Tag: mysql

From MySQL 8.0.0 to MySQL 8.0.1 – or any other dev milestone

Disclaimer: This post is aimed to you, the curious developer, sys-admin, technologist, whatever-title-you-use. DO NOT run the following lines on production. Not even in a stable environment, do this if you don’t care about the outcome of the current data.

If you want to keep up with the newest MySQL developer milestones I have news for you: there is no upgrade available for milestone versions. The way to go is to remove old version and install new one, according to their website:

Upgrades between milestone releases (or from a milestone release to a GA release) are not supported. For example, upgrading from 8.0.0 to 8.0.1 is not supported, as neither are GA status releases.

So if you, like me, had the 8.0.0 version and want to test the 8.0.1 (alhtough 8.0.3 milestone is already in development) you need to do something like the following (tutorial based on Debian/Ubuntu servers).

Stop your service:

$ sudo service mysql stop

Download Oracle’s repository and install it, as of now this is the current version, you can get the new package here:

$ wget https://dev.mysql.com/get/mysql-apt-config_0.8.6-1_all.deb
$ sudo dpkg -i mysql-apt-config_0.8.6-1_all.deb

Clean your old install, you will lose all the data. Be careful, back up is on you!

$ sudo apt-get remove --purge mysql-server mysql-client mysql-common
$ sudo apt autoremove
$ sudo apt-get autoclean
$ sudo apt-get install mysql-server

This is the way to go to test the new features such as Descending Indexes and others. Remember, the new default encoding was changed from latin1 to utf8mb4.

Short feature list:

The complete list is available here.

Converting comma separated fields to MySQL JSON – a case study

This post is a case study of a job I had to do in a legacy application, it doesn’t mean it will apply to you, but it might.

This is a table of contents:

There are many ways to store a tree in a relational database, this is not by far the best option to do it, however it is still common to see it happen.

One way it is called materialized path, which consist with values separated by a delimiter, in my case a comma ,.

You would have a tree stored in a manner like this:

id parent_id user_id depth asc_path node
1 0 1 1
2 1 2 2 ,1, L
3 1 13 2 ,1, R
4 2 3 3 ,2,1, L
5 2 61 3 ,2,1, R
6 13 23 3 ,13,1, L
7 13 22 3 ,13,1, R
8 3 4 4 ,3,2,1, L
9 3 156 4 ,3,2,1, R
10 22 1568 4 ,22,13,1, L
11 22 26 4 ,22,13,1, R
12 23 1476 4 ,23,13,1, L
13 23 690716 4 ,23,13,1, R
14 61 1051 4 ,61,2,1, L
15 61 62 4 ,61,2,1, R

The column asc_path stands for ascending path of a tree in where which node has two other ones, not necessarily being a binary tree, being stored in the database.

This column has commas in the beginning and in the end because how queries are made to search if an element is present in the path or not by using LIKE "%,id,%". If someone did a search to know if the number 2 was a node in any of the paths, without the commas, it would also return 23, 62 and any other number containing 2.

Performance

The only way to make it a bit faster is having a FULLTEXT index created in asc_path. Because a BTREE index starts indexing in the beginning of a string, since the presence of the wildcard % in the string search it makes it in possible to use said index.

This is the graphical representation of the example above:

Tree

Searching

To search an specific element the query would be:

SELECT
parent_id,
user_id,
depth,
asc_path,
node
FROM tree
WHERE asc_path LIKE '%,13,%';

Result:

parent_id user_id depth asc_path node
13 23 3 ,13,1, L
13 22 3 ,13,1, R
22 1568 4 ,22,13,1, L
22 26 4 ,22,13,1, R
23 1476 4 ,23,13,1, L
23 690716 4 ,23,13,1, R

Converting to a JSON array

Some databases, like PostgresSQL (section 9.42) have more modifiers functions to convert strings to JSON, in my case I wanted to store the ascending tree path in a JSON field which would give me the possibility of using JSON_CONTAINS(json_doc, val) to know the records that have a given node in its path.

