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Prometheus fundamentally stores all data as time series: streams of timestamped values belonging to the same metric and the same set of labeled dimensions. Timestamps have a millisecond resolution, while values are always 64-bit floats. The metric name specifies the general feature of a system that is measured. For example, a metric to count the total number of HTTP requests received by an API server might be called api_http_requests_total. Adding labels (key/value pairs) to this metric enables Prometheus's dimensional data model: any given combination of labels for the same metric name results in a separate time series. See More
A multi-dimensional data model, so that data can be sliced and diced at will, along dimensions like instance, service, endpoint, and method.
Alerts are defined using the same powerful query language described above. A separate binary, the Alertmanager, handles alert notifications and aggregations and enables silencing by any label set. If an alert fires, Alertmanager can send an email or page you through an external alerting service like PagerDuty. Nagios plugin is also offered as a bridge from the parts that are already monitored by Prometheus to the existing Nagios alerting set-up that many companies have in place. See More
Subscriptions to real-time updates from infrastructure systems gives users proactive detection of application anomalies, as well as performance, availability and capacity issues. This enables users to capture perishable insights into application issues that they would normally miss. See More
There are really only 2 things a user needs to do to show charts, graphs: 1) create a dashboard 2) Create viewlets. Each viewlets is bound with a JQKL query. JQKL queries can be typed in or built via a query builder for beginners. Pretty easy to get up and running without learning JQKL. There is also a UI tutorial once you logon to jKool. See More
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