There’s some interesting discussion around “Monitoring Sucks”, and has been for a while. (Go check the twitter hashtag #monitoringsucks). This is not a new opinion – the fact that I thought monitoring sucks is why I started LogicMonitor.
But it’s interesting to assess whether LogicMonitor meets the criteria for not sucking. Clearly our customers think we have great monitoring – but probably only 30% of our customers are SaaS type companies, and may or may not have the DevOps mentality.
So the initial criteria for why monitoring sucks, at least on the referenced blog post, were:
But does monitoring REALLY suck? Heck no! Monitoring is AWESOME. Metrics are AWESOME. I love it. Here's what I don't love: - Having my hands tied with the model of host and service bindings. - Having to set up "fake" hosts just to group arbitrary metrics together - Having to either collect metrics twice - once for alerting and another for trending - Only being able to see my metrics in 5 minute intervals - Having to chose between shitty interface but great monitoring or shitty monitoring but great interface - Dealing with a monitoring system that thinks IT is the system of truth for my environment - Perl
Let’s look at these points from the point of view of LogicMonitor
Having my hands tied with the model of host and service bindings. I’m not sure how you not tie someone’s hands to some degree, but LogicMonitor certainly tries to give flexibility. Services do generally have to associated with hosts – but can be associated by all sorts of things (hostname, group membership, SNMP agent OID, system description, WMI classes supported, kernel level, etc.)
Having to set up “fake” hosts just to group arbitrary metrics together. LogicMonitor avoids this mostly with custom graphs on dashboards, which allow you to group any metric (or set of metrics based on globs/regex’s) with any other set, filtered to the top 10, or not; aggregated together (sum, max, min, average) or not. Also, some meta-services are associated with groups, not hosts, to allow alerting on things like number of servers providing a service, rather than just whether a specific host is successfully providing the service.
Having to either collect metrics twice – once for alerting and another for trending. We certainly don’t require that. Any datapoint that is collected can be alerted on, graphed, both or neither. (Sometimes datapoints are collected as they are used in other calculated datapoints, derived from multiple inputs.)
Only being able to see my metrics in 5 minute intervals. Again, we don’t impose that restriction – you can specify the collection interval for each datasource, from 1 minute to once a day. (I know going to only 1 minute resolution is not ideal for some applications – but as a SaaS delivery model, we currently impose that limit to protect ourselves, until the next rewrite of the backend storage engine, which should remove that.)
Having to chose between shitty interface but great monitoring or shitty monitoring but great interface.I think we have a pretty good interface and great monitoring. Certainly our interface is orders of magnitude better than it was when we launched, and a lot of people give us kudos for it. But there’s lots of room for improvement.
Dealing with a monitoring system that thinks IT is the system of truth for my environment. LogicMonitor thinks it is the truth for what your monitoring should be monitoring – but it’s willing to listen. It’s easy to use the API to put hooks into puppet, kickstart, etc that automatically add hosts to monitoring, assign them to groups, etc. We’re looking at integration with Puppet Lab’s MCollective initiative and other things to get further along this issue.
Perl. Our collectors are agnostic when it comes to scripting. They support collection and discovery scripts in the native languages of whatever platform they are running on – so VBscript, powershell, C# on Windows; bash, ruby, perl, etc on linux. But as our collectors are Java based, we encourage Groovy as the scripting language for cross-platform goodness. The collectors expose a bunch of their own functionality (snmp, JMX, expect, etc) to groovy, so it makes a lot of things very easy. So it’s the language we use for writing and extending datasources for our customers. But if Perl is your thing, keep at it.
So, does LogicMonitor suck? I don’t think so, and hopefully DevOps Borat does not either.
I’ll be at the DevOps Days conference in Austin this coming week (LogicMonitor is sponsoring), so hopefully we’ll get some more feedback there.
Or post below to let us know what constitutes “non-sucky” monitoring.
IT people by nature are supposed to be gurus. They’re supposed to be able to build things from scratch. This expectation certainly applies to data center monitoring, where a common practice is to rely on open source monitoring tools such as Nagios. But when you consider the value of your time, these free tools can quickly wind up being far more costly than commercial tools. For instance, we did a survey and found that some system admins had spent over 100 hours to get their open source monitoring solution to do what they wanted. Further, there was ongoing work to try to keep the system up to date with frequent changes in their datacenter, and even then they only had, for the most part, coarse level monitoring (for example, monitoring only the CPU load of a load balancer, instead of monitoring the state of all the hundreds of VIPs on the load balancer.)
When the only alternatives were costly enterprise-class monitoring solutions, sweating it out with open source was understandable. But now that there are affordable tools that automate configuration and give you everything you need in 30 minutes, insisting on building your own doesn’t seem wise (especially in this era of understaffed data centers.) At the root of this DIY mentality is pride. With so many open source options available, Techies probably feel some sense of shame or embarrassment going to an IT director and asking for tools that cost money.
I’d suggest a better source of pride is being able to spend time on tasks that add value to the enterprise – writing Puppet scripts that automate machine and software deployments, and so greatly reduce the time to spin up machines; investigate cloud usage options; correlate resource expenses with revenue per business unit. There are a lot of things that should be done in any enterprise, that are not because of lack of time. A good systems administrator’s time is very valuable – much more valuable than going through a MIB to figure out which item is important to monitor.
And no matter how good a systems administrator you are, monitoring is not going to be your top priority (nor should it be). You’ll get monitoring going “good enough” – but there will be lots of cases that it failed to alert on, when a comprehensive monitoring system would have. Then after every outage, you’ll have to go back and extend the monitoring, adding in metrics that could have helped predict the specific case.
So given the cost of your time; the more in depth monitoring that you get immediately with LogicMonitor (a typical Nagios implementation may monitor 10 metrics on a linux host; a typical LogicMonitor deployment will monitor over 100); and the opportunity cost of the things you could be doing to add value with your time, if you weren’t configuring monitoring, then why not use an automated monitoring tool such as LogicMonitor that makes you a better system administrator, and doesn’t require a Fortune 500 budget to implement?
If you’d rather skip the tedious work, but want the peace of mind knowing that your infrastructure is properly monitored, and that you will be alerted of any issues early, it’s perfectly okay to go the automation route. You’ll feel a sense of satisfaction in preventing an outage, whether you wrote the code or not. And your CFO may even thank you for spending the money.
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