How to write an UPDATE query

My First UPDATE Statement

Last week we covered how to put information into a table using an INSERT statement.

This week we will learn how to make changes to data that is already in a table using an UPDATE statement. We are also going to learn all about why the WHERE clause is so important.

UPDATE

Updating information stored in a database can be as simple as identifying which column to update and specifying the new data to write in that column:

UPDATE [dbo].[Stores]
SET [StoreName] = N'West Edmonton Mall';
GO

The problem with this particular UPDATE statement, though, is that it will update every row, when we may only want to change one record. That’s why we need to add WHERE. The purpose of a WHERE clause is to restrict the data modification using a filter or limit.

The WHERE clause

In ANSI SQL (remember, ANSI is the standards organisation that defines how SQL works with different database platforms), a WHERE clause goes somewhere near the end of a query, to filter or limit the data being affected.

The WHERE keyword can be used on all four basic data manipulation queries: SELECT, INSERT, UPDATE and DELETE. The reason we’re only learning about it today, is that it is a critical part of the UPDATE and DELETE statements.

Limiting the damage

An UPDATE statement without a WHERE condition, or clause, will update the entire table.

Consider these two examples, and imagine what is happening in the table:

-- Without a WHERE clause
UPDATE [dbo].[Stores]
SET [StoreName] = N'West Edmonton Mall';
GO

-- With a WHERE clause
UPDATE [dbo].[Stores]
SET [StoreName] = N'West Edmonton Mall'
WHERE [StoreID] = 2;
GO;

In the first example, every single row in our table will now have ‘West Edmonton Mall’ for the store name. In the second example, only the row (or rows) that match the condition in the WHERE clause will be updated.

Notice how the WHERE condition relates to a value in the [StoreID] column, which is this table’s Primary Key. When updating individual rows in a table, it’s always better to use a unique value to guarantee that we only update a single row.

We could use a WHERE condition on a different column, or combinations of columns, using AND and OR logic statements. WHERE clauses can be extremely complex.

Note: the UPDATE statement will update whatever we tell it to. Even though the original value of the column was ‘West Edmonton Mall’, the database engine will happily update that value again and again, to the same string, if we ask it to.

SET

Astute readers will recognise a new keyword in the UPDATE statement: SET.

The first part of an UPDATE specifies the table we wish to modify.

Then, the SET keyword specifies the column we want to change. We can add more than one column to our SET clause, separated by commas, if we have more than one column in the same row (or rows) that we wish to update.

For instance, let’s assume the West Edmonton Mall store has a new manager. We can modify the [ManagerName] and [ManagerEmail] columns at the same time.

UPDATE [dbo].[Stores]
SET [ManagerName] = N'Wesley Wensleydale',
[ManagerEmail] = N'wesley@example.com'
WHERE [StoreID] = 2;
GO;

This operation, where more than one thing is being changed at the same time, is called a set-based operation. In other words, a set of data is being updated at once, instead of writing an UPDATE statement for every single column.

Set-based operations can run on the entire table, or on a filtered set of data specified by the WHERE clause.

Ambiguity Verboten

Unlike the INSERT statement, where column names were implied, an UPDATE statement has to explicitly list the column (or columns) that we are updating. In our examples above, we had [StoreName], [ManagerName] and [ManagerEmail]. This is because there can be absolutely no ambiguity when modifying data.

This is also why a WHERE clause is so important. I have personally run an UPDATE or DELETE without adding a WHERE clause, and it happens a lot in this field. Make sure to add a WHERE keyword before writing the rest of the statement.

Some tools that plug into SQL Server Management Studio will detect if we have missed a WHERE clause on an UPDATE or DELETE, but we can’t always rely on plugins. For example, we might have to use a different computer one day, or write our SQL code in a text editor like Notepad, and only good habits can avoid disaster.

The Final Results

If you’re following along in SQL Server Management Studio, we can run the three-column UPDATE statement like so:

UPDATE [dbo].[Stores]
SET [StoreName] = N'West Edmonton Mall',
[ManagerName] = N'Wesley Wensleydale',
[ManagerEmail] = N'wesley@example.com'
WHERE [StoreID] = 2;
GO

Once we have executed the statement (using F5 on the keyboard, or clicking the Execute button in the toolbar), we see a message in the Messages pane:

(1 row(s) affected)

Using our recently-learned skill to SELECT the data from this table, we will see the new result set, containing the new row:

Congratulations! We have modified a row of data in our table, and a SELECT query proved that it was inserted.

Next time, we will be removing data from a table using the DELETE command. Stay tuned.

Look me up on Twitter if you want to ask any questions about this series, at @bornsql.

How to write an INSERT query

My First INSERT Statement

Last week we covered how to get information out of a table, using a SELECT query.

This week, we will discover some of the myriad ways to put data into a table.

The good news is the concept is straightforward: we have a list of columns in a table, and each column has a datatype. We will insert a row into the table, according to the column order and datatype.

In reality, inserting data into a table is fraught with complexity.

Using our Stores table from before, the simplest way to write an INSERT statement is as follows:

INSERT INTO [dbo].[Stores]
VALUES (NULL,
N'West Edmonton Mall',
N'8882-170 Street, Edmonton, AB, T5T 4M2',
N'Stephanie West', N'stephanie@example.com');

Note: Remember that our store’s name and address, and manager’s name and email address, are all stored as Unicode (NVARCHAR), so the string has to be prefixed with an N. This guarantees that whatever is between the quotation marks is Unicode already and won’t be converted behind the scenes and potentially cause problems.

Ambiguity with column order

Our first problem is that it’s not clear which columns are being inserted into, nor their order.

