WEBVTT 0:00:11.120000 --> 0:00:14.720000 No SQL or NoSQL fundamentals. 0:00:14.720000 --> 0:00:19.740000 So welcome everyone to the NoSQL injection section of this course. 0:00:19.740000 --> 0:00:23.080000 Now this is going to be the final section of this course and I will typically 0:00:23.080000 --> 0:00:25.380000 consider it to be a bonus section. 0:00:25.380000 --> 0:00:29.600000 The reason for that is obviously primarily because we will not be diving 0:00:29.600000 --> 0:00:32.220000 too deep into NoSQL injection. 0:00:32.220000 --> 0:00:35.840000 I added it here as sort of a primer because we will be exploring it later 0:00:35.840000 --> 0:00:41.700000 on even in more advanced web application penetration testing certification. 0:00:41.700000 --> 0:00:45.960000 So in the beginning of the course, when we are talking about databases, 0:00:45.960000 --> 0:00:51.740000 I briefly introduced you to NoSQL databases and showed you how they operate 0:00:51.740000 --> 0:00:57.240000 or how they work in relation to your standard SQL or relational databases. 0:00:57.240000 --> 0:01:01.080000 So in this video, what I am going to do is again reintroduce you to NoSQL 0:01:01.080000 --> 0:01:06.160000 databases and sort of cover pretty much everything that you need to know 0:01:06.160000 --> 0:01:06.900000 or the essentials. 0:01:06.900000 --> 0:01:12.660000 And then we will also be taking a look at a practical live lab where we 0:01:12.660000 --> 0:01:18.360000 will be interacting with a MongoDB server or MongoDB as it were to show 0:01:18.360000 --> 0:01:24.540000 you how it differs from a typical or how it differs to a typical relational 0:01:24.540000 --> 0:01:29.540000 database with regards not only to its structure, but also the commands 0:01:29.540000 --> 0:01:31.580000 or the queries that you can run. 0:01:31.580000 --> 0:01:36.700000 So to begin with, what is NoSQL or a NoSQL database? 0:01:36.700000 --> 0:01:42.880000 Well, NoSQL databases, also known as not only SQL databases, are a class 0:01:42.880000 --> 0:01:46.820000 of database management systems that provide a non-relational approach 0:01:46.820000 --> 0:01:49.880000 for storing and retrieving data. 0:01:49.880000 --> 0:01:54.440000 Unlike traditional relational databases, which organize data into tables 0:01:54.440000 --> 0:01:59.600000 with predefined schemas as we have been able to see, NoSQL databases offer 0:01:59.600000 --> 0:02:04.400000 more flexible data models, which are useful in certain cases, that can 0:02:04.400000 --> 0:02:08.760000 handle unstructured, semi-structured and rapidly evolving data. 0:02:08.760000 --> 0:02:13.680000 NoSQL databases emerged as a response to the need for scalability, performance 0:02:13.680000 --> 0:02:17.180000 and agility in handling modern data types and workloads. 0:02:17.180000 --> 0:02:24.060000 Now, I can't or I will not dive into the nitty gritty of the advantages 0:02:24.060000 --> 0:02:29.360000 of NoSQL databases and their deployment models or the deployment environments 0:02:29.360000 --> 0:02:32.640000 where you typically see them deployed over relational databases. 0:02:32.640000 --> 0:02:39.140000 But the primary reason why some developers or some companies utilize NoSQL 0:02:39.140000 --> 0:02:43.940000 databases is primarily based on the type of data they're storing, if it's 0:02:43.940000 --> 0:02:49.340000 typically rapidly changing and secondly for scalability, as I've just 0:02:49.340000 --> 0:02:52.600000 shown, and I'll explain the scalability aspect in a few seconds. 0:02:52.600000 --> 0:02:57.620000 So when you talk about NoSQL databases, the important thing to note is 0:02:57.620000 --> 0:02:59.540000 that there are types of NoSQL databases. 0:02:59.540000 --> 0:03:03.580000 Now, this list is by no means comprehensive. 0:03:03.580000 --> 0:03:07.420000 This is just the common ones or these are examples of the common ones 0:03:07.420000 --> 0:03:08.620000 that you will see. 0:03:08.620000 --> 0:03:15.620000 So the first and the most popular are key value stores, right? 0:03:15.620000 --> 0:03:17.000000 Key value pairs. 0:03:17.000000 --> 0:03:22.300000 The value can be any type of data such as text, JSON or binary objects. 