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I assume most of your comment your code.
Well, you probably comment code most of the time.
I’d bet your comments have quite a bit of detail.
And you do this completely inconsistently.
That’s what I’d think, or maybe just what I want. Even the best developers I know will not consistently comment code. You can drift through any project on Github and see this. Those projects on GitHub might even be better documented because people know they are public. In most corporate environments I have worked in, I’ll find that when people get busy, or distracted, or even when they’re experimenting to find a solution, and they don’t write detailed comments. Usually only when someone fixes a bug, with a solution found quickly, do I get a really useful comment.
There are all sorts of ways that people think about commenting their code. I ran across a post from…
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With my (still-relatively) new job comes an introduction to new (to me) technology. In this case, the technology in question is Docker.
For those of you unfamiliar with Docker (like me), it is, in a nutshell, a tool for deploying and running an application within a container. It is an improvement over VM (virtual machine) in that it runs at the operating system, rather than the hardware, level, resulting in less overhead and a more efficient environment.
As part of my indoctrination into Docker, I looked up some resources to help me get started. I found this entry that seems to be very helpful. I’m sure I’ll find some others as well. I’ll post them as I go along. I also installed Docker on my work laptop and have been playing with it. At the moment, I am far from an expert on Docker (in fact, I’m not even close), but I feel like I’m starting to get the hang of how it works. Hopefully, I’ll be productive with it before long.
I also noticed that, in the schedule for our upcoming SQL Saturday, one of the sessions focuses on Docker. I intend to attend that session. At this point, any resource that helps me to learn this technology is definitely of interest.
Does anyone else have any suggested resources for helping me (and others) learn Docker? Feel free to comment below!
Let’s revisit the code that builds the Batting table again.
CREATE TABLE [dbo].[Batting]( [playerID] [varchar](50) NULL, [yearID] [varchar](50) NULL, [stint] [varchar](50) NULL, [teamID] [varchar](50) NULL, [lgID] [varchar](50) NULL, [G] [varchar](50) NULL, [AB] [varchar](50) NULL, [R] [varchar](50) NULL, [H] [varchar](50) NULL, [2B] [varchar](50) NULL, [3B] [varchar](50) NULL, [HR] [varchar](50) NULL, [RBI] [varchar](50) NULL, [SB] [varchar](50) NULL, [CS] [varchar](50) NULL, [BB] [varchar](50) NULL, [SO] [varchar](50) NULL, [IBB] [varchar](50) NULL, [HBP] [varchar](50) NULL, [SH] [varchar](50) NULL, [SF] [varchar](50) NULL, [GIDP] [varchar](50) NULL ) ON [PRIMARY]
Last time, I mentioned that every single column allowed NULL values (and I’d like to expand on that in a future article). Today, I’d like to talk about the fact that every single column is defined as varchar(50).
For those of you who are getting your feet wet with SQL Server (or don’t know what I’m talking about), let’s take a moment to discuss…
Imagine that you’re presented with a math problem (don’t worry; we’ll keep it simple). However, when you’re presented with the problem, it looks like this:
XII + IX
Want to give it a shot? The truth is, there’s a reason why we don’t normally use Roman numerals in our society. Unlike the Arabic numeral system that we use in everyday life, the Roman numeral system is inefficient and not very intuitive at a glance. For us to use Roman numerals, we’d first need to convert them into a format that we can easily understand.
So, knowing that XII = 12 and IX = 9, our new math problem is 12 + 9. We now know our answer is 21. (And if we want to convert back to Roman, it’d be XXI.)
Data types operate along the same principle. A number that’s stored as a data type of, let’s say, varchar(50) (for those of you who don’t yet know T-SQL, that’s “variable character of up to 50 characters”) needs to be converted to a numeric type of (for example) int (for “integer”) before it can be processed.
“Okay,” you might ask, “why is that such a big deal? Just let the system do the conversion and let it do the work.”
Let’s go back to that Roman numeral example for a moment. In order for you to understand what the Roman numerals were, your brain had to do some work to perform the conversion. Imagine that you had to process dozens, even hundreds, of those problems. That’s a lot of Roman numerals that you have to process. That makes for a lot of work.
Likewise, imagine that a computer system has to convert millions of data types. That’s a lot of extra work that your computer has to do. That can make for a very inefficient system. One of the big issues that a DBA or a developer often has to deal with is a slow system. Using proper data types can often help with trying to fix a slow system.
