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SQL Server Logical Reads – What do they really tell us?

By: Ami Levin
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SQL Server trace, the most common tool DBAs use to evaluate query performance, provides the ‘logical reads’ counter on which many DBAs rely for evaluating a query’s I/O performance. In this article, we will examine this counter’s true meaning and provide examples that prove it can sometimes be quite misleading…

I am sure you have all used SQL traces to evaluate the performance of queries and batches. The most common data columns used for this purpose are Duration, CPU, Writes and Reads. These are in fact the only true performance metrics available for a SQL Server trace event. A common misconception I've encountered in talking with numerous DBAs is that “reducing the number of reads a query performs is an important aspect of improving its performance”. Although this may prove to be true in many cases, in this article I want to draw your attention to the fact that "it ain’t necessarily so” ? sometimes the opposite is true.

First, we need to understand what a Read really is. Here is a quote from a Microsoft white paper about I/O architecture that clearly defines logical and physical reads: “The I/O from an instance of SQL Server is divided into logical and physical I/O. A logical read occurs every time the database engine requests a page from the buffer cache. If the page is not currently in the buffer cache, a physical read is then performed to read the page into the buffer cache. If the page is currently in the cache, no physical read is generated; the buffer cache simply uses the page already in memory.”

It is important to remember that SQL Trace reads are logical reads and not physical reads. I assume that this has to do with the fact that physical reads are common to all currently (and recently) executing batches on the server and therefore are not truly assigned to a specific event, even though they are always triggered by a specific event. Depending on the current content of the cache, the same query or batch may or may not trigger a physical read. Physical reads are the real cause of pain as far as performance is concerned because storage subsystems are unfortunately (still) inherently slow, as opposed to logical reads that occur in the super-fast modern memory modules.

* Physical reads metrics are available when using STATISTICS IO and from SQL Server DMVs.

I think the best way to make my point is by using an example. For this demo I have used SQL Server 2005 SP3 and the "AdventureWorks" sample database.

* As we cannot know in advance what the cache content will be at any given point in time, for the example code, I’ve explicitly flushed the cache to simulate the worst case scenario of an empty cache.

Let’s take the following example query and assume I was given the task of optimizing its performance:

SELECT C.CustomerID, SOH.SalesOrderID, SOH.OrderDate

FROM Sales.Customer C


Sales.SalesOrderHeader SOH

ON SOH.CustomerID = C.CustomerID

WHERE C.TerritoryID = 1 AND C.CustomerType = N'S'

The metrics for this query recorded on my PC using profiler are as follows:

Note: Numbers below indicate averages of several executions, using a cold cache.

CPU: ~40, Reads: ~840, Duration ~300ms.

The “missing indexes” option in management studio suggested that I add the following index:

Missing Index Details from logical_reads.sql - AMI-PC.AdventureWorks (DBSOPHIC\Ami (52))
The Query Processor estimates that implementing the following index could improve the query cost by 13.1751%.


USE [AdventureWorks]
ON [Sales].[Customer] ([TerritoryID],[CustomerType])
INCLUDE ([CustomerID])
Being an obedient DBA, I immediately obliged, created this index and then executed the same query again. This time, I got the following performance metrics in profiler:

CPU: ~20, Reads: ~4000, Duration ~200ms.

What happened here? Did adding this index improve the performance of the query or worsen it? It seems I have some conflicting information here. CPU and duration seem to have improved significantly but the logical reads have increased by nearly a factor of 5!

To understand what’s going on behind the scenes, we must consult the execution plan.

This is the original execution plan of the query before the suggested index was created:

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