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Displaying posts with tag: query tuning (reset)
EXPLAIN FORMAT=JSON: order_by_subqueries, group_by_subqueries details on subqueries in ORDER BY and GROUP BY

Another post in the EXPLAIN FORMAT=JSON is Cool! series! In this post, we’ll discuss how the EXPLAIN FORMAT=JSON provides optimization details for 

ORDER BY

 and  

GROUP BY

 operations in conjunction with 

order_by_subqueries

 and  

group_by_subqueries

EXPLAIN FORMAT=JSON

 can print details on how a subquery in

ORDER BY

 is optimized:

mysql> explain format=json select emp_no, concat(first_name, ' ', last_name) f2 from employees order by (select emp_no limit 1)G
*************************** 1. row *************************** …
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EXPLAIN FORMAT=JSON provides insights on optimizer_switch effectiveness

The previous post in the EXPLAIN FORMAT=JSON is Cool! series showed an example of the query

select dept_name from departments where dept_no in (select dept_no from dept_manager where to_date is not null)

, where the subquery was materialized into a temporary table and then joined with the outer query. This is known as a semi-join optimization. But what happens if we turn off this optimization?

EXPLAIN FORMAT=JSON

 can help us with this investigation …

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EXPLAIN FORMAT=JSON: everything about attached_subqueries, optimized_away_subqueries, materialized_from_subquery

EXPLAIN FORMAT=JSON

The regular

EXPLAIN

 command already provides some information about subquery optimization. For example, you can find out if the subquery is dependent or not, and (since version 5.6) if it was materialized:

mysql> explain select dept_name from departments where dept_no in (select dept_no from dept_manager where to_date is not null)G
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: departments
   partitions: NULL
         type: index
possible_keys: PRIMARY
          key: dept_name
      key_len: 42
          ref: NULL
         rows: 9
     filtered: 100.00
        Extra: Using where; Using index
*************************** 2. row ***************************
           id: 1
  select_type: SIMPLE
        table: …
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used_key_parts: EXPLAIN FORMAT=JSON provides insight into which part of multiple-column key is used

In the previous post for this ongoing “EXPLAIN FORMAT=JSON is Cool!” series, we discussed covered indexes and how the 

used_columns

  array can help to choose them wisely. There is one more type of multiple-column indexes: composite indexes. Composite indexes are just indexes on multiple columns. Covered indexes are a subgroup of the larger set “composite indexes.” In this post we’ll discuss how “used_key_parts” can help show …

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used_columns: EXPLAIN FORMAT=JSON tells when you should use covered indexes

In the “MySQL Query tuning 101” video, Alexander Rubin provides an excellent example of when to use a covered index. On slide 25, he takes the query

select name from City where CountryCode = ’USA’ and District = ’Alaska’ and population > 10000

 and adds the index

cov1(CountryCode, District, population, name)

 on table

City

. With Alex’s query tuning experience, making the right index decision is simple – but what about us mere mortals? If a query is more complicated, or simply uses more than one table, how do we know what to do? Maintaining …

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rows_examined_per_scan, rows_produced_per_join: EXPLAIN FORMAT=JSON answers on question “What number of filtered rows mean?”

At the end of my talk “Troubleshooting MySQL Performance” at the LinuxPiter conference, a user asked me a question: “What does the EXPLAIN ‘filtered’ field mean, and how do I use it?” I explained that this is the percentage of rows that were actually needed, against the equal or bigger number of resolved rows. While the user was happy with the answer, I’d like to better illustrate this. And I can do it with help of EXPLAIN FORMAT=JSON and its

rows_examined_per_scan, rows_produced_per_join

  statistics.

Let’s take a simple query that searches information about the …

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attached_condition: How EXPLAIN FORMAT=JSON can spell-check your queries

When you work with complicated queries, especially ones which contain subqueries, it is easy to make a typo or misinterpret column name. While in many cases you will receive a 

column not found

 error, sometimes you can get strange results instead.

Like finding 4079 countries in Antarctica:

mysql> select count(*) from City where CountryCode in (select CountryCode from Country where Continent = 'Antarctica');
+----------+
| count(*) |
+----------+
|     4079 |
+----------+
1 row in set (0.05 sec)

Or not finding any cities in Georgia:

mysql> select Name, Language from City join CountryLanguage using (CountryCode) where CountryCode in (select Code from Country where District='Georgia' and Continent='Asia');
Empty set (0.18 sec)

I used a standard …

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Advanced Query Tuning in MySQL 5.6 and MySQL 5.7 Webinar: Q&A

Thank you for attending my July 22 webinar titled “Advanced Query Tuning in MySQL 5.6 and 5.7” (my slides and a replay available here). As promised here is the list of questions and my answers (thank you for your great questions).

Q: Here is the explain example:

mysql> explain extended select id, site_id from test_index_id where site_id=1
*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: test_index_id
         type: ref
possible_keys: key_site_id
          key: key_site_id
      key_len: 5
          ref: const
         rows: 1
     filtered: 100.00
        Extra: Using where; Using index

why is site_id a covered index for the query, given the fact that a) we are selecting “id”, b) key_site_id only …

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Optimizer hints in MySQL 5.7.7 – The missed manual

In version MySQL 5.7.7 Oracle presented a new promising feature: optimizer hints. However it did not publish any documentation about the hints. The only note which I found in the user manual about the hints is:

  • It is now possible to provide hints to the optimizer by including /*+ ... */ comments following the SELECT, INSERT, REPLACE, …
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Introducing ‘MySQL 101,’ a 2-day intensive educational track at Percona Live this April 15-16

Talking with Percona Live attendees last year I heard a couple of common themes. First, people told me that there is a lot of great advanced content at Percona Live but there is not much for people just starting to learn the ropes with MySQL. Second, they would like us to find a way to make such basic content less expensive.

I’m pleased to say we’re able to accommodate both of these wishes this year at Percona Live! We have created a two-day intensive track called “MySQL 101” that runs April 15-16. MySQL 101 is designed for developers, system administrators and DBAs familiar with other databases but not with MySQL. And of course it’s ideal for anyone else who would like to expand their professional experience to include MySQL. The sessions are designed to lay a solid foundation on many aspects of MySQL development, design and …

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Showing entries 11 to 20 of 33
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