My previous post I finished with the graph with unstable results.
There I won’t analyze causes, but rather I want to show some different ways to present results.
I enjoy working with R, and though I am not even close to be proficient in it, I want to share some graphs you can build with R + ggplot2.
In this post, I’ll cover the new MySQL monitoring plugins we created for Nagios, and explain their features and intended purpose.
I want to add a little context. What problem were we trying to solve with these plugins? Why yet another set of MySQL monitoring plugins?
In my previous post I explained how it could be possible to recover, on some specific cases, a single table from a full backup in order to save time and make the recovery process more straightforward. Now the scenario is worse because we don’t have a backup or the backup restore process doesn’t work. How can I recover deleted rows?
SHOW PROFILES shows how much time MySQL spends in various phases of query execution, but it isn’t a full-featured profile. By that, I mean that it doesn’t show similar phases aggregated together, doesn’t sort them by worst-first, and doesn’t show the relative amount of time consumed.
I’ll profile the “nicer_but_slower_film_list” included with the Sakila sample database to demonstrate:
A quick reminder. I’m speaking on two MySQL meetups in North Carolina this week. Tuesday,Feb 21 on Raleigh MySQL/PHP Meetup and when Wednesday, Feb 22 on Charlotte Queen City PHP Note the last meetup date has changed, it was originally planned for February 23 but had to be rescheduled due to conflicts.
In response to the release of our new MySQL monitoring plugins on Friday, one commenter asked why the new Nagios plugins don’t use caching. It’s worth answering in a post rather than a comment, because there is an important principle that needs to be understood to monitor servers correctly. But first, some history.