WEBVTT 0:00:09.540000 --> 0:00:19.500000 All right. Now what if we wanted to identify or check how many and I'll 0:00:19.500000 --> 0:00:22.980000 go back to the database here, I'll list all the databases. 0:00:22.980000 --> 0:00:29.980000 Let's utilize the city database here. 0:00:29.980000 --> 0:00:32.440000 There we are. We have city there. 0:00:32.440000 --> 0:00:37.320000 So we can say db.city, but we can say db.find. 0:00:37.320000 --> 0:00:45.120000 db.city.find to list out the documents here. 0:00:45.120000 --> 0:00:48.100000 You can see these are all the actual records of documents. 0:00:48.100000 --> 0:00:49.980000 So we have multiple fields. 0:00:49.980000 --> 0:00:55.260000 We have the ID, the city, the location, which points to latitude and longitude 0:00:55.260000 --> 0:01:00.400000 coordinates and then population and the state. 0:01:00.400000 --> 0:01:06.240000 All right. So what if we wanted to find how many of these documents, and 0:01:06.240000 --> 0:01:09.380000 I'm referring to the number, how many of these documents all have the 0:01:09.380000 --> 0:01:15.120000 state of, let's say, in this case, MA, which I'm assuming is Massachusetts, 0:01:15.120000 --> 0:01:16.760000 I might be wrong, but let's try and do that. 0:01:16.760000 --> 0:01:23.040000 The way we would do this, we would say, is city db.city.find and then 0:01:23.040000 --> 0:01:25.600000 in here we would put in our parameters or criteria. 0:01:25.600000 --> 0:01:29.040000 So the field in this case is city. 0:01:29.040000 --> 0:01:34.020000 And then we would say that's going to be equal to MA or just MA, as it 0:01:34.020000 --> 0:01:38.300000 were, and we'll close the curly braces and then the count, right? 0:01:38.300000 --> 0:01:45.580000 So that'll tell us how many documents here essentially meet that criteria. 0:01:45.580000 --> 0:01:52.800000 We'll hit zero. So essentially what we did is we found out how many cities 0:01:52.800000 --> 0:01:59.300000 of the state Massachusetts are present in the collection city, in the 0:01:59.300000 --> 0:02:00.600000 database city, right? 0:02:00.600000 --> 0:02:03.860000 So a little bit abstract, but that's how that would work. 0:02:03.860000 --> 0:02:07.920000 And you can, again, utilize any field to perform your queries there. 0:02:07.920000 --> 0:02:13.480000 We could also do this to, for example, the population field seems interesting. 0:02:13.480000 --> 0:02:19.080000 And this is where I wanted to show you the logical operators in the Mongo 0:02:19.080000 --> 0:02:23.500000 query language. So for example, we can try and find out how many cities 0:02:23.500000 --> 0:02:28.140000 have a population greater than, let's say, 15,000 in the collection city 0:02:28.140000 --> 0:02:29.460000 in the database city. 0:02:29.460000 --> 0:02:31.920000 To do that, we would say use city. 0:02:31.920000 --> 0:02:36.360000 We currently are using that database. 0:02:36.360000 --> 0:02:39.860000 And now if we say show collections, we only have one collection called 0:02:39.860000 --> 0:02:45.560000 city. Again, we can say db.city.find and we can then utilize the field 0:02:45.560000 --> 0:02:50.340000 population. So it needs to match exactly population. 0:02:50.340000 --> 0:02:55.080000 And then we say in here, when we utilize an logical operation, we can 0:02:55.080000 --> 0:02:57.540000 utilize the dollar symbol. 0:02:57.540000 --> 0:03:00.980000 And we say GT, which stands for greater than. 0:03:00.980000 --> 0:03:05.920000 There's also not equal to or equal to, which I'll also show you in the 0:03:05.920000 --> 0:03:11.760000 next video and we'll then say the value here. 0:03:11.760000 --> 0:03:18.200000 So 15,000. And yeah, so we'll close this curly brace, the original one 0:03:18.200000 --> 0:03:21.080000 and then the actual bracket there. 