0:03:22.300000 --> 0:03:27.820000 Examples of key value store databases include Redis, React and Amazon 0:03:27.820000 --> 0:03:31.560000 DynamoDB. Redis being the most popular of them all and we'll touch on 0:03:31.560000 --> 0:03:35.460000 Redis shortly. We then have document databases, which is what we're going 0:03:35.460000 --> 0:03:37.820000 to be taking a look at in the practical demo. 0:03:37.820000 --> 0:03:42.680000 So document databases store and retrieve data in JSON-like documents. 0:03:42.680000 --> 0:03:46.780000 And documents can vary in structure and the database provides features 0:03:46.780000 --> 0:03:50.260000 for querying and indexing based on the documents content. 0:03:50.260000 --> 0:03:54.500000 MongoDB and Couchbase serve our popular document databases. 0:03:54.500000 --> 0:03:57.140000 We then have columnar databases. 0:03:57.140000 --> 0:04:01.580000 Columnar databases organize data into columns rather than rows, making 0:04:01.580000 --> 0:04:05.140000 them efficient for analytical workloads and handling large volumes of 0:04:05.140000 --> 0:04:10.140000 data. Examples of these are Apache Cassandra and Apache HBase. 0:04:10.140000 --> 0:04:12.400000 So those are the primary types. 0:04:12.400000 --> 0:04:16.740000 Now, that brings us to the critical point here. 0:04:16.740000 --> 0:04:21.080000 And that is what is the difference between a traditional SQL relational 0:04:21.080000 --> 0:04:25.480000 database and a NoSQL database? 0:04:25.480000 --> 0:04:28.540000 Well, firstly, we have the type, right? 0:04:28.540000 --> 0:04:32.540000 So in this particular case, SQL databases are most likely going to be 0:04:32.540000 --> 0:04:38.960000 relational and NoSQL databases are not relational or non-relational. 0:04:38.960000 --> 0:04:43.180000 And I explained how relational databases work, so I'll not dive into that 0:04:43.180000 --> 0:04:48.080000 right now. As for the data storage model, you can see that in SQL databases, 0:04:48.080000 --> 0:04:52.060000 you typically have tables with fixed rows and columns. 0:04:52.060000 --> 0:04:57.520000 In NoSQL databases, the data storage model is typically unstructured. 0:04:57.520000 --> 0:05:02.960000 The storage in JSON files and there's typically use of key value pairs. 0:05:02.960000 --> 0:05:07.400000 So tables and rows with dynamic columns, as you'll see once we get started. 0:05:07.400000 --> 0:05:12.060000 As for the database schema with SQL databases, you typically see that 0:05:12.060000 --> 0:05:18.340000 the static or rigid schema, which means very less likely to change or 0:05:18.340000 --> 0:05:22.820000 are pretty much static because of the relational nature or the relational 0:05:22.820000 --> 0:05:25.080000 nature of the actual data. 0:05:25.080000 --> 0:05:29.520000 With NoSQL databases, the schema is dynamic or flexible for obvious reasons 0:05:29.520000 --> 0:05:32.800000 because data is rapidly changing. 0:05:32.800000 --> 0:05:36.540000 The structure of rows in this particular columns is dynamic. 0:05:36.540000 --> 0:05:40.980000 So as a result, the schema needs to be dynamic or flexible. 0:05:40.980000 --> 0:05:44.160000 Now this is the key point here and that's scalability. 0:05:44.160000 --> 0:05:48.280000 So with SQL databases, it's vertical scalability where you need to increase 0:05:48.280000 --> 0:05:54.120000 resources, physical or hardware resources, in order to scale performance. 0:05:54.120000 --> 0:05:57.900000 With NoSQL databases, it's horizontal scaling. 0:05:57.900000 --> 0:06:02.540000 As for the language, SQL databases is fairly simple. 0:06:02.540000 --> 0:06:05.980000 It utilizes the structured query language as we've been able to see. 0:06:05.980000 --> 0:06:10.600000 With the NoSQL database, it's the unstructured query language. 0:06:10.600000 --> 0:06:15.020000 Typically, the NoSQL databases that I've just highlighted the examples 0:06:15.020000 --> 0:06:19.160000 of, which we'll take a look at shortly, usually have their own query language 0:06:19.160000 --> 0:06:25.400000 in some cases and also have some the capability for Boolean or logical 0:06:25.400000 --> 0:06:27.800000 operations which will also highlight. 