In a word, many. A number of web sites list data types for various systems, including MSDN and W3Schools. Since I’m writing primarily about SQL Server, I’ll focus on SQL Server data types. I won’t talk about all SQL Server data types (I’ll leave you to look at the links for that); rather, I’ll just discuss a few that are relevant to our conversation.
Let’s start first with the varchar data type, since that’s what’s defined in our tables. Varchar is for variable character. What that means is the data type accepts character strings up to a length that you specify. A data type of varchar(50) means that the field accepts strings up to 50 characters.
There is also a char data type. Like the varchar type, you specify the number of characters allowed by the data column. Unlike the varchar type, however, it is a fixed length string. A column of type char(50) means that the data will always have 50 characters. If data that is fewer than 50 characters is stored, it pads the rest with spaces. This is important to consider, because those characters (even the space characters) take up space. If you’re storing millions of rows of data, and you’re using char data types for data that takes up fewer than the number of characters you specify, that’s a lot of wasted space.
So, let’s revisit our Batting table.
When we built our primary key, we created a composite primary key from the playerID, yearID, and stint columns. Can we improve upon these columns?
Let’s look at playerID. Do we need to reserve fifty characters for this column? To find out, I ran the following query.
select distinct playerID, len(playerID) from batting order by len(playerID) desc
To translate this query into English*, this means “give me each distinct playerID, along with the length of the playerID, from the batting table, and give me the results in descending order of length.”
(*For those of you who don’t speak T-SQL, yes, I do intend to write an article about it.)
The results looked like this:
So what does this mean? It means the longest playerID is nine characters. Could it be longer than nine? Possibly. Will it ever get up to fifty? Unlikely. So while the varchar(50) will definitely accommodate this, my thinking is that it’s overkill. I’m going to shorten the column. I’m going to cut it down to 25 from 50.
Now, let’s look at the yearID column. I ran the same query as earlier, but this time, I replaced playerID with yearID. Every column was four characters long.
SQL Server provides us with a number of date and time data types. My first thought was to use one of these data types, but at that point, I stopped to think. What, exactly, is the purpose of the yearID column? It identifies a season. That’s it. Will I perform any calculations with it? It’s possible, but unlikely. Will it ever change from four characters/digits? No. I’m going to make this a fixed four-character field. For that, we’ll use char(4).
Now, let’s look at stint. What is this column? It keeps track of a player’s stint with a team in a season. So, let’s see how this works.
I went into the database and looked to see what stint values were in the Batting table. There were five. I looked up the player who had five stints, and found a man named Frank Huelsman, who played in the early 1900s. I ran a query that gave me this.
In 1904, he had five stints with four different teams, having played for the White Sox (twice), Tigers, Browns, and Senators.
So, what data type should we use for stint? Let’s think about this. We could use a char(1), but I don’t think we should. Is it possible that a player could have ten or more stints in a year? Unlikely, but yes. So we’d need at least two characters. Let’s try char(2) — or should we?
I say no. I say that we should use a tinyint data type for stint. Why?
I say it for a couple of reasons. First, there the matter of ordering. Let’s order the numerals 1-10. How would you expect them to be ordered if they were stored as characters? If you say 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, you’d be wrong. If it was defined as a character data type, the order would look like this: 1, 10, 2, 3, 4, 5, 6, 7, 8, 9. When we’re talking about characters, 10 comes after 1.
Second, there’s a matter of memory. tinyint uses only 1 byte, as opposed to 4 bytes for int. The memory used by char and varchar data types are dependent on their sizes (i.e. how much is allocated). Also, tinyint accommodates values from 0 to 255, which is plenty for our purposes.
So, let’s start by redefining the data types for our composite primary key. Because T-SQL in SQL Server won’t let us alter multiple columns at once, we need to run them as separate queries. I wrote three statements like this:
alter table Batting alter column playerID varchar(25) alter table Batting alter column yearID char(4) alter table Batting alter column stint tinyint
This should do the trick — or so I thought. When I ran the queries, SSMS gave me error messages that looked like this:
Msg 5074, Level 16, State 1, Line 1 The object 'PK_playerID_yearID_stint' is dependent on column 'playerID'. Msg 4922, Level 16, State 9, Line 1 ALTER TABLE ALTER COLUMN playerID failed because one or more objects access this column.
What’s going on here? Keep in mind what we’re trying to change: three columns that are part of a primary key. So before we can alter these columns, let’s drop our primary key.
alter table Batting drop constraint PK_playerID_yearID_stint
Now when we run our alter table statements, we should be set.