0:03:21.080000 --> 0:03:26.900000 So essentially saying can you find, can you find how many cities have 0:03:26.900000 --> 0:03:30.920000 a population greater than 15,000 in the collection city in the database 0:03:30.920000 --> 0:03:33.820000 city? And then we say count this. 0:03:33.820000 --> 0:03:35.660000 Can you tell us the total number here? 0:03:35.660000 --> 0:03:39.120000 So 5,785 cities. 0:03:39.120000 --> 0:03:42.420000 And you know, we can change this so we can say how many cities have a 0:03:42.420000 --> 0:03:50.620000 population greater than let's say 100,000, only four of them. 0:03:50.620000 --> 0:03:54.480000 All right. So that's how this works. 0:03:54.480000 --> 0:03:57.720000 Now if we get rid of count, it'll actually show us those cities. 0:03:57.720000 --> 0:04:01.780000 So in this case, New York, Brooklyn, Chicago, as you can see here, which 0:04:01.780000 --> 0:04:06.600000 is pretty cool. So you're starting to get a feel as to why some web applications 0:04:06.600000 --> 0:04:10.380000 need this type of databases opposed to a relational database. 0:04:10.380000 --> 0:04:15.760000 So it all comes down to, you know, what type of data you're going to be 0:04:15.760000 --> 0:04:19.180000 storing and how fast you need to access it, right? 0:04:19.180000 --> 0:04:21.580000 Now we can also run some advanced ones. 0:04:21.580000 --> 0:04:25.860000 So for example, we can say how many cities in the state of let's say, 0:04:25.860000 --> 0:04:33.120000 let's say New York have a, you know, maybe we can say population, but 0:04:33.120000 --> 0:04:34.300000 let's try a different one. 0:04:34.300000 --> 0:04:40.680000 So we can say DB dot city dot find. 0:04:40.680000 --> 0:04:44.100000 Let's see what other states we have here. 0:04:44.100000 --> 0:04:50.620000 So we have, we need to type in it to display the others. 0:04:50.620000 --> 0:04:55.900000 So it we're pretty much seeing just Massachusetts. 0:04:55.900000 --> 0:05:05.080000 Let's see, Massachusetts still, we can say DB dot city dot find. 0:05:05.080000 --> 0:05:10.000000 Let's see if we can find whether there's other states here. 0:05:10.000000 --> 0:05:16.760000 So I'm going to say in here, I will just put in state. 0:05:16.760000 --> 0:05:22.100000 And I'll say the state equals something like FL for Florida. 0:05:22.100000 --> 0:05:26.020000 And we'll close that up there and let's hit enter. 0:05:26.020000 --> 0:05:28.300000 So there we are Florida as an example. 0:05:28.300000 --> 0:05:31.400000 So we can say DB dot city dot find. 0:05:31.400000 --> 0:05:37.480000 And in here now coming back to utilizing a different or a logical operator 0:05:37.480000 --> 0:05:41.400000 to use the logical operator like we did in the structured query language 0:05:41.400000 --> 0:05:44.680000 instead of saying and or typing it out, we use the dollar symbol. 0:05:44.680000 --> 0:05:51.500000 And then we say and and then we use a colon and then specify our set here. 0:05:51.500000 --> 0:05:58.280000 So we're saying and we want to find the population greater than. 0:05:58.280000 --> 0:06:03.220000 So we're going to say within this set, we'll just say population. 0:06:03.220000 --> 0:06:05.900000 That's going to be greater than. 0:06:05.900000 --> 0:06:08.220000 So GT and then the value. 0:06:08.220000 --> 0:06:14.600000 So let's say, you know, 15,000, for example, and we'll close those two 0:06:14.600000 --> 0:06:15.840000 curly braces there. 