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Reliable Insights A blog on monitoring, scale and operational Sanity Trying to improve alerting piecemeal can be difficult. If you're in a situation where you are being inundated with low-value alerts, trying to gradually improve things can be a never-ending struggle. I once joined a team that was getting a few hundred email alerts per week, which the oncall was meant to handle all of. This was un
Reliable Insights A blog on monitoring, scale and operational Sanity The node exporter exposes filesystem metrics out of the box, so let's take a look. The usual way to look at filesystem space usage is df: $ df Filesystem 1K-blocks Used Available Use% Mounted on udev 16406128 0 16406128 0% /dev tmpfs 3289028 3280 3285748 1% /run /dev/md0 32881520 21934588 9253652 71% / tmpfs 16445128 624400 15820
Reliable Insights A blog on monitoring, scale and operational Sanity I previously looked at ingestion memory for 1.x, how about 2.x? Prometheus 2.x has a very different ingestion system to 1.x, with many performance improvements. This time I'm also going to take into account the cost of cardinality in the head block. To start with I took a profile of a Prometheus 2.9.2 ingesting from a single targ
Reliable Insights A blog on monitoring, scale and operational Sanity It's a best practice with Prometheus that target labels should be constant over a target's entire lifetime. On the other hand it's useful to aggregate metrics across all the machines that are currently Apache servers. How can we do that? A key concept in Prometheus is that you want continuity in your time series, that is that the
Reliable Insights A blog on monitoring, scale and operational Sanity In this blogpost we'll run you through a quick 'hello world' example instrumenting a Rails application with the Prometheus ruby client. (The completed sample created in this blogpost can be found here.) Create a new rails application: $ rails new prom-example Add the Prometheus client and rack gems to your Gemfile and install all
Reliable Insights A blog on monitoring, scale and operational Sanity High CPU load is a common cause of issues. Let's look at how to dig into it with Prometheus and the Node exporter. On a Node exporters' metrics page, part of the output is: # HELP node_cpu Seconds the cpus spent in each mode. # TYPE node_cpu counter node_cpu_seconds_total{cpu="0",mode="guest"} 0 node_cpu_seconds_total{cpu="0",mod
Prometheus 0.16.1 was just released, and with it brings my addition of the irate function. This offers more responsive graphs and higher resolution dashboards. The rate function takes a time series over a time range, and based on the first and last data points within that range (allowing for counter resets) calculates a per-second rate. As it's based on the whole range, it's effectively an average
Reliable Insights A blog on monitoring, scale and operational Sanity There's a common misunderstanding when dealing with Prometheus counters, and that is how to apply aggregation and other operations when using the rate and other counter-only functions. Aggregation is core functionality of Prometheus, and it's most commonly applied to counters. As you'll recall from a previous article counters onl
Reliable Insights A blog on monitoring, scale and operational Sanity Prometheus labels allow you to model your application deployment in the manner best suited to your organisation. As directly supporting every potential configurations would be impossible, we offer relabelling to give you the flexibility to configure things how you'd like. How labels propagate can be a bit tricky to get your head
Reliable Insights A blog on monitoring, scale and operational Sanity There are four standard types of metric in Prometheus instrumentation: Gauge, Counter, Summary and Histogram. Today we'll have a look at the principles around Counters, and how Prometheus differs from other monitoring systems. A counter counts things. Sounds simple, right? That's not much use on its own though. What you really wa
Reliable Insights A blog on monitoring, scale and operational Sanity A single Prometheus server can easily handle millions of time series. That's enough for a thousand servers with a thousand time series each scraped every 10 seconds. As your systems scale beyond that, Prometheus can scale too. Initial Deployment When starting out it's best to keep things simple. A single Prometheus server per dat
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