Docker MongoDB中的数据导出带过滤条件的excel CSV 文件

进入容器
docker exec -it mongo /bin/sh

导出csv
mongoexport -u root -p root -d iot -c sensorData --type=csv -f deviceId,id,name,data,unitname,ctime -q ‘{“id”:8022}’ -o /home/iot.csv

导出csv带过滤条件
mongoexport -u root -p root -d iot -c sensorData --type=csv -f deviceId,id,name,data,unitname,ctime -q ‘{“id”:8022}’ -o /home/iot.csv

从容器复制出来
docker cp f857853678a0:/home/iot.csv /root/

mongoexport 参数：
-h：指明数据库宿主机的IP
-u：指明数据库的用户名
-p：指明数据库的密码
–port：指明端口号
-d：指明数据库的名字
-c：指明collection的名字
–type：指明要导入的文件格式
-f：指明要导出那些列
-q：指明导出数据的过滤条件
-o：指明要导出的文件名
–authenticationDatabase：指明保存用户凭证的数据库
–authenticationMechanism：指明身份验证机制

excel时间加上8小时公式：=B1+TIME(8,0,0) 设置单元格格式：yyyy/m/d h:mm:ss
CSV中文乱码：UE打开UTF-8，另存为GBK编码，再用excel打开就正常了。
excel多条件排序：选自定义排序，如下
在这里插入图片描述

不如直接用NoSQLBooster for MongoDB客户端方便，一下就出来了。但试用过期就要钱了。

import * as path from "path";
import * as fs from "fs";
const promisify = require("bluebird").promisify;
const appendFileAsync = promisify(fs.appendFile);


const BATCH_SIZE = 2000;

const connection = "123.123.123.123";//
const db = "iot";
let targetPath = "C:\\Users\\wuqimei\\Desktop\\chenhao";

if (!fs.existsSync(targetPath))
    require("mkdirp").sync(targetPath);

let exportCollections = [
    {
        collection: "sensorData", query: {}, projection: {}, sort: {}, skip: 0, limit: 0, filename: "iot.sensorData.csv", delimiter: ",", fields: [
            "id",
            "name",
            "pid",
            "deviceId",
            "data",
            "unitname",
            "ctime"
        ]
    }
];
let totalDocs = 0;
let collectionResult = {};//collectionResult:{[name:string]:number}

function exportCollection(collectionParams) {
    let { collection, filename, query, projection, sort, skip, limit, fields, delimiter } = collectionParams;

    let filepath = path.resolve(targetPath, mb.sanitizeFile(filename || (db + "." + collection + ".csv")));

    console.log(`export docs from ${connection}:${db}:${collection} to ${filepath} start...`);

    if (fs.existsSync(filepath))
        fs.unlinkSync(filepath);

    if (!_.isEmpty(fields)) {
        projection = {};
        fields.forEach(field => {
            projection[field] = 1;
        })
    } else {
        fields = mb.tryGetFields({ connection, db, collection });
    }

    collectionResult[collection] = 0;

    let cursor = mb.getCursorFromCollection({ connection, db, collection, sort, skip, limit, query, projection });
    let isFirstRead = true;

    const totalCount = cursor.size ? cursor.size() : cursor.count();
    const appendFilePromises = [];

    await(mb.batchReadFromCursor(cursor, BATCH_SIZE, (docs) => {
        return async(() => {
            let readLength = docs.length;
            if (!readLength) {
                return;
            }

            let csvContent = mb.docsToCSV({ docs, fields, delimiter, withColumnTitle: isFirstRead });
            isFirstRead = false;
            appendFilePromises.push(appendFileAsync(filepath, csvContent));
            collectionResult[collection] += readLength;
            const percent = (collectionResult[collection] / totalCount * 100).toFixed(1);
            console.log(`${percent}%	 ${collectionResult[collection]}/${totalCount} docs exported to "${path.basename(filepath)}".`);
            totalDocs += docs.length;
        })();
    }));

    await(Promise.all(appendFilePromises));
    sleep(100);
    console.log(`export ${collectionResult[collection]} docs from ${connection}:${db}:${collection} to ${filepath} finished.`);
}

exportCollections.forEach(it => exportCollection(it));
_.delay(() => mb.openFolder(targetPath), 1000);

if (exportCollections.length > 1)
    console.log(`Total ${totalDocs} document(s) of ${exportCollections.length} collections successfully exported.`, collectionResult);

Docker MongoDB中的数据导出带过滤条件的excel CSV 文件

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