Prometheus is an open-source monitoring and alerting toolkit primarily designed for reliability and scalability in cloud-native and microservices environments. Its architecture is built around several components that work together to collect, store, and query time-series data while providing alerting capabilities.
Key components of Prometheus architecture
1. Prometheus Server
The Prometheus Server is the core component of the Prometheus system, responsible for the core functionalities of data collection, querying, and storage. It operates by scraping metrics from configured endpoints and storing them in its time-series database.
Functions of Prometheus Server:
Scraping: The Prometheus server scrapes metrics from target endpoints at specified intervals. These targets can be application endpoints, services, or exporters that expose metrics in a format that Prometheus can understand.
Data Storage: Prometheus stores the scraped data in a time-series database, which is highly optimized for storing data indexed by time. It supports powerful querying capabilities, allowing users to retrieve and aggregate metrics data efficiently.
PromQL: The Prometheus Query Language (PromQL) is used by the Prometheus server to query and aggregate time-series data. PromQL supports complex queries such as rate calculations, averages, sums, etc.
Data Retention: Prometheus keeps the data for a configurable retention period (default 15 days), after which old data is discarded. This helps keep storage requirements manageable.
Key Responsibilities:
Scrapes metrics from monitored targets (via HTTP endpoints).
Stores time-series data in its own database.
Provides a web interface for querying data.
Executes PromQL queries for alerting and dashboards.
2. Exporters
Exporters are small programs or scripts that expose metrics from third-party systems, applications, or infrastructure components in a format that Prometheus can scrape. Exporters are typically used when a service or system does not natively expose Prometheus-compatible metrics.
Types of Exporters:
Node Exporter: Exposes hardware and OS-level metrics, such as CPU usage, memory, disk space, and network I/O.
cAdvisor: Exposes container-level metrics (e.g., Docker containers), such as container resource usage (CPU, memory).
MySQL Exporter: Exposes MySQL database metrics, such as query performance, replication status, and connection counts.
Blackbox Exporter: Allows you to monitor endpoints over HTTP, HTTPS, DNS, and ICMP to perform basic health checks on services and websites.
How Exporters Work:
Metrics Exposure: Exporters expose metrics via an HTTP endpoint, often on a specific port (e.g., localhost:9100Â for Node Exporter).
Scraping by Prometheus: Prometheus server scrapes data from the exporter’s HTTP endpoint, storing the metrics in its time-series database.
Example of an exporter configuration in Prometheus:
scrape_configs:
- job_name: 'node_exporter'
static_configs:
- targets: ['localhost:9100']3. Alertmanager
The Alertmanager is a component responsible for managing and routing alerts generated by Prometheus based on user-defined alerting rules. It receives alert notifications from Prometheus and takes actions such as sending notifications to different communication channels.
Functions of Alertmanager:
Alert Aggregation: Alertmanager aggregates alerts received from Prometheus. If multiple alerts are triggered, it can group related alerts together to avoid spamming the notification system.
Alert Deduplication: If the same alert is triggered multiple times, Alertmanager can deduplicate it to avoid redundant notifications.
Alert Routing: Based on predefined routing rules, Alertmanager can send alerts to various notification channels such as email, Slack, PagerDuty, or custom webhooks.
Silencing Alerts: Alertmanager allows you to silence specific alerts for a certain period, which is useful for maintenance windows or known issues that do not require immediate action.
Inhibition: Alerts can be inhibited in certain cases. For example, you might want to inhibit a "down" alert for a service if a related "down" alert for its underlying infrastructure is already firing.
Key Responsibilities:
Receive alerts from Prometheus.
Group and deduplicate alerts.
Route alerts to external notification systems (e.g., Slack, email).
Manage silences and inhibitions.
4. PushGateway
The PushGateway is an optional component in the Prometheus ecosystem, designed for scenarios where it is difficult or impractical for Prometheus to pull metrics from an endpoint (i.e., a push-based model). PushGateway allows clients to "push" metrics to Prometheus.
Use Cases for PushGateway:
Short-lived Jobs: Some jobs, such as batch processes or cron jobs, run for a short period and do not remain alive long enough for Prometheus to scrape them. In this case, the job can push its metrics to PushGateway, which can be scraped by Prometheus.
Metrics from Push-Based Systems: Some systems or devices may not expose an HTTP endpoint for scraping. Instead, metrics can be pushed to PushGateway.
How PushGateway Works:
Metric Pushing: Clients (applications or services) push metrics to the PushGateway via an HTTP API.
Prometheus Scrapes PushGateway: Prometheus server periodically scrapes the PushGateway to retrieve the pushed metrics.