Without a list of columns in the INSERT statement, the database engine will insert in the order provided, into whatever columns are on the table, and it might even do implicit conversions on data types behind the scenes.

It is therefore good practice to include the column names when writing an INSERT statement, like so:

INSERT INTO [dbo].[Stores] (
[StoreCode],
[StoreName],
[Address],
[ManagerName],
[ManagerEmail]
)
VALUES (
NULL,
N'West Edmonton Mall',
N'8882-170 Street, Edmonton, AB, T5T 4M2',
N'Stephanie West', N'stephanie@example.com'
);

Now we are sure that the column order is correct, and even if the table somehow has a different column order (for instance, the ManagerEmail and ManagerName are swapped around), this statement will succeed.

Where’s the StoreID column?

The astute reader has noticed there is no reference to the StoreID column, which happens to be the Primary Key for this table.

This is one of the ways a lot of accidental DBAs (and even experienced DBAs) get tripped up. If we think back to the table definition, we used an IDENTITY value.

To refresh our memories, an IDENTITY value is an auto-incrementing integer value, generated by the database engine, in order to ensure that the Primary Key is unique.

Note: It is because the IDENTITY is set that this column is being excluded from the INSERT statement above. Primary Keys which don’t have an IDENTITY set must be included in INSERT statements, provided that the value(s) being inserted will be unique.

NULL Values

We spoke about null values in the beginning of the series, and in this example we can see how to insert a NULL value into a table, provided the column definition allows nulls in the first place. We happen to know that the StoreCode is a nullable column.

Default Values

It is possible to exclude columns that have default values on them. If we think about it, an IDENTITY column is a column that has a default value assigned to it, which just happens to be an auto-incrementing integer.

We might want to have columns that have a default value of the current date and time. This convention is used when auditing database events by adding a CreatedDate column (for example) that defaults to the current date and time, using DATETIME2(7), when a row is inserted.

Another example might be to use a default value of 0 in a bit column and update that value later on.

In these cases, columns with default values can be excluded from the INSERT statement, because the database engine will automatically put the default value into that column.

That being said, there’s nothing stopping us from using a different value for a column that has a default value. If we have a default value on a column, like our DATETIME2(7) example, we could override that default value as long as we include the column and a new value in the INSERT statement.

Adding a column to the table

What happens if, during the course of regular business, a column is added to the Stores table? Unless that column has a default value, both examples of the INSERT statement above will fail.

The final results

If you’re following along in SQL Server Management Studio, we can run the INSERT statement like so:

INSERT INTO [dbo].[Stores] (
[StoreCode],
[StoreName],
[Address],
[ManagerName],
[ManagerEmail]
)
VALUES (
NULL,
N'West Edmonton Mall',
N'8882-170 Street, Edmonton, AB, T5T 4M2',
N'Stephanie West', N'stephanie@example.com'
);

Once we have executed the statement (using F5 on the keyboard, or clicking the Execute button in the toolbar), we see a message in the Messages pane:

(1 row(s) affected)

Using our recently-learned skill to SELECT the data from this table, we will see the new result set, containing the new row:

Congratulations! We have put a new row of data into a table, and a SELECT query proved that it was inserted.

Notice that the NULL value has a different background colour to the rest of the data. This is a way to distinguish actual null values from string columns that might just have the word “NULL” stored there.

Next time, we will be updating data in a table using the UPDATE command. Stay tuned.

Look me up on Twitter if you want to ask any questions about this series, on @bornsql.

How to write a SELECT query

My First SELECT Statement

Microsoft SQL Server makes it really easy for us to query tables. In SQL Server Management Studio (SSMS) for instance, we can right-click on any table we have access to and select the top 1000 rows from that table.

Don’t do this

Please don’t query tables this way in a production environment. It’s a bad way to do it, and you should feel bad.

Writing a SELECT statement should be done manually, the way Ada Lovelace and Grace Hopper intended.

Let’s assume we want to get a list of the stores in our database that we created in the First Look at Normalization post.

The table had the following columns: StoreID, StoreCode, StoreName, Address, ManagerName, and ManagerEmail.

To get a list of all rows and all columns in a table, we write the following statement:

SELECT
[StoreID],
[StoreCode],
[StoreName],
[Address],
[ManagerName],
[ManagerEmail]
FROM
[dbo].[Stores];

Remember from previous posts that the square brackets are a matter of style, and we could just as easily exclude them.

I leave them in because humans are terrible at naming things, and the square brackets make the code less likely to fail.

You’ll notice that there is a semi-colon at the end of the statement. We could have placed the entire statement on one line, like so:

SELECT [StoreID], [StoreCode], [StoreName], [Address], [ManagerName], [ManagerEmail] FROM [dbo].[Stores];

This is more difficult to read. SQL Server doesn’t really care about white space, including carriage returns, so feel free to format your code nicely so that it’s easy for you to read.

If you’re typing this in Management Studio, you can now press the Execute button in the menu, or the F5 key on the keyboard, to run the command.

Tip: in Management Studio, we can select just the text (T-SQL code) we want to run, using the mouse, and then press F5. This will guarantee that no other part of the script will run as well. It’s a useful way to run portions of code in a longer script.

Once we run the SELECT statement, we see a result set.

Click to enlarge

Congratulations! We have asked SQL Server for data, and it has responded with the data we asked for.

Next time, we will be adding data to a table using the INSERT command. Stay tuned.

Look me up on Twitter if you want to ask any questions about this series, on @bornsql.

Querying a Database

When we want to retrieve information from a database, we query the structure with language appropriate to the database.

Remember right at the start of this series we saw that a database could be a phone book or a recipe book. So how do we find the phone number of Randolph West? By looking up the surnames starting with W, and going through all the Wests in alphabetical order until we get to that entry.

