While going through MySQL and PHP slow logs is a great way to find issues, modern reporting services that are integrated with your site help speed the process up tremendously. There are a few different systems to choose from, but at Pantheon we use New Relic® Performance Monitoring. This article explains how you can troubleshoot MySQL databases with New Relic APM.
From within Pantheon, go to the Site Dashboard for the site you suspect is having problems with MySQL query performance. Select the environment: Dev, Test, or Live. Click the New Relic tab, and select Go to New Relic.
By default New Relic® Performance Monitoring lists your Applications. Select the application the issue has been reported on (Dev/Test/Live).
Using the graph, locate the time period the issue occurred in. This is usually visually apparent via large spikes in the graph. If not, use the New Relic time period selection tool to broaden your search (30 min, 60 min, 3 hours, etc.) until you find the problem.
Highlight the spike in activity you want to investigate. New Relic will reload the page with the time frame you've selected.
Click Transactions. The default sort is "Most Time Consuming" but this can be a false positive, as it measures a sum of time loading specific transactions, not the time per individual transaction. If a particular item is called 10x more than another, but loads quickly, it's sum will send it to the top of the list even if it's behaving well. Choose "Slowest average result time" instead. This will resort the order, bringing the biggest speed (or lack thereof) offenders to the fore.
At times, systems like Drupal's Watchdog appear at the top. In general, that's an indication of a MySQL database under duress. Look for complex entities, such as Panels and Views, or custom functionality that's specific to the site in question.
Click on the most likely subject to see the details of that transaction. Scroll down, and note the transaction traces.
Select the worst transaction trace to see a complete stack trace of that particular transaction.
Get a more detailed breakdown by clicking on SQL statements. Scroll down until you find something suspicious.
Once located, you can see how and where it's happening by finding the path near the bottom of the page.
The New Relic® Performance Monitoring trace does not give the full query; it only shows the query with placeholders, which cannot be executed against MySQL as is. To do that, you'll need to look in the MySQL slow log. Go back to the site's panel on the Dashboard and get the SFTP connection information. Modify it per this article to connect to MySQL via SFTP through your terminal or an FTP program that supports the SFTP protocol.
Download database log files and review the
mysql-slow-query.log file. Search for the query within the log. If it isn't there, download and unzip any applicable archived slow logs (e.g.
mysqld-slow-query.log-20160606) and search there. The archived slow logs are created by date and time, so look for the one that corresponds with the trace you are working with.
Using the information from the New Relic trace, find the full query in the slow log. First, choose a distinctive part of the query. In this case I used
grep -c users_comment.uis AS users_comment_uid to get a count of the number of times that field has been included in the slow log. If the log is small enough (or if you have enough RAM), you can load it into your favorite text editor or IDE instead.
describe command shows that there is no primary key set on the UID field.
Use New Relic® Performance Monitoring to narrow and identify periods of time that have high load and/or slow response times.
Narrow down the scope to one of those time periods and find the worst performing transactions.
Within those transactions, go into the SQL trace to discover long running queries.
Using SFTP, download the appropriate MySQL Slow Log to retrieve the query in its entirety.
Connect to a safe MySQL server via CLI. Run the query to test the performance.
If the query result is poor, use the
EXPLAIN EXTENDEDMySQL command to get additional information. You can also examine the MySQL tables for structural issues using
Once identified, fix the issue. The fix may be to adjust the SQL query itself, or it can be within the application by redoing code or configurations that are creating the errant query.