yearly resentment (this would be the last) over a failed contribution:

• https://github.com/grafana/grafana/pull/7863#issuecomment-354004166
• https://github.com/grafana/grafana/issues/4693
• https://community.grafana.com/t/advanced-query-with-nested-object/4278/2

Disclaimer: Logging my experience of a quasi-success in executing $subject.

Specs

• Partitioning scheme: LVM based encrypted partitions
• Hardware - Lenovo T440p
• OS (earlier) - Fedora 24 Gnome
• OS (now) - Fedora 24 KDE

As if there aren’t enough cases already, this one found a grand entrance to the club of royally screwed ups, when it comes to messing up with your disk partitions.

On Scale tests

..on distributed systems, in companies with a global, collaborative and distributed workplace setup

Before running a scale test, make sure to have the following items checked off your list:

1. Is churn on product integrations, enough to simulate production environment?
2. During the tests, is the product going to run with defaults? Is this about baselining with defaults or are we going to let the system bleed while increasing throughputs?
3. Do you have exact expectations noted down prior to automating? (per se, monitoring enough parameters in the right way. This includes Telemetry, Logs and Configurations per iteration.)

My postgres parition was full and I desperately needed to increase its size, in order to report some performance numbers.

I couldn’t afford to loose the data (and my shit). So I followed these steps to safely increase the size of that partition based on xfs, which existed on a logical volume already.

Self help commands and assumptions:

• disk is /dev/vdb/
• xfs is mounted on /dev/vg_pg/lv_pg
• we grew the disk by 20 Gigs

1

xfs_info /dev/vg_pg/lv_pg -d 64G

https://jira.mariadb.org/browse/MDEV-11789

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

yum -y install MariaDB-devel  gcc gcc-c++ autoconf automake make libtool   zlib zlib-devel openssl-devel
yum install -y MySQL-server
 
# tried this, didn't work (where /home/native is the directory with mysql database is stored; configurable in my.cnf)
semanage fcontext -a -t mysqld_db_t "/home/native(/.*)?"
restorecon -Rv /home/native/
yum install selinux-policy
audit2allow -a
yum install yum-utils
yum-complete-transaction
 
# then tried this
yum provides sealert
yum install setroubleshoot-server
sealert -a /var/log/audit/audit.log
# this produces alerts from audit.log and provides hints as to what should be executed next..
 
# so i followed directions..
ausearch -c 'mysqld_safe_hel' --raw | audit2allow -M my-mysqldsafehel
semodule -i my-mysqldsafehel.pp

Satperf utilizes a visualization framework called Grafana, to present graphs.

Metrics Collected

An explanation of how the following metrics are placed in the satperf monitoring dashboard, is described in the ‘Grafana Dashboard’ section. If you wanna copy these metrics, refer to this file [2] in references

1. Intro

Satperf (link) is a Red Hat Satellite Performance Benchmarking & Automation tool that makes it super easy to quickly setup your own environment with satellite components and start running workloads. It uses ansible playbooks to manage remote execution and has built-in modules for various roles that Satellite Web UI has to offer. These are carried out with the use of hammer commands (read more on Hammer) as an equivalent of Satellite API.

Intro

Installing collectd could be trivial, although setting up monitoring for continuous time-series metric collection should be simpler. This post is aimed at helping sysadmins setup collectd and connect it to a graphite instance, so that all those metrics could later be viewed from Grafana instance.

I recently shifted from having a static page as my github.io link to making a blog out of the default .io page for my Github account. I found it pretty cool to be able to push stuff through markdown (although even that formatting is configurable under Rakefile of octopress source code).

So here’s a really short post on how to set it up.

1. What is it about?

Sarjitsu, or, SAR Jitsu, is a one stop shop for people who’re looking to visualize their system data, based on System Activity Report (SAR) data generated by Sysstat. Screenshots from the app are near the end of this post.