0:06:15.840000 --> 0:06:19.380000 And then the second operation, which is going to be the state. 0:06:19.380000 --> 0:06:23.720000 So the second match, in this case, let's say the state is Florida. 0:06:23.720000 --> 0:06:25.920000 So FL for Florida. 0:06:25.920000 --> 0:06:30.080000 And we'll close the curly braces there. 0:06:30.080000 --> 0:06:35.260000 And we then need to close the single bracket or the standard bracket and 0:06:35.260000 --> 0:06:36.880000 then normal bracket. 0:06:36.880000 --> 0:06:39.340000 And then we say count, right? 0:06:39.340000 --> 0:06:43.480000 Hit enter. So you can see this is a pretty advanced query. 0:06:43.480000 --> 0:06:45.920000 We're looking for very specific information here. 0:06:45.920000 --> 0:06:49.700000 And let's see where that can give us that information. 0:06:49.700000 --> 0:06:54.120000 So we're asking it to tell us DB dot city dot find. 0:06:54.120000 --> 0:07:02.180000 Can you please tell us the or run the operation where the first option 0:07:02.180000 --> 0:07:08.820000 is the population needs to be greater than 15,000. 0:07:08.820000 --> 0:07:16.800000 Secondly, it needs to essentially tell us the how many cities in the state 0:07:16.800000 --> 0:07:21.840000 of Florida have a population greater than 15,000 in the collection city 0:07:21.840000 --> 0:07:23.360000 and the database city. 0:07:23.360000 --> 0:07:24.660000 So I'll let enter. 0:07:24.660000 --> 0:07:25.860000 This is taking a while. 0:07:25.860000 --> 0:07:28.460000 I don't think it actually we actually got a result. 0:07:28.460000 --> 0:07:31.440000 So let's try a different state here. 0:07:31.440000 --> 0:07:34.280000 That operation did not run successfully. 0:07:34.280000 --> 0:07:36.400000 So let's try and copy this here. 0:07:36.400000 --> 0:07:39.960000 I was going to copy this and paste that in there. 0:07:39.960000 --> 0:07:46.440000 We can change this to maybe let's say New York, for example, hit enter. 0:07:46.440000 --> 0:07:50.240000 So for some reason that's not displaying that. 0:07:50.240000 --> 0:07:51.960000 Let's try a different one. 0:07:51.960000 --> 0:07:54.760000 One that might work here. 0:07:54.760000 --> 0:08:00.880000 Actually, what we could do is if we try and let me just paste in the query. 0:08:00.880000 --> 0:08:02.440000 So actually hold on. 0:08:02.440000 --> 0:08:04.200000 Let me just terminate that there. 0:08:04.200000 --> 0:08:11.800000 Let's go back into Mongo show DBS and we'll say use city and then we say 0:08:11.800000 --> 0:08:13.400000 show collections. 0:08:13.400000 --> 0:08:15.620000 I'm going to use that same query here. 0:08:15.620000 --> 0:08:19.380000 So if I run that again, let's see if we get any result from this. 0:08:19.380000 --> 0:08:23.160000 We may need to change the greater than here because they might not be 0:08:23.160000 --> 0:08:27.900000 city. Well, I assume they are, but they only might be a few documents 0:08:27.900000 --> 0:08:34.140000 with the state of Florida or that have the field of state that's equal 0:08:34.140000 --> 0:08:36.660000 to Florida or FL as it were. 0:08:36.660000 --> 0:08:41.660000 So we'll just give it a few seconds here. 0:08:41.660000 --> 0:08:43.260000 Let's try a different one. 0:08:43.260000 --> 0:08:45.620000 Maybe something like Indiana. 0:08:45.620000 --> 0:08:51.340000 So IN or we can try MA here as we did Massachusetts. 0:08:51.340000 --> 0:08:55.160000 That actually should work because we're just checking population and then 0:08:55.160000 --> 0:08:58.740000 greater than 15,000. 