完成C++代码。已知数据文件名为“input1000.csv”。数据文件的内容和格式，简述如下：数据文件由表头（首行）和具体一行行的股票数据构成。第一行为表头，ts_code 表示股票代码，trade_date 表示交易日期，open 表示开盘价，high 表示最高价，low 表示最低价，close 表示收盘价，pre_close 表示临近收盘价，change 表示涨跌幅，pct_chg 表示涨跌百分比，vol 表示交易量，amount 表示交易金额。其余各行为实际数据，每一行的各个数据项(字段)用“,”隔开。对股票数据进行排序 (必须使用外排序，用于排序的内存大小限制为 32MB，此限制的检查，需要通过资源管理器展现。)对原文“input1000.csv” 进行外排序，排序标准是首先按照股票代码的字典序进行排序，如果股票代码相同则按照日期从小到大进行排序，输出文件取名output.txt，输出文件第一行为表头.

以下是用C++完成对“input1000.csv”进行外排序的代码：

#include <iostream>
#include <fstream>
#include <string>
#include <vector>
#include <algorithm>
#include <queue>
#include <cstdio>
#include <ctime>
using namespace std;

const int MAXN = 4000000;
const int MAXM = 200000;

struct Stock {
    string ts_code;
    string trade_date;
    double open;
    double high;
    double low;
    double close;
    double pre_close;
    double change;
    double pct_chg;
    double vol;
    double amount;
};

struct Data {
    string ts_code;
    string trade_date;
    int idx;  // idx 表示该数据在文件中的行号
};

bool cmp(const Data& a, const Data& b) {
    if (a.ts_code != b.ts_code) {
        return a.ts_code < b.ts_code;
    } else {
        return a.trade_date < b.trade_date;
    }
}

void merge_files(int k, int n) {
    vector<ifstream> ifs(k);
    vector<Data> data(k);
    vector<Stock> stocks(k);
    vector<int> flag(k, 1);  // 标记每个文件是否读完
    priority_queue<Data, vector<Data>, decltype(&cmp)> pq(cmp);
    for (int i = 0; i < k; i++) {
        ifs[i].open("temp" + to_string(i) + ".csv");
        getline(ifs[i], data[i].ts_code, ',');
        getline(ifs[i], data[i].trade_date, ',');
        ifs[i] >> stocks[i].open >> stocks[i].high >> stocks[i].low >> stocks[i].close;
        ifs[i] >> stocks[i].pre_close >> stocks[i].change >> stocks[i].pct_chg;
        ifs[i] >> stocks[i].vol >> stocks[i].amount;
        data[i].idx = i * n + 1;
        pq.push(data[i]);
    }
    ofstream ofs("output.txt");
    ofs << "ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount" << endl;
    while (!pq.empty()) {
        Data tmp = pq.top();
        pq.pop();
        ofs << stocks[tmp.idx % n].ts_code << "," << stocks[tmp.idx % n].trade_date << ",";
        ofs << stocks[tmp.idx % n].open << "," << stocks[tmp.idx % n].high << ",";
        ofs << stocks[tmp.idx % n].low << "," << stocks[tmp.idx % n].close << ",";
        ofs << stocks[tmp.idx % n].pre_close << "," << stocks[tmp.idx % n].change << ",";
        ofs << stocks[tmp.idx % n].pct_chg << "," << stocks[tmp.idx % n].vol << ",";
        ofs << stocks[tmp.idx % n].amount << endl;
        if (ifs[tmp.idx / n].peek() == EOF) {
            flag[tmp.idx / n] = 0;
            ifs[tmp.idx / n].close();
        } else {
            getline(ifs[tmp.idx / n], data[tmp.idx / n].ts_code, ',');
            getline(ifs[tmp.idx / n], data[tmp.idx / n].trade_date, ',');
            ifs[tmp.idx / n] >> stocks[tmp.idx % n].open >> stocks[tmp.idx % n].high >> stocks[tmp.idx % n].low >> stocks[tmp.idx % n].close;
            ifs[tmp.idx / n] >> stocks[tmp.idx % n].pre_close >> stocks[tmp.idx % n].change >> stocks[tmp.idx % n].pct_chg;
            ifs[tmp.idx / n] >> stocks[tmp.idx % n].vol >> stocks[tmp.idx % n].amount;
            data[tmp.idx / n].idx++;
            pq.push(data[tmp.idx / n]);
        }
    }
    for (int i = 0; i < k; i++) {
        if (flag[i] == 1) {
            ifs[i].close();
        }
        remove(("temp" + to_string(i) + ".csv").c_str());
    }
}

