rustfs/.docker/observability

RustFS Observability Stack

This directory contains the comprehensive observability stack for RustFS, designed to provide deep insights into application performance, logs, and traces.

Components

The stack is composed of the following best-in-class open-source components:

• Prometheus (v2.53.1): The industry standard for metric collection and alerting.
• Grafana (v11.1.0): The leading platform for observability visualization.
• Loki (v3.1.0): A horizontally-scalable, highly-available, multi-tenant log aggregation system.
• Tempo (v2.5.0): A high-volume, minimal dependency distributed tracing backend.
• Jaeger (v1.59.0): Distributed tracing system (configured as a secondary UI/storage).
• OpenTelemetry Collector (v0.104.0): A vendor-agnostic implementation for receiving, processing, and exporting telemetry data.

By default, this stack uses Tempo in single-binary mode and does not require Kafka/Redpanda. If you want the Kafka-backed HA Tempo path, use docker-compose-example-for-rustfs.yml together with docker-compose-tempo-ha-override.yml.

Architecture

1. Telemetry Collection: Applications send OTLP (OpenTelemetry Protocol) data (Metrics, Logs, Traces) to the OpenTelemetry Collector.
2. Processing & Exporting: The Collector processes the data (batching, memory limiting) and exports it to the respective backends:
   • Traces -> Tempo (Primary) & Jaeger (Secondary/Optional)
   • Metrics -> Prometheus (via scraping the Collector's exporter)
   • Logs -> Loki
3. Visualization: Grafana connects to all backends (Prometheus, Tempo, Loki, Jaeger) to provide a unified dashboard experience.

Features

• Full Persistence: All data (Metrics, Logs, Traces) is persisted to Docker volumes, ensuring no data loss on restart.
• Correlation: Seamless navigation between Metrics, Logs, and Traces in Grafana.
  • Jump from a Metric spike to relevant Traces.
  • Jump from a Trace to relevant Logs.
• High Performance: Optimized configurations for batching, compression, and memory management.
• Standardized Protocols: Built entirely on OpenTelemetry standards.

GET Performance Optimization Dashboards

Three pre-built Grafana dashboards are included for monitoring RustFS GET performance optimization rollout:

Available Dashboards

Dashboard	File	Description
GET Rollout Health	grafana-get-rollout-health.json	Monitors optimization rollout: latency by reader path, early-stop hit rate, codec streaming usage, pipeline failures
GET Data Integrity	grafana-get-data-integrity.json	Monitors data safety: bitrot verify failures, decode errors, short reads, shard read outcomes
GET Resource Impact	grafana-get-resource-impact.json	Monitors resource usage: concurrent requests, IO queue utilization, disk permit wait, RSS trend
Object Data Cache	grafana-object-data-cache.json	Monitors the GET body cache (rustfs_object_data_cache_*): hit ratio, lookup/plan/fill outcomes, fill duration quantiles, hit vs fill throughput, entries/weighted bytes, inflight fills, memory-pressure skips, invalidations, and size-class breakdowns

Prometheus Alert Rules

The file prometheus-rules/rustfs-get-optimization-alerts.yaml contains pre-configured alerting rules:

Alert	Severity	Condition
GetP99Regression	Critical	GET p99 latency > 2x baseline for 10m
PipelineFailureSpike	Critical	Pipeline failure rate > 5x baseline for 5m
BitrotMismatchSpike	Critical	Bitrot mismatch rate > 3x baseline for 5m
EarlyStopInsufficientQuorum	Warning	Early-stop insufficient quorum rate > 0.1/s for 5m
CodecStreamingFallbackSpike	Warning	Codec streaming fallback > 10x baseline for 10m
IoQueueSaturation	Warning	IO queue utilization > 90% for 5m

The file prometheus-rules/rustfs-kms-alerts.yml contains alerting rules for the KMS backend operation metrics. Thresholds are conservative defaults pending staging baseline calibration; response procedures live in docs/operations/kms-observability-runbook.md, and the matching dashboard is deploy/observability/grafana/rustfs-kms-observability.json.

