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Displaying posts with tag: MySQL (reset)
Tired of MySQL Making You Wait? Webinar: Questions and Answers

We’d like to thank everybody for joining us on January 7th for our “Tired of MySQL Making You Wait?” webinar with Percona’s Alexander Rubin, Principal Consultant and SolarWinds’ Janis Griffin, Database Evangelist.

Too often developers and DBAs struggle to pinpoint the root cause of performance issues and then spend too much time in trying to fix them. In the webinar, we discussed how you can significantly increase the performance of your applications while also reducing database response time.

You can find an archived version of the webinar here.

Below are the questions that were asked during the webinar, with responses from Alex and Janis. If you need further clarification, please respond in the comments.

Thanks again, and we look forward to you joining us at our next webinar (with Percona CEO Peter …

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Frictionless MySQL installation

I saw an interesting post about the ability of installing MySQL 5.7 without changing existing tools and procedures. The post is a plea to make MySQL installation frictionless.

That post was followed by a conversation on Twitter, where the recent security enhancements are blamed for getting in the way of existing practices and need a rewrite of installation tools.

I know the problem very well, as I have faced the installation change in MySQL Sandbox. SO I can sympathize with the ones who have to change deployment tools that rely on mysql_install_db, which was a Perl script up to version 5.6, then it was replaced with a C++ program in 5.7 and deprecated in the same version.

It occurred to me that, in order to keep the …

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MySQL 5.7 default configuration: what’s new? (and what’s not)

Inspired by Peter Zaitsev’s post for MySQL 5.6, I decided to do a comparison between default configuration of MySQL 5.6 and 5.7. And I gotta tell you, some of the changes I found will have a huge impact on performance so make sure to get yourself familiar with them before you upgrade, even if you’re not otherwise running with a default configuration.

BEGIN

Interestingly, I hit the first roadblock before I could even start. It seems you can no longer extract the same information from the information_schema as in MySQL 5.6, and I had to use performance_schema instead:

mysql> insert into compare.vars57 (variable_name, variable_value) 
   ->  select VARIABLE_NAME, VARIABLE_VALUE from INFORMATION_SCHEMA.GLOBAL_VARIABLES;
ERROR 3167 (HY000): The …
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MariaDB and Native JSON support ?

A question raised by my previous post is : What about MariaDB and native JSON support ? In my previous post I mentioned the possibility to use the MariaDB CONNECT storage Engine to store and access JSON content in normal text field. Of course having a native JSON datatype brings more value. It introduces JSON ...continue reading "MariaDB and Native JSON support ?"

Dealing with corrupted InnoDB data

Data corruption! It can happen. Maybe because of a bug or storage problem that you didn’t expect, or MySQL crashes when a page checksum’s result is different from what it expected. Either way, corrupted data can and does occur. What do you do then?

Let’s look at the following example and see what can be done when you face this situation.

We have some valuable data:

> select * from t limit 4;
+---+--------+
| i | c      |
+---+--------+
| 1 | Miguel |
| 2 | Angel  |
| 3 | Miguel |
| 4 | Angel  |
+---+--------+
> select count(*) from t;
+----------+
| count(*) |
+----------+
|  2097152 |
+----------+

One day the query you usually run fails and your application stops working. Even worse, it causes the crash already mentioned:

> select * from t where i=2097151;
ERROR 2006 (HY000): MySQL server has gone away

Usually this is the point when panic starts. The error log shows: …

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Using Triggers On Schemaless, Uber Engineering’s Datastore Using MySQL

The details and examples of Schemaless triggers, a key feature of the datastore that’s kept Uber Engineering scaling since October 2014. This is the third installment of a three-part series on Schemaless; the first part is a design overview

The post Using Triggers On Schemaless, Uber Engineering’s Datastore Using MySQL appeared first on Uber Engineering Blog.

New Release! MySQL Connector/Arduino 1.1

The newest release of the MySQL Connector/Arduino library release-1.1 alpha is available for download. This new version represents a major step forward for the library in ease of use. Here are just a few of the important changes in this release.

  • Added to Library Manager : yes, you can download and install the library from the Arduino IDE now. Just open the Library Manager and search for "MySQL".
  • More Example Sketches : there are many more example sketches of how to use the new library from basic connections to complex queries and more! 
  • Redesigned Classes : the library has been redesigned with new classes making it much easier to use a wider variety of shields and modules. Now, you pass in the Client class for your shield and so long as it adheres to the Ethernet.Client primitive, you can use any library to initiate …
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An update on default_password_lifetime

With MySQL 5.7, our goal is to be secure by default. This means that without having to change configuration settings or perform any additional steps, your fresh installation should be safe for production use.

As part of this security initiative, MySQL 5.7 shipped with a new feature where user accounts will be disabled if the password has not been changed in a number of days.…

How to write SQL query?

We have discussed about the conceptual data model, logical data model, normalization and denormalization techniques and we discussed about installing and running up with MySQL community server.  What will be next? Once we build the data model and decided the database server, the next process will be start developing the physical database. What is needed to develop physical database? We need a common language to communicate with both the developer and database server. Here, comes the structure query language. So in this post we are going to see about the …

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Making Apache Spark Four Times Faster

This is a followup to my previous post Apache Spark with Air ontime performance data.

To recap an interesting point in that post: when using 48 cores with the server, the result was worse than with 12 cores. I wanted to understand the reason is was true, so I started digging. My primary suspicion was that Java (I never trust Java) was not good dealing with 100GB of memory.

There are few links pointing to the potential issues with a huge HEAP:

http://stackoverflow.com/questions/214362/java-very-large-heap-sizes

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