void external_sort() {
    ifstream ifs("input1000.csv");
    vector<string> buf(MAXM);
    int cnt = 0;
    while (!ifs.eof()) {
        string line;
        getline(ifs, line);
        if (line.empty()) {
            continue;
        }
        buf[cnt++] = line;
        if (cnt == MAXM) {
            sort(buf.begin(), buf.begin() + cnt);
            ofstream ofs("temp" + to_string(cnt / MAXM - 1) + ".csv");
            for (int i = 0; i < cnt; i++) {
                ofs << buf[i] << endl;
            }
            ofs.close();
            cnt = 0;
        }
    }
    if (cnt > 0) {
        sort(buf.begin(), buf.begin() + cnt);
        ofstream ofs("temp" + to_string(cnt / MAXM) + ".csv");
        for (int i = 0; i < cnt; i++) {
            ofs << buf[i] << endl;
        }
        ofs.close();
    }
    ifs.close();
    int k = ceil(32.0 * 1024 * 1024 / (MAXM * sizeof(Stock)));
    int n = MAXN / k;
    for (int i = 0; i < k; i++) {
        ifstream ifs("temp" + to_string(i) + ".csv");
        vector<Stock> stocks(n);
        for (int j = 0; j < n && !ifs.eof(); j++) {
            getline(ifs, stocks[j].ts_code, ',');
            getline(ifs, stocks[j].trade_date, ',');
            ifs >> stocks[j].open >> stocks[j].high >> stocks[j].low >> stocks[j].close;
            ifs >> stocks[j].pre_close >> stocks[j].change >> stocks[j].pct_chg;
            ifs >> stocks[j].vol >> stocks[j].amount;
            ifs.ignore();
        }
        sort(stocks.begin(), stocks.begin() + min(n, (int)stocks.size()), [](const Stock& a, const Stock& b) {
            if (a.ts_code != b.ts_code) {
                return a.ts_code < b.ts_code;
            } else {
                return a.trade_date < b.trade_date;
            }
        });
        ofstream ofs("temp" + to_string(i) + ".csv");
        for (int j = 0; j < min(n, (int)stocks.size()); j++) {
            ofs << stocks[j].ts_code << "," << stocks[j].trade_date << ",";
            ofs << stocks[j].open << "," << stocks[j].high << ",";
            ofs << stocks[j].low << "," << stocks[j].close << ",";
            ofs << stocks[j].pre_close << "," << stocks[j].change << ",";
            ofs << stocks[j].pct_chg << "," << stocks[j].vol << ",";
            ofs << stocks[j].amount << endl;
        }
        ofs.close();
        stocks.clear();
    }
    merge_files(k, n);
}

int main() {
    clock_t start = clock();
    external_sort();
    clock_t end = clock();
    cout << "Time used: " << (double)(end - start) / CLOCKS_PER_SEC << " s" << endl;
    return 0;
}

该代码首先将“input1000.csv”文件分为多个文件，每个文件最多包含MAXM行数据。然后对每个文件进行内部排序，再将这些排好序的文件合并成一个文件，即“output.txt”。在合并的过程中，使用了外排序的思想，每次从每个文件中选出一个数据，将它们放入一个小根堆中，每次取出堆顶的元素并输出，再从它所在的文件中读取一个新的数据放入堆中。这样就能保证输出的数据是按照题目要求排序的。