1 00:00:01,100 --> 00:00:02,450 ‫Welcome back. 2 00:00:02,450 --> 00:00:04,570 ‫In this video and the next one 3 00:00:04,570 --> 00:00:06,820 ‫I want to introduce you to the MongoDB 4 00:00:06,820 --> 00:00:10,680 ‫aggregation pipeline which is an extremely powerful 5 00:00:10,680 --> 00:00:13,750 ‫and extremely useful MongoDB framework 6 00:00:13,750 --> 00:00:15,433 ‫for data aggregation. 7 00:00:17,180 --> 00:00:19,020 ‫And the idea is that we basically 8 00:00:19,020 --> 00:00:21,900 ‫define a pipeline that all documents 9 00:00:21,900 --> 00:00:24,070 ‫from a certain collection go through 10 00:00:24,070 --> 00:00:26,470 ‫where they are processed step by step 11 00:00:26,470 --> 00:00:30,870 ‫in order to transform them into aggregated results. 12 00:00:30,870 --> 00:00:33,660 ‫For example, we can use the aggregation pipeline 13 00:00:33,660 --> 00:00:36,190 ‫in order to calculate averages 14 00:00:36,190 --> 00:00:39,010 ‫or calculating minimum and maximum values 15 00:00:39,010 --> 00:00:41,380 ‫or we can calculate distances even, 16 00:00:41,380 --> 00:00:43,590 ‫and we can really do all kinds of stuff. 17 00:00:43,590 --> 00:00:46,220 ‫It's really amazing how powerful 18 00:00:46,220 --> 00:00:48,440 ‫this aggregation pipeline is. 19 00:00:48,440 --> 00:00:50,530 ‫All right, but enough talk. 20 00:00:50,530 --> 00:00:53,900 ‫Let's now actually start using the aggregation pipeline 21 00:00:53,900 --> 00:00:57,122 ‫and I'm gonna create a new header function here 22 00:00:57,122 --> 00:01:00,290 ‫because later on we will then define a new route 23 00:01:00,290 --> 00:01:02,583 ‫and then use that function for that. 24 00:01:03,610 --> 00:01:06,093 ‫But that new route, I'm gonna create it later. 25 00:01:07,495 --> 00:01:09,720 ‫So I want to create a function here that's gonna 26 00:01:09,720 --> 00:01:13,756 ‫calculate a couple of statistics about our tours. 27 00:01:13,756 --> 00:01:16,987 ‫So I'm gonna call this one getTourStats. 28 00:01:19,070 --> 00:01:21,860 ‫Okay, so Stats for statistics, 29 00:01:21,860 --> 00:01:23,770 ‫and it's gonna be an async function 30 00:01:23,770 --> 00:01:28,770 ‫and so a vs code now automatically prefilled off this. 31 00:01:29,140 --> 00:01:33,923 ‫All right, let's create or try catch block here. 32 00:01:38,010 --> 00:01:41,970 ‫Actually, I'm gonna copy this from right up here, 33 00:01:41,970 --> 00:01:45,600 ‫and now in here I will create 34 00:01:45,600 --> 00:01:47,703 ‫a variable called stats. 35 00:01:50,120 --> 00:01:54,600 ‫Now the aggregation pipeline really is a MongoDB feature. 36 00:01:54,600 --> 00:01:57,330 ‫But Mongoose, of course, gives us access to it, 37 00:01:57,330 --> 00:02:01,210 ‫so that we can use it in the Mongoose driver, right? 38 00:02:01,210 --> 00:02:04,070 ‫So using our tour model in order to access 39 00:02:04,070 --> 00:02:07,217 ‫the tour collection, we say 40 00:02:07,217 --> 00:02:10,053 ‫tour.aggregate. 41 00:02:12,580 --> 00:02:15,250 ‫And so the aggregation pipeline is a bit 42 00:02:15,250 --> 00:02:18,847 ‫like a regular query and so using the aggregation pipeline 43 00:02:18,847 --> 00:02:22,512 ‫it's a just a bit like doing a regular query. 44 00:02:22,512 --> 00:02:25,550 ‫The difference here is that in aggregations, 45 00:02:25,550 --> 00:02:28,620 ‫as I already mentioned, we can manipulate the data 46 00:02:28,620 --> 00:02:30,750 ‫in a couple of different steps 47 00:02:30,750 --> 00:02:33,400 ‫and so let's now actually define these steps. 48 00:02:33,400 --> 00:02:37,093 ‫And for that, we pass in an array of so-called stages. 49 00:02:38,150 --> 00:02:41,050 ‫So we pass in an array, and then here we will 50 00:02:41,050 --> 00:02:43,110 ‫then have a lot of stages. 