The same goes for finding the recipe for lemon meringue in a recipe book. Start at the index at the back, look through the desserts, and then search for meringue.

In a RDBMS (relational database management system), the language for querying data is called Structured Query Language, or SQL. We can pronounce it like “sequel”, or sound out each letter.

SQL Server is commonly pronounced “Sequel Server”. MySQL is pronounced “My-S-Q-L”, sounding out the letters (some people pronounce it “My-Sequel”). It all depends on who’s saying it. PostgreSQL is just pronounced “Postgres”, because seriously.

These, along with Oracle, are the major players in the RDBMS industry.

ANSI SQL

When it comes to putting information into a database server, and getting information out, we can write queries that look very similar across platforms. This is thanks to a standards body called ANSI (American National Standards Institute), which (with proposals from each vendor) has suggested ANSI SQL syntax that all vendors should use.

For the sake of this series, we will assume that the vendors do follow the standard exactly, but in reality it isn’t that simple.

Putting data in: INSERT

Adding new data to a database is performed using an INSERT operation.

Changing data: UPDATE

Modifying existing data is done with an UPDATE operation.

Getting rid of data: DELETE

Removing rows from a table is performed with a DELETE operation.

Getting data out: SELECT

The vast majority of operations in a database has to do with data retrieval. To get data out, we SELECT it.

CRUD

In technical circles, between software developers and database developers, we might refer to these four operations using the mnemonic CRUD, which stands for Create, Read, Update, Delete.

When referring to a specific database or application, it could mean that the database is just being used as a data store (a virtual box of random stuff) and may not have proper relationships between the tables, nor be normalized.

There’s nothing necessarily evil about denormalized data, because the application code may handle that intelligence. Be wary though. Using an RDBMS to store non-relational data might cause headaches.

Next time we will write our first SELECT statement to query a database. Stay tuned!

Look me up on Twitter if you want to ask any questions about this series, on @bornsql.

Normalization, The Sequel

If there’s one thing that SQL Server is really good at, it’s relationships. After all, a relational database management system without the relationships is nothing more than a place to store your stuff.

Last week we briefly looked at a denormalized table, and then I suggested that breaking it up into five separate tables would be a good idea. So good, in fact, that it took me more than 2,000 words to explain just the first table in our highly contrived example.

Assuming you have read through all those words, let’s attempt a much more condensed look at the other four tables. If you recall, we had:

  • Transactions
  • Products
  • Customers
  • Salespersons
  • Stores

We tackled the Stores table first because everything is backwards when we design databases.

For the next three tables, I’m going to just show you how I would design them, without going into detail. Take a close look at Salespersons, though (which we’ll look at first) because it will give you a clue about how we link all the tables together finally in the Transaction table.

Then take a look at … PaymentTypes? ProductTypes? Colours? Categories? Sizes? Uh … What’s going on here? Where did all those tables come from? Luckily, T-SQL allows comments, which you’ll see below.

CREATE TABLE [Salespersons] (
[SalespersonID] SMALLINT NOT NULL IDENTITY(1,1),
[StoreID] SMALLINT NOT NULL,
[FirstName] NVARCHAR(255) NOT NULL,
[LastName] NVARCHAR(255) NOT NULL,
[EmailAddress] NVARCHAR(512) NOT NULL
CONSTRAINT [PK_Salespersons] PRIMARY KEY CLUSTERED ([SalespersonID] ASC)
);

-- List of possible payment types, (e.g. credit card, cheque)
CREATE TABLE [PaymentTypes] (
[PaymentTypeID] TINYINT NOT NULL IDENTITY(1,1),
[Description] VARCHAR(255) NOT NULL,
CONSTRAINT [PK_PaymentTypes] PRIMARY KEY CLUSTERED ([PaymentTypeID] ASC)
);

CREATE TABLE [Customers] (
[CustomerID] BIGINT NOT NULL IDENTITY(1,1),
[FirstName] NVARCHAR(255) NOT NULL,
[LastName] NVARCHAR(255) NOT NULL,
[EmailAddress] NVARCHAR(512) NOT NULL,
[Telephone] VARCHAR(25) NOT NULL,
[PaymentTypeID] TINYINT NOT NULL,
CONSTRAINT [PK_Customers] PRIMARY KEY CLUSTERED ([CustomerID] ASC)
);

-- List of possible product types (e.g. iPhone, iPhone cover, iPod)
CREATE TABLE [ProductTypes] (
[ProductTypeID] TINYINT NOT NULL IDENTITY(1,1),
[Description] VARCHAR(255) NOT NULL,
CONSTRAINT [PK_ProductTypes] PRIMARY KEY CLUSTERED ([ProductTypeID] ASC)
);

-- List of possible colours
CREATE TABLE [Colours] (
[ColourID] TINYINT NOT NULL IDENTITY(1,1),
[Description] VARCHAR(255) NOT NULL,
CONSTRAINT [PK_Colours] PRIMARY KEY CLUSTERED ([ColourID] ASC)
);

-- List of possible categories (5.5", 4.7", 4", 3.5")
-- This replaces "size", since we might use Size to denote storage
CREATE TABLE [Categories] (
[CategoryID] TINYINT NOT NULL IDENTITY(1,1),
[Description] VARCHAR(255) NOT NULL,
CONSTRAINT [PK_Categories] PRIMARY KEY CLUSTERED ([CategoryID] ASC)
);

-- List of possible sizes ("8GB", "16GB", "32GB", etc.)
-- Can also be used for other product types like laptops
CREATE TABLE [Sizes] (
[SizeID] TINYINT NOT NULL IDENTITY(1,1),
[Description] VARCHAR(255) NOT NULL,
CONSTRAINT [PK_Sizes] PRIMARY KEY CLUSTERED ([SizeID] ASC)
);