0:08:58.740000 --> 0:09:00.800000 That should work. 0:09:00.800000 --> 0:09:06.220000 And then the second operation is state, which is Massachusetts here. 0:09:06.220000 --> 0:09:09.820000 And oh, there we are. 0:09:09.820000 --> 0:09:12.680000 I think I found out why my query wasn't working. 0:09:12.680000 --> 0:09:13.880000 That's because of my syntax. 0:09:13.880000 --> 0:09:18.440000 I forgot to close the actual curly braces here. 0:09:18.440000 --> 0:09:19.820000 So we hit enter now. 0:09:19.820000 --> 0:09:20.600000 There we are. 378. 0:09:20.600000 --> 0:09:22.340000 So that took a while. 0:09:22.340000 --> 0:09:23.760000 So our initial query worked. 0:09:23.760000 --> 0:09:30.440000 So 378 cities in the state of Florida have a population greater than 15 0:09:30.440000 --> 0:09:33.460000 ,000 in the collection city in the database city. 0:09:33.460000 --> 0:09:36.200000 We get rid of the account operator there. 0:09:36.200000 --> 0:09:38.220000 It'll display them for us. 0:09:38.220000 --> 0:09:43.520000 And yeah, so you sort of getting the hang of things here now. 0:09:43.520000 --> 0:09:48.820000 I also want to show you the logical operator or and how to use that. 0:09:48.820000 --> 0:09:53.280000 So we can ask ourselves the question, how many cities have a population 0:09:53.280000 --> 0:09:58.660000 less than 100 or belong to the state of Florida or something like that. 0:09:58.660000 --> 0:10:01.800000 So to do this, we would say DB dot city. 0:10:01.800000 --> 0:10:03.340000 Let me type that in. 0:10:03.340000 --> 0:10:05.720000 So DB dot city dot find. 0:10:05.720000 --> 0:10:12.380000 And in here, we can say, for example, let's see, or so we'll say, or. 0:10:12.380000 --> 0:10:17.300000 And then we put in the actual criteria whenever utilizing a logical operator, 0:10:17.300000 --> 0:10:20.060000 you put in the logical operator, then the criteria. 0:10:20.060000 --> 0:10:22.660000 So we're saying either one of them can be correct. 0:10:22.660000 --> 0:10:25.540000 If either one of them is correct, display the results. 0:10:25.540000 --> 0:10:30.100000 So in here, we'll say a population is going to be and then we'll open 0:10:30.100000 --> 0:10:37.740000 up here. To say is less than we can say it. 0:10:37.740000 --> 0:10:41.040000 So it is another logical operator here. 0:10:41.040000 --> 0:10:43.240000 And then we set that to 100. 0:10:43.240000 --> 0:10:45.620000 Actually not it lt. 0:10:45.620000 --> 0:10:46.820000 Sorry, that's my bad. 0:10:46.820000 --> 0:10:52.360000 So lt. And we say 100. 0:10:52.360000 --> 0:10:58.700000 And then we can say in here, put in the state. 0:10:58.700000 --> 0:11:02.220000 And that's going to be equal to Florida. 0:11:02.220000 --> 0:11:08.000000 So FL. And yeah, so we'll close the first bracket here. 0:11:08.000000 --> 0:11:13.280000 And then of course we need to close the the square bracket and then the 0:11:13.280000 --> 0:11:18.960000 curly braces again for the initial set there and then the primary bracket 0:11:18.960000 --> 0:11:21.800000 there. And we say count. 0:11:21.800000 --> 0:11:24.760000 So one four nine nine. 0:11:24.760000 --> 0:11:32.800000 So in essence, how many cities have a population less than a hundred? 0:11:32.800000 --> 0:11:38.560000 Or belong to the state Florida in the collection city in the database 0:11:38.560000 --> 0:11:44.500000 city. So we've talked about and or and less than greater than etc. 0:11:44.500000 --> 0:11:48.260000 So you know, these would typically be so greater than in this case is 0:11:48.260000 --> 0:11:55.140000 just dollar symbol GT less than is just dollar symbol lt. 0:11:55.140000 --> 0:11:59.140000 The or operator is just or. 0:11:59.140000 --> 0:12:03.920000 And then we have of course and and we also have not. 0:12:03.920000 --> 0:12:08.860000 So that's you know, how we can learn around these logical operators. 