Litmus Edge Manager incorporates Prometheus as an widely used open-source systems monitoring and alerting toolkit.
It was originally created 2012 at SoundCloud but is now a standalone open source project and maintained independently of any company.
Prometheus collects and stores its metrics as time series data, i.e. metrics information is stored with the timestamp at which it was recorded, alongside optional key-value pairs called labels.
Prometheus's main features include:
a multi-dimensional data model with time series data identified by:
metric name
and key/value pairs
PromQL, a flexible query language to leverage this dimensionality
time series collection happens via a pull model over HTTP
pushing time series is supported via an intermediary gateway
multiple modes of graphing and dashboarding support
The main purpose is to collect what is called metrics or numerical measurements in layperson terms.
What users want to measure differs from application to application. For a web server, it could be request times; for a database, it could be the number of active connections or active queries, and so on.
Metrics play an important role in understanding why your application is working in a certain way.
For a system like Litmus Edge Manager, this would include the system performance like CPU, Memory or disk space.
As with more Litmus Edge Devices connected and more Companies and/or Projects, the originally allocated resources may run out and jobs like a Litmus Edge Update or Application push may be slowed down or even times out.
Prometheus scrapes metrics from instrumented jobs, either directly or via an intermediary push gateway for short-lived jobs. It stores all scraped samples locally and runs rules over this data to either aggregate and record new time series from existing data or generate alerts.
But users are not limited as other API consumers can be used to visualize the collected data too, such as logit.
By default, Litmus Edge Manager does provide some default Grafanadashboards which includes one for Prometheus Metrics.
But if your organization does make use of an existing centralized visualization and alerting platform such as for example Grafana Cloud or logit, Litmus Edge Manager can easily be connected to these platforms and becomes part of your overall IT infrastructure.
Example Use Case
Application performance monitoring of devices and/or applications using metrics such as:
response times
error rates
and resource utilization of different workflows
Load testing of infrastructure and applications under varying high loads using metrics like:
CPU utilization
memory utilization
network traffic
-> This data can be used to optimize applications to be more scalable and efficient.
Infrastructure monitoring for health and performance of components like
servers
databases
Analytics and anomaly detection to discover patterns and trends from real-time data
Enable security teams to detect and rectify anomalies before they pose a threat to the organization
Monitoring key performance indicators (KPIs) related to SLAs such as: