Lesson Learned #451:Deep Dive into Physical and Logical Reads in Azure SQL Database: Hands-On-Lab

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This post has been republished via RSS; it originally appeared at: New blog articles in Microsoft Community Hub.

This week, I addressed a customer's request to understand the differences between Physical and Logical Reads and how they affect performance. Here’s an explanation and an example to help clarify these concepts.



In the realm of Azure SQL Database, query performance is a paramount concern for database administrators and developers alike. A critical aspect of this performance is understanding how SQL Server interacts with data, particularly through physical and logical reads. This article delves into these two fundamental concepts, providing insights into their impact on database performance and a practical lab to observe these metrics in action.


Section 1: Deciphering Logical Reads

Logical reads refer to the number of data pages retrieved from the buffer cache. They are a key indicator of how efficiently SQL Server can access data in memory. Minimizing logical reads is crucial for query efficiency, as excessive logical reads can indicate suboptimal query design or indexing.


Section 2: Understanding Physical Reads

Physical reads occur when SQL Server fetches data pages from disk storage. These reads are typically more resource-intensive and can be a bottleneck in database performance. High physical reads might signal insufficient memory allocation, poor indexing, or other issues necessitating disk access.


Section 3: Analyzing Logical vs. Physical Reads

Balancing logical and physical reads is vital for optimal database performance. While logical reads are generally preferred for their speed, physical reads are sometimes inevitable. The key is to optimize queries and database design to minimize unnecessary disk access and make efficient use of the buffer cache.


Section 4: Practical Lab - Monitoring Reads in Azure SQL Database

Environment Setup

To begin, set up a test environment in Azure SQL Database. This setup includes creating a sample database and tables for testing purposes.


Lab Script

The following script creates a test table, populates it with data, and runs queries designed to generate logical and physical reads:



-- Create and populate a sample table CREATE TABLE TestTable (id int, Data varchar(8000)); begin transaction DECLARE @i int = 0; WHILE @i < 1000 BEGIN INSERT INTO TestTable VALUES (1, REPLICATE('X', 8000)); INSERT INTO TestTable VALUES (2, REPLICATE('X', 8000)); INSERT INTO TestTable VALUES (3, REPLICATE('X', 8000)); SET @i = @i + 1; END commit transaction -- Induce physical reads (Note: Use only in a test environment) CHECKPOINT; DBCC DROPCLEANBUFFERS; DBCC FREEPROCCACHE; SELECT * FROM TestTable WHERE id = 1; -- Generate logical reads SELECT * FROM TestTable WHERE id = 1;



Important Warning Regarding DBCC Commands

The DBCC commands DROPCLEANBUFFERS and FREEPROCCACHE are used here to clear the buffer and procedure cache. These commands can significantly impact performance and should only be used in a test environment. They are not recommended for production environments due to their potential disruptive effects.


Analyzing DMV Outputs

Post-execution, use this DMV query to analyze read patterns:



-- DMV Query to Analyze Reads SELECT (total_physical_reads / execution_count) AS avg_physical_reads, (total_logical_reads / execution_count) AS avg_logical_reads, total_physical_reads, total_logical_reads, execution_count, last_execution_time, statement_start_offset AS stmt_start_offset, sql_handle, plan_handle, st.text FROM sys.dm_exec_query_stats qs CROSS APPLY sys.dm_exec_sql_text(qs.sql_handle) AS st WHERE st.text LIKE '%TestTable%' ORDER BY total_physical_reads DESC;




Understanding the balance and behavior of physical and logical reads is crucial for optimizing query performance in Azure SQL Database. This article and the accompanying lab provide insights and practical experience in monitoring and improving these vital performance metrics. For example in the first execution: SELECT * FROM TestTable WHERE id = 1 we could see that all information is retrieved from the Storage and the second execution is retrieved from the buffer pool/memory. 







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