Introduction of Prometheus ?

Prometheus is an open-source monitoring and alerting toolkit designed for recording real-time metrics in a time-series database (TSDB), built especially for cloud-native and container-based environments like Kubernetes.

It collects and stores metrics as time series data (i.e., values with timestamps), supports powerful querying via PromQL, and integrates with Grafana and Alertmanager.

Key Features of Prometheus:

Feature	Description
Time Series Storage	Stores metrics as time series with labels
Pull-based model	Prometheus scrapes metrics from targets, unlike push-based systems
PromQL	Built-in query language for filtering, calculations, and alert conditions
Service Discovery	Automatically finds services via Kubernetes, Consul, EC2, etc.
Visualization	Built-in graph UI; best used with Grafana dashboards
Alerting	Define alerts based on thresholds; send notifications via Alertmanager

 Why Use Prometheus?

• Open source and widely used
• Lightweight and easy to install
• Perfect for microservices and containerized apps
• Strong community and support ecosystem
• Compatible with exporters for system, database, application monitoring

Prometheus Components?

Prometheus has several core components that work together to collect, store, query, and alert based on metrics.

Component	Description
Prometheus Server	Core component that collects (scrapes), stores, and queries metrics
Exporters	Services or agents that expose metrics in Prometheus format
Alerting Rules	PromQL-based rules to define alert conditions
Alertmanager	Manages alerts – grouping, silencing, routing to email, Slack, etc.
Pushgateway	Allows short-lived jobs to push metrics to Prometheus
Service Discovery	Auto-discovers scrape targets (e.g., Kubernetes pods, EC2 instances)
Prometheus UI	Built-in web interface to run queries, check targets, and view alerts
Grafana	External tool for beautiful dashboards and visualizations using Prometheus data

Prometheus vs. Other Monitoring Tools.

Feature	Prometheus	Graphite	Elastic Stack	Datadog
Architecture	Pull-based	Push & Pull	Pull	Agent-based
Primary Data Model	Time series	Time series	Logs	Custom metrics
Query Language	PromQL	Custom DSL	kibana query	Custom UI
Horizontal Scaling	Supported	Limited	Supported	Fully managed service
Open source	Yes	Yes	Yes	No

Basic Terminologies in Prometheus

basic terminologies in Prometheus explained simply, with practical context for better understanding:

Term	Meaning
Time Series	Prometheus stores metric values with timestamps, so it creates time series data.
Metric	Metric is a measured value collected from a system, like CPU, memory, or request count. (e.g., http_requests_total)
Label	Key-value pair to differentiate time series
Job	Logical group of scrape targets
Instance	A single scrape target (usually host:port)
Target	Actual endpoint Prometheus scrapes metrics from
Scraping	Prometheus pulling data from targets at regular intervals
PromQL	Prometheus Query Language to analyze and fetch data
Exporter	Service or agent that exposes metrics in Prometheus format
Recording Rule	Precomputed PromQL query result stored as a new time series
Alerting Rule	PromQL expression that triggers alerts when conditions are met
Alertmanager	Handles alerts: grouping, deduping, routing to email, Slack, etc.
Pushgateway	Allows short-lived jobs to push metrics to Prometheus
Retention	Duration Prometheus keeps time-series data (e.g., 15 days)
TSDB	Time Series Database used internally by Prometheus
Service Discovery	Automatically finds targets (Kubernetes, Consul, EC2, etc.)
Histogram	Metric type that counts observations in configurable buckets
Summary	Metric type similar to histogram but provides quantile estimation
Gauge	Metric that can go up or down (e.g., memory usage, temperature)
Counter	Monotonically increasing metric (e.g., number of requests)
Label Set	Complete set of labels attached to a metric time series
Expression Browser	Prometheus web UI for querying and visualizing time series data

Architecture of Prometheus.