0:12:08.860000 --> 0:12:11.860000 We can also utilize rejects. 0:12:11.860000 --> 0:12:17.500000 That's one of the great things with the no sequel or the Mongo query language 0:12:17.500000 --> 0:12:21.440000 as it were. The way we can do this is let's say we want to find cities 0:12:21.440000 --> 0:12:27.600000 that have their name starting with let's say. 0:12:27.600000 --> 0:12:35.720000 Let's see. Let's see city starting with. 0:12:35.720000 --> 0:12:39.660000 Let's try any OK, that might be interesting. 0:12:39.660000 --> 0:12:46.660000 So we can say. Let's say use city. 0:12:46.660000 --> 0:12:50.220000 We're currently using city here just trying to think of how we can run 0:12:50.220000 --> 0:12:53.160000 the query. So we'll say DB dot city dot find. 0:12:53.160000 --> 0:12:58.200000 And in here will say the will open up brackets and in here will say the 0:12:58.200000 --> 0:13:05.880000 city field is. We're going to need to utilize rejects will open up curly 0:13:05.880000 --> 0:13:10.060000 brackets here and will say dollar symbol rejects. 0:13:10.060000 --> 0:13:21.900000 And then we say rejects is going to be. 0:13:21.900000 --> 0:13:27.300000 So. So any actually know that might not work. 0:13:27.300000 --> 0:13:34.540000 Let's just try just trying to think here we put in the rejects. 0:13:34.540000 --> 0:13:43.640000 So. Starts with and then we can say for example. 0:13:43.640000 --> 0:13:55.800000 Let's say. So if we take a look at this here the city is in capital so 0:13:55.800000 --> 0:14:05.040000 we could say. So let's try H a for example just as an example and then 0:14:05.040000 --> 0:14:07.060000 we'll use wildcard there. 0:14:07.060000 --> 0:14:12.700000 And we'll close the curly brackets here and then the single the primary 0:14:12.700000 --> 0:14:14.420000 bracket as it were. 0:14:14.420000 --> 0:14:16.900000 And we'll just list them out. 0:14:16.900000 --> 0:14:20.440000 So looks like we have an issue with our query. 0:14:20.440000 --> 0:14:24.100000 So that's this one here this one here. 0:14:24.100000 --> 0:14:30.260000 To be.city.find and we close that there rejects H a you may have an issue 0:14:30.260000 --> 0:14:32.860000 with our rejects here. 0:14:32.860000 --> 0:14:42.100000 Let's try H. Unterminated string literal that's interesting. 0:14:42.100000 --> 0:14:47.340000 So I put out there we are okay my bad did not put the double quotes there. 0:14:47.340000 --> 0:14:52.020000 There we are so anything starting with H is going to be listed here. 0:14:52.020000 --> 0:14:55.880000 You can use rejects to do that again depending on what you want to do. 0:14:55.880000 --> 0:14:59.700000 So for example we can say in this case. 0:14:59.700000 --> 0:15:04.940000 We say H.O. that display Houston. 0:15:04.940000 --> 0:15:09.180000 No we don't have that at least in this data set or collection if we try 0:15:09.180000 --> 0:15:13.580000 for example. MI for Miami. 0:15:13.580000 --> 0:15:18.500000 We should see a match there for Miami hopefully if it's been added. 0:15:18.500000 --> 0:15:21.400000 So we only have Milan nothing else. 0:15:21.400000 --> 0:15:30.120000 Let's try. Let's try something else. 0:15:30.120000 --> 0:15:36.180000 Let's see. SA San Francisco. 