CREATE TABLE [Products] (
[ProductID] TINYINT NOT NULL IDENTITY(1,1),
[ProductTypeID] TINYINT NOT NULL,
[ColourID] TINYINT NOT NULL,
[CategoryID] TINYINT NOT NULL,
[SizeID] TINYINT NOT NULL,
[SellingPrice] SMALLMONEY NOT NULL,
CONSTRAINT [PK_Products] PRIMARY KEY CLUSTERED ([ProductID] ASC)
);

Those tables popped out of nowhere, didn’t they? Welcome to the world of normalization. To design a database properly, we come to the realisation that we can simplify the data input even more, reducing repeated values, and finding the most unique way of representing data. Products comprise product types. A single list of colours can be reused in various places. Payment types can be used for all sorts of transactional data.

We end up with a lot of tables when we normalize a database, and this is perfectly normal. When we want to read information out of the system the way senior management wants, we must join all these tables together.

The only way to join tables together in a safe and meaningful way is with foreign key relationships, where one table’s primary key is referenced in another table, with a matching data type.

The SalesPersons table has a StoreID column. As it stands, there’s no relationship between Stores and SalesPersons until we create the relationship using T-SQL.

ALTER TABLE [SalesPersons]
ADD CONSTRAINT FK_SalesPersons_Stores FOREIGN KEY (StoreID)
REFERENCES [Stores] (StoreID);

  • Line 1: Inform SQL Server that we are altering an existing table
  • Line 2: By adding a foreign key constraint (i.e. limiting what can go into the StoreID column)
  • Line 3: By forcing it to use the values from the Stores table’s StoreID column (i.e. the primary key).

In a relationship diagram, it looks like this (in SQL Server Management Studio’s Database Diagram tool):

The yellow key in each table is the Primary Key (StoreID and SalespersonID respectively). There is a StoreID column in both tables with the same data type (SMALLINT). The foreign key (FK) does not have to match the name of the primary key (PK), but it makes things a lot easier to have the same name for both sides of a relationship in large databases, so it’s a good habit.

Notice the direction of the relationship (FK_Salespersons_Stores) in the picture, with the yellow key on the table with the Primary Key. The name of the relationship is also sensible. To the casual eye, this says that there’s a Foreign Key constraint in the Salespersons table that points to the Primary Key in the Stores table.

Now we see why data types are so important with relational data. A relationship is not even possible between two tables if the data type is not the same in both key columns.

With this constraint enabled, whenever we insert data into the Salespersons table, we have to make sure that whatever we put into the StoreID column must already exist in the Stores table.

Let’s do the rest of the relationships so far, and then we’ll look at the Transactions table.

ALTER TABLE [Customers]
ADD CONSTRAINT FK_Customers_PaymentTypes FOREIGN KEY (PaymentTypeID)
REFERENCES [PaymentTypes] (PaymentTypeID);

ALTER TABLE [Products]
ADD CONSTRAINT FK_Products_ProductTypes FOREIGN KEY (ProductTypeID)
REFERENCES [ProductTypes] (ProductTypeID);

ALTER TABLE [Products]
ADD CONSTRAINT FK_Products_Colours FOREIGN KEY (ColourID)
REFERENCES [Colours] (ColourID);

ALTER TABLE [Products]
ADD CONSTRAINT FK_Products_Categories FOREIGN KEY (CategoryID)
REFERENCES [Categories] (CategoryID);

ALTER TABLE [Products]
ADD CONSTRAINT FK_Products_Sizes FOREIGN KEY (SizeID)
REFERENCES [Sizes] (SizeID);

As we can see now, we have more than one foreign key relationship in the Products table, to ProductTypes, Colours, Categories, and Sizes, which is a clue to how the Transactions table will look.

CREATE TABLE [Transactions] (
[TransactionID] BIGINT NOT NULL IDENTITY(1,1),
[TransactionDate] DATETIME2(3) NOT NULL,
[ProductID] TINYINT NOT NULL,
[DiscountPercent] DECIMAL(4,2) NOT NULL DEFAULT(0),
[SalesPersonID] SMALLINT NOT NULL,
[CustomerID] BIGINT NOT NULL,
[HasAppleCare] BIT NOT NULL DEFAULT(0),
CONSTRAINT [PK_Transactions] PRIMARY KEY CLUSTERED ([TransactionID] ASC)
);

Let’s assume we’ve created all the relationships as well, so that we’re left with the following relationships (click to enlarge):

Ten tables, compared to our original one denormalized table, is a significant increase in number of tables. However, let’s compare the data usage for adding transactions.

If we were to populate each row of each table with enough data to provide the same number of transactions as last week’s two purchases, it would look like this.

