DevOps: Monitoring and Observability

Production-grade monitoring, observability, and alerting patterns for microservices, data pipelines, and distributed systems.

Core Principles

1. Three pillars of observability -- Logs, metrics, and traces are complementary signals. Logs tell you what happened, metrics tell you how the system is performing, and traces tell you how a request flowed through services.
2. SLIs drive SLOs drive alerts -- Define Service Level Indicators (measurable signals), set Service Level Objectives (targets), and derive alerts from error budget burn rates. Never alert on raw thresholds disconnected from user impact.
3. Structured logging everywhere -- Emit JSON-formatted logs with consistent fields (timestamp, service, trace_id, level). Unstructured text logs are unsearchable at scale.
4. Instrument at boundaries -- Focus on service entry points, external calls (databases, APIs, queues), and critical business transactions. Over-instrumenting internal functions creates noise.
5. Alerts must be actionable -- Every alert should have a clear owner, a runbook, and a defined severity. If nobody needs to act, it should not be an alert.

Structured Logging Patterns

Emit structured JSON logs with correlation IDs for cross-service tracing and consistent fields for aggregation.

import structlog
import uuid

structlog.configure(
    processors=[
        structlog.contextvars.merge_contextvars,
        structlog.processors.add_log_level,
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.format_exc_info,
        structlog.processors.JSONRenderer(),
    ],
)
logger = structlog.get_logger()

def handle_request(request):
    """Bind correlation ID at request entry, propagate through all log calls."""
    correlation_id = request.headers.get("X-Correlation-ID", str(uuid.uuid4()))
    structlog.contextvars.bind_contextvars(
        correlation_id=correlation_id,
        service="order-service",
        endpoint=request.path,
    )
    logger.info("request_started", method=request.method, user_id=request.user_id)
    try:
        result = process_order(request)
        logger.info("request_completed", status="success", order_id=result.id)
        return result
    except Exception as e:
        logger.error("request_failed", error=str(e), exc_info=True)
        raise

See structured-logging.md for:

• Log level guidelines and when to use each
• Correlation ID propagation across async boundaries
• Python structlog and Node.js pino configuration
• Sensitive data redaction patterns

Prometheus Metrics

Use counters, gauges, and histograms to capture system and business behavior. Query with PromQL for dashboards and alerts.

from prometheus_client import Counter, Histogram, Gauge
import time

REQUEST_COUNT = Counter("http_requests_total", "Total HTTP requests",
    ["method", "endpoint", "status"])
REQUEST_DURATION = Histogram("http_request_duration_seconds", "Latency in seconds",
    ["method", "endpoint"],
    buckets=[0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0])
ACTIVE_CONNECTIONS = Gauge("active_connections", "Active connections", ["service"])

def handle_request(method, endpoint):
    ACTIVE_CONNECTIONS.labels(service="api").inc()
    start = time.monotonic()
    try:
        result = process(method, endpoint)
        REQUEST_COUNT.labels(method=method, endpoint=endpoint, status="200").inc()
        return result
    except Exception:
        REQUEST_COUNT.labels(method=method, endpoint=endpoint, status="500").inc()
        raise
    finally:
        REQUEST_DURATION.labels(method=method, endpoint=endpoint).observe(
            time.monotonic() - start)
        ACTIVE_CONNECTIONS.labels(service="api").dec()

See prometheus-patterns.md for:

• PromQL query patterns for RED and USE methods
• Recording rules for pre-computed aggregations
• Alerting rules with multi-window burn rates
• Histogram bucket selection strategies

OpenTelemetry Instrumentation

Use OpenTelemetry for distributed tracing with automatic context propagation across service boundaries.

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.resources import Resource

resource = Resource.create({"service.name": "order-service", "service.version": "1.2.0"})
provider = TracerProvider(resource=resource)
provider.add_span_processor(
    BatchSpanProcessor(OTLPSpanExporter(endpoint="http://otel-collector:4317")))
trace.set_tracer_provider(provider)
tracer = trace.get_tracer(__name__)

def process_order(order_id: str, items: list[dict]) -> dict:
    with tracer.start_as_current_span(
        "process_order",
        attributes={"order.id": order_id, "order.item_count": len(items)},
    ) as span:
        with tracer.start_as_current_span("validate_inventory"):
            available = check_inventory(items)
            span.set_attribute("order.items_available", available)
        with tracer.start_as_current_span("charge_payment"):
            payment = charge(order_id, items)
            span.add_event("payment_processed", {"payment.id": payment.id})
        return {"order_id": order_id, "status": "completed"}

See opentelemetry-patterns.md for:

• Auto-instrumentation for Flask, FastAPI, Django, SQLAlchemy
• Baggage and context propagation across async tasks
• Custom span attributes and events best practices
• Exporter configuration for Jaeger, Zipkin, and Grafana Tempo

Alerting Design

Design alerts around user impact with clear severity levels and multi-window burn rate thresholds to prevent alert fatigue.

groups:
  - name: slo_alerts
    rules:
      # Page: 2% budget consumed in 1 hour (fast burn)
      - alert: HighErrorBudgetBurn
        expr: |
          (sum(rate(http_requests_total{status=~"5.."}[1h]))
           / sum(rate(http_requests_total[1h]))) > (14.4 * 0.001)
          and
          (sum(rate(http_requests_total{status=~"5.."}[5m]))
           / sum(rate(http_requests_total[5m]))) > (14.4 * 0.001)
        for: 2m
        labels:
          severity: critical
        annotations:
          summary: "High error budget burn on {{ $labels.service }}"
          runbook: "https://runbooks.internal/slo-budget-burn"

See alerting-patterns.md for:

• Severity level definitions (P1-P4) and routing rules
• PagerDuty and Opsgenie integration patterns
• Runbook templates and incident response workflows
• Alert grouping, inhibition, and silencing strategies

SLIs, SLOs, and Error Budgets

Define SLIs from real user signals, set SLO targets, and use error budgets to balance reliability with velocity.

from dataclasses import dataclass

@dataclass
class SLODefinition:
    name: str
    sli_query: str        # PromQL query returning ratio 0-1
    target: float         # e.g. 0.999 for 99.9%
    window_days: int      # Rolling window (typically 30)
    burn_rate_pages: dict

    @property
    def error_budget(self) -> float:
        return 1.0 - self.target

    def budget_remaining(self, current_error_rate: float, elapsed_days: int) -> dict:
        budget_total = self.error_budget * self.window_days * 24 * 60
        budget_consumed = current_error_rate * elapsed_days * 24 * 60
        remaining_pct = max(0, (budget_total - budget_consumed) / budget_total)
        return {
            "remaining_pct": round(remaining_pct * 100, 2),
            "on_track": remaining_pct > (1 - elapsed_days / self.window_days),
        }

availability_slo = SLODefinition(
    name="api-availability",
    sli_query='sum(rate(http_requests_total{status!~"5.."}[5m])) / sum(rate(http_requests_total[5m]))',
    target=0.999, window_days=30,
    burn_rate_pages={"critical": 14.4, "warning": 6.0, "ticket": 3.0},
)

See slo-patterns.md for:

• SLI definitions for availability, latency, throughput, and correctness
• Error budget policies and decision frameworks
• Multi-window, multi-burn-rate alerting math
• SLO documentation templates and stakeholder reporting

Dashboard Design Patterns

Structure dashboards around the RED method (Rate, Errors, Duration) for services and the USE method (Utilization, Saturation, Errors) for resources.

# Grafana dashboard -- layered approach (Level 1: RED overview)
panels:
  - title: "Request Rate"
    expr: 'sum(rate(http_requests_total[5m])) by (service)'
  - title: "Error Rate (%)"
    expr: '100 * sum(rate(http_requests_total{status=~"5.."}[5m])) by (service)
           / sum(rate(http_requests_total[5m])) by (service)'
    thresholds: [{value: 1, color: "yellow"}, {value: 5, color: "red"}]
  - title: "p50 / p95 / p99 Latency"
    expr:
      - 'histogram_quantile(0.50, sum(rate(http_request_duration_seconds_bucket[5m])) by (le))'
      - 'histogram_quantile(0.99, sum(rate(http_request_duration_seconds_bucket[5m])) by (le))'
  - title: "Error Budget Remaining"
    expr: 'slo:budget_remaining:ratio * 100'
    type: gauge

Anti-Patterns

Performance

• Control metric cardinality -- Every unique label combination creates a new time series. Never use user IDs or unbounded values as labels
• Use recording rules -- Pre-compute frequently used PromQL aggregations to avoid expensive queries on every dashboard refresh
• Batch span exports -- Use BatchSpanProcessor to avoid blocking application threads
• Sample traces at scale -- Use head-based sampling (1-10%) for normal traffic, tail-based (100%) for errors
• Buffer logs asynchronously -- Use a local buffer (Fluent Bit, Vector) that ships in batches
• Right-size histogram buckets -- Align to SLO thresholds (e.g., 100ms, 250ms, 500ms, 1s, 2.5s)

source: Google SRE Book, OpenTelemetry documentation, Prometheus best practices, Grafana Labs documentation