#4 - Mastering the SELECT Statement: Your Gateway to SQL Power

0:00 Speaker 1 Welcome to the deep dive where we plunge into complex topics and emerge with well, hopefully crisp, understandable insights. 0:08 Speaker 2 That's the goal. 0:09 Speaker 1 In today's deep dive, we're peeling back the layers on something truly foundational to our Digital World sequel, and we're going to 0 in on its absolute heart, the the select statement. 0:22 Speaker 2 Yeah, you really can't talk about getting information out of databases without talking about SELECT. 0:27 Speaker 1 Exactly. Think about it. All the digital interactions you have every single day. You know, ordering a coffee online, checking your bank balance, managing a project at work, even just scrolling through your favorite app. 0:37 Speaker 2 It's everywhere. 0:38 Speaker 1 Behind almost all of it lies a database, right? And Sequel is how we ask that database, well, intelligent questions. 0:46 Speaker 2 It's the language they speak. 0:47 Speaker 1 So today we're not just going to learn how to maybe construct a simple query to pull information, we want to explore the why behind it too. 0:54 Speaker 2 Understanding the why is crucial, I think. Yeah. 0:57 Speaker 1 You'll discover the fascinating distinction between just raw data and, you know, truly meaningful information. And we'll uncover some clever ways to refine your results, like getting rid of those pesky duplicates or sorting your findings just right. 1:11 Speaker 2 Making the data actually useful. 1:13 Speaker 1 Get ready to harness the power of precise communication to unlock the stories hidden within, well, vast oceans of data. Let's jump in. 1:22 Speaker 2 Let's do it. It's absolutely true. The select statement isn't just another command. It really is the cornerstone of SQL Structured query language. 1:30 Speaker 1 The cornerstone. I like that. 1:32 Speaker 2 It is. It's the primary way we retrieve any information from these organized collections of data we call databases. Think of SQL as the universal translator for almost every digital system you interact with. 1:44 Speaker 1 Like a digital Rosetta Stone. 1:45 Speaker 2 Kind of, yeah. Whether you're making an online purchase, checking inventory for your business, looking up a client's history, chances are incredibly high that SQL is working diligently behind the scenes making sense of all that information. 1:58 Speaker 1 So if SQL is this this backbone, how do these massive systems and they can hold millions, even billions of pieces of data, how do they actually talk to that data right? How do they instantly know what specific bit to fetch when you click a button or type something in a search bar? 2:17 Speaker 2 That's precisely where SQL becomes, well, indispensable. While SQL has a whole range of functions, you know, creating tables, changing records, deleting old stuff. 2:26 Speaker 1 The whole life cycle. 2:27 Speaker 2 Exactly. But our focus for this deep dive and really much of the journey into SQL is on what we call data manipulation. And within that broad area, the SELECT statement just stands out. It's the ultimate tool for getting information out. Retrieval, retrieval. Exactly. 2:42 It empowers you to ask nearly any question you can think of. Who are our top customers? What products sold best last quarter? Where are my shipments, right? 2:50 Speaker 1 Now when did that thing happen? 2:51 Speaker 2 When did that specific event occur? Yeah. Or even more complex things like what if we bumped prices by 10%? Or how many unique visitors hit the website yesterday? 3:00 Speaker 1 Wow, OK. 3:01 Speaker 2 As long as the data actually exists and it's, you know, structured properly in your database, select is the key. It gets you the precise answers you need to make genuinely informed, data-driven decisions. 3:14 Speaker 1 So it's not just pulling numbers. 3:15 Speaker 2 No, not at all. It's about transforming those raw facts into actionable intelligence. 3:21 Speaker 1 I really like that analogy you use select being the heart of sequel. It really hammers home how central it is. So let's picture this database again as like a colossal digital filing cabinet. And we're not talking a few drawers here. Millions, maybe billions of documents, all meticulously categorized, stored in different folders, different sections. 3:42 Speaker 2 A massive library, essentially. And in this vast digital archive, the select statement isn't you just, you know, blindly rummaging through drawers hoping to find what you need. 3:52 Speaker 1 Right, that would take forever. 3:53 Speaker 2 It would be impossible. Instead, select acts as your highly specialized, incredibly efficient lightning fastest. 4:00 Speaker 1 System your data Butler. 4:01 Speaker 2 Huh. Yeah, your data Butler. You give this assistant a very precise instruction, a clear question, and in like milliseconds, it sifts through those millions of documents, isolates the specific facts you asked for, and then presents them to you in a perfectly clear, organized, digestible way. 4:21 It doesn't matter how specific or how broad or how complex your request is. This digital assistant powered by Select is designed to handle it with precision and speed. 4:31 Speaker 1 That sounds almost like magic, that level of control. 4:33 Speaker 2 Feel like it sometimes. 4:35 Speaker 1 And to really appreciate that power, we need to understand how this select operation is actually put together right. What are its like fundamental building block? 4:43 Speaker 2 Precisely, and to help simplify what can sometimes seem like a really complex topic, the overall select operation in Sequel can be broken down. We can think of it in three sort of interconnected components. 4:53 Speaker 1 OK, three parts. 4:54 Speaker 2 Yeah, there's the select statement, the select expression, and the select query. 4:58 Speaker 1 Statement expression query Got it. 5:00 Speaker 2 Each of these components has its own distinct keywords and clauses, offering immense flexibility to craft the exact question you need to ask the database. 5:09 Speaker 1 O what's the difference like select expression versus select statement? 5:13 Speaker 2 Good question. The SELECT expression basically refers to what goes inside the select clause itself. It could be a simple column name like product name or it could be a calculation, maybe retail price 1.05 to show a price with tax. 5:30 Speaker 1 So you can do math right in. 5:31 Speaker 2 There just absolutely or even manipulate text. These components, the statement expression query, they can be combined in all sorts of ways to answer increasingly sophisticated questions. It really sets the stage for, you know, deeper discussions later on beyond just simple data retrieval. 