0:15:36.180000 --> 0:15:39.960000 So these are just cities Sanco Sanford Yah right so you get the idea you 0:15:39.960000 --> 0:15:42.280000 can also utilize rejects. 0:15:42.280000 --> 0:15:48.480000 And I've already shown you the logical operators and what we can do there. 0:15:48.480000 --> 0:15:52.120000 We can also perform mathematical operations which is going to be the final 0:15:52.120000 --> 0:15:54.300000 example here that I'll show you. 0:15:54.300000 --> 0:15:59.360000 So while still working in the city database and the city collection what 0:15:59.360000 --> 0:16:04.280000 if we wanted to find the average population to do that we would say db 0:16:04.280000 --> 0:16:13.180000 .city.aggregate. db.city.aggregate and then we'll put in here the group 0:16:13.180000 --> 0:16:14.660000 will utilize the group functionality. 0:16:14.660000 --> 0:16:20.480000 So this is used to group data documents rather within a collection and 0:16:20.480000 --> 0:16:22.520000 then we'll say in here. 0:16:22.520000 --> 0:16:26.440000 We'll say the ID. 0:16:26.440000 --> 0:16:29.660000 So ID there's the field we want here. 0:16:29.660000 --> 0:16:31.740000 Actually we need to put that in double quotes. 0:16:31.740000 --> 0:16:41.520000 So we'll say ID and then we're going to say null average so a v g and 0:16:41.520000 --> 0:16:54.020000 then in here. Let's see what I want specifically is to we'll need to utilize 0:16:54.020000 --> 0:16:55.480000 the average operator. 0:16:55.480000 --> 0:17:00.360000 So a v g and then in here we would put in the parameter or the field here. 0:17:00.360000 --> 0:17:05.120000 So just population and then we would close that one there. 0:17:05.120000 --> 0:17:12.560000 The primary one and the secondary and tertiary bracket and then in here 0:17:12.560000 --> 0:17:15.920000 we can just hit enter. 0:17:15.920000 --> 0:17:18.720000 And string literal again. 0:17:18.720000 --> 0:17:25.940000 So group. Yeah I forgot to put this here in double quotes hit enter. 0:17:25.940000 --> 0:17:32.840000 So the average in this particular case is 8462 which is interesting. 0:17:32.840000 --> 0:17:39.520000 So there we are that's how to you know perform an average on a aggregate 0:17:39.520000 --> 0:17:43.560000 of documents as it were within a particular collection. 0:17:43.560000 --> 0:17:48.100000 With that being said that is the Mongo query language in a nutshell. 0:17:48.100000 --> 0:17:50.940000 I've not covered everything but hopefully this will give you an understanding 0:17:50.940000 --> 0:17:55.380000 as to how this differs from the structured query language. 0:17:55.380000 --> 0:17:58.300000 And with that being said that's going to conclude the practical demonstration 0:17:58.300000 --> 0:18:00.500000 side of this video. 0:18:00.500000 --> 0:18:06.360000 All right so that was an introduction to no SQL databases and we got an 0:18:06.360000 --> 0:18:11.400000 understanding as to the differences between SQL databases and no SQL databases. 0:18:11.400000 --> 0:18:16.440000 Examples of no SQL databases how they work with regards to how they store 0:18:16.440000 --> 0:18:21.120000 data. And we also took a look at a very popular no SQL database called 0:18:21.120000 --> 0:18:26.520000 Mongo DB as well as how to use it or interact with it using the Mongo 0:18:26.520000 --> 0:18:29.300000 query language or MQL. 0:18:29.300000 --> 0:18:32.740000 In the next video we're going to be taking a look at a practical example 0:18:32.740000 --> 0:18:38.520000 of what no SQL injection looks like primarily on Mongo DB. 0:18:38.520000 --> 0:18:41.920000 With that being said I'll be seeing you in the next video.