flink根据以上数据，统计每个省份有多少家外卖店，并按降序排序

假设数据已经被读入Flink的DataStream中，并且每个元素是一个JSON字符串，包含了外卖店所在的省份信息。可以通过Flink的DataStream API来进行统计和排序。

以下是可能的代码实现：

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.aggregation.AggregationFunction;
import org.apache.flink.streaming.api.functions.aggregation.SumFunction;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.json.JSONObject;

public class ProvinceCounter {

    public static void main(String[] args) throws Exception {
        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 读入数据流，假设每个元素是一个JSON字符串
        DataStream<String> input = ...;

        // 将每个JSON字符串解析为省份信息
        DataStream<String> provinces = input.map(new MapFunction<String, String>() {
            @Override
            public String map(String value) throws Exception {
                JSONObject json = new JSONObject(value);
                return json.getString("province");
            }
        });

        // 按省份分组，统计每个省份的外卖店数量
        DataStream<ProvinceCount> counts = provinces
                .keyBy(new KeySelector<String, String>() {
                    @Override
                    public String getKey(String value) throws Exception {
                        return value;
                    }
                })
                .timeWindow(Time.minutes(10)) // 滚动窗口，每10分钟统计一次
                .aggregate(new SumFunction<String>() {
                    @Override
                    public String add(String value1, String value2) throws Exception {
                        return value1; // 外卖店数量不需要累加，只需要计数
                    }
                }, new AggregationFunction<String, ProvinceCount>() {
                    @Override
                    public ProvinceCount createAccumulator() {
                        return new ProvinceCount("", 0);
                    }

                    @Override
                    public ProvinceCount add(String value, ProvinceCount accumulator) {
                        accumulator.province = value;
                        accumulator.count += 1;
                        return accumulator;
                    }

                    @Override
                    public ProvinceCount getResult(ProvinceCount accumulator) {
                        return accumulator;
                    }

                    @Override
                    public ProvinceCount merge(ProvinceCount a, ProvinceCount b) {
                        a.count += b.count;
                        return a;
                    }
                });

        // 按降序排序并输出结果
        counts
                .keyBy(new KeySelector<ProvinceCount, Integer>() {
                    @Override
                    public Integer getKey(ProvinceCount value) throws Exception {
                        return value.count;
                    }
                })
                .sortByDescending(new KeySelector<ProvinceCount, Integer>() {
                    @Override
                    public Integer getKey(ProvinceCount value) throws Exception {
                        return value.count;
                    }
                })
                .print();

        env.execute("Province Counter");
    }

    // 用于表示每个省份的统计信息
    public static class ProvinceCount {
        public String province;
        public int count;

        public ProvinceCount(String province, int count) {
            this.province = province;
            this.count = count;
        }

        @Override
        public String toString() {
            return "ProvinceCount{" +
                    "province='" + province + '\'' +
                    ", count=" + count +
                    '}';
        }
    }
}

本代码假设每个元素都是一个JSON字符串，其中包含了外卖店所在的省份信息。首先将每个JSON字符串解析为省份信息，然后按省份分组，并在滚动窗口内计数，统计每个省份的外卖店数量。最后按降序排序并输出结果。注意，Flink的DataStream API是流式处理的，因此需要使用窗口等操作将数据流转换为有限的数据集。