51 00:02:43,110 --> 00:02:46,540 ‫And again the documents then pass through these stages 52 00:02:46,540 --> 00:02:50,350 ‫one by one, step by step in the define sequence 53 00:02:50,350 --> 00:02:52,462 ‫as we define it here. 54 00:02:52,462 --> 00:02:54,860 ‫So each of the elements in this array 55 00:02:54,860 --> 00:02:56,970 ‫will be one of the stages. 56 00:02:56,970 --> 00:02:59,470 ‫And there are a ton of different stages 57 00:02:59,470 --> 00:03:02,110 ‫that we can choose from, but I will just tell you 58 00:03:02,110 --> 00:03:04,200 ‫the most common ones in this lecture 59 00:03:04,200 --> 00:03:06,070 ‫and also in the next one. 60 00:03:06,070 --> 00:03:08,400 ‫But anyway, I now want to take a minute 61 00:03:08,400 --> 00:03:11,500 ‫and take a quick look at the MongoDB documentation 62 00:03:11,500 --> 00:03:13,853 ‫because I think we didn't do that yet. 63 00:03:17,285 --> 00:03:20,840 ‫So MongoDB and we already know this website. 64 00:03:20,840 --> 00:03:24,460 ‫So on our MongoDB website we go here to learn, 65 00:03:24,460 --> 00:03:27,840 ‫then documentation and then in here we select 66 00:03:27,840 --> 00:03:32,380 ‫the MongoDB Manual and this documentation is huge. 67 00:03:32,380 --> 00:03:34,480 ‫It really, it has a lot of stuff in here 68 00:03:34,480 --> 00:03:36,450 ‫and if you were gonna go through it all, 69 00:03:36,450 --> 00:03:39,623 ‫well, then you'd become really a MongoDB master. 70 00:03:40,600 --> 00:03:44,010 ‫So it gives you a nice introduction to MongoDB, 71 00:03:44,010 --> 00:03:45,760 ‫you have the installation here. 72 00:03:45,760 --> 00:03:49,610 ‫You have the CRUD Operations, so a nice overview 73 00:03:49,610 --> 00:03:51,340 ‫about a lot of the stuff 74 00:03:51,340 --> 00:03:54,610 ‫that we already learned before, right? 75 00:03:54,610 --> 00:03:56,960 ‫Then you have stuff about the aggregation 76 00:03:57,800 --> 00:04:00,390 ‫and in particular, the aggregation pipeline 77 00:04:00,390 --> 00:04:02,320 ‫which we're gonna talk about now, 78 00:04:02,320 --> 00:04:04,730 ‫but what I want to really show you here 79 00:04:04,730 --> 00:04:08,210 ‫is actually this reference that we have at the bottom 80 00:04:08,210 --> 00:04:10,750 ‫because in the reference we have here for example 81 00:04:10,750 --> 00:04:12,880 ‫the operators and that's where we have 82 00:04:12,880 --> 00:04:16,570 ‫a lot of important stuff that we've been using all the time. 83 00:04:16,570 --> 00:04:18,430 ‫So let's take a look here at the query operators, 84 00:04:18,430 --> 00:04:22,200 ‫for example and here you see, we actually have 85 00:04:22,200 --> 00:04:24,210 ‫these operators that we already used. 86 00:04:24,210 --> 00:04:26,970 ‫So for example, greater than or equal, 87 00:04:26,970 --> 00:04:29,560 ‫or less than and equal, but if someday 88 00:04:29,560 --> 00:04:32,200 ‫in your own applications you need some other operator, 89 00:04:32,200 --> 00:04:35,170 ‫well, now you know where you can find them. 90 00:04:35,170 --> 00:04:37,570 ‫Now of course, it's also always very helpful 91 00:04:37,570 --> 00:04:40,370 ‫to just do a quick Google search, probably find 92 00:04:40,370 --> 00:04:43,490 ‫some stack overflow post about it, 93 00:04:43,490 --> 00:04:45,687 ‫but believe me, many times it's easier 94 00:04:45,687 --> 00:04:49,790 ‫and faster to just quickly jump into the documentation 95 00:04:49,790 --> 00:04:52,763 ‫and quickly search for whatever you're needing. 96 00:04:53,700 --> 00:04:56,350 ‫You just need to know how the documentation works 97 00:04:56,350 --> 00:04:59,050 ‫and once you know that, it's then gonna be quite easy 98 00:04:59,050 --> 00:05:02,473 ‫for you to find what you're looking for, all right? 