5:47 OK, that makes sense. Now just a quick note on terminology you might bump into if you're looking at other resources or older books. Oh yeah, sometimes you'll hear terms like relation instead of table. 5:57 Speaker 1 Relation. 5:57 Speaker 2 OK or tuple instead of row. 5:59 Speaker 1 Tuple sounds fancy. 6:01 Speaker 2 It does. Or attribute instead of column. These come from the more theoretical mathematical side of database theory, but to keep things clear and consistent with the official SQL standard and how frankly almost everyone in the industry actually talks about it. 6:16 Speaker 1 Right, the common language. 6:17 Speaker 2 We'll stick to the standard terms table, row, and column. So when we say table picture a spreadsheet, a row is just one entry in that spreadsheet, one record, and a column is a specific type of data like names or prices or dates. 6:35 Speaker 1 Table, row, column got it much clearer. 6:38 Speaker 2 Good, that consistent language will make this whole exploration much easier. 6:42 Speaker 1 That distinction and clarification helps a lot. So with that groundwork late, let's really zoom in on the the foundational piece, the select statement itself. 6:51 Speaker 2 Right. The select statement is truly the most fundamental building block. It's the start of every question you ask a database. When you actually construct one and then execute it or run it, you're. 7:00 Speaker 1 Querying. 7:00 Speaker 2 You're yes, you're querying the database. It sounds simple, maybe, yeah, But understanding that core action is critical to everything else we'll discuss. 7:07 Speaker 1 So querying is basically just the professional jargon for running a SELECT statement. 7:13 Speaker 2 You've got it. That's exactly right, and the way you run these statements can vary quite a bit. 7:19 Speaker 1 How so? 7:20 Speaker 2 Well, you might type them directly into like a command line interface, a black screen with text. 7:26 Speaker 1 OK, old school. 7:27 Speaker 2 Or you might use a visual tool, something with buttons and drag and drop features, often called query by example or QBE. More user friendly often, yeah. Or these statements might even be embedded deep inside a larger piece of software code, running automatically when you, say, load a web page or generate a report. 7:44 Speaker 1 That they can be hidden away very. 7:46 Speaker 2 Much so. But what's really fascinating and powerful is that regardless of how you define and run it, command line, visual tool, embedded code, the fundamental syntax. 7:55 Speaker 1 The grammar. 7:56 Speaker 2 The grammar, yes, The way you actually write a SELECT statement stays remarkably consistent across different database systems. Oracle, SQL Server, MySQL, PostgreSQL, they all understand this core language. It's. 8:10 Speaker 1 Like a universal language for data? That's amazing. 8:12 Speaker 2 It really is. It lets databases all over the world understand your requests. 8:17 Speaker 1 I often think of the select statement like crafting a really precise sentence. You're translating a need a question you have into a language the database understands perfectly without any ambiguity. 8:29 Speaker 2 That's a great way to put it. Precision is key. 8:31 Speaker 1 Ensuring you get exactly what you asked for, not something else. 8:35 Speaker 2 And here's a powerful bonus, especially for anyone who works with data regularly. Once you craft these precise statements, yeah, many database programs let you save them. Oh, nice. You can save them as a query, or sometimes it's called a view, or maybe a function, or even a stored procedure. 8:52 The name varies a bit depending on the system. 8:54 Speaker 1 But the ID is the same. 8:55 Speaker 2 The core idea is reusability. Instead of typing it out every single time, you need that same piece of information. 9:01 Speaker 1 You just run the saved thing. 9:03 Speaker 2 Exactly, you just run the saved statement. It's a massive time saver, especially for reports you need every week or every month. 9:09 Speaker 1 That's a huge efficiency gain right there. So a SELECT statement isn't just one big chunk of code Then? You mentioned it's built from distinct arts. You called them clauses. That's right. What are those essential components? 9:20 Speaker 2 A SELECT statement is composed of several clauses. Think of them as distinct keywords, each serving a specific purpose, used in various combinations to retrieve information. Some of these clauses are absolutely required. You have to have them for any SELECT statement to work. 9:37 Others are optional. You use them to refine your request, add filtering, sorting, that kind of thing. 9:43 Speaker 1 Got it required and optional parts. 9:45 Speaker 2 Exactly. If you were to visualize like a basic structure for a select statement, you'd always see the select keyword first. 9:53 Speaker 1 Always starts with. 9:54 Speaker 2 Always then comes the from keyword. 9:56 Speaker 1 Select then from. 9:57 Speaker 2 Right. And after those, you can have a series of optional clauses that add more power, which we'll touch on. 10:03 Speaker 1 OK, so let's focus on the required ones. First select and from. 10:06 Speaker 2 Perfect. Let's start with the SELECT clause. This is the number one absolutely essential primary clause. 10:12 Speaker 1 The most important one. 10:14 Speaker 2 You could argue that it's where you specify which columns or fields you actually want to see in your results. 10:20 Speaker 1 The what? 10:21 Speaker 2 The what exactly? Think of it as telling your data Butler or what pieces of information to pull out of that giant filing cabinet. These columns you list are always drawn from the tables or views that you'll identify in the next clause, the FROM clause. 10:35 Speaker 1 OK, they have to come from somewhere specific. 10:37 Speaker 2 Right. So for instance, if you want to see customer names and their cities, you would list customer name and city right there in the select clause. 10:45 Speaker 1 Simple enough. 10:46 Speaker 2 And it's really important to understand this clause isn't limited to just listing existing column names, you can also include calculations. 10:53 Speaker 1 Like the tax example? 