Key Responsibilities:
Accept metrics pushed by clients.
Expose metrics to Prometheus for scraping.
Can be used in scenarios where Prometheus's pull model is impractical.
Example of pushing metrics to PushGateway:
echo "job_duration_seconds 1.2" | curl --data-binary @- http://pushgateway:9091/metrics/job/my_batch_job5. Service Discovery
Service Discovery in Prometheus is a feature that allows Prometheus to automatically discover the list of services or targets to scrape for metrics without needing manual configuration. It plays a key role in dynamic environments where services come and go frequently, such as cloud environments, microservices architectures, and containerized systems.
Prometheus supports multiple service discovery mechanisms that work with various infrastructure components, such as cloud providers, container orchestrators, and more. This eliminates the need for static configuration and allows Prometheus to dynamically adapt to changes in the environment.
Service Discovery Methods in Prometheus:
Static Configuration: While not technically "discovery," this method allows you to manually configure the list of targets Prometheus should scrape. However, this approach is not scalable in dynamic environments.
Cloud Provider Integration: Prometheus can automatically discover targets in cloud environments like AWS, Google Cloud, and Azure. For example, it can query the cloud provider's API to discover instances, virtual machines, or other resources to scrape metrics from.
Example for AWS EC2 instance discovery:
scrape_configs:
- job_name: 'aws_ec2'
ec2_sd_configs:
- region: 'us-east-1'
access_key: 'AKIA...'
secret_key: '...'Kubernetes Service Discovery:
In Kubernetes environments, Prometheus can use the Kubernetes API to discover services, pods, and nodes dynamically. It can scrape metrics from containers and services running inside the cluster without manual configuration.
Example for Kubernetes discovery:
scrape_configs:
- job_name: 'kubernetes-pods'
kubernetes_sd_configs:
- api_server: 'https://kubernetes.default.svc'
role: 'pod'DNS-based Discovery: Prometheus can use DNS queries to discover targets. This is useful in environments where services are registered in a DNS service (e.g., Consul or etcd).
Consul: Prometheus can integrate with Consul’s service registry to discover services registered in Consul.
6. Prometheus Client Libraries
Prometheus Client Libraries allow developers to instrument their applications and expose custom metrics in a format that Prometheus can scrape. These libraries make it easy to collect application-specific metrics (e.g., request counts, response times, error rates) and provide insights into the performance and health of individual applications or services. Prometheus provides client libraries for a wide range of programming languages, ensuring that you can monitor virtually any type of application.
Key Features of Prometheus Client Libraries:
Custom Metrics: Developers can expose custom application-specific metrics such as HTTP request durations, error rates, or business-specific counters.
Instrumentation: By using the libraries, developers can instrument their code to gather specific data points (e.g., how many requests an app has handled in a given period).
Easy Integration: The libraries are easy to integrate into existing applications and follow the Prometheus format, ensuring compatibility with Prometheus scraping mechanisms.
Metric Types: Prometheus supports different types of metrics, which can be used depending on the data you want to capture:
Counter: A metric that only increases, such as the total number of requests handled by a service.
Gauge: A metric that can go up or down, such as memory usage or current active connections.
Histogram: A metric used to track distributions of data, such as response times or request sizes.
Summary: Similar to histograms, but also tracks quantiles (percentiles) of the data.
7. PromQL (Prometheus Query Language)
PromQL is a powerful query language used to filter, aggregate, and analyze time-series data. It supports:
Instant queries: Fetch the current value of a metric.
Range queries: Fetch values over a period for trend analysis.
Aggregation functions: Sum, average, min/max, percentiles, etc.
8. Time Series Database (TSDB)
Prometheus has its own embedded time-series database, optimized for fast ingestion and efficient storage. It handles:
Data compression: Uses block storage with write-ahead logs (WAL) for efficient data storage.
Label-based storage: Stores data using key-value pairs (labels) to enable powerful filtering and querying.
Summary of Prometheus Components
Prometheus Server:
Scrapes metrics, stores time-series data, and executes queries.
Provides the main query interface (PromQL) for alerting and dashboards.
Exporters:
Expose metrics from various systems, applications, and services.
Prometheus scrapes these metrics for storage and analysis.
Alertmanager:
Manages, aggregates, deduplicates, and routes alerts from Prometheus.
Sends notifications through various channels (email, Slack, PagerDuty, etc.).
PushGateway:
Allows clients to push metrics to Prometheus when the pull model isn't feasible (e.g., short-lived jobs).
Prometheus scrapes metrics from the PushGateway.
These components work together to form a robust, scalable, and flexible monitoring and alerting system that can be adapted to a wide range of use cases in both traditional and cloud-native environments.
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