Stores table: 172 bytes

  • StoreID: 2 bytes (IDENTITY value as SMALLINT)
  • StoreCode: 0 bytes (NULL)
  • StoreName: 30 bytes (“Chinook Centre” is 14 characters long including the space, so with Unicode that becomes 28 bytes, plus 2 for the overhead of using NVARCHAR)
  • Address: 86 bytes (“6455 Macleod Trail SW, Calgary, AB T2H 0K8” is 42 characters, so 84 with Unicode, plus 2 bytes for NVARCHAR overhead)
  • ManagerName: 22 bytes (“Bob Bobson” in Unicode, plus 2 bytes NVARCHAR overhead)
  • ManagerEmail: 32 bytes (“bob@example.com” in Unicode, plus 2 bytes NVARCHAR overhead)

Salespersons table: 70 bytes

  • SalespersonID: 2 bytes
  • StoreID: 2 bytes
  • FirstName: 14 bytes (“Thandi” in Unicode + 2 bytes)
  • LastName: 14 bytes (“Funaki” in Unicode + 2 bytes)
  • EmailAddress: 38 bytes (“thandi@example.com” in Unicode + 2 bytes)

PaymentTypes table: 14 bytes

  • PaymentTypeID: 1 byte
  • Description: 13 bytes (“Credit Card” + 2 bytes)

ProductTypes table: 23 bytes

  • ProductTypeID: 1 byte
  • Description: 8 bytes (“iPhone” + 2 bytes)
  • ProductTypeID: 1 byte
  • Description: 13 bytes (“iPhone Case” + 2 bytes)

Colours table: 21 bytes

  • ColourID: 1 byte
  • Description: 7 bytes (“Black” + 2 bytes)
  • ColourID: 1 byte
  • Description: 6 bytes (“Blue” + 2 bytes)
  • ColourID: 1 byte
  • Description: 5 bytes (“Red” + 2 bytes)

Categories table: 7 bytes

  • CategoryID: 1 byte
  • Description: 6 bytes (“5.5″” + 2 bytes)

Sizes table: 16 bytes

  • SizeID: 1 byte
  • Description: 7 bytes (“128GB” + 2 bytes)
  • SizeID: 1 byte
  • Description: 7 bytes (“256GB” + 2 bytes)

Products table: 27 bytes (3 products at 9 bytes each)

  • ProductID: 1 byte
  • ProductTypeID: 1 byte
  • ColourID: 1 byte
  • CategoryID: 1 byte
  • SizeID: 1 byte
  • SellingPrice: 4 bytes

Customers table: 192 bytes

  • CustomerID: 8 bytes
  • FirstName: 10 bytes (“I.M.” in Unicode + 2 bytes)
  • LastName: 18 bytes (“Customer” in Unicode + 2 bytes)
  • EmailAddress: 42 bytes (“customer@example.com” in Unicode + 2 bytes)
  • Telephone: 16 bytes (“(403) 555-1212” + 2 bytes)
  • PaymentTypeID: 1 byte
  • CustomerID: 8 bytes
  • FirstName: 10 bytes (“U.R.” in Unicode + 2 bytes)
  • LastName: 18 bytes (“Customer” in Unicode + 2 bytes)
  • EmailAddress: 44 bytes (“customer2@example.com” in Unicode + 2 bytes)
  • Telephone: 16 bytes (“(403) 665-0011” + 2 bytes)
  • PaymentTypeID: 1 byte

Transactions table: 96 bytes (32 bytes per transaction)

  • TransactionID: 8 bytes
  • TransactionDate: 7 bytes
  • ProductID: 1 byte
  • DiscountPercent: 5 bytes
  • SalesPersonID: 2 bytes
  • CustomerID: 8 bytes
  • HasAppleCare: 1 bit (expands to 1 byte)

GRAND TOTAL: 638 bytes to represent all three transactions

The denormalized version, for which we can see the original example below, works out as follows. Recall we said last week that each column was NVARCHAR(4000), or possibly even NVARCHAR(MAX).

A Wide Table
A Wide Table Appears – click to enlarge

At our most generous, we would need 1,166 bytes to record these three transactions. That’s almost double the data required, just for these three. Plus, the data has no foreign key relationships, so we cannot be sure that whatever is being added to the denormalized table is valid.

As time goes on, the normalized tables will grow at a much lower rate proportionally. Consider what a denormalized Transactions table would look like with an average row size of 388 bytes, for ten million rows (3.6GB).

Compare that to a normalized database, with ten million transactions for 8 million customers. Even assuming we have a hundred products, with twenty colours, and 30 product types, we would see only around 1GB of space required to store the same data.

We know Apple as being one of the most successful technology companies in terms of sales, so extrapolating to 1 billion transactions, we’d be comparing 361GB (for the denormalized table) with less than half that (178GB) if every single customer was unique and only ever bought one item.

Aside from the staggering savings in storage requirements, normalization gives us sanity checks with data validation by using foreign key constraints. Along with proper data type choices, we have an easy way to design a database properly from the start.

Sure, it takes longer to start, but the benefits outweigh the costs almost immediately. Less storage, less memory to read the data, less space for backups, less time to run maintenance tasks, and so on.

Normalization matters.

Next week, we talk briefly about bits and bytes, and then we will start writing queries. Stay tuned.

Find me on Twitter to discuss your favourite normalization story at @bornsql.

A First Look At Normalization

Phew! There’s a lot to take in with data types, collation, precision, scale, length, and Unicode, and we’re just getting warmed up. This week’s post is over 2,000 words long!

Over the last three weeks, we’ve gone fairly deep into data types, and now we are going to see how they come into play with normalization.

If we go back to the first post in this series, I mentioned normalization, and then apparently I forgot about it in the next two posts. What you didn’t see is that I was talking about it all along.

Continue reading “A First Look At Normalization”

Fundamentals of Data Types

Last week, we discussed storing text in a database. This week we will dive deeper into data types.

When storing data in our database, we want to make sure that it’s stored accurately and that we only use the required amount of space.

This is because when we access the data later, we want to make sure any calculations are accurate; plus reading the data takes up memory, and we want to be as efficient as we can with memory usage.

There are seven data type categories in SQL Server:

  • exact numerics
  • approximate numerics
  • date and time
  • character strings
  • Unicode character strings
  • binary strings
  • other

When we want to use these data types for our columns, we need to declare them. Some require a length, some require a precision and scale, and some can be declared without a length at all. For example:

No Length (implied in data type):
DECLARE @age AS TINYINT;

Explicit Length (length is supplied):
DECLARE @firstName AS VARCHAR(255);

Precision and Scale:
DECLARE @interestRate AS DECIMAL(9,3);

Let’s talk a bit about precision and scale, because those values between the brackets may not work the way we think they do.

