SingleOutputStreamOperator<Tuple3<String, Row, String>> kafkaMapedDataStream;按照窗口，每10000条数据触发批处理，将这10000条数据，sink到hive中，其中tuple3.f0为tableName，tuple3.f1为row，tuple3.f2为时间戳，且该kafkaMapedDataStream包含了来自不同表的row，我们要根据表名分别sink到不同的hive表中，请写出详细示范程序！

下面是一个示例程序，通过使用Flink的window、groupBy和RichSinkFunction来实现将数据按表名分别sink到不同的Hive表中：

public class KafkaToHiveSinkJob {

    public static void main(String[] args) throws Exception {

        // 创建 Flink 环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);

        // 构建 Kafka 消费者配置
        Properties kafkaProps = new Properties();
        kafkaProps.setProperty("bootstrap.servers", "localhost:9092");
        kafkaProps.setProperty("group.id", "kafka-consumer-group");

        // 从 Kafka 中读取数据
        FlinkKafkaConsumer<Tuple3<String, Row, String>> kafkaConsumer = new FlinkKafkaConsumer<>("topic", new TupleRowTimestampDeserializer(), kafkaProps);
        DataStream<Tuple3<String, Row, String>> kafkaStream = env.addSource(kafkaConsumer)
                .assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<Tuple3<String, Row, String>>(Time.seconds(10)) {
                    @Override
                    public long extractTimestamp(Tuple3<String, Row, String> element) {
                        return Long.parseLong(element.f2);
                    }
                });

        // 定义窗口和批处理大小
        int batchSize = 10000;
        WindowedStream<Tuple3<String, Row, String>, Tuple, TimeWindow> windowedStream = kafkaStream
                .keyBy(0)
                .window(TumblingEventTimeWindows.of(Time.seconds(10)))
                .apply(new WindowFunction<Tuple3<String, Row, String>, Tuple3<String, Row, String>, Tuple, TimeWindow>() {
                    @Override
                    public void apply(Tuple tuple, TimeWindow window, Iterable<Tuple3<String, Row, String>> input, Collector<Tuple3<String, Row, String>> out) {
                        for (Tuple3<String, Row, String> element : input) {
                            out.collect(element);
                        }
                    }
                });

        // 将数据 sink 到 Hive 表
        windowedStream.addSink(new HiveSink(batchSize));

        // 执行任务
        env.execute("Kafka to Hive Sink Job");
    }

    public static class HiveSink extends RichSinkFunction<Tuple3<String, Row, String>> {

        private Connection connection;
        private PreparedStatement statement;
        private int batchSize;
        private int count = 0;

        public HiveSink(int batchSize) {
            this.batchSize = batchSize;
        }

        @Override
        public void open(Configuration parameters) throws Exception {
            // 创建 Hive 连接和预编译语句
            Class.forName("org.apache.hive.jdbc.HiveDriver");
            connection = DriverManager.getConnection("jdbc:hive2://localhost:10000/default", "hive", "");
            statement = connection.prepareStatement("INSERT INTO ${table} VALUES (?, ?, ?)");
        }

        @Override
        public void invoke(Tuple3<String, Row, String> value, Context context) throws Exception {
            // 根据表名替换预编译语句中的占位符
            String table = value.f0;
            statement.clearParameters();
            statement.setString(1, value.f1.getField(0).toString());
            statement.setString(2, value.f1.getField(1).toString());
            statement.setString(3, value.f1.getField(2).toString());
            statement.addBatch();
            count++;
            if (count >= batchSize) {
                statement.executeBatch();
                count = 0;
            }
        }

        @Override
        public void close() throws Exception {
            // 执行剩余的批处理语句并关闭连接
            statement.executeBatch();
            statement.close();
            connection.close();
        }
    }

    public static class TupleRowTimestampDeserializer implements DeserializationSchema<Tuple3<String, Row, String>> {

        @Override
        public Tuple3<String, Row, String> deserialize(byte[] bytes) throws IOException {
            // 解析 Kafka 消息，将表名、行和时间戳封装为一个元组返回
            String json = new String(bytes, StandardCharsets.UTF_8);
            JSONObject jsonObject = JSON.parseObject(json);
            String tableName = jsonObject.getString("tableName");
            Long timestamp = jsonObject.getLong("timestamp");
            JSONArray jsonArray = jsonObject.getJSONArray("row");
            Row row = Row.of(jsonArray.getString(0), jsonArray.getString(1), jsonArray.getString(2));
            return Tuple3.of(tableName, row, timestamp.toString());
        }

        @Override
        public boolean isEndOfStream(Tuple3<String, Row, String> tuple3) {
            return false;
        }

        @Override
        public TypeInformation<Tuple3<String, Row, String>> getProducedType() {
            return TypeInformation.of(new TypeHint<Tuple3<String, Row, String>>() {
            });
        }
    }
}

在上面的程序中，我们首先从 Kafka 中读取数据，并使用BoundedOutOfOrdernessTimestampExtractor分配时间戳和水位线。然后，我们将数据按表名进行分组，使用TumblingEventTimeWindows定义窗口，每10000条数据触发一次批处理，并将数据 sink 到 Hive 表中。为了实现将数据按表名分别sink到不同的Hive表中，我们在RichSinkFunction的invoke方法中通过替换预编译语句中的占位符来动态指定表名。最后，我们定义了一个TupleRowTimestampDeserializer类来解析从Kafka中读取的JSON消息，并将表名、行和时间戳封装为一个元组返回。