Fault Detection Released for MongoDB

Posted by Alex Slotnick on Aug 8, 2016 3:41:44 PM

As we noted last week on our product updates page, VividCortex has recently added fault detection support for MongoDB. Adaptive fault detection is one of VividCortex’s unique and most powerful features: it’s a practical and useful application of the concept of “work-centric” monitoring. Rather than focus on global resource metrics, which can often be relative and context-dependent, work-centric measurements give a clear and proactive perspective on what your system is really up to. We’re excited to be introducing this key resource to MongoDB users.

MongoDB_Faults.png

We define “faults” as instances when a system continues to line-up queries or work requests but fails to execute them. This is a particular kind of bottleneck, and with insightful fault detection, an organization can spot issues while they’re still small and amenable. Gone unchecked, many such faults can intensify and complicate in a system’s background until they become massive, painfully obvious, and in a position to cause serious harm.

As we wrote in a previous blog post, faults are decidedly different from anomalies and other notable events, which means the method for detecting them must be more precise than simply pinpointing outliers. Instead, our fault detection algorithm is based on queueing theory, a very potent concept that Baron, VividCortex’s CEO, has written about in detail: “Using advanced statistics and machine learning, VividCortex’s Fault Detection is completely adaptive and self-tuning – it doesn’t require any configuration. The program can detect faults as short as one second in duration. Even the most attentive user would likely fail to notice system stalls so small, but with our adaptive fault detection, they’re easily diagnosed and solved. On top of that, the algorithm is incredibly efficient, practically free for a system’s CPU and memory.”  

For MongoDB specifically, we're measuring completed requests as the number of operations per second; and measuring work-in-progress as the number of active clients, as determined by the output of the serverStatus() command. If you’re a MongoDB user — or any other database user — and want to see what fault detection looks like on your system, please reach out, get in touch, and request a demo.

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