How to locate SQL performance bottlenecks through slow query logs?

The main steps to optimize SQL using slow query logs: 1. Turn on the slow query log and set the execution time threshold (for example, modify the my.cnf file in MySQL); 2. Analyze the slow query log and pay attention to execution time, SQL statements and additional information (such as execution plan); 3. Find performance bottlenecks based on the log information, such as missing indexes; 4. Take optimization measures, such as adding indexes (CREATE INDEX statements) or optimizing the SQL statement itself; 5. Combining database monitoring tools and comprehensive analysis of business logic, continuously monitor and optimize database performance. Ultimately, we will achieve the goal of improving database efficiency.

How to tame those slow SQL: The Secret of Slow Query Logs

Have you ever been tortured by inefficient database queries? That feeling was as mad as watching a snail crawl. Don't worry, you are not alone! This article will uncover the mystery of slow query logs, teaching you how to use it to scoop up the culprits of slowing down the database and ultimately rejuvenate your database. After reading this article, you will be able to analyze slow queries independently and master the skills to optimize SQL.

Let's start with the basics. Slow query logs, as the name suggests, record SQL statements whose execution time exceeds a certain threshold. You can set this threshold according to the actual situation, such as 1 second, or longer. It is like a database's "black box recorder", faithfully recording every "slow motion" of the database running. By understanding this, you will understand its value: it can accurately tell you which SQL statements need to be optimized.

The way to enable the slow query log varies from database system to database system. Taking MySQL as an example, you need to modify the configuration file my.cnf, add or modify the long_query_time parameter to set the threshold, and enable the slow_query_log parameter. This part of the content is explained in detail in the official MySQL documentation, so I won't go into details. Remember, after the configuration is completed, you need to restart the database service before it takes effect. Don't forget to check the location of the log files, otherwise you will not find the log files and all efforts will be in vain.

Now, let's explore in-depth the "connotation" of slow query logs. A typical slow query log entry usually contains execution time, SQL statements, and some additional information such as execution plan, etc. This information is crucial and they can help you analyze the performance bottlenecks of your query.

Let's look at an example: Suppose such a record appears in your slow query log:

 <code class="language-sql"># Time: 2024-03-08T10:00:00.000000</code><h1 id="User-Host-root-root-localhost"> User@Host: root[root] @ localhost []</h1><h1 id="Query-time-Lock-time-Rows-sent-Rows-examined"> Query_time: 2.500000 Lock_time: 0.000000 Rows_sent: 1000 Rows_examined: 1000000</h1><p> SELECT <em>FROM users WHERE last_login <p>This record shows that the execution time of this SQL statement reached 2.5 seconds, and 1 million rows of data were scanned to find 1,000 records that met the criteria. This is obviously a performance bottleneck. What's the problem? It is very likely that the index is missing. The <code>last_login</code> field should be indexed. </p>
<p>The solution is simple: add an index. In MySQL, you can use the <code>CREATE INDEX</code> statement to add an index: </p>
<pre class="brush:php;toolbar:false"> <code class="language-sql">CREATE INDEX idx_last_login ON users (last_login);</code> 

After adding the index, the database can quickly locate data that meets the conditions, thereby greatly improving query efficiency. Remember, although indexes can increase query speed, they will also increase the burden of write operations. Therefore, you need to be cautious when adding indexes and weigh the pros and cons according to actual situations. Blindly adding indexes will backfire.

In addition to adding indexes, there are other optimization methods, such as optimizing the query statement itself, avoiding the use of SELECT

, choosing the appropriate connection method, etc. These methods need to be analyzed in combination with specific SQL statements and database structure. Proficient in SQL optimization techniques requires a lot of practice and experience accumulation.

Lastly, I want to emphasize that slow query logs are just a tool to diagnose SQL performance issues and they don't solve all problems by themselves. You need to combine database monitoring tools such as MySQL's Performance Schema, and your business logic to comprehensively analyze it to find the most effective solution. Remember that continuous monitoring and optimizing database performance is a long-term process that requires you to continue to learn and practice. Don’t be afraid of trying or failing. Only by constantly exploring can you become an expert in database performance optimization!

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