10:54 Speaker 2 Like the tax example, quantity price to get a total. Or you could use functions like some hours worked to get a total for a group. These capabilities hint at much more advanced stuff we can explore later. 11:05 Speaker 1 Cool, so that's select. What about from? 11:08 Speaker 2 OK, the FROM clause. This is the other indispensable required clause. It specifies from which table or view the columns you just listed in your SELECT clause should be retrieved. 11:18 Speaker 1 The where? 11:19 Speaker 2 The where exactly? It tells the database where to look for the information you're asking for. So going back to our customer example, if you're selecting customer name and city. 11:28 Speaker 1 You'd say from customers. 11:30 Speaker 2 Precisely from customers. While it seems straightforward now with just one table, the FROM clause becomes incredibly powerful later when you need to combine information that's spread across multiple related tables. 11:44 Speaker 1 Joining tables together. 11:45 Speaker 2 Exactly. That's a whole other exciting area. 11:48 Speaker 1 OK, so SELECT tells you what columns, FROM tells you which table. Those are the bare minimum. 11:53 Speaker 2 That's the foundation for almost every query you'll write now. In addition to those two required clauses, Sequel gives you several optional ones. These let you build much more sophisticated, nuanced queries. 12:05 Speaker 1 The optional extras. 12:07 Speaker 2 Right, we'll just briefly introduce them now because each one really deserves its own deep dive later. 12:11 Speaker 1 Teasers. 12:12 Speaker 2 Exactly. First, there's the WHERE clause. This is used to filter the rows that your query returns. 12:19 Speaker 1 Filtering like only certain customers. 12:21 Speaker 2 Precisely. It's followed by some kind of condition, technically called a predicate, that evaluates to true, false, or maybe unknown. This lets you set very specific criteria, like you said, only show me customers from California or maybe only products priced over $50. 12:37 Speaker 1 OK, where are filters rows? What else? 12:39 Speaker 2 Then you have the GROUP by clause. This one is used together with what are called aggregate functions. Things like a sum count, AVG for average, Max MEN. 12:50 Speaker 1 The calculation functions. 12:51 Speaker 2 Right group by Y divides your information into distinct groups based on the values in one or more columns. Then it applies that aggregate function to each. 13:00 Speaker 1 Group example. 13:01 Speaker 2 Sure. Imagine you wanted to know the total sales per region group by region and would let you calculate that sum for each unique region you have in your data. 13:10 Speaker 1 OK, summarizing by groups. 13:12 Speaker 2 Exactly. And closely related to that is the halving clause. It's kind of like WHERE but it filters after the data has been grouped by group BY. 13:19 Speaker 1 Filters the groups. 13:21 Speaker 2 Yes. So building on the sales per region example, after you've grouped the sales by region using group BY, you could then use having to say only show the regions where the total sales were over $1,000,000. 13:36 Speaker 1 So where filters individual rows before grouping, having filters the group results after grouping. 13:42 Speaker 2 You nailed it. That's the key distinction. 13:44 Speaker 1 Wow, that's a truly powerful set of tools just within one statement type. But OK, for now, I agree. Let's keep our focus firmly on SELECT and FROM. 13:53 Speaker 2 Build the foundation first. 13:54 Speaker 1 Yeah, like learning the basic grammar before trying to write, you know, poetry. We need that rock solid understanding. 13:59 Speaker 2 Absolutely. 14:00 Speaker 1 So before we actually try to build our first practical sequel statement, let's pause for a second. You mentioned something earlier that sounded really important, a distinction about the building blocks we're working with. 14:09 Speaker 2 Yes, absolutely. This distinction is, I think, one of the most critical concepts for anyone working with data to really grasp. It's the fundamental difference between data and information. 14:19 Speaker 1 Data versus information, OK. 14:21 Speaker 2 In essence, data is the raw material. It's the facts, the static values, the things you actually store in the database. Think of it like individual bricks just sitting in a pile. 14:31 Speaker 1 OK, raw bricks. 14:32 Speaker 2 Information, on the other hand, is what you get back. It's what you retrieve after that raw data has been processed, organized and presented in a meaningful, useful way. 14:43 Speaker 1 The finish building made from the bricks. 14:45 Speaker 2 Exactly. It's those bricks assembled into something functional, something understandable. And this distinction is so important because it shapes how you think about databases. A database is fundamentally designed to provide meaningful information, right? Right. But it can only do that if the necessary data actually exists in the 1st place, and if the database itself is structured in a way that lets you transform that data effectively. 15:08 Speaker 1 Garbage in, garbage out. Kind of. 15:09 Speaker 2 Precisely. Let me give you a concrete example. Right. Imagine you just see a string of numbers 89931. OK by itself. That's just raw data. 15:17 Speaker 1 And as a human, seeing 89931 doesn't mean much without context. Is it azip code? Customer ID? Lottery numbers? My brain instantly wants to know what it is. 15:29 Speaker 2 Exactly. Our brains crave context. Raw data in isolation is often meaningless. A number like 89931, or even a name like Katherine Ehrlich alone. They're just static facts. We don't know if they're related, what they represent. 15:41 Speaker 1 Just noise, almost. 15:42 Speaker 2 Pretty much Now picture you're looking at a customer information screen on your computer. Suddenly that 89931 appears right next to a label that says Customer ID and below it you see fields like name Katherine Ehrlich, address 123 main saying Anytown 89931. 15:58 Speaker 1 OK, now it clicks yes. 16:00 Speaker 2 Those raw numbers and names are now associated. They're labeled, they're put into context. They tell a small story, That transformation where raw static values become meaningful and useful. That's information. 16:10 Speaker 1 Is the difference between a list of ingredients in a recipe maybe, or even the finished cake? 16:14 Speaker 2 Great analogies, yes. So when you use a SELECT statement, its primary job is to take that raw static data, process it according to your instructions, and transform it into dynamic, useful information. 