Constraints are sometimes annoying in real life, but no society can exist without rules and regulations. The same concept is found in Database Design: no good data can exist without constraints.
What they say in Heaven
Constraints define what is acceptable in the database and what does not comply with business rules. In Heaven, where the perfect database runs smoothly, no constraint is overlooked and all the data obeys to the rules of angels:
- Every column accepts only the data it was meant for, using the appropriate data type
- Every column that requires a value has a NOT NULL constraint
- Every column that references a key in a different table has a FOREIGN KEY constraint
- Every column that must comply with a business rule has a CHECK constraint
- Every column that must be populated with a predefined value has a DEFAULT constraint
- Every table has a PRIMARY KEY constraint
- Every group of columns that does not accept duplicate values has a UNIQUE constraint
Chaos belongs to hell
OK: Heaven is Heaven, but what about hell? Let’s see what will get you instant damnation:
- Using the wrong data type: we already found out that the SQL Server hell is full of Shaky Typers. The data type is the first constraint on your data: choose it carefully.
- No PRIMARY KEY constraints: In the relational model, tables have primary keys. Without a primary key, a table is not even a table (exception made for staging tables and other temporary objects). Do you want duplicate data and unusable data? Go on and drop your primary key.
- NULL and NOT NULL used interchangeably: NOT NULL is a constraint on your data: failing to mark required columns with NOT NULL will inevitably mean that you’ll end up having missing information in your rows. At the same time, marking all columns as NOT NULL will bring garbage data in the database, because users will start using dummy data to circumvent the stupid constraint. We already met these sinners in the First Circle of the SQL Server hell.
- No Foreign Key constraints: Foreign Keys can be annoying, because they force you to modify the database in the correct order, but following the rules pays off. Without proper constraints, what would happen if you tried to delete from a lookup table a key referenced in other tables? Unfortunately, it would work, silently destroying the correctness of your data.
What would happen if you tried to sneak in a row that references a non-existing key? Again, it would bring in invalid data.
- No CHECK constraints: Many columns have explicit or implicit constraints: failing to add them to the database schema means that values forbidden by the business rules will start to flow into the database. Some constraints are implicit, but equally important as the explicit ones. For instance:
- an order should never be placed in a future date
- a stock quantity should never be negative
- a ZIP code should only contain numeric characters
- a Social Security Number should be exactly 9 digits long
- Relying on the application to validate data: If I had €0.01 for every time I found invalid data in a database and the developers said “the application will guarantee consistency”, I would be blogging from my castle in Mauritius. Maybe the application can guarantee consistency for the data that it manipulates (and it won’t, trust me), but it can do nothing for other applications using the same database. Often the database is a hub for many applications, each with its own degree of complexity and each with its level of quality. Pretending that all these applications will independently guarantee that no invalid data is brought in is totally unrealistic.
The last circle of SQL Server hell dedicated to Database Design sins is the circle of Inconsistent Baptists, those who fail to comply to sensible naming conventions. Stay tuned!
Some days ago I blogged about a weird behaviour of “table-level” CHECK constraints. You can find that post here.
Somehow, I did not buy the idea that a CHECK with a scalar UDF or a trigger were the only possible solutions. Scalar UDFs are dog-slow and also triggers are evil.
Scalar UDFs are dog-slow because the function gets invoked RBAR (Row-By-Agonizing-Row, for those that don’t know this “Modenism”). If the UDF performs data access, the statement in the scalar function gets invoked for each row, hitting performance badly.
Triggers are evil, according to Glenn Berry (blog|twitter), because they are a “bad” way to implement referential integrity. Moreover, from a performance standpoint, even if triggers work with sets instead of rows (unlike UDFs), they fire an additional query (or even more than one).
However, I seem to have found a way to merge the “business logic” query plan into the same execution plan of the external DML statement that modifies the data.
The method I will discuss here makes use of Indexed Views.
First of all, we will need some tables.
And now some business rules:
- Users with a premium account can place orders with an unlimited total amount. Users with a normal account can place orders limited to a $1000 total amount.
- Minors cannot buy products in the ‘ADULT’ category.
Let’s create the tables and populate them with some sample data:
USE tempdb; GO -- Create users table CREATE TABLE Users ( user_id int PRIMARY KEY, user_name nvarchar(30) NOT NULL, birth_date date ) GO CREATE TABLE AccountTypes ( account_type_id int PRIMARY KEY, account_type_code char(3) NOT NULL UNIQUE, account_type_description nvarchar(255) NOT NULL ) GO -- Create account table CREATE TABLE Accounts ( account_id int PRIMARY KEY, user_id int FOREIGN KEY REFERENCES Users(user_id), balance decimal(10,2), account_type_id int FOREIGN KEY REFERENCES AccountTypes(account_type_id) ) GO -- Create product categories table CREATE TABLE ProductCategories ( product_category_id int PRIMARY KEY, product_category_code char(5) NOT NULL UNIQUE, product_category_description nvarchar(255) NOT NULL ) GO -- Create products table CREATE TABLE Products ( product_id int PRIMARY KEY, EAN_code char(18) NOT NULL, product_description nvarchar(255) NOT NULL, product_category_id int FOREIGN KEY REFERENCES ProductCategories(product_category_id), ) GO -- Create orders table CREATE TABLE Orders ( order_id int PRIMARY KEY, user_id int FOREIGN KEY REFERENCES Users(user_id), total_amount decimal(10,2) NOT NULL CHECK(total_amount > 0), order_date datetime NOT NULL ) GO -- Create order details table CREATE TABLE OrderDetails ( order_id int NOT NULL FOREIGN KEY REFERENCES Orders(order_id), order_line int NOT NULL CHECK(order_line > 0), product_id int NOT NULL FOREIGN KEY REFERENCES Products(product_id), quantity int NOT NULL CHECK(quantity > 0), PRIMARY KEY(order_id, order_line) ) GO -- Insert sample data INSERT INTO Users(user_id, user_name, birth_date) VALUES (1, N'Gianluca Sartori', '1977-11-25') –- This is me INSERT INTO Users(user_id, user_name, birth_date) VALUES (2, N'Mladen Prajdić', '1980-08-16') -- I suspect this is not Mladen’s birthday INSERT INTO Users(user_id, user_name, birth_date) VALUES (3, N'Giulia Sartori', '2009-07-02') -- This is my 2 year old baby girl INSERT INTO AccountTypes(account_type_id, account_type_code, account_type_description) VALUES (1, 'NOR', N'Normal account') INSERT INTO AccountTypes(account_type_id, account_type_code, account_type_description) VALUES (2, 'PRE', N'Premium account') INSERT INTO Accounts(account_id, user_id, balance, account_type_id) VALUES (1, 1, 520, 2) INSERT INTO Accounts(account_id, user_id, balance, account_type_id) VALUES (2, 2, 376, 2) INSERT INTO Accounts(account_id, user_id, balance, account_type_id) VALUES (3, 3, 31, 1) INSERT INTO ProductCategories(product_category_id, product_category_code, product_category_description) VALUES (1, 'MSCCD', N'Music CDs') INSERT INTO ProductCategories(product_category_id, product_category_code, product_category_description) VALUES (2, 'TOONS', N'Disney Cartoons') INSERT INTO ProductCategories(product_category_id, product_category_code, product_category_description) VALUES (3, 'ADULT', N'Adult stuff') INSERT INTO Products(product_id, EAN_code, product_description, product_category_id) VALUES (1, 'MMFAFGRCDGKDGQEJ10', N'AC/DC – Back in Black', 1) INSERT INTO Products(product_id, EAN_code, product_description, product_category_id) VALUES (2, 'DD245FS6D3KBNSDWNF', N'Finding Nemo', 2) INSERT INTO Products(product_id, EAN_code, product_description, product_category_id) VALUES (3, 'B87S0NFDKSDFSAP2IS', N'Pics of hot chicks with little or no clothes to share with your friends on Twitter', 3)
Now that sample data is ready, let’s enforce the business rule #1: orders from users with a normal account must be limited to $1000.
To achieve this, we have to create an additional “dummy” table that holds exactly two rows. This table exists with the only purpose to implement a cartesian product and violate a UNIQUE constraint in the indexed view.
-- Create dummy table to store exactly two rows CREATE TABLE TwoRows ( N int NOT NULL PRIMARY KEY ) INSERT INTO TwoRows VALUES(1) INSERT INTO TwoRows VALUES(2) GO
Everything is ready to create the view and the UNIQUE index bound to it:
CREATE VIEW CHECK_Orders_Amount WITH SCHEMABINDING AS SELECT 1 AS ONE FROM dbo.Orders AS ORD INNER JOIN dbo.Accounts AS ACCT ON ORD.user_id = ACCT.user_id INNER JOIN dbo.AccountTypes AS ACTY ON ACCT.account_type_id = ACTY.account_type_id CROSS JOIN dbo.TwoRows AS TR WHERE ORD.total_amount >= 1000 AND ACTY.account_type_code <> 'PRE' GO CREATE UNIQUE CLUSTERED INDEX IX_CHECK_Orders_Accounts ON dbo.CHECK_Orders_Amount(ONE) GO
We can now insert some sample data to test if the business rule gets enforced:
-- Insert order #1 for user #1 (me) and total amount $2500. Works. INSERT INTO Orders (order_id, user_id, total_amount, order_date) VALUES (1, 1, 2500.00, GETDATE()) -- Insert order #2 for user #2 (Mladen) and total amount $500. Works INSERT INTO Orders (order_id, user_id, total_amount, order_date) VALUES (2, 2, 500.00, GETDATE()) -- Insert order #3 for user #3 (My 2 year-old daughter) and total amount $5000. OUCH! Violates the UNIQUE constraint. INSERT INTO Orders (order_id, user_id, total_amount, order_date) VALUES (3, 3, 5000.00, GETDATE()) -- Insert order #3 for Giulia with total amount $100. Works INSERT INTO Orders (order_id, user_id, total_amount, order_date) VALUES (3, 3, 100.00, GETDATE())
If we look at the execution plan of the INSERT statements, we can see that the indexed view maintenance is merged into the INSERT query plan:
It may be interesting to note that SQL Server is smart enough to identify the statements that require updating the indexed view. For instance, if we try to update a column that is not used in the indexed view, we won’t see any index maintenance in the query plan.
For instance, we could update order_id and examine the query plan:
-- This statement does not update the indexed view, so it is not included in the plan UPDATE Orders SET order_id = 3 WHERE order_id = 2 GO
As you can see, there is no need to maintain the index on the view. To achieve the same with a trigger, you would have to explicitly define the behaviour of the code using IF UPDATE(ColumnName).
Moreover, the UNIQUE constraint gets evaluated whenever ANY table used in the indexed view gets modified: this would be very hard to achieve with a trigger.
Now that the first business rule is set, we can proceed with the second one: no ‘ADULT’ products can be ordered by minors.
This can get a tricky requirement, as we might be tempted to calculate the age of the user comparing it to GETDATE(). Unfortunately, non-deterministic functions cannot be used in indexed views. We will have to get around it by using the order_date column, that was set to GETDATE() previously.
CREATE VIEW CHECK_Orders_Adult WITH SCHEMABINDING AS SELECT 1 AS ONE FROM dbo.Orders AS ORD CROSS JOIN dbo.TwoRows INNER JOIN dbo.OrderDetails AS ODT ON ORD.order_id = ODT.order_id INNER JOIN dbo.Products AS PR ON ODT.product_id = PR.product_id INNER JOIN dbo.ProductCategories AS PRC ON PR.product_category_id = PRC.product_category_id INNER JOIN dbo.Users AS USR ON ORD.user_id = USR.user_id WHERE PRC.product_category_code = 'ADULT' AND DATEADD(year, 18, USR.birth_date) > ORD.order_date GO CREATE UNIQUE CLUSTERED INDEX IX_CHECK_Orders_Adult ON dbo.CHECK_Orders_adult(ONE) GO
With the constraint in place, we can try to verify if the business rule gets enforced:
-- I order the AC/DC album. I will listen to it in my car while driving. INSERT INTO OrderDetails (order_id, order_line, product_id, quantity) VALUES (1, 1, 1, 1) -- Mladen orders the hot chicks DVD to send the pics via Twitter. INSERT INTO OrderDetails (order_id, order_line, product_id, quantity) VALUES (2, 1, 3, 1) -- Giulia tries to buy the hot chicks DVD as well. She likes boobs. For the milk, I suspect. -- Fortunately, the INSERT statement fails. INSERT INTO OrderDetails (order_id, order_line, product_id, quantity) VALUES (3, 1, 3, 1) -- OK, Giulia: you'd better buy a Disney DVD INSERT INTO OrderDetails (order_id, order_line, product_id, quantity) VALUES (3, 1, 2, 1)
Indexed Views provide an elegant way to enforce business rules that go beyond the scope of a single row in a table, without the kludge of CHECK constraints with scalar UDFs or the pain of DML triggers.
However, some limitations apply:
- Not all queries can be expressed in a way that can be used in an Indexed View. You can’t use non-deterministic functions, common table expressions, subqueries or self joins.
- Indexed Views cannot perform cross-database queries. If the business rule must be verified against a table stored in a different database, this method cannot be used.
Picking the right tool among CHECK constraints and triggers can be a hard decision. But now, hopefully, you have another option. 😉