Alert	Severity	Condition
KmsBackendFatalErrors	Critical	Fatal (non-retryable) attempt failures > 0 for 5m
KmsBackendHighErrorRate	Critical	Non-success operation ratio > 5% for 10m (with traffic guard)
KmsBackendP99LatencyHigh	Warning	Operation p99 duration (incl. retries) > 2s for 10m
KmsBackendAttemptFailureSpike	Warning	Attempt failure rate > 0.5/s for 10m
KmsBackendRetryBudgetExhausted	Warning	budget_exhausted / deadline_exceeded outcomes > 0.05/s for 10m

Enabling Alert Rules

Add the alert rules file to your Prometheus configuration:

# prometheus.yml
rule_files:
  - "/etc/prometheus/rules/*.yml"

# Or mount the file in docker-compose.yml:
# volumes:
#   - ./prometheus-rules:/etc/prometheus/rules

Dashboard Usage

The dashboards are automatically provisioned when Grafana starts. They use the ${DS_PROMETHEUS} datasource variable, so you need a Prometheus datasource configured in Grafana.

Key panels to monitor during optimization rollout:

1. GET Latency by Reader Path - Compare codec_streaming vs legacy_duplex latency
2. Early-Stop Hit Rate - Verify early-stop is triggering effectively
3. Pipeline Failure Rate - Detect any new failure modes introduced by optimizations
4. Bitrot Verify Failures - Ensure data integrity is maintained

Quick Start

Prerequisites

• Docker
• Docker Compose

Deploy

Run the following command to start the entire stack:

docker compose up -d

High Availability Tempo

The default docker-compose.yml is the single-node stack. If you need the Kafka-backed HA Tempo configuration, start it with:

docker compose -f docker-compose-example-for-rustfs.yml -f docker-compose-tempo-ha-override.yml up -d

Access Dashboards

Service	URL	Credentials	Description
Grafana	http://localhost:3000	admin / admin	Main visualization hub.
Prometheus	http://localhost:9090	-	Metric queries and status.
Jaeger UI	http://localhost:16686	-	Secondary trace visualization.
Tempo	http://localhost:3200	-	Tempo status/metrics.

Configuration

Data Persistence

Data is stored in the following Docker volumes:

• prometheus-data: Prometheus metrics
• tempo-data: Tempo traces (WAL and Blocks)
• loki-data: Loki logs (Chunks and Rules)
• jaeger-data: Jaeger traces (Badger DB)

To clear all data:

docker compose down -v

Customization

• Prometheus: Edit prometheus.yml to add scrape targets or alerting rules.
• Grafana: Dashboards and datasources are provisioned from the grafana/ directory.
• Collector: Edit otel-collector-config.yaml to modify pipelines, processors, or exporters.

Verifying RustFS Traces

When RustFS points RUSTFS_OBS_ENDPOINT at this stack, treat the value as the OTLP/HTTP base URL, for example:

export RUSTFS_OBS_ENDPOINT=http://host.docker.internal:4318

RustFS automatically expands that base URL to:

• /v1/traces
• /v1/metrics
• /v1/logs

Important behavior notes:

• Logs and metrics usually appear during startup, so seeing those two signals first is expected.
• Visible trace data usually requires real HTTP/S3/gRPC request traffic after startup, because request-path spans are created on demand.
• RUSTFS_OBS_LOGGER_LEVEL=info keeps the top-level request span but filters many nested debug spans. If Tempo or Jaeger looks sparse, retry with RUSTFS_OBS_LOGGER_LEVEL=debug before suspecting collector or Tempo issues.

Minimal validation flow:

# 1. Start this observability stack.
docker compose up -d

# 2. Start RustFS with OTLP/HTTP export and richer span visibility.
export RUSTFS_OBS_ENDPOINT=http://host.docker.internal:4318
export RUSTFS_OBS_LOGGER_LEVEL=debug

# 3. Generate real request traffic.
curl -I http://127.0.0.1:9000/health
curl -I http://127.0.0.1:9000/health/ready

# 4. Inspect Grafana or Jaeger.
# Grafana: http://localhost:3000
# Jaeger:  http://localhost:16686

If logs and metrics are present but traces are sparse, the most common cause is "no real request traffic yet" or "info level filtered nested spans", not an OTLP routing failure.

Troubleshooting

• Service Health: Check the health of services using docker compose ps.
• Logs: View logs for a specific service using docker compose logs -f <service_name>.
• Otel Collector: Check http://localhost:13133 for health status and http://localhost:1888/debug/pprof/ for profiling.