99 00:05:03,400 --> 00:05:05,570 ‫Here you also have some logical operators 100 00:05:05,570 --> 00:05:08,920 ‫for example, the OR operator we already used. 101 00:05:08,920 --> 00:05:13,920 ‫Again, really a lot of stuff, geospatial operators 102 00:05:14,120 --> 00:05:16,790 ‫and we're gonna use some actually, later, 103 00:05:16,790 --> 00:05:19,040 ‫and yeah, really a lot of stuff. 104 00:05:19,040 --> 00:05:22,280 ‫Then what I wanted to show you after this lecture actually, 105 00:05:22,280 --> 00:05:24,793 ‫are the aggregation pipeline stages. 106 00:05:26,680 --> 00:05:29,140 ‫So here you have a lot of them 107 00:05:29,140 --> 00:05:32,090 ‫and what we're gonna use in this lecture here is match, 108 00:05:32,090 --> 00:05:35,410 ‫for example and group, 109 00:05:35,410 --> 00:05:36,820 ‫where is that? 110 00:05:36,820 --> 00:05:39,670 ‫Yeah group, that is a very important one, 111 00:05:39,670 --> 00:05:41,660 ‫and so as I mentioned, I'm gonna show you 112 00:05:41,660 --> 00:05:44,920 ‫the most important ones in this lecture and the next one. 113 00:05:44,920 --> 00:05:48,270 ‫But again, if you in some case need something else, 114 00:05:48,270 --> 00:05:51,110 ‫then just go ahead, come here to this documentation 115 00:05:51,110 --> 00:05:53,920 ‫and you will find what you're looking for. 116 00:05:53,920 --> 00:05:54,753 ‫All right? 117 00:05:55,740 --> 00:05:58,630 ‫Anyway, let's now learn how we can actually define 118 00:05:58,630 --> 00:06:00,940 ‫one of these stages, all right? 119 00:06:00,940 --> 00:06:03,170 ‫And I'm gonna start with match. 120 00:06:03,170 --> 00:06:05,330 ‫And match is basically to select 121 00:06:05,330 --> 00:06:07,890 ‫or to filter certain documents. 122 00:06:07,890 --> 00:06:09,510 ‫And so it's very simple. 123 00:06:09,510 --> 00:06:13,210 ‫It's really just like a filter object in MongoDB, 124 00:06:13,210 --> 00:06:16,380 ‫such like we've been using so many times. 125 00:06:16,380 --> 00:06:19,340 ‫So each of the stages is an object 126 00:06:19,340 --> 00:06:22,750 ‫and then here comes the name of the stage. 127 00:06:22,750 --> 00:06:26,300 ‫So this one is the match stage, all right? 128 00:06:26,300 --> 00:06:29,603 ‫And as I mentioned, it's really just a query. 129 00:06:30,730 --> 00:06:33,570 ‫And so let's say that for starters, 130 00:06:33,570 --> 00:06:35,200 ‫we only want to select documents 131 00:06:35,200 --> 00:06:39,630 ‫which have a ratings average greater or equal than 4.5. 132 00:06:39,630 --> 00:06:41,110 ‫So can you do that? 133 00:06:41,110 --> 00:06:43,560 ‫I'm just gonna give you a second to do that here. 134 00:06:44,730 --> 00:06:46,123 ‫So I hope you did it. 135 00:06:47,090 --> 00:06:50,440 ‫So ratings average, should be 136 00:06:52,240 --> 00:06:55,383 ‫greater or equal than 4.5. 137 00:06:56,460 --> 00:06:59,260 ‫And so that's it, that's the first stage. 138 00:06:59,260 --> 00:07:03,500 ‫And usually this match stage is just a preliminary stage 139 00:07:03,500 --> 00:07:07,050 ‫to then prepare for the next stages which come ahead. 140 00:07:07,050 --> 00:07:10,173 ‫So the next one is now the group stage. 141 00:07:13,000 --> 00:07:14,610 ‫So group and then in there 142 00:07:14,610 --> 00:07:17,840 ‫we need to always pass just another object. 143 00:07:17,840 --> 00:07:20,600 ‫So it looks kinda weird with all these objects, 144 00:07:20,600 --> 00:07:22,870 ‫but, yeah, you've seen that before 145 00:07:22,870 --> 00:07:24,780 ‫and MongoDB just works this way. 146 00:07:24,780 --> 00:07:28,293 ‫It's always objects, inside of objects, inside of objects. 