array(2) { ["docs"]=> array(10) { [0]=> array(10) { ["id"]=> string(3) "428" ["text"]=> string(77) "Visual Studio 2017 单独启动MSDN帮助(Microsoft Help Viewer)的方法" ["intro"]=> string(288) "目录 ECharts 异步加载 ECharts 数据可视化在过去几年中取得了巨大进展。开发人员对可视化产品的期望不再是简单的图表创建工具,而是在交互、性能、数据处理等方面有更高的要求。 chart.setOption({ color: [ " ["username"]=> string(8) "DonetRen" ["tagsname"]=> string(55) "Visual Studio 2017|MSDN帮助|C#程序|.NET|Help Viewer" ["tagsid"]=> string(23) "[401,402,403,"300",404]" ["catesname"]=> string(0) "" ["catesid"]=> string(2) "[]" ["createtime"]=> string(10) "1511400964" ["_id"]=> string(3) "428" } [1]=> array(10) { ["id"]=> string(3) "427" ["text"]=> string(42) "npm -v;报错 cannot find module "wrapp"" ["intro"]=> string(288) "目录 ECharts 异步加载 ECharts 数据可视化在过去几年中取得了巨大进展。开发人员对可视化产品的期望不再是简单的图表创建工具,而是在交互、性能、数据处理等方面有更高的要求。 chart.setOption({ color: [ " ["username"]=> string(4) "zzty" ["tagsname"]=> string(50) "node.js|npm|cannot find module "wrapp“|node" ["tagsid"]=> string(19) "[398,"239",399,400]" ["catesname"]=> string(0) "" ["catesid"]=> string(2) "[]" ["createtime"]=> string(10) "1511400760" ["_id"]=> string(3) "427" } [2]=> array(10) { ["id"]=> string(3) "426" ["text"]=> string(54) "说说css中pt、px、em、rem都扮演了什么角色" ["intro"]=> string(288) "目录 ECharts 异步加载 ECharts 数据可视化在过去几年中取得了巨大进展。开发人员对可视化产品的期望不再是简单的图表创建工具,而是在交互、性能、数据处理等方面有更高的要求。 chart.setOption({ color: [ " ["username"]=> string(12) "zhengqiaoyin" ["tagsname"]=> string(0) "" ["tagsid"]=> string(2) "[]" ["catesname"]=> string(0) "" ["catesid"]=> string(2) "[]" ["createtime"]=> string(10) "1511400640" ["_id"]=> string(3) "426" } [3]=> array(10) { ["id"]=> string(3) "425" ["text"]=> string(83) "深入学习JS执行--创建执行上下文(变量对象,作用域链,this)" ["intro"]=> string(288) "目录 ECharts 异步加载 ECharts 数据可视化在过去几年中取得了巨大进展。开发人员对可视化产品的期望不再是简单的图表创建工具,而是在交互、性能、数据处理等方面有更高的要求。 chart.setOption({ color: [ " ["username"]=> string(7) "Ry-yuan" ["tagsname"]=> string(33) "Javascript|Javascript执行过程" ["tagsid"]=> string(13) "["169","191"]" ["catesname"]=> string(0) "" ["catesid"]=> string(2) "[]" ["createtime"]=> string(10) "1511399901" ["_id"]=> string(3) "425" } [4]=> array(10) { ["id"]=> string(3) "424" ["text"]=> string(30) "C# 排序技术研究与对比" ["intro"]=> string(288) "目录 ECharts 异步加载 ECharts 数据可视化在过去几年中取得了巨大进展。开发人员对可视化产品的期望不再是简单的图表创建工具,而是在交互、性能、数据处理等方面有更高的要求。 chart.setOption({ color: [ " ["username"]=> string(9) "vveiliang" ["tagsname"]=> string(0) "" ["tagsid"]=> string(2) "[]" ["catesname"]=> string(8) ".Net Dev" ["catesid"]=> string(5) "[199]" ["createtime"]=> string(10) "1511399150" ["_id"]=> string(3) "424" } [5]=> array(10) { ["id"]=> string(3) "423" ["text"]=> string(72) "【算法】小白的算法笔记:快速排序算法的编码和优化" ["intro"]=> string(288) "目录 ECharts 异步加载 ECharts 数据可视化在过去几年中取得了巨大进展。开发人员对可视化产品的期望不再是简单的图表创建工具,而是在交互、性能、数据处理等方面有更高的要求。 chart.setOption({ color: [ " ["username"]=> string(9) "penghuwan" ["tagsname"]=> string(6) "算法" ["tagsid"]=> string(7) "["344"]" ["catesname"]=> string(0) "" ["catesid"]=> string(2) "[]" ["createtime"]=> string(10) "1511398109" ["_id"]=> string(3) "423" } [6]=> array(10) { ["id"]=> string(3) "422" ["text"]=> string(64) "JavaScript数据可视化编程学习(二)Flotr2,雷达图" ["intro"]=> string(288) "目录 ECharts 异步加载 ECharts 数据可视化在过去几年中取得了巨大进展。开发人员对可视化产品的期望不再是简单的图表创建工具,而是在交互、性能、数据处理等方面有更高的要求。 chart.setOption({ color: [ " ["username"]=> string(7) "chengxs" ["tagsname"]=> string(28) "数据可视化|前端学习" ["tagsid"]=> string(9) "[396,397]" ["catesname"]=> string(18) "前端基本知识" ["catesid"]=> string(5) "[198]" ["createtime"]=> string(10) "1511397800" ["_id"]=> string(3) "422" } [7]=> array(10) { ["id"]=> string(3) "421" ["text"]=> string(36) "C#表达式目录树(Expression)" ["intro"]=> string(288) "目录 ECharts 异步加载 ECharts 数据可视化在过去几年中取得了巨大进展。开发人员对可视化产品的期望不再是简单的图表创建工具,而是在交互、性能、数据处理等方面有更高的要求。 chart.setOption({ color: [ " ["username"]=> string(4) "wwym" ["tagsname"]=> string(0) "" ["tagsid"]=> string(2) "[]" ["catesname"]=> string(4) ".NET" ["catesid"]=> string(7) "["119"]" ["createtime"]=> string(10) "1511397474" ["_id"]=> string(3) "421" } [8]=> array(10) { ["id"]=> string(3) "420" ["text"]=> string(47) "数据结构 队列_队列实例:事件处理" ["intro"]=> string(288) "目录 ECharts 异步加载 ECharts 数据可视化在过去几年中取得了巨大进展。开发人员对可视化产品的期望不再是简单的图表创建工具,而是在交互、性能、数据处理等方面有更高的要求。 chart.setOption({ color: [ " ["username"]=> string(7) "idreamo" ["tagsname"]=> string(40) "C语言|数据结构|队列|事件处理" ["tagsid"]=> string(23) "["246","247","248",395]" ["catesname"]=> string(12) "数据结构" ["catesid"]=> string(7) "["133"]" ["createtime"]=> string(10) "1511397279" ["_id"]=> string(3) "420" } [9]=> array(10) { ["id"]=> string(3) "419" ["text"]=> string(47) "久等了,博客园官方Android客户端发布" ["intro"]=> string(288) "目录 ECharts 异步加载 ECharts 数据可视化在过去几年中取得了巨大进展。开发人员对可视化产品的期望不再是简单的图表创建工具,而是在交互、性能、数据处理等方面有更高的要求。 chart.setOption({ color: [ " ["username"]=> string(3) "cmt" ["tagsname"]=> string(0) "" ["tagsid"]=> string(2) "[]" ["catesname"]=> string(0) "" ["catesid"]=> string(2) "[]" ["createtime"]=> string(10) "1511396549" ["_id"]=> string(3) "419" } } ["count"]=> int(200) } 222 MongoDB中的数据导出为excel CSV 文件   - 爱码网

