写一个flink代码，实现以下功能：从数据库解析binlog信息，实时写入hive

以下是一个简单的Flink代码，用于从MySQL数据库解析binlog信息，并将其实时写入Hive中。

import org.apache.flink.api.java.utils.ParameterTool;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer;
import org.apache.flink.streaming.connectors.kafka.KafkaSerializationSchema;
import org.apache.flink.streaming.connectors.kafka.KafkaDeserializationSchema;
import org.apache.flink.streaming.util.serialization.SimpleStringSchema;
import org.apache.flink.streaming.api.functions.sink.SinkFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.serialization.SerializationSchema;
import org.apache.flink.api.common.serialization.DeserializationSchema;
import org.apache.flink.api.common.serialization.AbstractSerializationSchema;
import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.java.typeutils.TypeExtractor;
import org.apache.flink.api.java.tuple.Tuple2;

import java.util.Properties;

public class FlinkBinlogToHive {
    public static void main(String[] args) throws Exception {
        // 从命令行参数中获取配置信息
        ParameterTool parameterTool = ParameterTool.fromArgs(args);
        String bootstrapServers = parameterTool.get("bootstrapServers", "localhost:9092");
        String groupId = parameterTool.get("groupId", "flink-binlog-to-hive");
        String inputTopic = parameterTool.get("inputTopic", "binlog-topic");
        String outputTopic = parameterTool.get("outputTopic", "hive-topic");
        String mysqlHost = parameterTool.get("mysqlHost", "localhost");
        int mysqlPort = parameterTool.getInt("mysqlPort", 3306);
        String mysqlUser = parameterTool.get("mysqlUser", "root");
        String mysqlPassword = parameterTool.get("mysqlPassword", "");
        String hiveJdbcUrl = parameterTool.get("hiveJdbcUrl", "jdbc:hive2://localhost:10000/default");
        String hiveUser = parameterTool.get("hiveUser", "hive");
        String hivePassword = parameterTool.get("hivePassword", "");

        // 设置Flink流计算环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 设置Kafka消费者配置
        Properties kafkaConsumerProps = new Properties();
        kafkaConsumerProps.setProperty("bootstrap.servers", bootstrapServers);
        kafkaConsumerProps.setProperty("group.id", groupId);

        // 创建Kafka消费者
        FlinkKafkaConsumer<String> kafkaConsumer = new FlinkKafkaConsumer<>(inputTopic, new SimpleStringSchema(), kafkaConsumerProps);

        // 将binlog数据解析为Tuple2<数据库名, 表名>
        kafkaConsumer.map(new MapFunction<String, Tuple2<String, String>>() {
            @Override
            public Tuple2<String, String> map(String value) throws Exception {
                // 解析binlog数据，获取数据库名和表名
                String[] parts = value.split("\\.");
                String databaseName = parts[0];
                String tableName = parts[1];

                return new Tuple2<>(databaseName, tableName);
            }
        })
        // 将解析后的数据写入Hive
        .addSink(new SinkFunction<Tuple2<String, String>>() {
            @Override
            public void invoke(Tuple2<String, String> value) throws Exception {
                // 建立Hive连接
                Class.forName("org.apache.hive.jdbc.HiveDriver");
                Connection conn = DriverManager.getConnection(hiveJdbcUrl, hiveUser, hivePassword);

                // 创建Hive表
                String createTableSql = String.format("CREATE TABLE IF NOT EXISTS %s.%s (id INT, name STRING)", value.f0, value.f1);
                Statement stmt = conn.createStatement();
                stmt.executeUpdate(createTableSql);

                // 插入数据到Hive表
                String insertSql = String.format("INSERT INTO %s.%s VALUES (1, 'test')", value.f0, value.f1);
                stmt.executeUpdate(insertSql);

                // 关闭连接
                conn.close();
            }
        });

        // 执行Flink任务
        env.execute("Flink Binlog To Hive");
    }
}

这个代码使用Flink从Kafka中读取binlog数据，解析出数据库名和表名，然后将数据写入Hive中。在这个例子中，我们只是简单地创建了一个表并插入了一条数据。在实际应用中，您需要根据自己的需要修改代码。