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Flink新增特性 | CDC(Change Data Capture) 原理和实践应用
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使用flink sql进行数据同步,可以将数据从一个数据同步到其他的地方，比如mysql、elasticsearch等。
可以在源数据库上实时的物化一个聚合视图
因为只是增量同步，所以可以实时的低延迟的同步数据
使用EventTime join 一个temporal表以便可以获取准确的结果
数据库之间的增量数据同步
审计日志
数据库之上的实时物化视图
基于CDC的维表join
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Flink CDC使用方式
目前Flink支持两种内置的connector，PostgreSQL和mysql，接下来我们以mysql为例。
使用这种架构是好处有：
减少canal和kafka的维护成本，链路更短，延迟更低
flink提供了exactly once语义
可以从指定position读取
去掉了kafka，减少了消息的存储成本
<dependency> <groupId>com.alibaba.ververica</groupId> <artifactId>flink-connector-mysql-cdc</artifactId> <version>1.1.0</version></dependency>如果是sql客户端使用，需要下载 flink-sql-connector-mysql-cdc-1.1.0.jar 并且放到<FLINK_HOME>/lib/下面
连接mysql数据库的示例sql如下：
-- creates a mysql cdc table sourceCREATE TABLE mysql_binlog ( id INT NOT NULL, name STRING, description STRING, weight DECIMAL(10,3)) WITH ( 'connector' = 'mysql-cdc', 'hostname' = 'localhost', 'port' = '3306', 'username' = 'flinkuser', 'password' = 'flinkpw', 'database-name' = 'inventory', 'table-name' = 'products');import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;import org.apache.flink.streaming.api.functions.source.SourceFunction;import com.alibaba.ververica.cdc.debezium.StringDebeziumDeserializationSchema;import com.alibaba.ververica.cdc.connectors.mysql.MySQLSource; public class MySqlBinlogSourceExample { public static void main(String[] args) throws Exception { SourceFunction<String> sourceFunction = MySQLSource.<String>builder() .hostname("localhost") .port(3306) .databaseList("inventory") // monitor all tables under inventory database .username("flinkuser") .password("flinkpw") .deserializer(new StringDebeziumDeserializationSchema()) // converts SourceRecord to String .build(); StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); env .addSource(sourceFunction) .print().setParallelism(1); // use parallelism 1 for sink to keep message ordering env.execute(); }}<dependency> <groupId>org.apache.flink</groupId> <artifactId>flink-connector-kafka_2.11</artifactId> <version>1.11.0</version></dependency>CREATE TABLE topic_products ( id BIGINT, name STRING, description STRING, weight DECIMAL(10, 2)) WITH ( 'connector' = 'kafka', 'topic' = 'products_binlog', 'properties.bootstrap.servers' = 'localhost:9092', 'properties.group.id' = 'testGroup', 'format' = 'canal-json' -- using canal-json as the format)<dependency> <groupId>com.alibaba.ververica</groupId> <artifactId>flink-format-changelog-json</artifactId> <version>1.0.0</version></dependency>-- assuming we have a user_behavior logsCREATE TABLE user_behavior ( user_id BIGINT, item_id BIGINT, category_id BIGINT, behavior STRING, ts TIMESTAMP(3)) WITH ( 'connector' = 'kafka', -- using kafka connector 'topic' = 'user_behavior', -- kafka topic 'scan.startup.mode' = 'earliest-offset', -- reading from the beginning 'properties.bootstrap.servers' = 'localhost:9092', -- kafka broker address 'format' = 'json' -- the data format is json);
-- we want to store the the UV aggregation result in kafka using changelog-json formatcreate table day_uv ( day_str STRING, uv BIGINT) WITH ( 'connector' = 'kafka', 'topic' = 'day_uv', 'scan.startup.mode' = 'earliest-offset', -- reading from the beginning 'properties.bootstrap.servers' = 'localhost:9092', -- kafka broker address 'format' = 'changelog-json' -- the data format is json);
-- write the UV results into kafka using changelog-json formatINSERT INTO day_uvSELECT DATE_FORMAT(ts, 'yyyy-MM-dd') as date_str, count(distinct user_id) as uvFROM user_behaviorGROUP BY DATE_FORMAT(ts, 'yyyy-MM-dd');
-- reading the changelog back againSELECT * FROM day_uv;版权声明：
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