16:27 Speaker 1 And this information comes back, as you called it, a result set. 16:30 Speaker 2 That's right, a result set. It sounds a bit formal, maybe a little bit, but it's really just the answer the database gives. Back to your question, it's the collection of rows, maybe one row, maybe thousands that contain the information you asked for. 16:43 Speaker 1 So the output of the query. 16:44 Speaker 2 Exactly. It's called a set because relational databases are built on mathematical set theory. You're always working with collections or sets of data, even if that set only contains one item or even no items. This result set is what you typically see on your screen, or in a report, or what an application uses. 17:02 It's the tangible output. 17:04 Speaker 1 OK, that makes perfect sense. So we understand what we're trying to get information from data. Now let's get into the really practical part. How do we actually ask for it? How do we translate our thoughts into sequel? This feels like where the rubber meets the road. 17:16 Speaker 2 It really is. When you need information from a database, your thought process usually starts with a question, right? Or maybe a statement that implies a question, like which cities do our customers live in? Or show me a current list of employees and their phone numbers. Maybe what kind of classes do we offer? 17:33 These are just normal everyday ways we ask for things. 17:36 Speaker 1 Right. How do we bridge that gap? How do we turn that natural question into the strict language SQL needs? 17:43 Speaker 2 We use a pretty straightforward, almost formulaic 3 step technique. You can think of it as using a core translation pattern. Select item from the source. 17:52 Speaker 1 Select item from source. 17:54 Speaker 2 OK, here's how you apply it in practice 1. Formulate your request. Just figure out clearly what you want to know. Say it or write it in plain English. Don't worry about SQL yet. 18:03 Speaker 1 OK. Just the question. 18:04 Speaker 2 Two, translate to the basic form. Select item from the source. Look at your plain English request. Replace words like list, show me what, which, who with the keyword select. Then identify the key nouns. 18:20 Which noun is the item, the column you want to see, Which noun is the source? The table where that item lives? Plug those into the item in source spots. 18:28 Speaker 1 Item is the column, source is the table. Got it. 18:31 Speaker 2 Three, clean up for SQL syntax. Now you look at your translated sentence and mentally cross out any words that aren't an actual column name, a table name, or in sequel keyword like SELECT or FROM. You're stripping away the conversational filler words like the list of. 18:47 Speaker 1 Get rid of the fluff. 18:48 Speaker 2 Exactly. Let's try it with that first example. Which cities do our customers live in? OK, Step 2. Translate select city from the customers table. 18:57 Speaker 1 Select city from the customers table makes sense. 18:59 Speaker 2 Step 3. Clean up. We drop the end table. What's left? 19:02 Speaker 1 Select city from customers. 19:04 Speaker 2 You've got a perfect functional SQL statement ready to go. 19:07 Speaker 1 Wow, that's actually surprisingly simple when you break it down like that. 19:11 Speaker 2 It is. 19:11 Speaker 1 So for beginners, that's a really solid technique to bridge the gap. But I guess with practice, those steps just become kind of automatic in your head. 19:20 Speaker 2 Exactly. After a little while you just start thinking in SELECT and FROM. But what happens when the request isn't quite so clear cut? What if it's a bit vague or you don't immediately know the exact column name you need? 19:33 Speaker 1 Yeah, people don't always ask questions in perfectly structured ways. 19:36 Speaker 2 Tell me about it. That's a super common scenario in the real world. Natural language is messy, so you need strategies to figure out the precise column names for your select statement. There are two main techniques we usually rely on. 19:51 Speaker 1 OK. 19:51 Speaker 2 Strategy 1. Technique 1 is basically reviewing the table structure to find the column names. Let's say someone asks. I need the names and addresses of all our employees. This. 20:01 Speaker 1 Is simple enough. Look in the employees table. 20:03 Speaker 2 Right, you know the table, but names and addresses are general terms. They probably aren't the actual column names in a well structured database. 20:11 Speaker 1 Right, it might be split up. 20:12 Speaker 2 Exactly. So you'd need to look at the actual design, the structure of that employees table. Imagine looking at a list of all the columns defined in that table. You might see things like M first name, M last name, M street address, M city, M state, M zip code. 20:27 Speaker 1 First Name Last Name Street, City, State Zip. 20:30 Speaker 2 OK, so to properly fulfill the request for names and dresses, you actually need to use all six of those specific column names. Your translation process would go request. I need names and addresses of employees translation attempt. Select first name, last name, street address, city, state, ZIP code from the employees table. 20:51 Clean up. Remove extra words SQL Select M first name, M Last name M street address, M City M state M zip code from employees. 21:00 Speaker 1 So you break down the general request into the specific database fields. 21:03 Speaker 2 Precisely that's technique. 21:04 Speaker 1 One What's technique 2? 21:06 Speaker 2 Technique 2 involves searching for implied columns or using synonyms. Sometimes the request doesn't even hint at a column name directly. Like what? Like what kind of classes do we currently offer? There's no obvious column name in that sentence. 21:19 Speaker 1 Yeah, kind isn't a column. 21:21 Speaker 2 Right, so you look for words that imply a column name, or think about synonyms in what kind of classes like. The word kind strongly suggests something like category or type. 21:33 Speaker 1 OK, category makes sense for classes. 21:35 Speaker 2 So you check your classes table structure. If you find a column named category, bingo, you found your item. Got it. The request What kind of classes do we offer? Translates to select category from the classes table. Clean it up and you get select category from classes. 21:53 Speaker 1 So you're using context clues and synonyms. 21:55 Speaker 2 Exactly. Thinking about synonyms and implied meanings is really powerful for bridging that gap between how people talk and how databases are structured. 