0:06:27.800000 --> 0:06:32.120000 And that's where the whole NoSQL injection comes into play. 0:06:32.120000 --> 0:06:35.620000 We've talked about the schema, but I wanted to dive deeper a little bit 0:06:35.620000 --> 0:06:39.820000 into that or a little more into that where to sort of highlight what I 0:06:39.820000 --> 0:06:40.740000 was talking about. 0:06:40.740000 --> 0:06:45.900000 But with SQL databases, the schema is bound to the relationship or relationships 0:06:45.900000 --> 0:06:51.100000 and NoSQL databases typically have a non-rigid schema. 0:06:51.100000 --> 0:06:55.780000 As for the query complexity, this is where you factor in the query language. 0:06:55.780000 --> 0:06:59.240000 So in the case of the structured query language, as you're able to tell, 0:06:59.240000 --> 0:07:04.360000 it supports really complex queries that can perform multiple things at 0:07:04.360000 --> 0:07:09.320000 one go, where you can combine different queries into one and combine their 0:07:09.320000 --> 0:07:15.100000 results. Let's say a select statement using the union operator, so on 0:07:15.100000 --> 0:07:18.260000 and so forth. So it supports really complex queries. 0:07:18.260000 --> 0:07:23.720000 The disadvantage with NoSQL databases is while databases or NoSQL databases 0:07:23.720000 --> 0:07:28.040000 like MongoDB have their own query language, they don't really support 0:07:28.040000 --> 0:07:33.640000 advanced or complex queries in the same way that SQL database would, given 0:07:33.640000 --> 0:07:35.760000 the fact that is relational. 0:07:35.760000 --> 0:07:40.260000 So they support some really cool queries and some really advanced operations, 0:07:40.260000 --> 0:07:45.600000 but not to the level that you would see with standard SQL database. 0:07:45.600000 --> 0:07:49.540000 So those are the key differences between the two types here. 0:07:49.540000 --> 0:07:53.280000 And again, I've not gone over all of them with regards to the technical 0:07:53.280000 --> 0:07:56.860000 stuff, because I don't think that that's really important at this phase. 0:07:56.860000 --> 0:07:59.220000 But this is what I'm referring to typically. 0:07:59.220000 --> 0:08:03.520000 So if we take a look at on the left here, we have a standard SQL relational 0:08:03.520000 --> 0:08:10.480000 database, where you have columns and rows and a column refers to the attribute. 0:08:10.480000 --> 0:08:13.520000 And the rows are referred to records, right? 0:08:13.520000 --> 0:08:20.540000 So particular entries with a NoSQL database like MongoDB, where you have 0:08:20.540000 --> 0:08:25.320000 documents or the use of documents or records as it were, you can see that 0:08:25.320000 --> 0:08:29.480000 data is stored in documents and its key value. 0:08:29.480000 --> 0:08:33.700000 So prop one and then the data and the data could consist of multiple fields, 0:08:33.700000 --> 0:08:35.360000 but we'll get to that. 0:08:35.360000 --> 0:08:37.340000 And that's how it's stored. 0:08:37.340000 --> 0:08:40.000000 And you may be asking yourself, why would you want to do that? 0:08:40.000000 --> 0:08:43.560000 As I said, I'm not really going to dive deep into why you would want to 0:08:43.560000 --> 0:08:47.240000 use one over the other, because again, that would go over the scope of 0:08:47.240000 --> 0:08:51.140000 this course. But this is essentially the difference between them. 0:08:51.140000 --> 0:08:54.980000 So it's very important that you keep this in mind. 0:08:54.980000 --> 0:08:59.180000 Now, of course, we've talked a lot about this, but what are the most popular 0:08:59.180000 --> 0:09:03.860000 NoSQL databases that you run into when performing a web app pen test? 0:09:03.860000 --> 0:09:06.780000 And of course, this is by no means comprehensive. 0:09:06.780000 --> 0:09:10.520000 Just from my experience, you'll typically find MongoDB being used quite 0:09:10.520000 --> 0:09:14.580000 a lot. So MongoDB, as I've stated before, is a document database that 0:09:14.580000 --> 0:09:17.840000 stores data in flexible JSON-like documents. 0:09:17.840000 --> 0:09:22.200000 And it provides high scalability, automatic sharding, and a powerful query 0:09:22.200000 --> 0:09:27.620000 language called the Mongo Query Language, or MQL, which I'll actually 0:09:27.620000 --> 0:09:30.960000 be helping you with or helping you understand shortly. 