To do it, I had to transform the string in a JSON array.

1st step: remove the leading commas

Removing the leading commas, but before any update, lets test what we are doing:

SELECT
parent_id,
user_id,
depth,
asc_path,
TRIM(BOTH ',' FROM asc_path) AS trimmed_commas
FROM tree

Results:

parent_id user_id depth asc_path trimmed_commas
0 1 1
1 2 2 ,1, 1
1 13 2 ,1, 1
2 3 3 ,2,1, 2,1
2 61 3 ,2,1, 2,1
13 23 3 ,13,1, 13,1
13 22 3 ,13,1, 13,1
3 4 4 ,3,2,1, 3,2,1
3 156 4 ,3,2,1, 3,2,1
22 1568 4 ,22,13,1, 22,13,1

2nd step: add brackets to the string

A JSON array is formed around brackets [], and we need to have it in our string to be a valid JSON document:

SELECT
parent_id,
user_id,
depth,
asc_path,
TRIM(BOTH ',' FROM asc_path) AS trimmed_commas,
CONCAT("[", TRIM(BOTH ',' FROM asc_path), "]") AS added_brackets
FROM tree;

Results:

parent_id user_id depth asc_path trimmed_commas added_brackets
0 1 1
1 2 2 ,1, 1 [1]
1 13 2 ,1, 1 [1]
2 3 3 ,2,1, 2,1 [2,1]
2 61 3 ,2,1, 2,1 [2,1]
13 23 3 ,13,1, 13,1 [13,1]
13 22 3 ,13,1, 13,1 [13,1]
3 4 4 ,3,2,1, 3,2,1 [3,2,1]
3 156 4 ,3,2,1, 3,2,1 [3,2,1]
22 1568 4 ,22,13,1, 22,13,1 [22,13,1]

3rd step: validate if the changes works

Let’s use JSON_VALID() to see if it will accept our new string as a JSON, keep in mind that when the argument is NULL the return is also NULL:

SELECT
parent_id,
user_id,
depth,
asc_path,
TRIM(BOTH ',' FROM asc_path) AS trimmed_commas,
CONCAT("[", TRIM(BOTH ',' FROM asc_path), "]") AS added_brackets,
JSON_VALID(CONCAT("[", TRIM(BOTH ',' FROM asc_path), "]")) AS json_valid
FROM tree;

Results:

parent_id user_id depth asc_path trimmed_commas added_brackets json_valid
0 1 1
1 2 2 ,1, 1 [1] 1
1 13 2 ,1, 1 [1] 1
2 3 3 ,2,1, 2,1 [2,1] 1
2 61 3 ,2,1, 2,1 [2,1] 1
13 23 3 ,13,1, 13,1 [13,1] 1
13 22 3 ,13,1, 13,1 [13,1] 1
3 4 4 ,3,2,1, 3,2,1 [3,2,1] 1
3 156 4 ,3,2,1, 3,2,1 [3,2,1] 1
22 1568 4 ,22,13,1, 22,13,1 [22,13,1] 1
22 26 4 ,22,13,1, 22,13,1 [22,13,1] 1
23 1476 4 ,23,13,1, 23,13,1 [23,13,1] 1
23 690716 4 ,23,13,1, 23,13,1 [23,13,1] 1
61 1051 4 ,61,2,1, 61,2,1 [61,2,1] 1
61 62 4 ,61,2,1, 61,2,1 [61,2,1] 1

Replacing 1st step and 2nd step with a function

So that your query gets easier to use and not messy, you can create a function, I decided to create to_json_array(input_string, delimiter_char):

Running the query only with to_json_array on MySQL:

SELECT
parent_id,
user_id,
depth,
asc_path,
to_json_array(asc_path, ',') AS to_json_array,
JSON_VALID(to_json_array(asc_path, ',')) AS is_to_json_array_valid,
node
FROM tree;

