pom
代码
注意开启checkpoint 和不开启是有区别的（savepoint也可以 启动的flink指定时候 -s savepath）
不开启，如果项目重启了，会重新读取所有的数据
开启了，项目重启了额，会根据保留的信息去读取变化的数据
import com.ververica.cdc.connectors.mysql.MySqlSource;
import com.ververica.cdc.connectors.mysql.table.StartupOptions;
import com.ververica.cdc.debezium.DebeziumSourceFunction;
import com.ververica.cdc.debezium.JsonDebeziumDeserializationSchema;
import com.ververica.cdc.debezium.StringDebeziumDeserializationSchema;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
public class FlinkCDCTest {
public static void main(String[] args) throws Exception {
//1.获取Flink 执行环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
//1.1 开启CK
// env.enableCheckpointing(5000);
// env.getCheckpointConfig().setCheckpointTimeout(10000);
// env.getCheckpointConfig().setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE);
// env.getCheckpointConfig().setMaxConcurrentCheckpoints(1);
//
// env.setStateBackend(new FsStateBackend("hdfs://hadoop102:8020/cdc-test/ck"));
//2.通过FlinkCDC构建SourceFunction
DebeziumSourceFunction
.hostname("9.134.70.1")
.port(3306)
.username("root")
.password("xxxxxxx")
.databaseList("cc")
.tableList("cc.student")
// .deserializer(new StringDebeziumDeserializationSchema())
.deserializer(new JsonDebeziumDeserializationSchema())
.startupOptions(StartupOptions.initial())
.build();
DataStreamSource
//3.数据打印
dataStreamSource.print("==FlinkCDC==");
//4.启动任务
env.execute("FlinkCDC");
}
}
mysql
数据库表
增加一条数据
打印日志 op:c 是create
==FlinkCDC==> {"before":null,"after":{"id":1,"name":"name1","age":"1"},"source":{"version":"1.5.4.Final","connector":"mysql","name":"mysql_binlog_source","ts_ms":1689245998000,"snapshot":"false","db":"cc","sequence":null,"table":"student","server_id":1,"gtid":null,"file":"mysql-bin.000001","pos":2781,"row":0,"thread":null,"query":null},"op":"c","ts_ms":1689246005928,"transaction":null}
修改一条数据 age=1 ->age=2 op=u 是update
flink打印日志
==FlinkCDC==> {"before":{"id":1,"name":"name1","age":"1"},"after":{"id":1,"name":"name1","age":"2"},"source":{"version":"1.5.4.Final","connector":"mysql","name":"mysql_binlog_source","ts_ms":1689246092000,"snapshot":"false","db":"cc","sequence":null,"table":"student","server_id":1,"gtid":null,"file":"mysql-bin.000001","pos":3049,"row":0,"thread":null,"query":null},"op":"u","ts_ms":1689246099578,"transaction":null}
删除这条数据 op=d 是delete
==FlinkCDC==> {"before":{"id":1,"name":"name1","age":"2"},"after":null,"source":{"version":"1.5.4.Final","connector":"mysql","name":"mysql_binlog_source","ts_ms":1689246163000,"snapshot":"false","db":"cc","sequence":null,"table":"student","server_id":1,"gtid":null,"file":"mysql-bin.000001","pos":3333,"row":0,"thread":null,"query":null},"op":"d","ts_ms":1689246170857,"transaction":null}
由于打印的日志太多 我们可以用fastjson稍微封装下 然后传给sink去处理，根据update delete insert实时更新下游数据，还有一个op=r 是读取的数据
还可以使用flinksql
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;
public class FlinkSqlCdc {
public static void main(String[] args) throws Exception {
//1.获取执行环境
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);
StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
//2.使用FLINKSQL DDL模式构建CDC 表
tableEnv.executeSql("CREATE TABLE user_info ( " +
" id STRING primary key, " +
" name int, " +
" age STRING " +
") WITH ( " +
" 'connector' = 'mysql-cdc', " +
" 'scan.startup.mode' = 'latest-offset', " +
" 'hostname' = '9.134.70.1', " +
" 'port' = '3306', " +
" 'username' = 'root', " +
" 'password' = 'xxxxx', " +
" 'database-name' = 'cc', " +
" 'table-name' = 'student' " +
")");
//3.查询数据并转换为流输出
Table table = tableEnv.sqlQuery("select * from user_info");
DataStream
retractStream.print();
//4.启动
env.execute("FlinkSQLCDC");
}
}
同样是增删改查，flink打印日志如下
精彩链接
您阅读本篇文章共花了：
发表评论