production-monitoring

name	production-monitoring
description	Production monitoring with Grafana, Prometheus, Loki, and OpenTelemetry for containerized Python services on AWS EKS
triggers	["monitoring","grafana","prometheus","loki","opentelemetry","otel","alerting","SLO","SLI","dashboard","metrics","observability"]

Production Monitoring

Monitoring stack for containerized Python services: Prometheus (metrics), Loki (logs), Grafana (dashboards), OpenTelemetry (instrumentation).

OpenTelemetry Python Instrumentation

from opentelemetry import trace, metrics
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.instrumentation.fastapi import FastAPIInstrumentor

def setup_telemetry(app, service_name: str = "jarvis"):
    provider = TracerProvider(resource=Resource.create({"service.name": service_name}))
    provider.add_span_processor(BatchSpanProcessor(OTLPSpanExporter()))
    trace.set_tracer_provider(provider)
    FastAPIInstrumentor.instrument_app(app)

tracer = trace.get_tracer(__name__)

@tracer.start_as_current_span("process_agent_task")
async def process_task(task_id: str):
    span = trace.get_current_span()
    span.set_attribute("task.id", task_id)
    # ... processing

Prometheus Metrics

from prometheus_client import Counter, Histogram, Gauge, start_http_server

REQUEST_COUNT = Counter("http_requests_total", "Total requests", ["method", "endpoint", "status"])
REQUEST_LATENCY = Histogram("http_request_duration_seconds", "Request latency", ["endpoint"])
ACTIVE_AGENTS = Gauge("jarvis_active_agents", "Currently active agents")
TASK_DURATION = Histogram("jarvis_task_duration_seconds", "Task execution time", ["agent_type"])

PromQL Queries

# Request rate (5m window)
rate(http_requests_total[5m])

# P99 latency
histogram_quantile(0.99, rate(http_request_duration_seconds_bucket[5m]))

# Error rate percentage
100 * rate(http_requests_total{status=~"5.."}[5m]) / rate(http_requests_total[5m])

# Agent utilization
jarvis_active_agents / jarvis_max_agents * 100

Alert Rules

groups:
  - name: jarvis-alerts
    rules:
      - alert: HighErrorRate
        expr: rate(http_requests_total{status=~"5.."}[5m]) / rate(http_requests_total[5m]) > 0.05
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "High error rate (>5%)"

      - alert: HighLatency
        expr: histogram_quantile(0.99, rate(http_request_duration_seconds_bucket[5m])) > 2
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "P99 latency >2s"

      - alert: AgentStuck
        expr: jarvis_task_duration_seconds_count == 0 and jarvis_active_agents > 0
        for: 10m
        labels:
          severity: warning

SLO Definitions

Service	SLI	SLO	Measurement
API	Availability	99.9% uptime	1 - (5xx / total) over 30d
API	Latency	P99 < 500ms	histogram_quantile(0.99, ...)
Agents	Success rate	>95% tasks complete	succeeded / total per hour
Agents	Duration	P95 < 30s	Task duration histogram