Written by Luther Rochester on — 05:51 reading time
As we’ve moved increasing portions of our infrastructure to AWS, we’ve found more use cases for stashing things in s3 that we used to use traditional backup software to store. We keep our SSIS code in version control (currently TFS, grumble grumble), but we aren’t storing a copy of each database object definiton there. Recently we undertook setting up a job that would script out each object’s DDL and store it in an s3 bucket. We’re already doing this for our Postgres servers using pg_dump; we found a great little Python app called mssql-scripter that works similarly to script out the objects for us. This runs in a batch script, along with an aws-cli command to upload the resulting files. The batch script is then called from a SQL Agent job and run on a schedule. The AWS bucket has a lifecycle policy to handle retention for us.
You’ll need a few tools
The server that will run the code will need a few things installed on it. It’s easiest if you use the same server that SQL Agent...
Written by Luther Rochester on — 05:45 reading time
Our web systems live on UNIX-y hosts, and we’ve got a robust Nagios implementation to monitor and alert us for all those systems. However, our BI platform is Windows and SQL Server based, and we didn’t want to have a separate monitoring system for those servers and databases. We came up with some tricks that have worked well for us to integrate Nagios into our Windows ecosystem.
Install Nsclient++ on Windows boxes
In order to get monitoring stats into Nagios, we’ve installed the Nsclient++ application on all our Windows machines. This is a very lightweight, handy client that enables all sorts of monitoring data. For our purposes, we’ve got it configured to pass Nagios checks to Windows Performance Monitor counters via the check_nt protocol.
Nsclient++ utilizes a simple ini file to set the basic configuration. Our Nagios server was added to the Allowed Hosts section, and “NSClientserver” was set =1 to allow the check_nt command to flow through.