#Prometheus

Question count - 0     Last updated    - V3 (23-Apr-2023)

What is Prometheus? Features? Architecture? Components?

What is an Alert?

What is Prometheus server

What is Push Gateway

What is Alert Manager

What is data model.

What is counters and gauges.

What is an Instance? What is a Job?

What core metrics types Prometheus supports?

What is an exporter? What is it used for?

What is Traffic?

What is Error rate?

What is Latency?

How to get total requests in a given period of time?

How do you join two metrics? Histograms? Summaries?

How to write a query that returns the value of a label?

How do you convert cpu_user_seconds to cpu usage in percentage?

Can you compare Prometheus to other solutions like InfluxDB for example?

Topic	Content
Prometheus	Prometheus is a system monitoring and alerting toolkit Gather time-series data by pulling data through metrics from the codebase. Specify a random list of labels for multi-dimensionality. Store data as metrics that can be queried. Easily build queries using time-series functions found in its native PromQL. Generate and deliver threshold-based alerts through its Alertmanager. Several dashboards and graphing modes support Representation of time series assemblage from any pull model on HTTP Multidimensional data model presenting time series data, i.e., recognized with the name of the metric or with KVP (Key-value pairs) Capability to apply PromQL for supporting the multidimensionality feature of a data model Zero reliance over distributed storage and autonomous individual server nodes Target discovery from service discovery or static configuration Capability to manage time series from any intermediary gateway
PromQL	It enables users to aggregate and select data. Specifically, PromQL is adjusted for working in assemblage with the Time-Series database. Prometheus contains four types of metrics which are listed below: Gauge Counter Summary Histogram Apply gauges for times-series metrics, i.e., straightforward. Apply counters for many things we know for increasing monotonically. For example, if we are counting time numbers something happens. Apply histogram for measurements of latency using general buckets. For example, a single bucket for "over SLO" and another for "under SLO".

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