Logical and Physical IOs: A chili-cooking analogy

I was teaching a class today, covering the topic of indexes with some hands-on activities.

A group of students get some card-stock pages with data on them, each one representing an 8K data or index page. With these, we go through a number of database read operations and look at the IOs that are generated.

This is a great exercise, because it focuses attention on IO, rather than computation.

However, I found that the students had some trouble differentiating between a physical IO and a logical IO. Or realizing that an IO was either logical or physical, and wasn't be both.

So I came up with a chili-cooking analogy.

Suppose I am cooking chili and need a can of kidney beans.

If I go to my cupboard, and there is no can of beans, then I'll need to get in the car and go to the store.

Dang.

That would be like a physical IO. The can of beans is not in my cupboard, and it's a pain (in time and effort) to go to the store for the can of beans.

If I go to the cupboard, and the can of beans is already there, then I don't need to go to the store.

Cool.

That would be like a logical IO. The can of beans is right there handy, and I don't need to spend the time and effort to go to the store.

This is not a perfect analogy, but it has some good parallels.

• I need a can of beans. This is like the SQL Engine needing a data page. It would be considered an IO request. But we don't yet know whether it will be a logical or physical IO.
• The can is in my cupboard or it is not.
• It's in my cupboard. This is like a logical IO. No reason to go to disk (store).
• It's not in my cupboard. This is like a physical IO. Have to go to the disk (store).

So I'm either going to the store (physical IO) or I'm not (logical IO). 

So why count logical IOs? 

Well, logical IOs are kind of bonus; you can't count on them. (Sometimes that can of beans is in the cupboard, and sometimes its not.) If you have an operation that takes 1000 IOs, it might be all physical IOs the first time, and all logical the next time. So, you should be looking at total IOs as a measure of potential IOs for the operation. 

Always define a Unique Grouping

I've written before about providing a unique ordering. The idea being that downstream systems and developers could begin to depend on records always coming back in the same order. Without having something truly unique in your ORDER BY clause (like a primary key), results could be coming back in different orders.

When using GROUP BY, its even more important to ensure that the grouping is always going to be unique.

Let's look at a fairly obvious example using the Products table from the Northwind database.

Consider this statement:

    SELECT   ProductName,
             AVG(unitPrice) AS [AvgUnitPrice]
    FROM     Products
    GROUP BY ProductName
    ORDER BY ProductName

This is perfectly valid syntax. The resulting records will look fine also.

However, there is a chance that two (or five) products will have the same name, and will get placed into the same group. In this case, the results of this statement are not correct.

The safest thing to do, for this query is to add the ProductID to the GROUP BY clause like this:

    SELECT   ProductName,
             AVG(unitPrice) AS [AvgUnitPrice]
    FROM     Products
    GROUP BY ProductName,
             ProductID
    ORDER BY ProductName

This ensures that the grouping is correct, and that the averages will be correct. However, it still has the problem that the ordering is now not necessarily unique. (As addressed here.)

To also provide a unique ordering, you'd add the ProductID to the ORDER BY also:

    SELECT   ProductName,
             AVG(unitPrice) AS [AvgUnitPrice]
    FROM     Products
    GROUP BY ProductName,

             ProductID
    ORDER BY ProductName,
             ProductID

Always define a unique ordering

Your SELECT statements should always have a unique ordering.

This ensures that "downstream" systems/code that consumes the data doesn't assume (wrongly) that the data is always in the same order.

Let me describe this using the Products table from the MS Northwind database.

Consider this SQL:

SELECT   ProductName

FROM     Products

ORDER BY ProductName

This is syntactically correct SQL, and we'll assume that it returns the correct records.

If all the products have a different ProductName, this will provide a unique ordering of the records. In other words, each time the SELECT statement is executed, the records come back in the same order.

However, in a dynamic environment, with a large number of records, it is likely that two products could have the same ProductName, e.g., "Socks", "Lipstick", "Hammer". (Unless uniqueness is enforced on ProductName.)

The danger here is that downstream systems or consumers of the data from this SELECT statement might actually depend on the records always being in the exact same order. And when they are not, things can go wrong.

So why would a programmer downstream assume that the records are in order? Well, why not? At first glance, they look like they are in order. But with a million records that are changing, how could the programmer be sure? And why sort a million records that look like they are already in order?

A simple safe SQL practice is to always specify a unique ordering. In this case, adding ProductID to the ORDER BY statement would be easy:

SELECT   ProductName

FROM     Products

ORDER BY ProductName, ProductID

This is not likely to be noticed by anyone, and may never make a difference. But it can prevent future errors.

By the way, I encountered a very similar situation on a large software project. The database people said the data was sorted, the programmers wrote their code to depend on the sort. And visual inspection of the data made us think it was sorted. We ended up noticing the changing order of records as we were stepping through code in a debugger. In other words, we were convinced it was a logic error in the code.

Self-Referential Join Example on Northwind Database

Self-Referential Join Example on Northwind Database

In the Microsoft Northwind database, there is an example of a self-referential table, Employee (see data model).

Let's look at some of the data with a simple query:

SELECT   EmployeeID,
         LastName,
         ReportsTo
FROM     Employees
ORDER BY EmployeeID

Which gives the data shown.

So you can see that EmployeeID=6 with the LastName=Suyama reports to EmployeeID=5 with the LastName=Buchanan.

So how do we get this all in one table?







The primary constraint here is that you cannot use the same table name twice in the same FROM statement. So, we need to use a table alias.

SELECT   Employees.EmployeeID,
         Employees.LastName,
         Employees.ReportsTo,
         Boss.EmployeeID,
         Boss.LastName
FROM     Employees
   JOIN  Employees Boss    ON Employees.reportsTo = Boss.EmployeeID

ORDER BY Employees.EmployeeID


Which gives this:

The FROM statement uses the Employees table twice, but one is aliased to be Boss. Based on that alias, the correct join is to use Employees.reportsTo as the foreign key, and Boss.EmployeeID as the primary key; because Employees report to Bosses.

Here's how that join looks in the Query Designer.

Regardless of how the database engine actually accomplishes the results, it's good to view this as two complete copies of the (same) table. They both have exactly the same fields and all the same records.