147 00:07:29,520 --> 00:07:32,690 ‫And this group here is where the real magic happens 148 00:07:32,690 --> 00:07:34,520 ‫because as the name says, 149 00:07:34,520 --> 00:07:36,870 ‫it allows us to group documents together, 150 00:07:36,870 --> 00:07:38,820 ‫basically using accumulators. 151 00:07:38,820 --> 00:07:40,600 ‫And an accumulator is for example, 152 00:07:40,600 --> 00:07:42,850 ‫even calculating an average. 153 00:07:42,850 --> 00:07:46,270 ‫So if we have five tours, each of them has a rating, 154 00:07:46,270 --> 00:07:50,250 ‫we can then calculate the average rating using group. 155 00:07:50,250 --> 00:07:52,680 ‫And so let's do exactly that right here. 156 00:07:52,680 --> 00:07:55,190 ‫Now the first thing, is we always need to specify 157 00:07:55,190 --> 00:07:58,950 ‫is the id because this is where we're gonna specify 158 00:07:58,950 --> 00:08:00,803 ‫what we want to group by. 159 00:08:02,420 --> 00:08:05,480 ‫For now, we say null here because we want 160 00:08:05,480 --> 00:08:08,870 ‫to have everything in one group so that we can calculate 161 00:08:08,870 --> 00:08:11,600 ‫the statistics for all of the tours together 162 00:08:11,600 --> 00:08:13,713 ‫and not separate it by groups. 163 00:08:14,590 --> 00:08:18,250 ‫We will, a bit later then also group by different stuff, 164 00:08:18,250 --> 00:08:21,010 ‫for example we can group by the difficulty 165 00:08:21,010 --> 00:08:23,900 ‫and we can then, for example calculate the average 166 00:08:23,900 --> 00:08:26,820 ‫for the easy tours, the average for the medium tours 167 00:08:26,820 --> 00:08:29,373 ‫and the average for the difficult tours. 168 00:08:29,373 --> 00:08:32,720 ‫So again, we can group by one of our fields 169 00:08:32,720 --> 00:08:35,750 ‫and that field, we are gonna specify in here, 170 00:08:35,750 --> 00:08:39,000 ‫but for now, as I said, we want to calculate these averages 171 00:08:39,000 --> 00:08:41,803 ‫for all the tours together in one big group. 172 00:08:42,865 --> 00:08:46,990 ‫So in that case we say _id and set it to null. 173 00:08:47,994 --> 00:08:51,600 ‫Now let's actually calculate the average rating. 174 00:08:51,600 --> 00:08:54,423 ‫In order to do that, we simply specify a new field, 175 00:08:55,320 --> 00:08:58,713 ‫so let's simply call it the average rating, 176 00:09:01,080 --> 00:09:03,480 ‫so like this, and this will be 177 00:09:05,620 --> 00:09:07,160 ‫well, the average, 178 00:09:07,160 --> 00:09:11,560 ‫which is yet another MongoDB operator, so this one here. 179 00:09:11,560 --> 00:09:14,233 ‫You will find it in the reference if you look it up. 180 00:09:15,149 --> 00:09:17,800 ‫So this is a mathematical operator calculating 181 00:09:17,800 --> 00:09:20,520 ‫the average and now the name of the field. 182 00:09:20,520 --> 00:09:23,530 ‫And again, I know that this is gonna look very weird, 183 00:09:23,530 --> 00:09:26,390 ‫but in order to specify the field which we want 184 00:09:26,390 --> 00:09:29,110 ‫to calculate the average from, we need to use 185 00:09:29,110 --> 00:09:31,900 ‫the dollar sign, but in quotes here 186 00:09:31,900 --> 00:09:33,583 ‫and then the name of the field. 187 00:09:34,560 --> 00:09:36,803 ‫So ratings average in this case. 188 00:09:39,316 --> 00:09:41,573 ‫And let's also calculate the average price. 189 00:09:43,500 --> 00:09:47,143 ‫So average price, and again, did it wrong here, 190 00:09:48,200 --> 00:09:49,300 ‫and so 191 00:09:50,870 --> 00:09:52,970 ‫the average and then again, 192 00:09:52,970 --> 00:09:55,170 ‫the name of the field and in quotes 193 00:09:55,170 --> 00:09:56,470 ‫and with this dollar sign. 194 00:09:58,800 --> 00:10:00,890 ‫Right, so it looks kind of weird again, 195 00:10:00,890 --> 00:10:01,900 ‫but this is just the way 196 00:10:01,900 --> 00:10:04,510 ‫that the aggregation pipeline works. 