Precision and Scale

Data types with decimal places are defined by what we call fixed precision and scale. Let’s look at an example:

123,456.789

In the above number, we see a six-digit number (ignoring the thousand separator) followed by a decimal point, and then a fraction represented by three decimal places. This number has a scale of 3 (the digits after the decimal point) and a precision of 9 (the digits for the entire value, on both sides of the decimal point). We would declare this value as DECIMAL(9,3).

This is confusing at first glance, because we have to declare it “backwards”, with the precision first, and then the scale. It may be easier to think of the precision in the same way we think of a character string’s length.

Date and time data types can also have decimal places, and SQL Server supports times accurate to the nearest 100 nanoseconds. The most accurate datetime is DATETIME2(7), where 7 decimal places are reserved for the time.

Before SQL Server 2008, we used DATETIME, which is only accurate to the nearest 3 milliseconds, and uses 8 bytes. A drop-in replacement for this is DATETIME2(3), using 3 decimal places, and accurate to the nearest millisecond. It only needs 7 bytes per column.

Be mindful that, as higher precision and scale are required, a column’s storage requirement increases. Accuracy is a trade-off with disk space and memory, so we may find ourselves using floating point values everywhere.

However, in cases where accuracy is required, always stick to exact numerics. Financial calculations, for example, should always use DECIMAL and MONEY data types.

Exact Numerics

Exact Numerics are exact, because any value that is stored is the exact same value that is retrieved later. These are the most common types found in a database, and INT is the most prevalent.

Exact numerics are split up into integers (BIGINT, INT, SMALLINT, TINYINT, BIT) and decimals (NUMERIC, DECIMAL, MONEY, SMALLMONEY). Decimals have decimal places (defined by precision and scale), while integers do not.

Integers have fixed sizes (see table below), so we don’t need to specify a length when declaring this data type.

Type Bytes Range
BIGINT 8 bytes -2^63 to 2^63-1
INT 4 bytes -2^31 to 2^31-1
SMALLINT 2 bytes -2^15 to 2^15-1
TINYINT 1 byte 0 to 255
BIT 1 bit 0 to 1
  • BIT is often used for storing Boolean values, where 1 = True and 0 = False.
  • Yes, BIGINT can store numbers as large as 2 to the power of 63 minus 1. That’s 19 digits wide, with a value of 9,223,372,036,854,775,807, or 9.2 quintillion.

Decimals may vary depending on the precision and scale, so we have to specify those in the declaration.

Type Bytes Range
DECIMAL 5 to 17 bytes Depends on precision and scale.
38 digits is the longest possible precision.
NUMERIC
  • DECIMAL and NUMERIC are synonyms and can be used interchangeably. Read more about this data type, and how precision and scale affects bytes used, here.

Although the MONEY and SMALLMONEY data types do have decimal places, they don’t require the precision and scale in the declaration because these are actually synonyms for DECIMAL(19,4) and DECIMAL(10,4) respectively. Think of these data types for convenience more than anything.

Type Bytes Range
MONEY 8 bytes -922,337,203,685,477.5808 to 922,337,203,685,477.5807
SMALLMONEY 4 bytes -214,748.3648 to 214,748.3647

Approximate Numerics

Approximate Numerics mean that the value stored is only approximate. Floating point numbers would be classified as approximate numerics, and these comprise FLOAT and REAL.

Declaring a FLOAT requires a length, which represents the number of bits used to store the mantissa. REAL is a synonym of FLOAT(24).

The mantissa means the significant digits of a number in scientific notation, which is how floating point numbers are represented. The default is FLOAT(53). Generally, we stick to the defaults, and use REAL if we want to save space, forgoing some accuracy of the larger FLOAT(53).

Type Bytes Range
FLOAT 4 or 8 bytes -1.79E+308 to -2.23E-308, 0 (zero),
and 2.23E-308 to 1.79E+308
REAL 4 bytes -3.40E+38 to -1.18E-38, 0 (zero),
and 1.18E-38 to 3.40E+38

Date and Time

Date and time data types are slightly more complex. For storing dates (with no time), we use DATE. We store times (with no dates) using TIME. For storing both date and time in the same column, we can use DATETIME2, DATETIME, or SMALLDATETIME. Finally, we can even store timezone-aware values comprising a date and time and timezone offset, using DATETIMEOFFSET.

DATETIME2, TIME, and DATETIMEOFFSET take a length in their declarations, otherwise they default to 7 (accurate to the nearest 100 nanoseconds).

Character Strings

As we saw last week, characters can be fixed-length (CHAR) or variable-length (VARCHAR), and can support special Unicode character types (NCHAR and NVARCHAR respectively). Collation should also be taken into account.

Length can be 1 to 8000 for CHAR and VARCHAR, or 1 to 4000 for NCHAR and NVARCHAR. For storing values larger than that, see the Large Objects section below.

Binary Strings

Sometimes we want to store binary content in a database. This might be a JPEG image, a Word document, an SSL certificate file, or anything that could traditionally be saved on the file system. SQL Server provides the BINARY and VARBINARY data types for this (and IMAGE for backward compatibility).

Length can be 1 to 8000 for BINARY and VARBINARY. For storing values larger than that, see the Large Object section below.

Large Objects

SQL Server 2008 introduced a new MAX length for several data types, including CHAR, NCHAR, VARCHAR, NVARCHAR, BINARY and VARBINARY.

