1 00:00:00,240 --> 00:00:00,720 Beautiful. 2 00:00:00,900 --> 00:00:02,310 We're done with our first step. 3 00:00:02,640 --> 00:00:07,050 We matched all the jobs that belong to certain user. 4 00:00:07,440 --> 00:00:10,650 Now we want to group them and we want to group them based on one. 5 00:00:11,070 --> 00:00:17,610 Well, on a status card because we have three options pending interview or cancelled. 6 00:00:18,270 --> 00:00:20,640 And then each of them, I want to count. 7 00:00:21,180 --> 00:00:23,950 So how many are there pending? 8 00:00:23,970 --> 00:00:25,350 How many are reconciled? 9 00:00:25,560 --> 00:00:32,369 And hopefully you see where I'm going with this and the way we'll do that, we'll go with group operators 10 00:00:32,369 --> 00:00:34,220 and now we're setting up our second step. 11 00:00:34,230 --> 00:00:35,520 And yes, that's the syntax. 12 00:00:35,820 --> 00:00:40,950 As you're setting up these steps in the aggregation pipeline, you need to go with comma and then add 13 00:00:40,950 --> 00:00:42,240 another object. 14 00:00:42,600 --> 00:00:44,190 So that's going to be our next step. 15 00:00:44,460 --> 00:00:48,990 Now we want to group them and effectively, this is what we're returning. 16 00:00:49,380 --> 00:00:55,640 So when you're looking at this object, this is what we'll get back right now in the stats. 17 00:00:56,010 --> 00:01:03,300 So unlike the match where we basically got all of the jobs now, get back the object with this underscore 18 00:01:03,300 --> 00:01:04,050 add property. 19 00:01:05,069 --> 00:01:09,780 And I will be equal to again, either pending interior or decline. 20 00:01:10,170 --> 00:01:13,800 And then how many of them are there? 21 00:01:14,040 --> 00:01:15,750 How many declined, how many interior? 22 00:01:16,020 --> 00:01:16,640 And all that. 23 00:01:16,650 --> 00:01:18,590 So let's try this one out. 24 00:01:18,830 --> 00:01:19,050 One. 25 00:01:19,060 --> 00:01:21,870 Go to jobs, comptroller one. 26 00:01:21,900 --> 00:01:23,250 Let's add that second step. 27 00:01:23,760 --> 00:01:25,920 I'll copy and paste just so can be the sample. 28 00:01:25,920 --> 00:01:29,310 But remember, the operator is group in this case. 29 00:01:29,850 --> 00:01:38,190 And as far as the turn we're looking for on the Score I.D. and then now will be equal to the stats property. 30 00:01:38,580 --> 00:01:45,360 So technically again, you can provide a different property, for example, you could go job that and 31 00:01:45,370 --> 00:01:47,580 that is going to count what kind of job types you're at. 32 00:01:47,580 --> 00:01:49,460 But in our case, that's not what we're looking for. 33 00:01:49,480 --> 00:01:50,340 We're looking for that. 34 00:01:50,640 --> 00:01:55,500 And then the syntax in this case is following where we go with quotation marks, one dollar sign and 35 00:01:55,500 --> 00:01:56,700 then we say status. 36 00:01:56,830 --> 00:01:59,760 Now we're accessing the spatters property value. 37 00:02:00,090 --> 00:02:01,340 None of let's go with comma. 38 00:02:02,400 --> 00:02:07,110 We're going to come up with another property that I'll place in the subject and that is going to be 39 00:02:07,110 --> 00:02:10,320 the count and that will be equal to another object. 40 00:02:10,320 --> 00:02:16,080 Again, this index comes from the MongoDB docks, where we go with another operator, which is some. 41 00:02:16,470 --> 00:02:18,180 And we set it equal to one. 42 00:02:18,960 --> 00:02:26,070 So once we save and then we navigate back to the stats one, what we should see right now is the array 43 00:02:26,250 --> 00:02:33,660 that we're getting back and notice how each option for the stats one is an object. 44 00:02:33,990 --> 00:02:36,510 It has this property of underscore Eddy. 45 00:02:36,840 --> 00:02:38,790 And I notice our nicely count. 46 00:02:39,660 --> 00:02:46,590 So we have 24 pending 24 decline and we have twenty seven in three years. 47 00:02:47,130 --> 00:02:51,750 So that's the first setup where we have the aggregation pipeline first. 48 00:02:52,730 --> 00:02:59,900 We grab the jobs that belong to a certain user and then we group them based on the status.