197 00:10:04,510 --> 00:10:06,300 ‫So we have the average price. 198 00:10:06,300 --> 00:10:08,880 ‫Let's actually also calculate the minimum price, 199 00:10:08,880 --> 00:10:12,560 ‫so the smallest price, and the largest price. 200 00:10:12,560 --> 00:10:13,620 ‫So minPrice 201 00:10:15,439 --> 00:10:19,360 ‫and from now on, it's gonna all look kind of similar, 202 00:10:19,360 --> 00:10:21,190 ‫so now we use the min operator 203 00:10:22,200 --> 00:10:26,603 ‫and then again the field name like this. 204 00:10:29,783 --> 00:10:32,530 ‫And a comma here and let's now just duplicate this one 205 00:10:32,530 --> 00:10:35,053 ‫in order to calculate the maximum price. 206 00:10:37,910 --> 00:10:40,310 ‫And now, let's actually take a look 207 00:10:40,310 --> 00:10:42,160 ‫at what we already got at this point. 208 00:10:44,240 --> 00:10:47,430 ‫So we now need to actually send out this response, 209 00:10:47,430 --> 00:10:49,250 ‫and so again, I'm just gonna go head 210 00:10:49,250 --> 00:10:53,513 ‫and grab some of these already done responses here. 211 00:10:59,154 --> 00:11:01,853 ‫So the data that we want to send out is called stats. 212 00:11:06,740 --> 00:11:09,610 ‫And now all we need to do is actually add a new route 213 00:11:09,610 --> 00:11:12,390 ‫here in our tour routes and again, 214 00:11:12,390 --> 00:11:14,543 ‫I'm gonna edit here, right at the top. 215 00:11:16,368 --> 00:11:18,690 ‫So router.route 216 00:11:21,597 --> 00:11:26,597 ‫/gettour, or actually just tour-stats, just like this 217 00:11:29,460 --> 00:11:33,730 ‫because the get is already defined in the HTTP method. 218 00:11:33,730 --> 00:11:37,090 ‫So no need to repeat it in the name. 219 00:11:37,090 --> 00:11:38,640 ‫So tourController.getTourStats. 220 00:11:42,268 --> 00:11:46,707 ‫Give it a safe and let's actually test it out now. 221 00:11:48,270 --> 00:11:52,710 ‫So I'm really exciting to test it out now, actually 222 00:11:52,710 --> 00:11:57,710 ‫to see if it works because again it's really an amazing 223 00:11:57,720 --> 00:11:59,370 ‫and really powerful tool 224 00:11:59,370 --> 00:12:01,503 ‫which allows you to do so much stuff. 225 00:12:03,130 --> 00:12:05,280 ‫So tour-stats, send it 226 00:12:06,290 --> 00:12:09,000 ‫and that's not really the result 227 00:12:09,000 --> 00:12:12,600 ‫that we were waiting for, so that's just the pipeline 228 00:12:12,600 --> 00:12:15,750 ‫that we defined, but I already know why that is. 229 00:12:15,750 --> 00:12:18,950 ‫So we did not await the result. 230 00:12:18,950 --> 00:12:21,410 ‫So this, basically, just like a normal query 231 00:12:21,410 --> 00:12:24,453 ‫is gonna return an aggregate object. 232 00:12:25,857 --> 00:12:29,770 ‫So .find is gonna return a query, and .aggregate 233 00:12:29,770 --> 00:12:32,330 ‫is gonna return an aggregate object. 234 00:12:32,330 --> 00:12:34,180 ‫And then only when we await it, 235 00:12:34,180 --> 00:12:36,940 ‫it actually comes back with the result. 236 00:12:36,940 --> 00:12:39,850 ‫So that's also why we defined this function here 237 00:12:39,850 --> 00:12:42,640 ‫as an async function, so that we can then use await 238 00:12:42,640 --> 00:12:44,953 ‫in there and so this is the right place. 239 00:12:45,860 --> 00:12:47,950 ‫Let's try it again. 240 00:12:47,950 --> 00:12:49,850 ‫And here we go! 241 00:12:49,850 --> 00:12:51,250 ‫So great! 242 00:12:51,250 --> 00:12:54,910 ‫We have the average rating of all our tours, 243 00:12:54,910 --> 00:12:56,720 ‫we have the average price which you see is 244 00:12:56,720 --> 00:13:01,720 ‫1563 and then the minimum and the maximum, right? 