1、打开命令行,进入我们所安装的mongodb路径下的bin文件夹

2、我们采用bin文件夹下的mongoexport方法进行导出, 

1
mongoexport -d myDB -c user -f _id,name,password,adress --csv -o ./user.csv
  1. -d  标示 数据库  
  2.  -c   标示  数据表  
  3.  -f   需要提取的field用逗号分隔  
  4.  -o  输出路径  

 

 

ongoexport query with using date

Sometimes we might want to export only a specific part of our collection with query support of mongoexport.

Suppose this is our notebook collection, and each document refers to a notebook with their production date.

{
        "_id" : ObjectId("531ce460000000019b9643bc"),
        "company" : "Samsung",
        "date" : ISODate("2014-03-09T22:00:00Z"),
        "price" : 2000,
        "brand" : "Ultrabook",
}
{
        "_id" : ObjectId("531ce460000000019b9643ba"),
        "company" : "Sony",
        "date" : ISODate("2014-03-08T22:00:00Z"),
        "price" : 1500,
        "brand" : "Vaio",
}
{
        "_id" : ObjectId("531ce460000000019b9643bd"),
        "company" : "Apple",
        "date" : ISODate("2014-03-07T22:00:00Z"),
        "price" : 2250,
        "brand" : "MacbookPro",
}
{
        "_id" : ObjectId("531ce460000000019b9643be"),
        "company" : "Apple",
        "date" : ISODate("2014-03-06T22:00:00Z"),
        "price" : 1200,
        "brand" : "MacbookAir",
}
{
        "_id" : ObjectId("531ce460000000019b9643bf"),
        "company" : "Samsung",
        "date" : ISODate("2014-03-05T22:00:00Z"),
        "price" : 1000,
        "brand" : "Ultrabook",
}

The original way of mongoexport is defined by

mongoexport --db <database> --collection <collection> --query <JSON query> --out <file>

The major problem is we can not use ISODate(“”) objects to represent dates within a query, so that we have to convert each of them object into a Date object.

