Generic server monitoring with Monitis & M3

Had I been told to monitor a cat chasing a mouse with Monitis, my answer would have been – “Yes, it’s probably possible”.
With the not-so-recent addition of M3 to the arsenal of monitoring tools Monitis can utilize, it is possible to monitor anything. However this alone is far from being enough. Smart implementations of proper applicative monitoring is what should be practiced.

New feature provides significantly faster insight and root cause analysis

SAN JOSE, Calif., February, 15, 2012 – Monitis, the leading cloud and web application monitoring software provider, today announces that it has added comprehensive MySQL database monitoring to its award-winning Application Performance Management & Monitoring platform. The robust Software-as-a-Service (SaaS) tool enables users to gain significantly faster insight when conducting root cause analysis.

The MySQL monitoring feature includes 246 monitoring variables and more than 21 different metrics to provide one of the easiest to use, yet comprehensive database monitoring tools available. It was first introduced into the free Monitor.Us platform back in June last year and has seen the code battle hardened by many hundred free users over the last 8 months.

Unleashing the power of M3 & timers

During the lifespan of M3 (Monitis Monitor Manager) there has always been something lacking – timers.

M3 execution procedure was outlined in this previous article.

The execution mentioned in the latter was a one-time-execution, whereas server monitoring requires periodic invocation of monitors in order to actually provide counters over time, graphing performance.

The periodic invocation method suggested up until today was to integrate M3 with crontab.

Crontab, in a nutshell, is a Linux/Unix service for periodic invocation of executables. Implementing M3 with crontab properly meant M3 would run every X minutes, producing a Monitis counter update every X minutes, should everything run properly.

In the following article I’ll outline the changes done to support timers in M3.

Monitoring In a previous Monitoring Performance on MongoDB – Mongo Basics article we went over some basics about Mongo and monitoring Mongo performance.  In this article we are going to examine some interesting services, that can help with our Mongo monitoring.

Basically, we have two sets of statistics we would like to collect from our Mongo instances.  First, the basic computer stats that you need to collect for any machine in your fleet, and second we want to collect all these great DB stats from the Mongo HTTP Console.

We want to collect all these statistics, but we also need somewhere to store and view them.  That’s where Monitis comes into play!

Pluggable M3 (Monitis Monitor Manager) Framework

Who needs an introduction about M3? – Perhaps no one!
After gaining some reputation with M3, providing extra-easy integration of any monitor into Monitis it was time to take it to the next level.

Generally speaking, the work flow of M3 was described in detail in this article.

After some thought and design, we’ve decided it’d be best if M3 was pluggable. Pluggable in terms of being able to easily add execution and parsing plugins.
The interface and behavior of M3 stayed exactly the same, however now it is much easier to obtain data from any source and parse the data the way you want it.
Saying that, it was time to put the new design for a test. We tried to integrate the DBI support into M3.
Guess what – it was much easier than expected!

Cassandra is a robust and highly scalable NoSQL datastore that usually consists of multiple nodes spread out across multiple datacenters.  If you are the system administrator for a large Cassandra deployment then you might be curious as to how your cluster is doing.  In fact your job probably depends on it!  So how can you combine a great service like Monitis with Cassandra to make sure you cluster is buzzing along smoothly?

The Internet is a great resource, but it is sometimes too much to wade through hundreds of web pages looking for tips about using Tomcat with a database. So we at Monitis have done it for you! Below are some of the more useful tips found in various places on the Internet.

Hadoop, which was named after a toy elephant, is a serious framework for doing distributed data processing across thousands of nodes and petabytes of data.  It’s loosely based on some big time Google projects, MapReduce, Big Table, and Google File System.  A key thing to understand about Hadoop is that it’s not just one monolithic project. Hadoop has a number of different pieces that all work together to help you crunch some very big data.

Hadoop is all about processing data, and Hadoop’s MapReduce engine is what makes all this processing possible. MapReduce is a process that Google came up with for churning through petabytes of data on commodity hardware. Here’s how it works.