22:04 Speaker 1 Those are fantastic strategies. They really give you the tools to turn a vague idea into a precise, actionable instruction for the database. Super useful they are. OK, so now we know how to find the right columns even for tricky requests. Let's talk about getting more than one column back, or maybe even getting all of them without listing them. 22:22 Speaker 2 Yeah, absolutely. Retrieving multiple columns is thankfully very easy. You just list the names of the columns you want in the SELECT clause, separated by commas. 22:30 Speaker 1 Just list them out with commas. 22:32 Speaker 2 Just list them out with commas, like making a shopping list select column A, column B, column C from table name. Simple, very. And this lets you see a much broader picture in a single result set, right? You get more context without running lots of separate queries. It's much more efficient. 22:47 Speaker 1 So give me an example. 22:48 Speaker 2 Sure, someone asks show me a list of our employees and their phone numbers. Your sequel would be a select M plus name, M first name, M phone number from employees. You're asking for those three specific pieces of info. 23:00 Speaker 1 Last name, first name, phone number. Got it. 23:02 Speaker 2 Or maybe a more detailed request, what are the names and prices of the products we carry and what category is each item listed under? 23:11 Speaker 1 OK, three things there. 23:12 Speaker 2 Right. So it would be select product name, retail price, category from products. You get those 3 distinct attributes for every product in your table. 23:20 Speaker 1 Nice. Does the order I list them in the select part matter? 23:24 Speaker 2 Great question. The order you list the columns in the SELECT clause doesn't affect the underlying data itself, but it absolutely does determine the order the columns appear in your results. 23:34 Speaker 1 So it controls the display order. 23:36 Speaker 2 Exactly. It gives you flexibility in how you present the information. Let's say you have a subjects table with columns like subject, name, category ID, subject code. Someone asks to see the name first, then the category, then the code. 23:51 Even if the columns are stored differently in the table itself, you can control the output order like this. So like subject name, category ID, subject code from subjects the results that will have columns in that specific order name, category code. 24:05 Speaker 1 That's really good to know, having control over the presentation. But what if I don't care about the order or I just want to see everything? Maybe a table has 50 columns. 24:12 Speaker 2 Or 100? 24:13 Speaker 1 Typing all those out sounds like a nightmare. Is there like a magic button? A shortcut. 24:18 Speaker 2 There absolutely is, and it's probably one of the most commonly used shortcuts in SQL, especially when you're first exploring data. It's the asterisk. 24:24 Speaker 1 The star symbol. 24:26 Speaker 2 The star symbol, yes, it's convenient shorthand you put in the SELECT clause instead of column names. It basically means give me all columns from the table specified in the FROM clause. 24:37 Speaker 1 So instead of typing select subjected category ID, subject code, subject name, subject description from subjects. 24:45 Speaker 2 You just type select from subjects. 24:48 Speaker 1 Wow, OK, that saves a ton of typing. 24:49 Speaker 2 It really does. It's super quick for getting a general overview, but like most shortcuts it comes with some important trade-offs. Some pros and cons you really need they need to be aware of. 24:58 Speaker 1 The catch? OK, what are they? 25:00 Speaker 2 Well, the Pro is obvious. Massive time saver, especially with wide tables. Perfect for that quick and dirty look when you just want to see what's in there. 25:08 Speaker 1 Makes sense. What are the cons? 25:10 Speaker 2 There are a few significant ones. First, the columns in your result set will change automatically if someone adds or deletes columns from the underlying table after you wrote your select query. 25:20 Speaker 1 So the output isn't stable. 25:22 Speaker 2 Exactly. That might be fine for exploring, but if your query is part of an automated report or an application that expects a specific set of columns in a specific order, things can break unexpectedly. I've seen critical processes fail because a new column was added and the select suddenly started pulling it in, causing errors downstream. 25:40 Speaker 1 Yikes. OK, stability is 1 con. 25:42 Speaker 2 Second, using SELECT is less self documenting. Just looking at select from some table, you have no idea which columns are actually being returned unless you separately go and look up the table structure. It makes the queries intent harder to understand at a glance. 25:57 Speaker 1 That's clear. 25:57 Speaker 2 Got it. And 3rd, especially in systems dealing with large amounts of data or sensitive information, pulling all columns when you I only need two or three can be inefficient. It uses more network bandwidth, more database resources, and potentially it could expose sensitive data columns that you didn't actually intend to retrieve. 26:16 Speaker 1 Performance and security risks. 26:17 Speaker 2 OK, so the general rule of thumb is use the asterisk for quick ad hoc exploratory queries when you just need to see everything. But for anything more critical, anything that will be run repeatedly or used by an application, it's always best practice to explicitly list the specific columns you need. 26:36 Speaker 1 Be specific for important stuff. 26:38 Speaker 2 Exactly. It ensures reliability, clarity, performance and security. 26:43 Speaker 1 That's a really crucial distinction. It goes way beyond just saving keystrokes. So we can get specific columns, we can get all columns, but what about the data within those columns? What if there are duplicates like the same city appearing multiple times and I only want to see each city once? 26:58 Speaker 2 Yes, that's where things get even more refined. When your SELECT statement might return rows that are identical or contain duplicate values you don't need to see multiple times. The DISTINCT keyword is your best friend. 27:10 Speaker 1 Distinct. OK. 27:11 Speaker 2 It's an optional keyword you place immediately after select and right before you list your columns like select distinct city. 27:17 Speaker 1 So select distinct instead of just select. 