Result:

parent_id user_id depth asc_path to_json_array is_to_json_array_valid node
0 1 1
1 2 2 ,1, [1] 1 L
1 13 2 ,1, [1] 1 R
2 3 3 ,2,1, [2, 1] 1 L
2 61 3 ,2,1, [2, 1] 1 R
13 23 3 ,13,1, [13, 1] 1 L
13 22 3 ,13,1, [13, 1] 1 R
3 4 4 ,3,2,1, [3, 2, 1] 1 L
3 156 4 ,3,2,1, [3, 2, 1] 1 R
22 1568 4 ,22,13,1, [22, 13, 1] 1 L

Disclaimer

This function is not native, and its use in production is not guaranteed.

Notice that the database returns the JSON as valid making it possible to convert that TEXT to a new column asc_path_json:

ALTER TABLE tree
ADD COLUMN asc_path_json JSON
AFTER asc_path;

UPDATE tree
SET asc_path_json = to_json_array(asc_path, ',');

Which gives us the ability to check more quickly if an item is in the path for that node:

SELECT *
FROM tree
WHERE json_contains(asc_path_json, "13");

Result:

id parent_id user_id depth asc_path asc_path_json node
6 13 23 3 ,13,1, [13, 1] L
7 13 22 3 ,13,1, [13, 1] R
10 22 1568 4 ,22,13,1, [22, 13, 1] L
11 22 26 4 ,22,13,1, [22, 13, 1] R
12 23 1476 4 ,23,13,1, [23, 13, 1] L
13 23 690716 4 ,23,13,1, [23, 13, 1] R
MySQL version poll: a not so scientific analysis

MySQL version poll: a not so scientific analysis

Prior to my talk at LaraconEU 2016 I was curious to know how much adoption for MySQL 5.7 was in within the community.

I tweeted this:

Twitter polls only gives you up to 4 items to choose. What I wanted to know is if people were using MariaDB or other forks like Percona, but I didn’t had the proper space, and I  only put three options.

This January I managed to get a bit more syndication on my tweet and more people replied. I added a 4th option, “Other”. This option could include the fork data as well as people using even the MySQL 4:

Analysis results

This have no scientific foundation whatsoever. Most of the people on my twitter bubble work on tech and try to be using cutting edge technology, but I could see a bit of a trend (taking into the consideration also the amount of people that now replied).

August 2016 January 2017

It is possible to notice that 5.7 got more market where 5.5 was the most common version to those people. I would like to think they upgraded first to 5.6 to then upgrade to 5.7 and not just jumped versions disabling and doing this to make it work:

SET @@GLOBAL.sql_mode = '';

Again, this is the equivalent of disabling errors in any language because you are not gonna fix them, just want swipe under the carpet. Don’t do that.

It is nice to see that 5.5 is losing ground (again, a pinch of salt here) to newer and modern versions.

What should I not consider?

Well, you can actually ignore the whole poll as a trend indicator. The first one ran only for a day and it got 85 votes with not all options on it, and the second one had 669 votes and it was a week long poll. Plus the fact there is no way to do a control group to calculate the error margin.

What does this really mean?

MySQL 5.7 was released with General Availability around October 2015, major hosting  and cloud companies started to make it available on February/March 2016. Adoption always take a bit of a time to be absorbed, specially if you have to do any code change to support the new version of the database (hint, you probably will have to). It also means that those companies may at any point stop providing support for versions older than 5.6 (5.5, 5.1, etc.).

Also take into consideration that MySQL 8.0 is under development and most of the strictness embedded by default on 5.7 will continue to come on 8.0. So if you are reading this blogpost and starting a new project, go ahead and start with 5.7 already so when version 8.0 comes out you won’t have trouble upgrading.

If you have a legacy application then, there are ways of adapting your code so you can enjoy everything the new version has to offer. Just a final reminder, disabling strictness on the server to be able to use the JSON feature may sound as a smart idea in the beginning, but that also means putting your data consistency at risk.