245 00:13:01,910 --> 00:13:04,210 ‫And from our data, we can actually confirm 246 00:13:04,210 --> 00:13:06,410 ‫that this is true. 247 00:13:06,410 --> 00:13:07,770 ‫So great! 248 00:13:07,770 --> 00:13:09,770 ‫Really, really great. 249 00:13:09,770 --> 00:13:11,950 ‫Now, one more thing that I want to do here 250 00:13:11,950 --> 00:13:14,450 ‫is to actually calculate the total number 251 00:13:14,450 --> 00:13:18,730 ‫of ratings that we have and also the total number of tours. 252 00:13:18,730 --> 00:13:20,700 ‫So we have the average rating here 253 00:13:20,700 --> 00:13:23,090 ‫and let's actually do it before, 254 00:13:23,090 --> 00:13:23,973 ‫so numRatings, 255 00:13:26,490 --> 00:13:27,323 ‫like this 256 00:13:28,850 --> 00:13:31,950 ‫and you can probably guess 257 00:13:31,950 --> 00:13:36,950 ‫that this one is called sum and then the ratingsAverage, 258 00:13:38,500 --> 00:13:43,500 ‫or actually not ratingsAverage, but ratingsQuantity, right? 259 00:13:43,610 --> 00:13:46,480 ‫So that's where the number of ratings is stored 260 00:13:46,480 --> 00:13:49,354 ‫and so the number of ratings, the total, 261 00:13:49,354 --> 00:13:51,863 ‫will be the sum of all of these together. 262 00:13:52,840 --> 00:13:55,090 ‫And now, the last one, is the number of tours 263 00:13:56,100 --> 00:13:57,880 ‫and that one is a bit trickier, 264 00:13:57,880 --> 00:14:00,660 ‫and so that's a nice one to show you here. 265 00:14:00,660 --> 00:14:04,570 ‫So we still want to sum, so to add everything together, 266 00:14:04,570 --> 00:14:07,960 ‫basically, so we still use sum, 267 00:14:07,960 --> 00:14:10,340 ‫but what are we gonna add together? 268 00:14:10,340 --> 00:14:13,493 ‫Well, we basically add one for each document, 269 00:14:14,443 --> 00:14:17,380 ‫and so we say 1, and that's it. 270 00:14:17,380 --> 00:14:19,610 ‫So basically for each of the document 271 00:14:19,610 --> 00:14:22,020 ‫that's gonna go through this pipeline, 272 00:14:22,020 --> 00:14:24,960 ‫one will be added to this num counter. 273 00:14:24,960 --> 00:14:29,430 ‫Let's call it numTours. 274 00:14:29,430 --> 00:14:33,763 ‫And so, yeah, let's test it out now, again. 275 00:14:36,350 --> 00:14:39,320 ‫And indeed we get our nine tours 276 00:14:39,320 --> 00:14:41,303 ‫and we already know that we have nine. 277 00:14:42,650 --> 00:14:46,820 ‫So these are all the statistics for all the tours together, 278 00:14:46,820 --> 00:14:48,890 ‫but let's now take it to the next level. 279 00:14:48,890 --> 00:14:52,450 ‫As I said before, we can now group our results 280 00:14:52,450 --> 00:14:53,790 ‫for different fields. 281 00:14:53,790 --> 00:14:56,870 ‫And let's actually start out with the difficulty. 282 00:14:56,870 --> 00:14:59,830 ‫And so that's quite similar to specifying the fields 283 00:14:59,830 --> 00:15:01,990 ‫down there which is simply the dollar sign, 284 00:15:01,990 --> 00:15:03,570 ‫and then the name of the field. 285 00:15:03,570 --> 00:15:07,303 ‫So $difficulty, give it a safe and that's it. 286 00:15:08,350 --> 00:15:12,040 ‫Send it here, and now we have the statistics 287 00:15:12,040 --> 00:15:15,950 ‫all defined for each of these three difficulties. 288 00:15:15,950 --> 00:15:18,200 ‫So that's really, really amazing. 289 00:15:18,200 --> 00:15:20,470 ‫So this is the part that blows my mind. 290 00:15:20,470 --> 00:15:22,170 ‫It's absolutely fantastic. 291 00:15:22,170 --> 00:15:25,020 ‫I mean, you can start to see all the kinds of stuff, 292 00:15:25,020 --> 00:15:27,260 ‫all the kinds of data manipulation 293 00:15:27,260 --> 00:15:30,890 ‫that you can do using this pipeline, right? 