(The XML data type uses MAX under the covers as well.)

This new specification allows up to 2 GB of data to be stored in a column with that declared length. We should take care not to use 2 GB when inserting data into these columns, but it provides greater flexibility when inserting more than 8000 bytes into one of these columns.

Other Data Types

SQL Server supports other types of data, which fall outside the scope of text and numerics. These include CURSOR, TABLE, XML, UNIQUEIDENTIFIER, TIMESTAMP (not to be confused with the date and time types), HIERARCHYID, SQL_VARIANT, and Spatial Types (GEOGRAPHY and GEOMETRY).

Next week, we will see how normalization and data types work together, now that we have a good overview of the different data types in a database.

If you have any thoughts or comments, please find me on Twitter at @bornsql.

Data Types and Collation

Last week we started with a very simple definition of a database: a discrete set of information, with a specific structure and order to it.

We briefly looked at normalization, which is a way to store as little of the information as possible, so that it stays unique.

We will cover more normalization as we move forward through this series, but first we will talk about how the information, or data, is stored. (This does affect normalization and relationships, even if that is not immediately clear.)

For this week’s discussion, we need to consider a spreadsheet application, like Microsoft Excel or Google Sheets.

Columns and Rows

In a spreadsheet, we have columns and rows. Usually we will also have a header row, so we can distinguish between each column.

Each column, in turn, may be formatted a certain way so that we can easily see what kind of information is in that column. For instance, we may want to format a column as a currency, with leading symbol and the two decimal places at the end.

We may left-align text values, and we may decide that numbers have no decimal places and are right-aligned. Dates and times get their own formatting.

If we were to compare this structure to that of a database, we can imagine that each sheet is a table, and each column and row is a column and row in the table.

In some applications like Microsoft Access, we may hear different terminology for columns and rows, namely fields and records. However, in SQL Server, we maintain the same convention as Excel and call them columns and rows.

Because SQL Server doesn’t care about how our data looks, we have to specify those traits when we create the table. Whether creating from scratch or from an import process through an external application (Excel, for example), we need to specify the data type for each column.

There are several key reasons why we want to do this.

In the case of numbers that will be summarized in some way (sum, average, minimum, maximum, mean, mode), we want SQL Server’s database engine to treat these as numbers internally so that it doesn’t have to convert anything, which in turn makes the calculations much faster.

The same goes for dates, times, and datetimes (where both the date and time is in one column) because the database engine understands date and time calculations, provided the data types are correct.

Text values are also very important but for a fundamentally different reason. While computers understand numbers, it’s humans that understand text.

We will focus the rest of this week’s discussion on storing strings in a database.

Collation

Imagine we are developing a database for international customers, and we need to support accented characters or an entirely different alphabet. Database systems use a catch-all term for this, and that is collation.

When we install SQL Server, we are asked what the “default” is, then we are presented with some arcane terminology which may be confusing, so we leave the defaults and click Next.

Collation has to do with how data is sorted, and thus the order in which we see it when data is returned.

Note that collation only affects text columns.

The Windows regional settings, for the user installing SQL Server, will affect the default collation of a SQL Server installation. If we were to install SQL Server on a machine that is configured with U.S. regional settings, it will have a very different default collation than a server that is set for Canada or Finland.

The default SQL Server collation for US regional settings (SQL_Latin1_General_CP1) may need to be changed to match what is required for the user databases that will be running on a server.

The above values mean the following:

  • General – the sort order follows 0-9, A-Z;
  • CP1 – code-page 1, the US English default;
  • Case Insensitivity and Accent Sensitivity are implied (see below).

When not using US English, or the Latin alphabet, we need to be aware that the data’s sort order is taken into account.

Even more confusingly, some vendor products require a specific collation for their database. For example, Microsoft’s own SharePoint database uses the collation Latin1_General_CI_AS_KS_WS:

  • CICase Insensitive – no difference between upper and lower case when sorting data;
  • ASAccent Sensitive – distinguishes between accented characters, for instance, the Afrikaans words “sê” and “se” are considered different;
  • KSKana Sensitive – distinguishes between different Japanese character sets;
  • WSWidth Sensitive – distinguishes between characters that can be expressed by both single- or double-byte characters.

(Read up more about collation options here.)

Text Data Types

Now that we have a very basic grasp of collation, let’s look at text data types.

We tend to use only four text data types in SQL Server these days:

CHAR(n), NCHAR(n), VARCHAR(n), and NVARCHAR(n), where n may be a number between 1 and 8,000 or the keyword MAX.

Why 8,000?

For historic reasons, SQL Server set their data page size (the amount of storage available on each data page, including headers and footers) to 8KB many years ago. This means that the largest amount of data we can store on a single page is 8,192 bytes. Once we take away the header and the slot array at the end, we are left with slightly more than 8,000 bytes for our data.

When we store a text value, we need to decide if the characters can be expressed in a single byte or as double-byte characters (also known as Unicode, using two bytes per character). Alphabets like Kanji, Chinese (Simplified or Traditional), and Turkish, will require double-byte characters, for each character in their alphabet.

(Some code pages need more than two bytes for a character. That is outside of the scope of this discussion.)

So CHAR or VARCHAR uses one byte per character, while NCHAR and NVARCHAR uses two bytes per character (the N represents Unicode).

Thus, the longest a CHAR or VARCHAR string can be is 8000, while the longest an NCHAR or NVARCHAR string can be is 4000 (at two bytes per character).

MAX Data Type

In SQL Server 2008, several new data types were introduced, including the MAX data type for strings and binary data. The underlying storage mechanism was changed to allow columns longer than 8,000 bytes, where these would be stored in another section of the database file under certain conditions.

