The natively exposed ThingWorx Platform performance metrics can be extremely valuable to understanding overall platform performance and certain of the core subsystem operations, however as a development platform this doesn't give any visibility into what your built solution is or is not doing. Here is an amazing little trick that you can use to embed custom performance metrics into your application so that they show up automatically in your Prometheus monitoring system. What you do with these metrics is up to your creativity (with some constraints of course). Imaging a request counter for specific services which may be incredibly important or costly to run, or an exception metric that is incremented each time you catch an exception, or a query result size metric that informs you of how much data is being queried from the database. Refer to Resources > MetricsServices: GetCounterMetric GetGaugeMetric IncrementCounterMetric DecrementCounterMetric SetGaugeMetric You'll need to give your metric a name - identified by key - and this is meant to be dotted notation* which will then be converted to underscores when the metric is exposed on the OpenMetrics endpoint. Use sections/domains in the dotted notation to structure your metrics in-line with your application design. COUNTER type metrics are the most commonly used and relate to things happening through time. They are an index which will get timestamped as they're collected by Prometheus so that you will be able to look back in time and analyse and investigate what happened when and what the scale or impact was. After the fact functions and queries will need to be applied to make these metrics most useful (delta over time, increase, rate per second). Common examples of counter type metrics are: requests, executions, bytes transferred, rows queried, seconds elapsed, execution time. Resources["MetricServices"].IncrementCounterMetric({
basetype: "LONG",
value: 1,
key: "__PTC_Reported.integration.mes.requests",
aggregate: false
}); GAUGE type metrics are point-in-time status of some thing being measured. Common gauge type metrics are: CPU load/utilization, memory utilization, free disk space, used disk space, busy/active threads. Resources["MetricServices"].SetGaugeMetric({
basetype: "NUMBER",
value: 12,
key: "__PTC_Reported.Users.ConnectedOperatorCount",
aggregate: true
}); Be aware of the aggregate flag, as it will make this custom metric cluster level which can have some unintended consequences. Normally you always want performance metrics for the specific node as you then see what work is happening where and can confirm that it is being properly distributed within the cluster. There are some situations however where you might want the cluster aggregation however, like with this concurrently connected operators. Happy Monitoring!
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