27:19 Speaker 2 Exactly. It tells the database evaluate all the values in the specified columns for each row as a single unit and then only return the rows that are unique. Get rid of any exact duplicates. 27:32 Speaker 1 Give me an example. 27:33 Speaker 2 OK, let's use that city example. Imagine you're querying a bowlers table. If you just run select city from bowlers and you have say 20 bowlers from Bellevue, 7 from Kent and 14 from Seattle. 27:46 Speaker 1 You'll see Bellevue 20 times, Kent 7 times, Seattle 14 times. 27:51 Speaker 2 Right, which is probably not what you want. If the question is just which cities are represented, it's redundant. But if you use SELECT DISTINCT CITY from bowlers, the database looks at all those city names, sees the duplicates and returns Bellevue only once, Kent once, and Seattle once, along with any other unique cities. 28:09 Speaker 1 Much cleaner. Just the list of unique cities. 28:11 Speaker 2 Exactly. It's not just about tidiness, it's about getting a specific kind of insight, the unique set of values. Think about market analysis. You want the unique states your customers are in, not a list with California repeated 1000 times. 28:23 Speaker 1 Makes sense. Can you use DISTINCT with multiple columns? 28:26 Speaker 2 Yes, absolutely. If you query SELECT DISTINCT city state from bowlers, it looks at the combination of city and state as the unit. 28:35 Speaker 1 So Portland, ME is different from Portland, OR. 28:38 Speaker 2 Precisely, it would return both of those as distinct rows because the combination of values across the specified columns is unique. It evaluates the the entire set of columns listed after distinct together. 28:50 Speaker 1 OK, that's powerful for finding unique combinations. Is there a catch with distinct? 28:55 Speaker 2 There is one important caution, especially if you ever think about modifying data through your query results. SELECT statements that use DISTINCT typically produce result sets that are read only. You usually cannot update the data through them. Why not? Think about it? 29:10 If multiple original rows, say 5 bowlers from Bellevue, were condensed down into one distinct row Bellevue, and you tried to change something in that single result row, how would the database know which of the original 5 rows you intended to modify? 29:24 Speaker 1 It's ambiguous. It doesn't know which source road to change. 29:27 Speaker 2 Exactly. So the database plays it safe and usually makes distinct results read only. Perfect for analysis and reporting, not for direct data updates. 29:35 Speaker 1 That's a really important operational detail. OK, so distinct handles uniqueness. What about the order you mentioned? Databases don't guarantee order unless you ask. How do we ask? 29:46 Speaker 2 Right. By default, results just come back. However, the database finds them efficiently. There's no inherent sequence. To guarantee a specific predictable order, you absolutely must use the ORDER BY clause. 29:59 Speaker 1 Order by. 30:00 Speaker 2 Yes, and this is often why we make a subtle distinction in terminology. A plain SELECT statement might not have an order, but a SELECT query often implies that an ORDER BY clause has been added. It's the only reliable way to ensure your output is sorted the way you want it. 30:14 Speaker 1 So ORDER BY is the key to sorting. Where does it go in the statement? 30:17 Speaker 2 It always goes at the very end. If you visualize the structure you have select then FROM then maybe optional clause clauses like WHERE or GROUP BY or HAVING and finally right at the end comes ORDER BY. 30:28 Speaker 1 Always last, OK. 30:30 Speaker 2 ORDER BY lets you sort your results based on the values in one or more columns. For each column you want to sort by, you can specify the direction. You can add ASC after the column name for ascending order A-Z, lowest to highest. That's actually the default, so if you just say order by column name, it assumes ascending. 30:48 Speaker 1 OK, ASC is default. 30:49 Speaker 2 Or you can explicitly add DESC for descending order Z to a highest to lowest. 30:55 Speaker 1 ASC for ascending, Dec for descending. Can I sort by columns that aren't in my select list? 31:01 Speaker 2 Generally best practice and the SQL standard say you should only sort by columns that you've actually included in your select list. Some specific database systems might allow you to sort by other columns from the FROM table, but it's not guaranteed to work everywhere and can be less clear. Sticking to sorting by selected columns is safer and more portable. 31:18 OK. 31:19 Speaker 1 Stick to sorting by what you selected makes sense. 31:21 Speaker 2 Now just a quick technical aside, but it can be important for precision the exact sort order. Like does lowercase A come before uppercase a? Do numbers come before letters? Do symbols come first? That depends on something called the collating sequence set up in your database system itself. 31:40 Speaker 1 Collating sequence. 31:40 Speaker 2 Yeah, it's usually configured when the database is installed or set up. It defines the character by character. Comparison rules usually don't set it per query, but it's good to be aware it exists, especially if you work with different languages or see sorting that seems slightly odd for certain characters. 31:57 It explains those subtle differences. 31:59 Speaker 1 Good to know it exists even if I don't control it directly. 32:01 Speaker 2 Exactly. So with ORDER BY we can now expand that translation pattern again. Select item from the source, maybe add where condition and order by column with ASC or DSC. 32:12 Speaker 1 OK, let's see some examples with ORDER BY. 32:14 Speaker 2 Sure, simple one. List the categories of classes we offer and show them in alphabetical order. SQL select category from classes order by category ASC or since ASC is default just order by category. 32:26 Speaker 1 Easy enough. How about descending? 32:28 Speaker 2 Request show me a list of vendor names sorted by their zip code. But I want the highest zippy codes first SQL select ven name then zip code from vendor's order by ven zip code de ace. 32:42 Speaker 1 DSE for descending. Got it. Now what about sorting by more than one thing? Like by last name then first name. 32:49 Speaker 2 Multi column sorting. This is where order BY gets really useful request. Display employee names, phone number and ID. List them alphabetically by last name and for people with the same last name, list them alphabetically by first name. SQL select M plus name and first name, M phone number, employee Ed from employees order by and plus name and first name. 33:10 Speaker 1 So you just list the columns in the order by separated by comma. 