Severalnines Blog
The automation and management blog for open source databases

Severalnines blog

Filter by:
Clear
Apply (1) filters
81 blog posts in 1 category

Webinar Replay: Completing the MySQL Query Tuning Trilogy: working with optimizer & SQL tuning

This webinar was the third and last part of our webinar trilogy on MySQL Query Tuning, in which we discussed query tuning and related tools. In this trilogy we’ve covered SQL tuning, indexing, the optimizer and how to leverage EXPLAIN to gain insight into execution plans. Part 3 focussed on working with the optimizer and SQL tuning.

How to monitor MongoDB (if you’re really a MySQL DBA) - Webinar Replay & Slides

These are the replay details for the second webinar of the ‘Become a MongoDB DBA’ series, during which we demonstrated how to monitor MongoDB and discussed the most important metrics to keep an eye on.

Become a MongoDB DBA: Monitoring and Trending (part 2)

This is our fourth post in the “Become a MongoDB DBA” blog series. It will give a deep dive in monitoring MongoDB: which metrics should you pay attention to, and why?

Become a MongoDB DBA: Monitoring and Trending (part 1)

This is our third post in the “Become a MongoDB DBA” blog series. It will give a primer in monitoring MongoDB: how to ship metrics using free open source tools.

High Availability Log Processing with Graylog, MongoDB and ElasticSearch

This blog post discusses how to deploy a Graylog cluster, with a MongoDB Replica Set deployed using ClusterControl.

Automate your Database with CCBot: ClusterControl Hubot integration

This post describes how to automate your database with CCBot. integrate ClusterControl with your ChatOps infrastructure via Hubot. We will go through the steps.

Integrating ClusterControl Alarms with Splunk

This blog shows how to integrate ClusterControl with a Log Management Tool like Splunk.

Syslog Plugin for ClusterControl

This blog shows how to install and configure ClusterControl’s syslog plugin to forward alarms to the syslog facilities.

Deep Dive SQL Workload Analysis using pt-query-digest

In our previous post, we showed you how to interpret reports generated by pt-query-digest. Today we’d like to cover some of its more advanced features, as it is a pretty extensive tool with lots of functionality. We’ll also show you what information you should be looking for, and how to derive conclusions based on that data. 

Analyzing Your SQL Workload Using pt-query-digest

Mid to large size applications tend to have hundred of SQL statements distributed throughout a large code base, with potentially hundreds of queries running every second. That can generate a lot of data. How do we identify causes of bottlenecks slowing down our applications? Obviously, going through the information query by query would not be great - we’ll get drowned with all the entries. We need to find a way to aggregate the data and make sense of all that.