Why Prometheus Became the Leading Cloud‑Native Monitoring Solution

Introduction

Prometheus is both a time‑series database and a complete monitoring system, forming a robust monitoring ecosystem. In the February 2020 ranking of time‑series databases, Prometheus rose to third place, surpassing OpenTSDB, Graphite, RRDtool, and KairosDB.

History

Prometheus traces its roots to Google’s Borg and Borgmon systems. Borg managed large clusters, while Borgmon provided monitoring. In 2012, Google SRE engineer Matt Proud began developing Prometheus as a research project; after joining SoundCloud, he and Julius Volz open‑sourced it in early 2015. Prometheus graduated as the second CNCF project after Kubernetes in 2016, and version 1.0 was released the same year, followed by version 2.0 in 2017 with a new storage layer.

Main Features

Prometheus distinguishes itself with four primary capabilities:

Flexible multi‑dimensional data model and query language (PromQL).

Open metric standards and easy‑to‑write exporters.

PushGateway for receiving pushed metrics.

Both VM and containerized deployments.

Additional strengths include being written in Go, pull‑based data collection with optional push, binary‑only or containerized deployment, client libraries for many languages, high‑performance local storage, efficient compression, horizontal scalability via federation and sharding, rich visualisation (built‑in UI, Grafana integration), precise alerting with grouping, inhibition and silencing, extensive service‑discovery mechanisms, and openness to third‑party integrations.

Limitations are also noted: focus on performance/availability metrics (not logs, events, tracing), short‑term data retention (default 15 days), limited local storage requiring remote back‑ends for long‑term data, lack of a unified global view in federation, no built‑in unit definitions, and potential inaccuracies for exact accounting use‑cases.

Architecture Overview

The architecture consists of six core modules: Prometheus Server, Pushgateway, Job/Exporter, Service Discovery, Alertmanager, and Dashboard. These components interact to discover targets, scrape metrics, store data, query via PromQL, and send alerts.

Job/Exporter

Jobs (long‑running or short‑lived) and Exporters expose metrics for Prometheus to scrape. Exporters exist for thousands of third‑party systems; custom exporters can be built when needed. Exporters can be managed centrally with tools like Telegraf.

Pushgateway

Pushgateway enables short‑lived or batch jobs to push metrics to Prometheus. It acts as an intermediary for metrics that cannot be scraped directly. However, it does not provide health checks (UP metric) and can become a single point of failure.

curl -X DELETE http://pushgateway.example.org:9091/metrics/job/some_job/instance/ some_instance

Service Discovery

Prometheus supports file‑based discovery and integrations with Kubernetes, DNS, Zookeeper, Azure, EC2, GCE, and more. Relabeling rules allow selective scraping based on environment, team, or other labels.

Prometheus Server

The server handles scraping, storage, and querying. Scraped data is stored locally (recommended SSD, limited to ~1 month) or remotely via adapters (OpenTSDB, InfluxDB, Elasticsearch, etc.). The storage engine can ingest millions of samples per second, using efficient compression (≈1.3 B per sample).

Dashboard

Prometheus provides a built‑in UI and expression browser; Grafana is commonly used for richer visualisation. Clients can query data via the HTTP API.

Alertmanager

Alertmanager receives alerts from Prometheus, deduplicates, groups, silences, and routes them to email, Slack, webhook, or other receivers. It can be deployed in a highly available cluster.

Source: Distributed Lab