For instance; if we try to find the notebooks produced between 2014-03-09T22:00:00Z and 2014-03-07T22:00:00Z by Apple with the given query; 

mongoexport --db test --collection notebooks --query  '{ company:"Apple", date: { $lt: ISODate("2014-03-09T22:00:00Z") , $gte: ISODate("2014-03-07T22:00:00Z")} }' --out example.json

we will probably have an error like;

ERROR: too many positional options

There are two common ways to convert ISODates into Date objects which are;

    • we can use a simple javascript in mongo shell like;
var a = ISODate('2014-03-10T22:00:00Z');
a.getTime()

Now we have correct Date times to use them in our query.

mongoexport --db test --collection notebooks --query  "{ company:"Apple", date: { $lt: new Date(1394402400000) , $gte: new Date(1394229600000)} }" --out example.json

Finally, we will have a json file (example.json) in our current directory which includes only one document which is;

{
        "_id" : ObjectId("531ce460000000019b9643bd"),
        "company" : "Apple",
        "date" : ISODate("2014-03-07T22:00:00Z"),
        "price" : 2250,
        "brand" : "MacbookPro",
}

1、打开命令行,进入我们所安装的mongodb路径下的bin文件夹

2、我们采用bin文件夹下的mongoexport方法进行导出, 

1
mongoexport -d myDB -c user -f _id,name,password,adress --csv -o ./user.csv
  1. -d  标示 数据库  
  2.  -c   标示  数据表  
  3.  -f   需要提取的field用逗号分隔  
  4.  -o  输出路径  

 

 

ongoexport query with using date

Sometimes we might want to export only a specific part of our collection with query support of mongoexport.

Suppose this is our notebook collection, and each document refers to a notebook with their production date.

{
        "_id" : ObjectId("531ce460000000019b9643bc"),
        "company" : "Samsung",
        "date" : ISODate("2014-03-09T22:00:00Z"),
        "price" : 2000,
        "brand" : "Ultrabook",
}
{
        "_id" : ObjectId("531ce460000000019b9643ba"),
        "company" : "Sony",
        "date" : ISODate("2014-03-08T22:00:00Z"),
        "price" : 1500,
        "brand" : "Vaio",
}
{
        "_id" : ObjectId("531ce460000000019b9643bd"),
        "company" : "Apple",
        "date" : ISODate("2014-03-07T22:00:00Z"),
        "price" : 2250,
        "brand" : "MacbookPro",
}
{
        "_id" : ObjectId("531ce460000000019b9643be"),
        "company" : "Apple",
        "date" : ISODate("2014-03-06T22:00:00Z"),
        "price" : 1200,
        "brand" : "MacbookAir",
}
{
        "_id" : ObjectId("531ce460000000019b9643bf"),
        "company" : "Samsung",
        "date" : ISODate("2014-03-05T22:00:00Z"),
        "price" : 1000,
        "brand" : "Ultrabook",
}

The original way of mongoexport is defined by

mongoexport --db <database> --collection <collection> --query <JSON query> --out <file>

The major problem is we can not use ISODate(“”) objects to represent dates within a query, so that we have to convert each of them object into a Date object.

For instance; if we try to find the notebooks produced between 2014-03-09T22:00:00Z and 2014-03-07T22:00:00Z by Apple with the given query; 

mongoexport --db test --collection notebooks --query  '{ company:"Apple", date: { $lt: ISODate("2014-03-09T22:00:00Z") , $gte: ISODate("2014-03-07T22:00:00Z")} }' --out example.json

we will probably have an error like;

ERROR: too many positional options

There are two common ways to convert ISODates into Date objects which are;

    • we can use a simple javascript in mongo shell like;
var a = ISODate('2014-03-10T22:00:00Z');
a.getTime()

Now we have correct Date times to use them in our query.

mongoexport --db test --collection notebooks --query  "{ company:"Apple", date: { $lt: new Date(1394402400000) , $gte: new Date(1394229600000)} }" --out example.json

Finally, we will have a json file (example.json) in our current directory which includes only one document which is;

{
        "_id" : ObjectId("531ce460000000019b9643bd"),
        "company" : "Apple",
        "date" : ISODate("2014-03-07T22:00:00Z"),
        "price" : 2250,
        "brand" : "MacbookPro",
}

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