By Jason Dixon
Why is Graphite some of the most well known instruments for tracking and graphing the functionality of computers? This sensible booklet is helping you utilize this open resource software to trace the operational facts you must visual display unit your structures and music application-level metrics for profiling your prone. you are going to how to correlate all of that info with enterprise intelligence that will help you make trained judgements approximately your IT thoughts. This booklet is perfect for approach directors and engineers who've familiarity with handling servers or improvement environments from the command line.
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Additional info for Monitoring with Graphite: Tracking Dynamic Host and Application Metrics at Scale
This particular schema would only “cost” you 4MB a file. To contrast, storing one-minute precision for three years would be a 20MB database. This might not sound like a huge difference, but now multiply that by a million metrics. Trust me when I say a million metrics is totally feasible. Most Graphite deployments become very popular once users see how easy it is to send metrics and visualize data. If you’re not prepared for the explosion of “metric growth” your systems will be over‐ whelmed far sooner than you expected.
A rules-based setup will demand more ongoing maintenance, at least in terms of moni‐ toring the storage use over time. Conversely, it will be far less painful to add nodes to your cluster if you get to choose precisely where your metrics are routed. I like to think of the rules method as amortizing the pain of upgrading your Graphite cluster storage over time, versus inflicting all that pain on yourself at the time of the upgrade. I’ve seen large companies start with consistent-hashing, switch to rules after a particularly thorny upgrade cycle, and then back again once they had better synchro‐ nization tooling in place (similar to Carbonate).
Summing two metrics to create a new unique metric) before passing it on to the storage layer. Graphite’s carbon-aggregator is an example of this type of service, and we’ll cover it at greater length in the next chapter. We’ve already touched on StatsD in terms of the library support it offers developers for easy instrumentation of their applications. But one of my favorite features is its ability to export “value-added” statistics for timer measurements. Not only will it tell you the average (mean) for all timers in an interval, it will also give you: the upper and lower boundaries, the total count of timers reported, the timer_ps of timers per second, the sum and sum_squares, and the median and std standard deviation.
Monitoring with Graphite: Tracking Dynamic Host and Application Metrics at Scale by Jason Dixon