Or are we? I tried to recreate the primary key (I decided to rename the primary key, while I was at it).
alter table Batting add constraint PK_Batting primary key (playerID, yearID, stint)
Instead, it gave me the following error:
Msg 8111, Level 16, State 1, Line 1 Cannot define PRIMARY KEY constraint on nullable column in table 'Batting'. Msg 1750, Level 16, State 0, Line 1 Could not create constraint or index. See previous errors.
Now what’s going on? Apparently, I’d forgotten to include the NOT NULL statement when I altered the columns. So this time, I’ll rewrite the statements like this and rerun them.
alter table Batting alter column playerID varchar(25) not null alter table Batting alter column yearID char(4) not null alter table Batting alter column stint tinyint not null
After running these statements (which ran successfully), I retried my statement that added the primary key. This time, it worked.
So, our primary key columns are now altered. Let’s look at the rest of our table.
Our first two columns are teamID (for “team ID”) and lgID (for “league ID”). I checked the data in these two columns (I’ll save you the trouble of looking at the T-SQL I used). I found that they are never null, and they are always 3 characters and 2 characters, respectively. They are also not unique. For our table, I’ll redefine them as char(3) and char(2).
Now let’s look at the rest of the table. For the benefit of anyone who doesn’t understand baseball, let’s write out what these columns are.
|RBI||Runs Batted In|
|BB||Base On Balls (Walks)|
|IBB||Intentional Base On Balls (Walks)|
|HBP||Hit By Pitch|
|GIDP||Grounded Into Double Play|
They’re all statistics, and they’re all integer values. For now, we’ll make them all integer values. Do we make them NOT NULL? Not necessarily. Why? Because for some records, certain statistics didn’t exist during their time, so they might not exist. Some of these numbers may very well be NULL. So we won’t add the NULL constraint.
Here’s what I wrote for my T-SQL:
alter table Batting alter column [teamID] char(3) not null alter table Batting alter column [lgID] char(2) not null alter table Batting alter column [G] int alter table Batting alter column [AB] int alter table Batting alter column [R] int alter table Batting alter column [H] int alter table Batting alter column [2B] int alter table Batting alter column [3B] int alter table Batting alter column [HR] int alter table Batting alter column [RBI] int alter table Batting alter column [SB] int alter table Batting alter column [CS] int alter table Batting alter column [BB] int alter table Batting alter column [SO] int alter table Batting alter column [IBB] int alter table Batting alter column [HBP] int alter table Batting alter column [SH] int alter table Batting alter column [SF] int alter table Batting alter column [GIDP] int
The query, which took 20 seconds to run, ran successfully.
Note that I only dealt with a few data types in this article. I didn’t talk about decimal data types or computed values. (What if, for example, we wanted to keep track of batting averages?) I’ll cover those in a later article. Hopefully, at this point, you’ll have a better idea of what data types are and what they can do.
My friend Steve Jones ‘blogged this, and this spoke to me enough that I thought it was worth a re-blog.
I was listening to a web developer talk about some fundamental changes in a web platform. In this case, an older system was being replaced completely with a new one, and as one of the reasons, the developer showed some typos that had existed on the old site for years and hadn’t been fixed. The reason? This quote:
“Very few people understand how the entire system works that are still in the building … The thought of deploying [changes] brought people to tears.”
That can’t happen. Ever. We can’t be afraid to touch systems. When this happens we get paralyzed, and we don’t do good work. Or we’re not a good fit for a project. Or perhaps, we’ve got a bad attitude.
I’ve worked in a few companies where developers were afraid to touch a system. It’s amazing how quickly this attitude becomes contagious, even scaring management from considering change…
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One thing I’ve been doing to improve my skill set is teach myself PowerShell. For those of you who don’t know what that is, here’s my description in a nutshell: it’s the command prompt on steroids.
So far, I’ve come across some references, some good, some not so good, to guide me in this endeavor. For my own reference (and maybe yours!), listed below are some of my favorite PowerShell references.
This is only a partial list, and I fully expect it to change. As I find more references that I like, I’ll update the above list!
Solving your business document and writing needs
Just me and the databases...
Hand-Crafted SQL and PowerShell from New York's Finger Lakes
SQL Blogs by Monica Rathbun
musings of a husband, dad, developer, teacher
Microsoft Azure, Data Platform, SQL, Speaking, Community
Ideas are the hard part...the rest is just code
Writings from Steve Jones, the Voice of the DBA
Thoughts on SQL, Disasters and thinking
Big Data and Data Warehousing
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