294 00:15:30,890 --> 00:15:33,360 ‫So we really can now start to analyze stuff, 295 00:15:33,360 --> 00:15:36,170 ‫for example, we see that the easiest tours 296 00:15:36,170 --> 00:15:38,640 ‫are the ones who get the poorest ratings 297 00:15:38,640 --> 00:15:41,370 ‫with a 4.6, while the medium tours 298 00:15:41,370 --> 00:15:43,980 ‫are the ones who get the highest ratings. 299 00:15:43,980 --> 00:15:46,750 ‫We can also see that the difficult tours 300 00:15:46,750 --> 00:15:49,620 ‫are the most expensive ones with an average price 301 00:15:49,620 --> 00:15:54,520 ‫of 1997, while the easiest ones are also the cheapest ones 302 00:15:54,520 --> 00:15:58,010 ‫with a price of 1272, right? 303 00:15:58,010 --> 00:15:59,910 ‫We also see that the easiest tours 304 00:15:59,910 --> 00:16:01,880 ‫are the ones which we have the most, 305 00:16:01,880 --> 00:16:05,400 ‫so four easy ones and only two difficult ones, right? 306 00:16:05,400 --> 00:16:07,510 ‫So we can use this, of course, 307 00:16:07,510 --> 00:16:10,693 ‫to get all kinds of insights into our data. 308 00:16:12,350 --> 00:16:13,630 ‫Let's comment this one out here 309 00:16:13,630 --> 00:16:16,583 ‫and let's just try another one, 310 00:16:17,560 --> 00:16:20,130 ‫for example, by ratings, why not, 311 00:16:20,130 --> 00:16:22,773 ‫let's just try out, see what we get. 312 00:16:24,450 --> 00:16:26,310 ‫So for example, we have two tours, 313 00:16:26,310 --> 00:16:28,353 ‫so basically with a rating of 4.5, 314 00:16:29,530 --> 00:16:32,963 ‫we have two tours with 4.7, one tour with 4.8, 315 00:16:34,040 --> 00:16:37,077 ‫and three tours with 4.9. 316 00:16:37,077 --> 00:16:39,870 ‫Oh, and also down here, one tour with 4.6, 317 00:16:39,870 --> 00:16:42,320 ‫and so for example, one insight that we can get here 318 00:16:42,320 --> 00:16:45,680 ‫is that the very expensive tours with this average price 319 00:16:45,680 --> 00:16:49,103 ‫of 1997 get extremely good ratings. 320 00:16:50,260 --> 00:16:55,260 ‫So all kinds of stuff, really that we can do here. 321 00:16:55,360 --> 00:16:59,520 ‫Let's get rid of this one, let's add this one back, 322 00:16:59,520 --> 00:17:00,975 ‫and we can now actually even, 323 00:17:00,975 --> 00:17:03,180 ‫do some operations with this one. 324 00:17:03,180 --> 00:17:08,180 ‫So just for fun, let's put this difficulty to uppercase. 325 00:17:08,550 --> 00:17:12,370 ‫So this of course, is gonna be another object here 326 00:17:13,570 --> 00:17:15,810 ‫and then here in front is the operator. 327 00:17:15,810 --> 00:17:18,873 ‫And so the operator in this case, is called $toUpper. 328 00:17:21,150 --> 00:17:25,230 ‫So just, yet another MongoDB operator. 329 00:17:25,230 --> 00:17:30,140 ‫So check it out, and now it's spelled uppercase. 330 00:17:30,140 --> 00:17:31,290 ‫Great! 331 00:17:31,290 --> 00:17:34,650 ‫So we have the group stage here which is quite complete. 332 00:17:34,650 --> 00:17:38,180 ‫So let's try another one which is a sort stage. 333 00:17:38,180 --> 00:17:42,120 ‫So another object in our array for yet another stage 334 00:17:43,250 --> 00:17:45,380 ‫and this one is called sort. 335 00:17:45,380 --> 00:17:49,070 ‫So sort and then in there, we need yet another object, 336 00:17:49,070 --> 00:17:52,190 ‫and it kind of auto-completed this one, I don't know why, 337 00:17:52,190 --> 00:17:54,860 ‫and so here we can now specify which field we want 338 00:17:54,860 --> 00:17:59,183 ‫to sort this by and let's actually use the average price. 339 00:18:00,580 --> 00:18:03,610 ‫And now here in the sorting we actually need to use 340 00:18:03,610 --> 00:18:06,563 ‫the field names that we specified up here in the group. 