0:09:30.960000 --> 0:09:32.180000 We then have Cassandra. 0:09:32.180000 --> 0:09:35.620000 Apache Cassandra is a distributed columnar database designed to handle 0:09:35.620000 --> 0:09:39.760000 large amounts of data across multiple commodity servers. 0:09:39.760000 --> 0:09:44.260000 It offers high availability, fault tolerance, and a linear scalability. 0:09:44.260000 --> 0:09:47.320000 We then have Redis, which again is very popular. 0:09:47.320000 --> 0:09:51.100000 Redis is an in-memory key value store that can be used as a database cache 0:09:51.100000 --> 0:09:52.580000 or message broker. 0:09:52.580000 --> 0:09:56.780000 It's typically used as a cache or message broker, and it supports a wide 0:09:56.780000 --> 0:10:02.100000 range of data structures and provides high performance and low latency. 0:10:02.100000 --> 0:10:06.280000 Now, this brings us to the NoSQL database query language, or what language 0:10:06.280000 --> 0:10:09.380000 NoSQL database is utilized now. 0:10:09.380000 --> 0:10:13.540000 NoSQL databases typically have their own query languages or interfaces 0:10:13.540000 --> 0:10:16.360000 for data retrieval and manipulation. 0:10:16.360000 --> 0:10:20.600000 And here are some examples of query languages used in popular NoSQL databases. 0:10:20.600000 --> 0:10:24.600000 So as I've said, in the case of MongoDB, it utilizes a query language 0:10:24.600000 --> 0:10:28.300000 called the MongoDB query language, also known as MQL. 0:10:28.300000 --> 0:10:31.820000 And it provides a rich set of operators and functions for querying and 0:10:31.820000 --> 0:10:35.960000 manipulating documents in the database, not as extensive as the structured 0:10:35.960000 --> 0:10:38.400000 query language, but still pretty powerful. 0:10:38.400000 --> 0:10:40.120000 We then have Redis. 0:10:40.120000 --> 0:10:44.040000 Redis is primarily an in-memory data structure store and does not have 0:10:44.040000 --> 0:10:45.820000 a traditional query language. 0:10:45.820000 --> 0:10:49.260000 It provides a set of commands that operate on different data structures 0:10:49.260000 --> 0:10:52.800000 like strings, lists, sets, and hashes. 0:10:52.800000 --> 0:10:55.780000 Redis commands are typically used to perform operations such as reading 0:10:55.780000 --> 0:10:59.880000 and writing data, data manipulation, and data exploration. 0:10:59.880000 --> 0:11:03.240000 So essentially, modification of the data store. 0:11:03.240000 --> 0:11:08.060000 And that brings us to the practical section of this video. 0:11:08.060000 --> 0:11:11.400000 Where we'll be interacting with a MongoDB server. 0:11:11.400000 --> 0:11:14.560000 And I'll be showing you some a little bit of the Mongo query language 0:11:14.560000 --> 0:11:16.920000 and how you can sort of navigate around the database. 0:11:16.920000 --> 0:11:21.860000 So you have an understanding, at least at a fundamental level of how NoSQL 0:11:21.860000 --> 0:11:25.100000 databases work, primarily how they store data. 0:11:25.100000 --> 0:11:29.020000 So I'm not going to highlight the syntax and the slides because as I said, 0:11:29.020000 --> 0:11:30.940000 this is not really important at this phase. 0:11:30.940000 --> 0:11:33.940000 I just want to introduce you to NoSQL injection. 0:11:33.940000 --> 0:11:38.700000 And we start off by understanding how these databases work with regards 0:11:38.700000 --> 0:11:40.400000 to the actual database itself. 0:11:40.400000 --> 0:11:44.420000 So this lab will provide you with a terminal session where we'll be able 0:11:44.420000 --> 0:11:47.660000 to immediately interact with the MongoDB server. 0:11:47.660000 --> 0:11:50.520000 So you don't need to use your own Kali Linux system. 0:11:50.520000 --> 0:11:52.660000 We don't need to use any other tools. 0:11:52.660000 --> 0:11:54.080000 We'll just get started. 0:11:54.080000 --> 0:11:57.060000 And again, you can go through the video first. 