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prometheus-go-code-review

Reviews Prometheus instrumentation in Go code for proper metric types, labels, and patterns. Use when reviewing code with prometheus/client_golang metrics.

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Prometheus Go Code Review

Review Checklist

  • Metric types match measurement semantics (Counter/Gauge/Histogram)
  • Labels have low cardinality (no user IDs, timestamps, paths)
  • Metric names follow conventions (snake_case, unit suffix)
  • Histograms use appropriate bucket boundaries
  • Metrics registered once, not per-request
  • Collectors don't panic on race conditions
  • /metrics endpoint exposed and accessible

Hard gates (sequenced)

Complete in order before recording a finding. Skip gates that clearly do not apply to the diff.

  1. Evidence scope — Enumerate the files you are reviewing that touch Prometheus (prometheus/client_golang, promauto, promhttp, or MustRegister). Pass: you have a concrete path list (from the diff or an explicit file set); no repo-wide claim without at least one path.

  2. Label cardinality — For each *Vec or labeled metric in scope, list label names and where values come from (constants, bounded codes, vs request-derived strings). Pass: no label uses unbounded values (e.g. raw user_id, full URL path, timestamps) unless the code uses a bounded mapping and you cite it.

  3. Registration lifecycle — For metric definitions in scope, confirm constructors run once (package-level var, init, or sync.Once), not inside per-request handlers. Pass: no pattern that allocates/registers a new Counter/Histogram/*Vec on every request for the same logical metric.

  4. Finding shape — Each finding names a file (and line or symbol where possible), states which gate (2 or 3) would fail if the issue is real, and ties to observed code. Pass: no standalone style nit when gates 2–3 are satisfied for that code.

Metric Type Selection

MeasurementTypeExample
Requests processedCounterrequests_total
Items in queueGaugequeue_length
Request durationHistogramrequest_duration_seconds
Concurrent connectionsGaugeactive_connections
Errors since startCountererrors_total
Memory usageGaugememory_bytes

Critical Anti-Patterns

1. High Cardinality Labels

// BAD - unique per user/request
counter := promauto.NewCounterVec(
    prometheus.CounterOpts{Name: "requests_total"},
    []string{"user_id", "path"},  // millions of series!
)
counter.WithLabelValues(userID, request.URL.Path).Inc()

// GOOD - bounded label values
counter := promauto.NewCounterVec(
    prometheus.CounterOpts{Name: "requests_total"},
    []string{"method", "status_code"},  // <100 series
)
counter.WithLabelValues(r.Method, statusCode).Inc()

2. Wrong Metric Type

// BAD - using gauge for monotonic value
requestCount := promauto.NewGauge(prometheus.GaugeOpts{
    Name: "http_requests",
})
requestCount.Inc()  // should be Counter!

// GOOD
requestCount := promauto.NewCounter(prometheus.CounterOpts{
    Name: "http_requests_total",
})
requestCount.Inc()

3. Registering Per-Request

// BAD - new metric per request
func handler(w http.ResponseWriter, r *http.Request) {
    counter := prometheus.NewCounter(...)  // creates new each time!
    prometheus.MustRegister(counter)       // panics on duplicate!
}

// GOOD - register once
var requestCounter = promauto.NewCounter(prometheus.CounterOpts{
    Name: "http_requests_total",
})

func handler(w http.ResponseWriter, r *http.Request) {
    requestCounter.Inc()
}

4. Missing Unit Suffix

// BAD
duration := promauto.NewHistogram(prometheus.HistogramOpts{
    Name: "request_duration",  // no unit!
})

// GOOD
duration := promauto.NewHistogram(prometheus.HistogramOpts{
    Name: "request_duration_seconds",  // unit in name
})

Good Patterns

Metric Definition

var (
    httpRequests = promauto.NewCounterVec(
        prometheus.CounterOpts{
            Namespace: "myapp",
            Subsystem: "http",
            Name:      "requests_total",
            Help:      "Total HTTP requests processed",
        },
        []string{"method", "status"},
    )

    httpDuration = promauto.NewHistogramVec(
        prometheus.HistogramOpts{
            Namespace: "myapp",
            Subsystem: "http",
            Name:      "request_duration_seconds",
            Help:      "HTTP request latencies",
            Buckets:   []float64{.005, .01, .025, .05, .1, .25, .5, 1, 2.5, 5, 10},
        },
        []string{"method"},
    )
)

Middleware Pattern

func metricsMiddleware(next http.Handler) http.Handler {
    return http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
        timer := prometheus.NewTimer(httpDuration.WithLabelValues(r.Method))
        defer timer.ObserveDuration()

        wrapped := &responseWriter{ResponseWriter: w, status: 200}
        next.ServeHTTP(wrapped, r)

        httpRequests.WithLabelValues(r.Method, strconv.Itoa(wrapped.status)).Inc()
    })
}

Exposing Metrics

import "github.com/prometheus/client_golang/prometheus/promhttp"

func main() {
    http.Handle("/metrics", promhttp.Handler())
    http.ListenAndServe(":9090", nil)
}

Review Questions

  1. Are metric types correct (Counter vs Gauge vs Histogram)?
  2. Are label values bounded (no UUIDs, timestamps, paths)?
  3. Do metric names include units (_seconds, _bytes)?
  4. Are metrics registered once (not per-request)?
  5. Is /metrics endpoint properly exposed?

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

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日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

Use when writing or formatting an ADR document using the MADR template, applying Definition of Done (E.C.A.D.R.) criteria, or verifying ADR completeness. Triggers on "write the ADR", "format as MADR", "check ADR quality", "mark gaps in ADR". Also triggers when a decision has been extracted and needs to become a document. Does NOT extract decisions from conversations (use adr-decision-extraction) or orchestrate the full extract-confirm-write workflow (use write-adr).

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

Use when auditing an agent codebase against the 12-Factor Agents methodology, reviewing LLM-powered system architecture, or assessing agentic app compliance. Triggers on "analyze agent architecture", "12-factor audit", "how compliant is this agent", or "evaluate this LLM app". Also applies when comparing frameworks or planning agent improvements. Not for quick checklists — this performs deep per-factor codebase analysis with file-level evidence.

日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

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日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

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日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

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日本語の概要は準備中です。原文の説明を表示しています。

existential-birds/beagle822026年9月16日 更新

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