The MAX data type allows up to 2 GB (more than two billion bytes) for every row that column is used.

So we have to consider three distinct things when deciding how we store text: collation, Unicode, and string length.

Because my readers are terribly intelligent, you’ve already deduced that the VAR in VARCHAR means “variable length”, and you’d be correct.

We use VARCHAR (and its Unicode equivalent NVARCHAR) for columns that will contain strings with variable lengths, including names, addresses, phone numbers, product names, etc. In fact, along with INT (meaning a 4-byte integer), VARCHAR is probably the most common data type in any database today.

CHAR (and NCHAR), on the other hand, are fixed-length data types. We use this type for string lengths that are unlikely to change. For example, IMEI numbers, Vehicle Identification Numbers, social security numbers (where the dash forms part of the number), product codes, serial numbers with leading zeroes, and so on. The point here is that the length is fixed.

So why don’t we just use VARCHAR instead of CHAR everywhere?

Let’s start with why VARCHAR was introduced in the first place, and why we would use it instead of CHAR.

For columns with unpredictably long strings, we don’t want to reserve all 8,000 bytes per row for a string that may only take up 2,000 bytes—and end up wasting 6,000 (not to mention the storage required for a MAX column)—so we switch to VARCHAR, and each row only uses as many bytes as it needs.

However, SQL Server needs to keep track of the length of a VARCHAR column for each row in a table. There is a small overhead of a few bytes per row for every VARCHAR for SQL Server to keep track of this length. The reason we don’t replace CHAR and NCHAR outright, is ironically to save space.

It doesn’t make sense for a table containing millions or billions of rows to use VARCHAR for fixed-length columns because we would be adding on another few bytes per row as unnecessary overhead. Adding just one byte per million rows is roughly 1 MB of storage.

Extrapolating that extra byte to the memory required to hold it, maintenance plans when updating indexes and statistics, backups, replicated databases, and so on, we are now looking at extra megabytes, and possibly gigabytes, for the sake of convenience.

We must make sure that we pick the correct character type for storing strings, beyond just the length of the string. Both CHAR and VARCHAR have their place.

While we did spend most of this discussion on collations and text, we’ve only scratched the surface.

Next week, we will discuss how to pick the right data type for your columns, with concrete examples. This matters a lot with how numbers are stored.

If you have any feedback, find me on Twitter at @bornsql.

Testing for Object Existence: CREATE OR ALTER

For the longest time, T-SQL writers have had to wrestle with ways of testing for an object’s existence so that it can either be dropped and recreated, or modified as needed.

Last week we covered the new DROP ... IF EXISTS syntax. This week goes into how we handle changes to objects.

We’ve spent many hours of our lives fighting with an object existence check so that ALTER commands don’t fail.

The common implementation pattern now is to CREATE a dummy object with the appropriate name and then use ALTER to write the actual code. That way, future changes can just be done by using the ALTER keyword.

We can see this in a famous example: sp_WhoIsActive, an extremely popular stored procedure written by Adam Machanic, which I highly recommend installing when setting up a new SQL Server instance.

USE master;
GO

IF NOT EXISTS (SELECT * FROM INFORMATION_SCHEMA.ROUTINES
WHERE ROUTINE_NAME = 'sp_WhoIsActive')
EXEC ('CREATE PROC dbo.sp_WhoIsActive AS
SELECT ''stub version, to be replaced''');
GO
...
ALTER PROC dbo.sp_WhoIsActive (...);

Look at all that unnecessary code. It’s messy; it’s prone to errors. There are so many ways to do it, which makes it inconsistent, meaning that automatically searching through a code base isn’t reliable. Additionally, if we miss a single quotation mark, the entire script fails.

In a word: Ugh!

What happens if we forget to run the stub first? What happens if we have an existing object and run the CREATE accidentally, then we have to manually change it to an ALTER

(Side note: I spent a good few minutes one day a few years ago not remembering the ALTER keyword.)

SQL Server 2016 Service Pack 1 has finally added in a feature that many DBAs and database developers have been clamouring for: CREATE OR ALTER.

In the above example, the entire IF NOT EXISTS section can be replaced with:

USE master;
GO

CREATE OR ALTER PROC dbo.sp_WhoIsActive (...);

While this has been a long time coming, causing many anguished cries from people writing T-SQL scripts over the years, we are going to love using this new, small, yet significant, syntax.

Share your best ALTER story with me on Twitter, at @bornsql.

Testing for Object Existence: DROP … IF EXISTS

For the longest time, T-SQL writers have had to wrestle with ways of testing for an object’s existence so that it can either be dropped and recreated, or modified as needed.

This is especially common in the case of temp tables or table variables. If the object already exists when our script runs, the script will fail and leave our workflow in an inconsistent state.

Consider this script:

IF OBJECT_ID('tempdb..#doTheThing') IS NOT NULL
DROP TABLE #doTheThing;

CREATE TABLE #doTheThing (
ImportantColumn1 BIGINT,
ImportantColumn2 TINYINT,
ImportantColumn3 NVARCHAR(255)
);

With SQL Server 2016, we can now do this:

DROP TABLE IF EXISTS #doTheThing;

CREATE TABLE #doTheThing (
ImportantColumn1 BIGINT,
ImportantColumn2 TINYINT,
ImportantColumn3 NVARCHAR(255)
);

DROP ... IF EXISTS can be used on many objects, including DATABASE, FUNCTION, INDEX, PROCEDURE, ROLE, SCHEMA, SEQUENCE, SYNONYM, TABLE, TRIGGER, TYPE, USER and VIEW.

Share your object existence check nightmares with me on Twitter at @bornsql.