0:11:57.060000 --> 0:11:58.920000 I would highly recommend that you do that. 0:11:58.920000 --> 0:12:02.020000 However, if you're familiar with the Mongo query language, you can go 0:12:02.020000 --> 0:12:05.220000 through the lab and then go through the video or the practical section 0:12:05.220000 --> 0:12:08.480000 of this video. With that being said, let me start up the lab and switch 0:12:08.480000 --> 0:12:12.340000 over and we can get started. 0:12:12.340000 --> 0:12:17.040000 All right. So I am back within the lab environment. 0:12:17.040000 --> 0:12:20.800000 And this is the lab environment you'll be provided with in a browser tab. 0:12:20.800000 --> 0:12:26.160000 It'll provide you with a web SSH session on a as the user student. 0:12:26.160000 --> 0:12:31.440000 So this particular Linux server already has a MongoDB or a MongoDB server 0:12:31.440000 --> 0:12:36.940000 running. And in order to interact with the MongoDB server, we simply need 0:12:36.940000 --> 0:12:42.060000 to type in Mongo and it enter and it's going to open up the MongoDB shell. 0:12:42.060000 --> 0:12:44.060000 And from this point, we can get started. 0:12:44.060000 --> 0:12:47.740000 Now the most important command here is going to be the help command. 0:12:47.740000 --> 0:12:52.000000 If you ever lost with regards to commands or queries, just open it up. 0:12:52.000000 --> 0:12:56.120000 And you can see that you can also utilize the db. 0:12:56.120000 --> 0:13:00.200000 .help command right over here to get help on db methods. 0:13:00.200000 --> 0:13:04.900000 You can also utilize the db.mycollection .help command here, which will 0:13:04.900000 --> 0:13:06.900000 give you help on collection methods. 0:13:06.900000 --> 0:13:11.700000 Now I'll explain what collections are because I think this is quite important. 0:13:11.700000 --> 0:13:16.420000 You can list out the databases collections, users, the profile, the logs, 0:13:16.420000 --> 0:13:18.840000 etc. So very, very simple to use. 0:13:18.840000 --> 0:13:22.060000 Not simple in a bad way, but just really functional. 0:13:22.060000 --> 0:13:24.520000 Now we need to understand a few concepts here. 0:13:24.520000 --> 0:13:28.200000 When we refer to a database, this is very similar to a SQL database. 0:13:28.200000 --> 0:13:30.580000 We're referring to an actual database. 0:13:30.580000 --> 0:13:35.500000 So if we say, for example, here, show DBS, right? 0:13:35.500000 --> 0:13:40.460000 And in this particular case, it's typically recommended that you pretty 0:13:40.460000 --> 0:13:45.120000 much end your queries with the actual semicolon, as you would do with 0:13:45.120000 --> 0:13:47.180000 the structured query language. 0:13:47.180000 --> 0:13:50.860000 But you can just hit enter and that's going to show you your databases. 0:13:50.860000 --> 0:13:54.480000 So we have various databases here that look like they're storing different 0:13:54.480000 --> 0:13:58.660000 types of data. So we have one called admin, city flag, local stats and 0:13:58.660000 --> 0:14:03.320000 users. Now it's after this point when you go into each database that things 0:14:03.320000 --> 0:14:07.420000 change from a standard relational database or a SQL database. 0:14:07.420000 --> 0:14:13.500000 So if I was to navigate into a database, let's say in MySQL, what I would 0:14:13.500000 --> 0:14:15.860000 be greeted with at that point is tables. 0:14:15.860000 --> 0:14:20.100000 All right. Now what you'll be greeted with in the case of MongoDB is something 0:14:20.100000 --> 0:14:21.040000 called a collection. 0:14:21.040000 --> 0:14:25.660000 All right. Now a collection is a grouping of documents inside the database. 0:14:25.660000 --> 0:14:30.160000 It's pretty much the same thing or a similar to a table in SQL. 0:14:30.160000 --> 0:14:35.540000 And it stores different types of data based on its data type. 0:14:35.540000 --> 0:14:40.040000 You know, essentially what you do with a relational database. 0:14:40.040000 --> 0:14:43.920000 So we can go ahead and list out the collections, but I'll show you how 0:14:43.920000 --> 0:14:46.020000 we would typically go through this. 0:14:46.020000 --> 0:14:50.980000 The first thing you need to know how to do is obviously to to actually 0:14:50.980000 --> 0:14:52.720000 select a particular database. 