<String> clean(SingleOutputStreamOperator<String> source) {return source.flatMap(new FlatMapFunction<String, String>() {@Overridepublic void flatMap(String row, Collector<String> out) throws Exception {try {LOG.info("============================row:{}", row);JSONObject rowJson = JSON.parseObject(row);String op = rowJson.getString("op");//history,insert,updateif (Arrays.asList("r", "c", "u").contains(op)) {out.collect(rowJson.getJSONObject("after").toJSONString());} else {LOG.info("filter other op:{}", op);}} catch (Exception ex) {LOG.warn("filter other format binlog:{}", row);}}});}
处理业务逻辑，过滤掉了部分数据
private static SingleOutputStreamOperator<String> logic(SingleOutputStreamOperator<String> cleanStream) {return cleanStream.filter(new FilterFunction<String>() {@Overridepublic boolean filter(String data) throws Exception {try {JSONObject dataJson = JSON.parseObject(data);String id = dataJson.getString("id");Integer bizType = dataJson.getInteger("biz_type");if (StringUtils.isBlank(id) || bizType == null) {return false;}// 只处理上岗卡数据return bizType == 9;} catch (Exception ex) {LOG.warn("filter other format binlog:{}", data);return false;}}});}
3、创建自定义sink，将数据写出去
private static class CustomDealDataSink extends RichSinkFunction<String> {private transient Connection cloudConnection;private transient PreparedStatement cloudPreparedStatement;private String insertSql = "INSERT INTO dws_profit_record_hdj_flink_api (id, show_profit_id, order_no, from_user_id, from_user_type, user_id,n" +" user_type, amount, profit_time, state, acct_circle, biz_type,n" +" contribute_user_id, relation_brand_owner_id, remark, add_time)n" +"VALUES (?, ?, ?, ?, ?, ?, ?, ?,n" +" ?, ?, ?, ?, ?, ?, ?, ?)";private String deleteSql = "delete from dws_profit_record_hdj_flink_api where id = '%s'";@Overridepublic void open(Configuration parameters) throws Exception {super.open(parameters);// 在这里初始化 JDBC 连接cloudConnection = DriverManager.getConnection("jdbc:mysql://10.20.1.11:3306/cloud_test", "root", "123456");cloudPreparedStatement = cloudConnection.prepareStatement(insertSql);}@Overridepublic void invoke(String value, Context context) throws Exception {JSONObject dataJson = JSON.parseObject(value);String id = dataJson.getString("id");String showProfitId = dataJson.getString("show_profit_id");String orderNo = dataJson.getString("order_no");String fromUserId = dataJson.getString("from_user_id");Integer fromUserType = dataJson.getInteger("from_user_type");String userId = dataJson.getString("user_id");Integer userType = dataJson.getInteger("user_type");Integer amount = dataJson.getInteger("amount");Timestamp addTime = dataJson.getTimestamp("add_time");Integer state = dataJson.getInteger("state");Timestamp profitTime = dataJson.getTimestamp("profit_time");String acctCircle = dataJson.getString("acct_circle");Integer bizType = dataJson.getInteger("biz_type");String remark = dataJson.getString("remark");String contributeUserId = dataJson.getString("contribute_user_id");String relationBrandOwnerId = dataJson.getString("relation_brand_owner_id");Timestamp profitTimeTimestamp = Timestamp.valueOf(DateFormatUtils.format(profitTime.getTime(), "yyyy-MM-dd HH:mm:ss", TimeZone.getTimeZone("GMT")));Timestamp addTimeTimestamp = Timestamp.valueOf(DateFormatUtils.format(addTime.getTime(), "yyyy-MM-dd HH:mm:ss", TimeZone.getTimeZone("GMT")));cloudPreparedStatement.setString(1, id);cloudPreparedStatement.setString(2, showProfitId);cloudPreparedStatement.setString(3, orderNo);cloudPreparedStatement.setString(4, fromUserId);cloudPreparedStatement.setInt(5, fromUserType);cloudPreparedStatement.setString(6, userId);cloudPreparedStatement.setInt(7, userType);cloudPreparedStatement.setInt(8, amount);cloudPreparedStatement.setTimestamp(9, profitTimeTimestamp);cloudPreparedStatement.setInt(10, state);cloudPreparedStatement.setString(11, StringUtils.isBlank(acctCircle) ? "PG11111" : acctCircle);cloudPreparedStatement.setInt(12, bizType);cloudPreparedStatement.setString(13, contributeUserId);cloudPreparedStatement.setString(14, relationBrandOwnerId);cloudPreparedStatement.setString(15, remark);cloudPreparedStatement.setTimestamp(16, addTimeTimestamp);cloudPreparedStatement.execute(String.format(deleteSql, id));cloudPreparedStatement.execute();}@Overridepublic void close() throws Exception {super.close();// 在这里关闭 JDBC 连接cloudPreparedStatement.close();cloudConnection.close();}}
代码里有2个夹子，一个是API方式的，一个是SQL方式的，每种方式了放了2个例子，代码地址如下：https://github.com/yclxiao/flink-cdc-demo.git
如果在实践的过程中碰到问题，可以在这里找到我：http://www.mangod.top/articles/2023/03/15/1678849930601.html
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