In this blog post, I want to share a case we worked on a few days ago. I’ll show you how we approached the resolution of a MySQL performance issue and used Percona Monitoring and Management PMM to support troubleshooting. The customer had noticed a linear high CPU usage in one of their MySQL instances and was not able to figure out why as there was no much traffic hitting the app. We needed to reduce the high CPU usage on MySQL. The server is a small instance:

Models | 6xIntel(R) Xeon(R) CPU E5-2430 0 @ 2.20GHz
10GB RAM

This symptom can be caused by various different reasons. Let’s see how PMM can be used to troubleshoot the issue.

CPU

It’s important to understand where the CPU time is being consumed: user space, system space, iowait, and so on. Here we can see that CPU usage was …

Some time ago, a customer had a performance issue with an internal process. He was comparing, finding, and reporting the rows that were different between two tables. This is simple if you use a LEFT JOIN and an 

IS NULL

  comparison over the second table in the WHERE clause, but what if the column could be null? That is why he used UNION, GROUP BY and a HAVING clauses, which resulted in poor performance.

The challenge was to be able to compare each row using a LEFT JOIN over NULL values.

The challenge in more detail

I’m not going to use the customer’s real table. Instead, I will be comparing two sysbench tables with the same structure:

CREATE TABLE `sbtest1` (
  `id` int(10) unsigned NOT NULL AUTO_INCREMENT,
  `k` int(10) unsigned DEFAULT NULL,
  `c` char(120) DEFAULT NULL,
  `pad` char(60) DEFAULT NULL,
  PRIMARY …

Lately, I worked on several queries which started returning wrong results after upgrading MySQL Server to version 5.7 The reason for the failure was derived merge optimization which is one of the default

optimizer_switch

  options. Issues were solved, though at the price of performance, when we turned it

OFF

 . But, more importantly, we could not predict if any other query would start returning incorrect data, to allow us to fix the application before it was too late. Therefore I tried to find reasons why

derived_merge

  can fail. Analyzing the problem

In the first run, we turned SQL Mode

ONLY_FULL_GROUP_BY

on, and this removed most of the problematic queries. That said, few of the queries that were successfully working with

ONLY_FULL_GROUP_BY

  were affected.

A quick search in the …

Please join Percona’s MySQL Database Administrator, Brad Mickel as he presents How to Analyze and Tune MySQL Queries for Better Performance on Thursday, June 21st, 2018, at 10:00 AM PDT (UTC-7) / 1:00 PM EDT (UTC-4).

Register Now

Query performance is essential in making any application successful. In order to finely tune your queries you first need to understand how MySQL executes them, and what tools are available to help identify problems.

In this session you will learn:

1. The common tools for researching problem queries
2. What an Index is, and why you should use one
3. Index limitations
4. When to rewrite the …

This blog is an EXPLAIN FORMAT=JSON wrap-up for the series of posts I’ve done in the last few months.

In this series, we’ve discussed everything unique to

EXPLAIN FORMAT=JSON

. I intentionally skipped a description of members such as

table_name

,

access_type

  or

select_id

, which are not unique.

In this series, I only mentioned in passing members that replace information from the

Extra

 column in the regular

EXPLAIN

 output, such as

using_join_buffer

 ,

partitions

,

using_temporary_table

  or simply

Once again it’s time for another EXPLAIN FORMAT=JSON is cool! post. This post will discuss how EXPLAIN FORMAT=JSON allows the nested_loop command to make the JOIN operation hierarchy transparent.

The regular

EXPLAIN

  command lists each table that participates in a 

JOIN

  operation on a single row. This works perfectly for simple queries:

mysql> explain select * from employees join titles join salariesG
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: employees
   partitions: NULL
         type: ALL
possible_keys: NULL
          key: NULL
      key_len: …

Time for another entry in the EXPLAIN FORMAT=JSON is cool! series of blog posts. This time we’ll discuss how using EXPLAIN FORMAT=JSON allows you to see that

cost_info

  knows why the optimizer prefers one index to another.

Tables often have more than one index. Any of these indexes can be used to resolve query. The optimizer has to make a choice in this case. One of the metrics that can be used to help make the choice is the potential cost of the query evaluation.

For example, let’s take the table

titles

  from the standard employees database:

Time for another entry in the EXPLAIN FORMAT=JSON is cool! series. Today we’re going to look at how you can view the buffer result using JSON (instead of the regular

EXPLAIN

 command.

Regular

EXPLAIN

 does not identify if

SQL_BUFFER_RESULT

 was used at all. To demonstrate, let’s run this query:

mysql> explain select * from salariesG
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: salaries
   partitions: NULL
         type: ALL
possible_keys: NULL
          key: NULL
      key_len: NULL
          ref: NULL
         rows: 2557022
     filtered: 100.00
        Extra: …

Ready for another post in the EXPLAIN FORMAT=JSON is Cool series! Great! This post will discuss how to see all the information that is contained in optimized queries with

UNION

 using the

union_result

 and

query_specifications

 commands.

When optimizing complicated queries with

UNION

, it is easy to get lost in the regular

EXPLAIN

  output trying to identify which part of the output belongs to each part of the

UNION

.

Let’s consider the following example:

mysql> explain
    ->     select emp_no, last_name, 'low_salary' from employees
    ->     where emp_no in (select emp_no from salaries
    ->         where salary < (select avg(salary) …

Over several previous blog posts, we’ve already discussed what information the 

EXPLAIN FORMAT=JSON

 output provides for some subqueries. You can review those discussions here, here and here. EXPLAIN FORMAT=JSON shows many details that you can’t get with other commands. Let’s now finish this topic and discuss the output for the rest of the subquery types.

…