0:14:52.720000 --> 0:14:57.840000 And then we can say, for example, in this case, let's try and use the 0:14:57.840000 --> 0:14:59.820000 users database or interact with it. 0:14:59.820000 --> 0:15:05.180000 In order to do that, we say use and we then say users or the actual name 0:15:05.180000 --> 0:15:05.800000 of the database. 0:15:05.800000 --> 0:15:08.640000 So there is going to say switch to DB users. 0:15:08.640000 --> 0:15:10.920000 And then we can say show collections. 0:15:10.920000 --> 0:15:12.900000 And that's the equivalent of show tables. 0:15:12.900000 --> 0:15:14.100000 And there we are. 0:15:14.100000 --> 0:15:17.580000 So we can see that in this particular database called users, we have the 0:15:17.580000 --> 0:15:19.040000 following collections. 0:15:19.040000 --> 0:15:20.940000 We have banned current and passed. 0:15:20.940000 --> 0:15:22.580000 All right. So this makes sense. 0:15:22.580000 --> 0:15:27.860000 This particular database looks like it's storing user information. 0:15:27.860000 --> 0:15:32.440000 And within the database, we have collections or tables that will probably 0:15:32.440000 --> 0:15:35.720000 have banned users, current users and past users. 0:15:35.720000 --> 0:15:40.800000 Right. So let's go through some practical examples. 0:15:40.800000 --> 0:15:43.920000 And I'm going to, you know, of course, this particular lab will have a 0:15:43.920000 --> 0:15:46.080000 set of questions and I'm going to help you answer them. 0:15:46.080000 --> 0:15:51.040000 So the first question is, you know, how we can find information, you know, 0:15:51.040000 --> 0:15:54.640000 so what I'm going to do is I'll say show DBS. 0:15:54.640000 --> 0:15:57.860000 And you can see we have a database called flag here. 0:15:57.860000 --> 0:15:59.660000 So I'm going to say use flag. 0:15:59.660000 --> 0:16:06.360000 And we can say show collections in this particular database. 0:16:06.360000 --> 0:16:11.180000 And now what we can do is if we wanted to find, you know, the value of 0:16:11.180000 --> 0:16:16.380000 a flag from within that particular, from this particular collection, as 0:16:16.380000 --> 0:16:19.840000 it were, or from this particular database. 0:16:19.840000 --> 0:16:25.280000 And collection, what we could do is say, you know, db.flag, sorry, db 0:16:25.280000 --> 0:16:28.340000 .flag. And then we say find, all right. 0:16:28.340000 --> 0:16:32.740000 So put in a two or put in your brackets here. 0:16:32.740000 --> 0:16:38.880000 We hit enter. And what is going to do is it's going to find the value 0:16:38.880000 --> 0:16:42.900000 of the flag that, you know, obtained from the MongoDB cluster. 0:16:42.900000 --> 0:16:47.380000 All right. Now we can switch into that particular collection. 0:16:47.380000 --> 0:16:52.820000 And, you know, for example, in this case, we're just using the query language, 0:16:52.820000 --> 0:16:55.060000 the Mongo query language here to find the flag. 0:16:55.060000 --> 0:16:59.420000 But if we say, you know, show collections once more, show collections. 0:16:59.420000 --> 0:17:04.440000 And we say, in this case, let's see, use flag. 0:17:04.440000 --> 0:17:09.640000 Or we can just, you know, in this case, you know, in order to view it, 0:17:09.640000 --> 0:17:13.120000 we can just say db.flag and then display the actual flag. 0:17:13.120000 --> 0:17:15.340000 So we say db.flag. 0:17:15.340000 --> 0:17:19.460000 In this particular case, you can use the auto completion here to display 0:17:19.460000 --> 0:17:24.520000 other commands. So for example, we can find which you've already done. 0:17:24.520000 --> 0:17:29.380000 You can also aggregate count, et cetera. 0:17:29.380000 --> 0:17:30.900000 Interesting commands we can run. 0:17:30.900000 --> 0:17:41.020000 So if we go back to, we say, show dbs, if we go to the user's database, 0:17:41.020000 --> 0:17:45.840000 and we, let's try and find a particular user, right? 0:17:45.840000 --> 0:17:52.800000 In this particular case, the question is asking us to find a, to find 0:17:52.800000 --> 0:17:54.320000 a particular user. 0:17:54.320000 --> 0:17:55.800000 So for example, how would we do that? 0:17:55.800000 --> 0:18:00.780000 So, you know, if we say show collections, we don't need to go into any 0:18:00.780000 --> 0:18:04.640000 of them, but we could say db.current.find. 0:18:04.640000 --> 0:18:09.000000 And then in brackets, we can open up brackets and then use curly braces 0:18:09.000000 --> 0:18:11.780000 and say the user field. 