33:12 Speaker 2 Exactly. And here's the crucial point. The order you list them in, the order BY clause matters immensely. 33:17 Speaker 1 Also. 33:18 Speaker 2 The database sorts first by the 1st column M plus name in our example. Then only for rows where the M plus name is identical tie, it uses the second column and first name to sort those tied rows. If there was a third column listed, it would break ties in the second column and so on. 33:32 Speaker 1 It's like layers of sorting. Primary sort, then secondary, then tertiary. 33:37 Speaker 2 Perfect analogy. Layers of sorting and you can even mix the directions. What if you wanted last names descending Z to A, but first names ascending A-Z within each last name? 33:48 Speaker 1 Oh, OK, How? 33:48 Speaker 2 You just specify the direction for each column. Select M plus name, M first name, M phone number, employee ID from employees order by M plus name decisi M first name ASC. 34:00 Speaker 1 Wow, DSC on the first, ASC on the 2nd. That gives you incredibly fine grained control. 34:05 Speaker 2 It really does. This is where you start to truly shape the presentation of your information, making it not just present, but genuinely intelligible and actionable. 34:14 Speaker 1 Yeah, the order you present data can totally change how someone understands it, right? It guides their focus. 34:19 Speaker 2 Absolutely controlling the narrative of your data. As you put it earlier, it can highlight trends or outliers just by the sequence you choose. 34:26 Speaker 1 So it's not just about neatness, it's about effective communication. 34:29 Speaker 2 Precisely. And just to mention, some database vendors offer even more advanced options here. Microsoft SQL Server and Access, for example, have top N or top N percent keywords. Yeah, you can combine it with order by to say something like give me the top five most expensive products or the top 10% of customers by sales. 34:48 It lets you grab just a specific slice from the top or bottom of your sorted results. 34:52 Speaker 1 Well, that sounds incredibly useful for some reason, reports. 34:55 Speaker 2 It is. It shows how vendors sometimes add powerful extensions to the standard SQL capabilities. 35:02 Speaker 1 OK, so we figured out how to ask for data select from, how to filter it where, how to group it, group Y having, how to make it unique, distinct, and how to sort it order by Y. That's a lot of power. 35:16 Speaker 2 It really is the core toolkit for data retrieval. 35:19 Speaker 1 Now, after putting in all that thought to craft the perfect query, you mentioned saving it. That seems like the next crucial step, right? Who wants the Tye of complex query over and over? That's a definite pain point. 35:31 Speaker 2 Absolutely. Saving your work is crucial for efficiency and consistency. After you've carefully constructed a SELECT statement, especially a complex 1, you really don't want to have to recreate it from scratch every time. No way. So thankfully, all major database software programs provide robust ways to save your SELECT statements. 35:48 This is a massive time saver, especially for reports or analysis you need to run regularly. 35:54 Speaker 1 How do you save them effectively? Good. 35:56 Speaker 2 Question Best practice #1 Give your save statement a meaningful, descriptive name. 36:02 Speaker 1 Not just query one or my query. 36:04 Speaker 2 Please know something like monthly sales by category or Active customers California. Something that tells you or someone else exactly what information it provides. 36:15 Speaker 1 Makes sense? What else? 36:16 Speaker 2 If the database tool allows it, and most do, add a description, a short sentence explaining the purpose, maybe who requested it, or any assumptions it makes, You will thank yourself six months down the line when you've completely forgotten why you built it. 36:28 Speaker 1 Future you will be grateful. 36:30 Speaker 2 Absolutely. Now the terminology for these saved objects can vary. As we mentioned, it might be called a query like in Microsoft Access, a view, a function or a stored procedure. 36:39 Speaker 1 Views. Stored procedures? Those sound more complex. 36:43 Speaker 2 They can be. A view is often like a saved select statement that acts like a virtual table. Users can query the view without needing to know the complex SQL behind it. It's great for simlifying access and ensuring consistency. A stored rocedure can contain more complex logic variables, even loop's and often takes parameters making it very dynamic. 37:04 Speaker 1 So different names, but the core idea is saving the logic for reuse. 37:08 Speaker 2 Exactly, saving the logic. And you can usually execute these saved objects either interactively like clicking on its name in a list, or programmatically by calling it from application code. 37:20 Speaker 1 So developers can use them too. 37:21 Speaker 2 Heavily. For most end users doing analysis though, just running it interactively is usually enough. 37:27 Speaker 1 So knowing how to save and reuse queries isn't just a minor convenience, it's a fundamental skill for working efficiently with data. Turns A1 off effort into a reusable asset. 37:37 Speaker 2 Kind of said it better myself. It's about working smarter, not harder. 37:40 Speaker 1 OK, we've covered a lot of theory, the clauses, the concepts like dataverse information, the techniques for translation, refining results. Now let's try to bring it all together. Let's see some concrete examples in action, maybe from different kinds of databases. 37:54 Speaker 2 Excellent idea. Seeing the sequel applied to different scenarios really helps solidify the concepts. We'll walk through a few diverse examples, stating the business need and then showing the sequel to meet it. Perfect. OK, let's start with the typical sales orders database. Super basic request. 38:10 First the need. Your manager just wants a simple list. Show me the names of all our vendors. The Sequel Select Vend name from Vendors analysis can't get much simpler than that. Select the Vendor Name column from the Vendors table. Straightforward retrieval. 38:25 Speaker 1 OK, nice and easy start. 38:27 Speaker 2 Now let's get slightly more info from the same database. The need The product team asks what are the names and the retail prices of all the products we sell. The sequel Select Product name, Retail Price from Products analysis. Here we're selecting 2 specific columns, Product name and Retail price from the products table. 