High Availability

Prometheus is designed for simplicity and flexibility, but achieving high availability (HA) and horizontal scalability in production environments requires intentional architecture choices. This section outlines patterns for deploying Prometheus in HA environments and scaling metric collection across distributed systems.

High Availability Patterns¶

1. Redundant Prometheus Server Clusters¶

Prometheus itself is not inherently HA-aware, but you can deploy multiple Prometheus servers in active-active or active-passive configurations: - Active-Active: Use Prometheus Federation to aggregate metrics from multiple instances. For example, a central Prometheus server can federate data from regional instances, ensuring redundancy. - Active-Passive: Deploy Prometheus servers in different zones or regions with shared remote storage. Use a load balancer to route traffic to healthy instances during failures.

Example:

# prometheus.yml (federated setup)
global:
  scrape_interval: 15s
scrape_configs:
  - job_name: 'local-metrics'
    static_configs:
      - targets: ['localhost:9090']
  - job_name: 'federated-metrics'
    metrics_path: '/federate'
    params:
      match[]:
        - '{job="remote-server"}'
    remote_url: 'http://remote-prometheus:9090/api/v1/query_range'

2. Remote Storage for Data Persistence¶

Use remote storage (e.g., Thanos, Cortex, or Object Storage) to persist metrics and avoid single points of failure: - Thanos: Provides a distributed, scalable architecture with a sidecar for each Prometheus instance. Thanos Querier aggregates data across multiple stores. - Cortex: A horizontally scalable, distributed time-series database for long-term storage.

Example:

# prometheus.yml (remote_write to Thanos)
remote_write:
  - url: 'http://thanos-store:10901/api/v1/write'

3. Load Balancing and Failover¶

Deploy Prometheus servers behind a load balancer (e.g., NGINX, HAProxy) to distribute traffic. Use health checks to route requests to healthy instances.

Scalability Patterns¶

1. Horizontal Scaling with Remote Write¶

Scale metric collection by offloading data to remote storage: - Remote Write: Send metrics to a scalable backend (e.g., Cortex, Thanos) instead of storing them locally. This reduces memory and CPU usage on Prometheus servers. - Service Discovery: Use Kubernetes, Consul, or etcd to dynamically discover targets and avoid manual configuration.

Example:

# prometheus.yml (remote_write to Cortex)
remote_write:
  - url: 'http://cortex-api:9009/api/v1/write'

2. Prometheus Federation¶

Aggregate metrics from multiple Prometheus instances using federation: - Federated Queries: Use http://prometheus-federator:9090/api/v1/federate to combine data from distributed Prometheus servers. - Scalable Aggregation: Distribute scraping workloads across instances and federate results for centralized querying.

3. Distributed Scraping with Service Meshes¶

Integrate Prometheus with service meshes (e.g., Istio, Linkerd) to scrape metrics from microservices. Use sidecar proxies to expose metrics and reduce network overhead.

Diagram: High-Availability Prometheus Architecture¶

+----------------+        +----------------+        +----------------+
|  Prometheus    |--------|   Load Balancer |--------|  Remote Storage |
|  (Regional)    |        |                |        |  (Thanos/Cortex)|
+----------------+        +----------------+        +----------------+
          |                           |                           |
          |                           |                           |
          v                           v                           v
+----------------+        +----------------+        +----------------+
|  Prometheus    |--------|   Federation   |--------|  Remote Write  |
|  (Central)     |        |  (Prometheus)  |        |  (Remote Write)|
+----------------+        +----------------+        +----------------+

Key takeaways¶

Use remote storage (Thanos, Cortex) for HA and long-term persistence.
Federate Prometheus instances to scale querying and distribute scraping workloads.
Leverage Kubernetes or service meshes for dynamic target discovery and horizontal scaling.
Implement load balancing and health checks to ensure failover resilience.