0:18:11.780000 --> 0:18:16.120000 What we want to do is find a particular user that matches, let's say, 0:18:16.120000 --> 0:18:21.840000 Alexis, right? And we can enclose the curly braces here and hit enter. 0:18:21.840000 --> 0:18:25.240000 Now it doesn't find a user called Alexis, but let's try another user. 0:18:25.240000 --> 0:18:29.780000 And in this case, I can say, for example, find the user header, no match, 0:18:29.780000 --> 0:18:31.300000 because this is case sensitive. 0:18:31.300000 --> 0:18:34.040000 So let's try that with an upper case H. 0:18:34.040000 --> 0:18:39.220000 There we are. So we're able to find a particular, we're able to find a 0:18:39.220000 --> 0:18:40.060000 particular user. 0:18:40.060000 --> 0:18:45.340000 Now let me walk you through how you can view, how you can view data stored 0:18:45.340000 --> 0:18:47.060000 within collections. 0:18:47.060000 --> 0:18:52.300000 So if you wanted to list, you know, the actual documents within a particular 0:18:52.300000 --> 0:18:55.820000 collection, what you can do is again, let's start off from the beginning 0:18:55.820000 --> 0:19:00.540000 show databases and let's say use the database called users. 0:19:00.540000 --> 0:19:02.600000 And we say show collections here. 0:19:02.600000 --> 0:19:04.540000 So I'm going to say show collections. 0:19:04.540000 --> 0:19:06.720000 And we have, let's say, current ones, right? 0:19:06.720000 --> 0:19:11.240000 So let's say db.users.find. 0:19:11.240000 --> 0:19:16.960000 And then, sorry, let me type that incorrectly here, db.users.find. 0:19:16.960000 --> 0:19:24.120000 In this particular case, there we are. 0:19:24.120000 --> 0:19:28.720000 So this is the data stored within that particular collection, right? 0:19:28.720000 --> 0:19:33.860000 Or the data stored within that particular, the documents, rather as it 0:19:33.860000 --> 0:19:35.980000 was stored within that particular collection. 0:19:35.980000 --> 0:19:39.940000 So in here, we can see now the individual data, which is key value. 0:19:39.940000 --> 0:19:42.100000 And there's multiple fields here. 0:19:42.100000 --> 0:19:46.160000 So, you know, if I was to zoom out and display this, so it's displayed 0:19:46.160000 --> 0:19:50.420000 or rendered correctly, I can hit enter, you can see that we have the ID 0:19:50.420000 --> 0:19:56.160000 object ID, and then the user, the actual name here, the join date, the 0:19:56.160000 --> 0:19:58.960000 email, etc. So very similar to a relational database. 0:19:58.960000 --> 0:20:02.680000 So I just want to go through that again, because it can be quite difficult 0:20:02.680000 --> 0:20:04.280000 to understand the abstraction. 0:20:04.280000 --> 0:20:07.300000 So we have databases, right? 0:20:07.300000 --> 0:20:11.540000 Databases are just the same as they are in a relational database. 0:20:11.540000 --> 0:20:14.500000 They store tables, or in this case, collections, all right? 0:20:14.500000 --> 0:20:20.660000 So if we say, use a particular database like users, we can now say show 0:20:20.660000 --> 0:20:23.860000 collections, the equivalent of saying show tables. 0:20:23.860000 --> 0:20:29.380000 So show essentially in no SQL databases, collections are just tables, 0:20:29.380000 --> 0:20:34.620000 right? And collections are a collection of documents, right? 0:20:34.620000 --> 0:20:38.220000 And documents store, you know, specific data, right? 0:20:38.220000 --> 0:20:42.660000 So for example, in this particular case, you know, if we wanted to find 0:20:42.660000 --> 0:20:45.700000 out, for example, there's another, you know, command we can run. 0:20:45.700000 --> 0:20:49.100000 So we can say, DB dot past or band users. 0:20:49.100000 --> 0:20:55.740000 So we can say DB dot band dot find, and within say dot count to show, 0:20:55.740000 --> 0:21:00.800000 you know, the total number of band users or entries within that particular, 0:21:00.800000 --> 0:21:04.880000 within that particular collection, it'll show us the amount of documents. 0:21:04.880000 --> 0:21:06.300000 So we'll let enter. 0:21:06.300000 --> 0:21:07.900000 So they're 91 documents. 