Best Way to Write Basic SQL Queries

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SQL Server Query writing strategies is something I have yet to find in any book. When enthusiastic SQL students do this, they experience a revelation. The number of errors drops significantly and the speed at writing complex queries increases immediately. Knowing how to narrow down what we are looking for amongst a vast list of choices helps immensely. Grabbing the right tables first and then the fields second is akin to grabbing the right menu before ordering an item from it. In fact, one student named Tim took this back to his team of SQL developers and they immediately implemented this process. We are all used to following steps. Most of the time, actions are sequential from top to bottom or left to right. Other times we complete things in phases. The two phases we are going to use in this exercise apply to joining tables. This is easy to implement as we only need remember to organize first and clean up second. When visiting a new restaurant, we will ask to see the menu, because we want to see all they have to offer. The odds are that we might be interested in half of the items, but only need a few dishes for our group at the table. Looking at the menu is like starting a query with a ‘SELECT *’ statement. Once we have looked at all the fields, we narrow our choice(s) to only the items we want at the time. Sometimes restaurants have multiple menus. My favorite restaurant has a kids’ menu, an adult menu, a gluten-free menu and a drink menu. These menus were each gathered at our table. Ultimately, in my head, a selection was narrowed to what was needed. Phase I: Organize. When building a new query from many tables, we often find ourselves wondering, “Where do I start?” First, lay the steps out by identifying which tables contain the essential data. Second, get all the table joins working with a basic ‘SELECT *’ statement. Third, add any basic filtering criteria. Phase II: Itemize. Once all joins and criteria, such as SELECT, FROM and WHERE are working, we are ready for Phase II. This entails going back and changing our ‘SELECT *’ to an itemized SELECT field list as the final step. Let’s explore how this two-phase process of Organize and then Itemize can be a big time-saver. We are going to use one of the challenges from the last lab. In Lab 3.2 (Outer Joins), Skill Check 2, we needed to get four fields from two different tables. If we were to list all four desired fields and test one table at time, we will get an error as seen on the right side of the figure below. In the figure below we write a SELECT statement and part of the FROM clause. When completed, the FROM clause will have two tables, but for now we just want to get the Location table working. By using the ‘SELECT *’ strategy, we remove any possible errors from line 1. From there, we can focus on the more complicated logic used for joining tables together. We can add tables one at a time until everything is working. This is the Organize phase. NOTE: SELECT * never results in an Error message stating “invalid column name”, however; a SELECT list with itemized field(s) can have this error. After our query is organized and working, we can go back and itemize the SELECT field list to display only the fields that are necessary. This is done during Phase II (Itemize). The steps for this system are broken down as follows: Since SELECT is always the first statement in a query, it’s natural to want to write the field names before writing the FROM clause. However; we can save time and trouble by using the ‘*’ until the entire query is working properly. When this is complete, it is very easy to itemize the field list, with the confidence of knowing it will not cause any problems. Exercise: Simple membership for a club: Table1 (id_Member,name_Member, email_member, title, age, company) Table2 (id_Club,club_name, club_Location, address) Table3 (id_club, id_member,membership_date) Q1: Show all member names, email, age, and company from Table1 Q2: Show all club name, location, and address data Q3: Show member name, club name, club location (hint: Use joins and all 3 tables) 

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