Prometheus and Grafana Enterprise Observability

Data Archiving and Tiered Storage¶

Historical metrics data often accumulates rapidly, leading to storage costs and performance degradation. To balance cost, accessibility, and retention requirements, organizations implement tiered storage strategies and data archiving. This section outlines best practices for structuring storage tiers, automating data movement, and leveraging cost-effective solutions for long-term metric retention.

Tiered Storage Strategies¶

Tiered storage categorizes metrics based on access frequency and criticality, using different storage mediums (e.g., SSDs, HDDs, object storage) to optimize cost and performance.

1. Hot vs. Cold Storage¶

Hot storage: Retain recent, frequently accessed metrics (e.g., last 7 days) in fast, expensive storage (e.g., SSD-backed Prometheus or Prometheus Remote Write to a high-performance backend).
Cold storage: Archive older metrics (e.g., >30 days) to cheaper, slower storage (e.g., object storage like S3, Google Cloud Storage, or tape).

Example:

# Prometheus configuration for tiered retention
global:
  scrape_interval: 30s
  retention_time: 7d  # Hot storage: keep 7 days of data
remote_write:
  - url: http://remote-write-endpoint/write?name=hot-storage
  - url: http://remote-write-endpoint/write?name=cold-storage

2. Retention Policies with Time-Series Databases¶

Use time-series databases (e.g., Cortex, Thanos, or VictoriaMetrics) to define retention policies for different tiers. For example:
- Short-term retention: 7 days for critical metrics.
- Long-term retention: 1 year for historical trends.

Example:

# Cortex retention policy (config.yaml)
retention_period: 31d  # Cold storage: retain data for 31 days

Data Archiving Techniques¶

Archiving involves moving historical data to cheaper storage while ensuring it remains accessible for analysis.

1. Prometheus Remote Write to Object Storage¶

Use Prometheus' Remote Write feature to send metrics to object storage (e.g., S3) for archival. This requires a remote storage adapter (e.g., s3 or gcs).

Example:

remote_write:
  - url: http://s3-adapter/write?bucket=archived-metrics
    queue_config:
      capacity: 500
      max_shards: 10

2. Automated Archiving with Thanos¶

Thanos provides a unified view of metrics across storage tiers. Use its Object Storage integration to archive data to S3/GCS and query it via Grafana.

Example:

# Thanos sidecar configuration (sidecar.yaml)
storage:
  objectStorage:
    type: s3
    bucket: thanos-archived
    endpoint: s3.amazonaws.com
    accessKey: YOUR_ACCESS_KEY
    secretKey: YOUR_SECRET_KEY

3. Data Lifecycle Management¶

Automate data movement using scripts or tools like prometheus-archiver to periodically transfer metrics from hot to cold storage.

Example:

# Archive metrics older than 30 days to S3
prometheus-archiver --source=http://prometheus/api/v1/query_range --target=s3://archived-bucket --age=30d

Tools and Integrations¶

Tool	Purpose	Example Use Case
S3/GCS	Cost-effective cold storage	Archive metrics older than 30 days
Cortex	Distributed TSDB with retention policies	Tier metrics by retention period
Thanos	Unified query across storage tiers	Query archived data via Grafana
VictoriaMetrics	High-performance TSDB with archiving	Combine hot/cold storage for mixed workloads

Diagram: Tiered Storage Architecture¶

[Prometheus]  
    |  
    v  
[Hot Storage (SSD)]  
    |  
    v  
[Remote Write to Cold Storage (S3/GCS)]  
    |  
    v  
[Thanos/Query Frontend]  
    |  
    v  
[Grafana (Visualization)]

Key takeaways¶

Tiered storage balances cost and performance by separating hot (frequent access) and cold (archival) data.
Remote Write to object storage (e.g., S3) enables cost-effective long-term retention.
Thanos and Cortex provide scalable solutions for querying archived metrics.
Automate data movement with scripts or tools to ensure consistent retention policies.
Always validate storage costs and access latency for each tier to avoid performance bottlenecks.