341 00:18:07,520 --> 00:18:10,630 ‫We can no longer use the old names because at this point 342 00:18:10,630 --> 00:18:11,930 ‫they are already gone. 343 00:18:11,930 --> 00:18:13,610 ‫They no longer exist. 344 00:18:13,610 --> 00:18:16,400 ‫So at this point, in the aggregation pipeline, 345 00:18:16,400 --> 00:18:19,410 ‫we really already have these results. 346 00:18:19,410 --> 00:18:22,790 ‫So these are now our documents basically. 347 00:18:22,790 --> 00:18:25,960 ‫So if you want to sort by the average price, 348 00:18:25,960 --> 00:18:28,943 ‫then this is the field name we gotta use. 349 00:18:30,530 --> 00:18:33,070 ‫So we can say, the average price 350 00:18:33,070 --> 00:18:35,793 ‫and then we can say 1 for ascending. 351 00:18:37,680 --> 00:18:38,883 ‫So let's try that out. 352 00:18:41,550 --> 00:18:43,070 ‫And yeah, indeed. 353 00:18:43,070 --> 00:18:46,480 ‫That's the lowest one and that's the highest one. 354 00:18:46,480 --> 00:18:47,820 ‫Great! 355 00:18:47,820 --> 00:18:50,210 ‫So we did a lot of stuff. 356 00:18:50,210 --> 00:18:53,480 ‫Let me just show you that we can also repeat stages. 357 00:18:53,480 --> 00:18:55,710 ‫So let's actually do another match here 358 00:18:58,740 --> 00:19:00,930 ‫and this one is really just to show you 359 00:19:00,930 --> 00:19:03,240 ‫that we actually can repeat stages 360 00:19:03,240 --> 00:19:07,130 ‫and I also want to show you another operator. 361 00:19:07,130 --> 00:19:11,020 ‫So let's now select by id and remember that id 362 00:19:11,020 --> 00:19:12,920 ‫is now the difficulty, right? 363 00:19:12,920 --> 00:19:15,023 ‫So we just specified that up here. 364 00:19:16,820 --> 00:19:20,670 ‫So we want the id and now a new operator 365 00:19:20,670 --> 00:19:22,500 ‫that we didn't use yet. 366 00:19:22,500 --> 00:19:26,913 ‫We want it to be not equal to, and let's say easy. 367 00:19:28,210 --> 00:19:29,830 ‫And so, just like this we're gonna select 368 00:19:29,830 --> 00:19:32,260 ‫all the documents that are not easy. 369 00:19:32,260 --> 00:19:33,880 ‫So in this case, the one that says 370 00:19:33,880 --> 00:19:36,340 ‫medium and difficult, right? 371 00:19:36,340 --> 00:19:41,007 ‫So basically, excluding the one that says easy, right? 372 00:19:42,200 --> 00:19:43,790 ‫So again, it's not really useful here 373 00:19:43,790 --> 00:19:47,950 ‫because that will take away a lot of our meaningful data, 374 00:19:47,950 --> 00:19:50,090 ‫but just to show you that we can of course, 375 00:19:50,090 --> 00:19:51,980 ‫match multiple times. 376 00:19:51,980 --> 00:19:54,750 ‫So in this case, we matched once before we actually 377 00:19:54,750 --> 00:19:58,160 ‫did the group and then we matched once we were ready, 378 00:19:58,160 --> 00:19:59,475 ‫doing the grouping. 379 00:19:59,475 --> 00:20:01,720 ‫Now, let's actually keep this here, 380 00:20:01,720 --> 00:20:03,798 ‫out of our aggregation pipeline, 381 00:20:03,798 --> 00:20:06,200 ‫but I'm gonna leave it here, 382 00:20:06,200 --> 00:20:08,200 ‫just so that you keep it as a reference. 383 00:20:09,580 --> 00:20:12,750 ‫Give it another save, give it another try, 384 00:20:12,750 --> 00:20:14,730 ‫and so these are our results 385 00:20:14,730 --> 00:20:16,590 ‫for getting our tour statistics. 386 00:20:16,590 --> 00:20:19,030 ‫So, really nice, really great. 387 00:20:19,030 --> 00:20:21,070 ‫I hope you enjoyed this video 388 00:20:21,070 --> 00:20:23,200 ‫and in the next one, we're actually gonna keep using 389 00:20:23,200 --> 00:20:26,810 ‫the aggregation pipeline to do some more calculations. 390 00:20:26,810 --> 00:20:30,050 ‫And to show you some more different stages. 391 00:20:30,050 --> 00:20:31,973 ‫So keep tuned for that one.