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Deploying WordPress on OCI with MySQL Database Service: the easy way !

During the MDS webinar on how to deploy WordPress on OCI using MDS (slides & video), I briefly explained how to deploy the full architecture on OCI using Resource Manager and Stacks.

The Stack for that architecture is now available on my github: https://github.com/lefred/oci-wordpress-mds/releases/tag/0.0.1

To deploy it, it’s very easy. In OCI’s Dashboard, go on “Resource Manager” and then choose “Stacks“:

Create a new stack and just drop the …

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Using MySQL Database Service for WordPress

Today we will see how to use MySQL Database Service aka MDS with WordPress.

To achieve this easy task, we will use the architecture we already deployed in this article.

We have then two Compute Instances on OCI, 1 running WordPress (Apache and PHP) and one running MySQL 8.0.

The Plan

This is how we will proceed to migrate to MDS with minimal maintenance time, we will:

  1. create a MDS instance
  2. verify if the database is ready to act as replication source
  3. dump the MySQL instance running on OCI for being migrated to MDS.
  4. load the dump in MDS
  5. create a user dedicated to the replication
  6. create a replication channel on MDS (from OCI to MDS)
  7. modify WordPress config to point to MDS

Create a MDS …

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MySQL mysql_config_editor & expect

This is just a note to help out anyone that might want to use the mysql_config_editor command in their automation tools. 

the mysql_config_editor does not take a password argument so automation tools that might have before set your password in the .my.cnf file trying to use mysql_config_editor fails. 

It is possible and quite simple though with the expect tool. 

 yum -y install expect  

it works for apt-get also. 


So in this example, I will show a simple bash script version. 

1st.. my login path does not work... 

mysql --login-path=local

ERROR 1045 (28000): Access denied for user


Set this with expect 

You would execute this via your bash script.  

expect <<EOD

spawn …

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MySQL: Encoding fields for great profit.

Iterating schemas over time is not an uncommon thing. Often requirements emerge only after you have data, and then directed action is possible. Consequently, working on existing data, and structuring and cleaning it up is a common task.

In todays example we work with a log table that logged state transitions of things in freeform VARCHAR fields. After some time the log table grew quite sizeable, and the log strings are repeated rather often, contributing to the overall size of the table considerably.

We are starting with this table:

CREATE TABLE `log` (
  `id` int NOT NULL AUTO_INCREMENT,
  `device_id` int NOT NULL,
  `change_time` datetime NOT NULL,
  `old_state` varchar(64) NOT NULL,
  `new_state` varchar(64) NOT NULL,
  PRIMARY KEY (`id`)
) ENGINE=InnoDB

That is, our log table has an id field to allow individual row addressing, and then logs the state change of a …

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Recover Table Structure From InnoDB Dictionary

When a table gets dropped, MySQL removes the respective .frm file. This post explains how to recover the table structure if the table was dropped.

You need the table structure to recover a dropped table from the InnoDB tablespace. The B+tree structure of the InnoDB index doesn’t contain any information about field types. MySQL needs to know that in order to access records of the InnoDB table. Normally, MySQL gets the table structure from the .frm file. But when MySQL drops a table the respective frm file removed too.

Fortunately, there’s one more place where MySQL keeps the table structure. It’s the InnoDB dictionary.

The InnoDB dictionary is a set of tables where InnoDB keeps information about the tables. I reviewed them in detail in a separate InnoDB Dictionary post earlier. After the DROP, InnoDB deletes records related to the dropped table from …

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Watch The Replay: High Volume MySQL HA Use Case Webinar - SaaS Continuous Operations with Terabytes of Data

You can now watch the replay of our High Volume MySQL HA Use Case Webinar: SaaS Continuous Operations with Terabytes of Data; and learn how to guarantee continuous operations for a SaaS provider with billions of daily transaction and terabytes of data with Tungsten MySQL Clusters.

Tags:  Webinar MySQL use case tungsten clustering mysql cluster marketo High Availability

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Using JSON features to restructure results

Recently there was a question about which clients were connected to a server being asked in the MySQL Community Slack. The relevant information is available from performance schema, as most connectors will send information about themselves when connecting:

select * from performance_schema.session_connect_attrs;
+----------------+-----------------+------------------------+------------------+
| PROCESSLIST_ID | ATTR_NAME       | ATTR_VALUE             | ORDINAL_POSITION |
+----------------+-----------------+------------------------+------------------+
|            130 | _pid            | 17412                  |                0 |
|            130 | _platform       | x86_64                 |                1 |
|            130 | _os             | Linux-5.4.0            |                2 |
|            130 | _source_host    | maniacmansion          |                3 |
|            130 | …
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Easily Achieve Continuous MySQL Operations In The AWS Cloud: Introducing the New Continuent Tungsten Cluster (AMI)

Continuent is pleased to announce the availability of our new Tungsten Cluster (AMI) - the complete continuous operations solution for MySQL database clusters - on the AWS Marketplace.

Tags:  tungsten cluster AWS amazon marketplace tungsten clustering MySQL mysql high availability mysql disaster recovery

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Auditing Selection of Classified Data Stored in MySQL 8.0

The Challenge Often with sensitive information, you need to have an audit log. Not just that a table had a select run, but that specific cells within the table were accessed.  Frequently data such as this will contain a classification level as part of the row, defining policies for how it is handled, audited, etc.… Facebook Twitter LinkedIn

Basic Data Analysis with MySQL Shell Python mode

I recently watched a fantastic Python Pandas library tutorial series on YouTube. Without a doubt, Pandas is great for all sorts of data stuff. On the same token, MySQL Shell in Python mode is quite powerful in the sense that Python and the MySQL Shell (version >= 8.0) are somewhat united in the same environment. Although Pandas is in a league all its own when it comes to data analysis, between the power of MySQL and Python, we can also perform some basic analysis easily in MySQL Shell Python mode. In this blog post, I will cover some basic data analysis using Python mode in the MySQL Shell. Continue reading to see examples…

Business vector created by freepik – www.freepik.com

OS, Software, and DB used:

  • OpenSuse Leap …
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