0:21:07.900000 --> 0:21:11.520000 So a document in this case is what you typically consider a record. 0:21:11.520000 --> 0:21:15.660000 So if we go back to the previous command here, where we listed out the, 0:21:15.660000 --> 0:21:22.800000 the current, the actual content of the current collection, you can see 0:21:22.800000 --> 0:21:27.280000 that these here are all entries, but they're all individual documents. 0:21:27.280000 --> 0:21:37.360000 All right, so for every user here, these are all storing data is preferred. 0:21:37.360000 --> 0:21:42.860000 Now, as I said, there's multiple types of queries we can perform. 0:21:42.860000 --> 0:21:47.680000 And what we can do is let's say show collections, we can say, in this 0:21:47.680000 --> 0:21:54.040000 particular case, DB dot, DB dot band dot find, let's see what users have 0:21:54.040000 --> 0:22:00.280000 been banned, band dot find. 0:22:00.280000 --> 0:22:04.740000 You can see these are all the band users here, and these are all documents. 0:22:04.740000 --> 0:22:08.120000 So we can try and perform some additional queries. 0:22:08.120000 --> 0:22:11.720000 You know, if we were interested in finding some particular information, 0:22:11.720000 --> 0:22:13.740000 which I just showed you how to do. 0:22:13.740000 --> 0:22:20.680000 So for example, if I said DB dot band dot find, I could in here put in 0:22:20.680000 --> 0:22:23.360000 my query or the criteria, right? 0:22:23.360000 --> 0:22:29.040000 So I would say for the field, for the field user, can you find a user 0:22:29.040000 --> 0:22:30.580000 with the following value? 0:22:30.580000 --> 0:22:32.440000 So we'll say user. 0:22:32.440000 --> 0:22:37.000000 And this is where we got that criteria early on, we can say find a user 0:22:37.000000 --> 0:22:39.520000 called Mona, which I know exists. 0:22:39.520000 --> 0:22:41.280000 And we just hit enter. 0:22:41.280000 --> 0:22:41.800000 And there we are. 0:22:41.800000 --> 0:22:44.700000 So it's going to give us that particular document, right? 0:22:44.700000 --> 0:22:49.860000 And again, you can do this for any of the, for any of the fields here. 0:22:49.860000 --> 0:22:56.200000 So for example, we could again, search for emails or phone numbers, so 0:22:56.200000 --> 0:22:57.500000 on and so forth. 0:22:57.500000 --> 0:23:00.980000 And based on this, you can see how this is, how this is useful, because 0:23:00.980000 --> 0:23:04.920000 this simple query can, you know, find data and then it refers to the actual 0:23:04.920000 --> 0:23:08.280000 document storing the data and also lists out the data within the document, 0:23:08.280000 --> 0:23:10.080000 right? So pretty cool. 0:23:10.080000 --> 0:23:15.440000 So again, we can change that to something like, for example, we said, 0:23:15.440000 --> 0:23:19.340000 hmm, let's see phone. 0:23:19.340000 --> 0:23:22.080000 Or let's try, yeah, let's use phone. 0:23:22.080000 --> 0:23:25.040000 And you know, in this case, we put in our own phone number. 0:23:25.040000 --> 0:23:28.920000 So let's say someone was trying to log in, a better option might be their 0:23:28.920000 --> 0:23:33.500000 email. The query would be something that checks for this particular value 0:23:33.500000 --> 0:23:39.360000 within the current user collection, not the band one, but there we are. 0:23:39.360000 --> 0:23:44.960000 So you know, we can say, Alexis at i Knee.com. 0:23:44.960000 --> 0:23:47.520000 And let me type that in correctly here. 0:23:47.520000 --> 0:23:51.460000 And I'll just zoom out, because this is too zoomed in, hit enter, so it 0:23:51.460000 --> 0:23:53.080000 doesn't find anything. 0:23:53.080000 --> 0:23:57.180000 Now, I've already shown you how to, you know, identify the, so if I say 0:23:57.180000 --> 0:24:03.920000 DB dot band, DB dot band dot count, that's to tell the total number of 0:24:03.920000 --> 0:24:07.540000 records or in the case of a new SQL database, the total number of documents 0:24:07.540000 --> 0:24:15.020000 within a particular collection, 91, if we say DB dot current, we can see 0:24:15.020000 --> 0:24:18.940000 the current users or the documents within that collection. 0:24:18.940000 --> 0:24:20.300000 So very, very simple to understand.