Maintaining years of fine-grained telemetry data for capacity planning, compliance audits, and seasonal trend analysis requires decoupling compute from time-series storage. Running single-node Prometheus instances with massive local SSD volumes creates single points of failure and operational fragility.
The Block Storage Microservices Architecture
Modern horizontal time-series systems like Cortex and Grafana Mimir break down the ingestion and query pipeline into specialized, horizontally scalable microservices:
- Distributors: Stateless reverse proxies that validate incoming Prometheus remote-write payloads, enforce tenant rate limits, and fan out samples across an ingestion ring using consistent hashing.
- Ingesters: In-memory write buffers that hold active TSDB blocks before flushing compacted 2-hour blocks to durable object storage (such as Amazon S3, Google Cloud Storage, or MinIO).
- Store-Gateways: Index search workers that maintain local block index caches and fulfill historical PromQL range queries without loading raw sample chunks into memory.
- Compactors: Background workers that merge overlapping 2-hour blocks into 12-hour and 24-hour blocks while applying downsampling routines.
Tuning Compaction and Downsampling
Without proper compactor sizing, storage buckets quickly accumulate thousands of fragmented index files, degrading long-range PromQL query performance. We configure hierarchical downsampling schedules (e.g., raw data for 30 days, 5-minute rollups for 6 months, and 1-hour rollups for 3 years), reducing total storage footprint by up to 85% while keeping dashboard query times under 500 milliseconds.