Amazon CloudWatch is the monitoring tool for all the Amazon Cloud services. It offers both White Box and Black Box monitoring for services managed by Amazon and can be extended to work with user-generated monitoring data.
This text covers the integration of a simple MySQL monitoring script with Amazon CloudWatch.
Let’s assume that we want to monitor the number of active connections to the MySQL server and have an indication on when this figure becomes close to the maximal value defined in the configuration file (max_connections). In order to be portable, we may want to also report this value to the monitoring engine, even if it’s unlikely that a change may occur without explicit human intervention.
MySQL provides 3 numeric figures we may be interested in:
In this text I intend to discuss some practical aspects related to the “multiple 9” percentages that are advertised by vendors regarding their reliability. Oh, and how to achieve 100% uptime (NOT).
SLA stands for Service Level Agreement and it is a binding contract between the vendor and the customer. It is usually expressed as the percentage of the time reference window (e.g. a year) when the Service should be functioning normally, delivering its desired output.
The Uptime of the Service represents the numeric portion of the agreement above, expressed either as a percentage or by using time units.
Note: the uptime/downtime definitions above do not completely apply with Services provided through different infrastructure sets, e.g. to different geographical regions, from different data centers. A downtime in one geographical region does not mean that the Service is unavailable to just every customer out there so a different calculation method must be figured out. A solution may be to estimate the number of requests not served during the downtime by looking historical data up and then do the Service availability estimates from that particular numeric figure.
How many 9s?
AWS provides a complete monitoring engine called CloudWatch; it works with metrics – including custom, user-provided metrics – and is able to raise alarms when any such metric crosses a certain threshold. This is the tool that is used for all perfomance monitoring tasks within AWS.
This text will cover a monitoring scenario regarding deploying an arbitrary appplication to the cloud and being able to determine what causes the performance limits to be met, be it the application code itself or resource limits enforced by Amazon.
Let’s assume that one has just started using Amazon Web Services and is deploying applications on free tier or other general purpose (T2) instances. One learns that the general purpose instances work with “credits” that allow dealing with short spikes through performance bursting – but once the credits are exhausted the performance is reverted to some baseline. All the particular details do not make a lot of sense but one needs to know if the application can meet the desired service limits within this setup.