MongoDB based on time aggregate example

You need to add translatefields values to the following collection according to Lastupdate by day.

Rs_test:secondary> db.new_result.find ();   {  "_id"  : objectid (" 57fb0756e31f84a56ed41889 "), " Lastupdate " : isodate (" 2016-09-02t01:35:02.471z "), " Translatefields " : 9 }    { " _id " : objectid (" 57fb0756e31f84a56ed4188a "), " Lastupdate " : isodate (" 2016-09-05t11:13:28.344z "), " Translatefields " : 10 }    { " _id " : objectid (" 57fb0756e31f84a56ed4188b "), " Lastupdate " : isodate (" 2016-09-05t09:26:41.016z "), " Translatefields " : 33 }    { " _id " : objectid (" 57fb0756e31f84a56ed4188c "), " Lastupdate " : isodate (" 2016-09-02t13:34:50.114z "), " Translatefields " : 12 }    { " _id " : objectid (" 57fb0756e31f84a56ed4188d "), " Lastupdate " : isodate (" 2016-08-26t03:49:52.369z "), " TranslaTefields " : 17 } 

If it is in SQL Server, the grouping statistics should be written like this:

SELECT CONVERT (varchar,lastupdate,112), SUM (translatefields) from Dbo.new_result GROUP by CONVERT (Varchar,lastupdate, ) ORDER by 1;

So in MongoDB, there are 3 ways to aggregate: Group, aggregate, and MapReduce.

2.6 Version Aggregate method    db.new_result.aggregate (           {             $group  : {                _id : {  year: {  $year:  "$LastUpdate"  }, month: {  $month:  "$LastUpdate"  }, day: {  $dayOfMonth:  "$LastUpdate"  } },                totalTime: {  $sum:  "$ Translatefields " }            }           },           {             $sort  : {                 "_id.year": 1,  "_id.month": 1,  "_id.day": 1             }           }    )

3.0 version Aggregate method    db.new_result.aggregate (           {             $group  : {                yearmonthday: {   $dateToString: { format:  "%y-%m-%d", date:  "$LastUpdate"  } },                totaltime: { $ sum:  "$TranslateFields"  }            }           },           {             $sort  : {                 "Yearmonthday":  1             }          }     )

The group method Db.new_result.group ({keyf:function (doc) {var date = new Date (DOC).      Lastupdate);      var DateKey = "" +date.getfullyear () + "-" + (Date.getmonth () +1) + "-" +date.getdate ();    return {' Day ':d Atekey}; }, initial: {"Time": 0}, Reduce:function (Doc, prev) {prev.time + = doc.        Translatefields;    }, Finalize:function Finalize (out) {return out; }    }    });

First Save As Date     //1    db.tmp_result.find ({"Value"). Status ": 3},{" value. Translatefields ": 1," value. Lastupdate ": 1}". ForEach (        function (item) {              db.new_result.save ({"LastUpdate": Item.value.LastUpdate.getFullYear () + "-" +                   (Item.value.LastUpdate.getMonth () +1) + "-" +                  item.value.lastupdate.getdate (),                   " Translatefields ": Item.value.TranslateFields});        }      )     //2    db.new_result.aggregate (           {             $group  : {                _id: "$LastUpdate",                totaltime: { $ sum:  "$TranslateFields"  }            }           }          ,{"$sort": {"_id":1}}    )

For the aggregate method, it is best to $match before $group, reducing the amount of data, and if the filtered key is indexed, the query will also go through the index.

Db. Translateticket.aggregate (    {         "$match":         {               "lastupdate":  {"$gte": Isodate ("2016-06-19t00:00:00.000z"),  "$lt": Isodate (" 2016-09-19t00:00:00.000z ")},             " Status ": 3        }    },     {         "$group":         {          _id : { month: { $ month:  "$LastUpdate"  }, day: {  $dayOfMonth:  "$LastUpdate"  }, year:  {  $year:  "$LastUpdate"  } },          totaltime: {  $sum: "$CharactersCount"  }        }    },     {         "$sort":         {             "_id.year": 1, "_ Id.month ": 1," _id.day ":1        }    }     )

In this case, it is best to create the following index:

Db. Translateticket.createindex ({"Lastupdate": 1, "Status": 1},{background:1})

This article is from the SQL Server deep Dive blog, so be sure to keep this source http://ultrasql.blog.51cto.com/9591438/1861003

MongoDB based on time aggregate example