38:45 The result gives you that two column list for every product. 38:48 Speaker 1 Still pretty basic, just multiple columns. 38:50 Speaker 2 Exactly. Now let's use distinct. The need marketing is planning a campaign. Which unique states do our customers actually live in? I just need the list of states the sequel select Distinct Cus state from customers analysis. This is where Distinct shines. 39:05 Instead of getting CA hundreds of times, if you have many Californian customers, you get CA once, NY once, TX once, etcetera. Concise list of unique states. 39:14 Speaker 1 Very useful for that kind of question. 39:16 Speaker 2 Let's switch contexts. Imagine an entertainment agency database. We need sorting the need. The director wants a sorted list. Give me all entertainers and their home cities sorted alphabetically by city first, then by the entertainer stage name within each city. 39:31 The sequel select in city and stage name from entertainers. Order by N city ASC and stage name ASC analysis. This shows that multi level sorting. It sorts everyone by N city first Arizona, then for all entertainers in the same city it sorts them by in stage name Arizona. 39:47 The ASC is explicit but optional here. 39:49 Speaker 1 That layered sorting in action? Cool. 39:51 Speaker 2 How about using the asterisk shortcut? Let's say for a school scheduling database. The need an administrator needs a quick overview. Can I just see all the information we have about our classes? The sequel select from classes analysis. Perfect use case for us. Instead of listing classes, subject, instructor, rid room, nums, schedule, credits select just grabs every column to find in the classes table. 40:12 Quick and comprehensive look. 40:13 Speaker 1 The quick and dirty approach. 40:15 Speaker 2 Exactly. And finally a more complex sorting example, maybe from a bowling league database. The need the lead organizer needs a schedule. List all tournament dates and locations. I want the newest tournaments first descending date, but if multiple tournaments are on the same date, list their locations alphabetically ascending the sequel. 40:35 Select tourney date tourney location from tournaments order by tourney date dis C tourney location ASC analysis. This really highlights mixing sort orders and the importance of the order by column sequence. It sorts primarily by tourney date, descending most recent first. 40:51 Then for any tournaments on the same date, it sorts secondarily by attorney location ascending AZ. 40:56 Speaker 1 That's a great example of combining DSC and ASC for a very specific view. You can really see how just these few commands give you incredible control. 41:04 Speaker 2 Right from simple lookups to sorting and uniqueness, it's a flexible toolkit. 41:08 Speaker 1 It really shows the power to pull out exactly what you need, exactly how you need to see it. Fantastic. OK, wow, that was quite the journey through the select statement. Let's let's try and unpack everything we covered in this deep dive. 41:20 Speaker 2 Sounds good. We really did plunge into the heart of Sequel today. The SELECT operation we talked about, it's three sort of conceptual parts, the statement, the expression, the query. And we really focused on the SELECT statement itself as the fundamental way you ask a database for information. 41:39 Speaker 1 Yeah, we established that a query is built with those essential clauses SELECT telling it what columns you want and from telling it where to find them. 41:46 Speaker 2 Exactly. 41:47 Speaker 1 And we also dug into that really crucial difference between just raw static data and the dynamic processed information that actually helps us make decisions. 41:56 Speaker 2 That data to information transformation is key. We then walked through that practical 3 step process for translating a natural language request just how you'd normally ask a question into precise Sequel syntax. 42:09 Speaker 1 Including those neat strategies for figuring out call names even when the request is it bit vague, using the table structure or synonyms that felt really useful for bridging the gap. 42:19 Speaker 2 It's a common challenge, so yeah, important techniques. 42:21 Speaker 1 We also discovered how easy it is to get multiple columns back just listing them with commas, and we look at the asterisk shortcut for grabbing everything convenient, but with those important caveats about stability, clarity, and performance, use with. 42:35 Speaker 2 Care. And finally, we learned how to truly refine our results, getting rid of those redundant rows using the DISTINCT keyword, turning a long list into a concise set of unique values. 42:47 Speaker 1 Transforming raw lists into actual insights sometimes. 42:50 Speaker 2 Exactly, and then mastering how to precisely order our findings using the powerful order by clause. Understanding how ASCDSC and the order of columns in the clause dramatically impact what you see and how you interpret it. 43:05 Speaker 1 Yeah, that multi level sorting is really powerful. And hey, don't forget saving your work. 43:09 Speaker 2 Oh, definitely not. Saving those queries as views or procedures makes your life so much easier down the road. Turns that effort into a reusable asset. 43:16 Speaker 1 So thinking about all this, what does it mean for you listening right now? Think about all the information you interact with everyday. News feeds, Online stores, work systems, apps on your phone. 43:26 Speaker 2 It's everywhere. 43:27 Speaker 1 How might understanding these fundamental SQL concepts even just select change how you perceive the organized data humming away behind the scenes? Or maybe maybe how might you start thinking about the specific questions you want to ask of your own information, whether it's personal or professional, now that you have a glimpse of the language used to get those answers? 43:48 Speaker 2 It opens up possibilities, doesn't it? Knowing how to ask the right questions. 43:53 Speaker 1 It really does. This journey into sequel is just beginning, but the control you've gained today over retrieving information is genuinely powerful. 44:01 Speaker 2 We certainly hope this deep dive has given you a solid foundation, a powerful shortcut maybe to understanding the fascinating and incredibly practical world of sequel queries. 44:10 Speaker 1 Join us next time for another deep dive.