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statsd git:20260313.02a8e7cA
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---
name: statsd
description: >-
StatsD metric instrumentation: push metrics over UDP, choose the right metric
type, name consistently. Invoke whenever task involves any interaction with
StatsD metrics — emitting counters, gauges, timers, histograms, sets, or
distributions; configuring DogStatsD tags; tuning sampling rates; or
integrating with Graphite, Prometheus, or Telegraf backends.
---
# StatsD
Choose the right metric type, name it with dot-delimited hierarchy, tag dimensions instead of encoding them in names.
StatsD is fire-and-forget: UDP means zero latency impact on your application, but wrong metric types or bad naming
corrupt your data silently.
## References
| Topic | Reference | Contents |
| --------------- | ----------------------------------------------------- | ----------------------------------------------------------------------------- |
| Metric types | [`${CLAUDE_SKILL_DIR}/references/metric-types.md`] | Wire format details, type comparison, sampling correction |
| Naming | [`${CLAUDE_SKILL_DIR}/references/naming.md`] | Graphite namespace mapping, character rules, naming examples |
| DogStatsD | [`${CLAUDE_SKILL_DIR}/references/dogstatsd.md`] | Events format, service checks, protocol versions, distributions vs histograms |
| Aggregation | [`${CLAUDE_SKILL_DIR}/references/aggregation.md`] | Flush mechanics, Graphite downsampling, DogStatsD aggregation, timestamps |
| Client patterns | [`${CLAUDE_SKILL_DIR}/references/client-patterns.md`] | High-throughput tuning, error handling, K8s deployment, UDS configuration |
| Backends | [`${CLAUDE_SKILL_DIR}/references/backends.md`] | statsd_exporter config, Telegraf setup, migration guides |
## Metric Types
Wire format: `<metric_name>:<value>|<type>[|@<sample_rate>][|#<tags>]`
### Decision Matrix
| Question | Type |
| ------------------------------------- | ---------------------------------- |
| How many times did X happen? | Counter (`c`) |
| What is X right now? | Gauge (`g`) |
| How long did X take? | Timer (`ms`) |
| What is the distribution of X? | Histogram (`h`) |
| How many unique X occurred? | Set (`s`) |
| What is the global distribution of X? | Distribution (`d`, DogStatsD only) |
Wrong metric type = wrong math at the server. A gauge used as a counter loses data between flushes. A counter used as a
gauge produces meaningless rates.
### Counter (`|c`)
Measures rate of events over time. Server sums all values during flush interval, resets to 0 after flush, reports both
raw count and per-second rate.
- Use for: request counts, error counts, event occurrences (cache hits, logins)
- Sample rate correction: value multiplied by `1/rate`
- Supports sampling (`|@<rate>`)
### Gauge (`|g`)
Instantaneous value at a point in time. Server stores last value received, retains between flushes (sticky).
- Use for: queue depth, active connections, memory/CPU usage, thread pool size
- Signed values (`+N`, `-N`) modify current value incrementally
- Cannot set to a negative number directly — set to 0 first, then decrement
- **Do not sample gauges** — server cannot correct for sampling on point-in-time values
### Timer (`|ms`)
Duration of an operation in milliseconds. Server computes per flush interval: count, mean, upper (max), lower (min),
sum, stddev, median, configurable percentiles (p90, p95, p99).
- Use for: HTTP request latency, DB query duration, function execution time
- Supports sampling (`|@<rate>`)
### Histogram (`|h`)
Distribution of values over time. Identical to timer in most implementations. DogStatsD treats histograms as the native
distribution type.
- Use for: request payload sizes, response body sizes, batch sizes
- Conceptually: timers measure duration, histograms measure arbitrary distributions
### Set (`|s`)
Count of unique values per flush interval. Server tracks distinct values, reports cardinality at flush, resets.
- Use for: unique users, unique IPs, unique error codes per interval
- **Do not sample sets** — sampling breaks uniqueness tracking
### Distribution (`|d`) — DogStatsD Only
Global distribution across all hosts. Raw values sent to Datadog servers (not aggregated locally). Use when you need
accurate fleet-wide percentiles.
See `${CLAUDE_SKILL_DIR}/references/dogstatsd.md` for distributions vs histograms comparison and protocol version
details.
## Naming
Format: `<namespace>.<subsystem>.<target>.<metric>.<unit>`
Example: `myapp.api.request.duration.ms`, `myapp.cache.hit.count.total`
### Naming Rules
- Always namespace by service name — `myapp.api.requests` not just `requests`
- Use dot-delimited hierarchy
- Include the unit: `.ms`, `.bytes`, `.total`, `.items`
- Dimensions go in tags, not metric names (when tags are available)
- Use lowercase everywhere — some backends are case-sensitive
- Use underscores within path segments: `http_request` not `httpRequest`
- No dashes — they break Graphite navigation
See `${CLAUDE_SKILL_DIR}/references/naming.md` for Graphite namespace mapping, character rules table, and naming
anti-patterns.
## Tags (DogStatsD)
Format: `metric.name:1|c|#key1:value1,key2:value2` — comma-separated, no spaces.
### Tag Rules
- Use tags for dimensions you will filter or group by — not metric names
- Keep cardinality bounded — each unique tag combination creates a separate time series
- No spaces in tag values — use underscores: `region:us_east`
### Unified Service Tagging
Set these as global/constant tags on the client — attach to every metric automatically:
| Tag | Purpose | Example |
| --------- | ---------------------- | --------------------- |
| `env` | Deployment environment | `env:production` |
| `service` | Service name | `service:payment-api` |
| `version` | Deployed version | `version:2.1.0` |
### Tag Cardinality
Rule of thumb: if a tag can have >1000 distinct values, do not use it. Use logs or traces for high-cardinality data.
| Tag | Cardinality | Acceptable? |
| --------------------- | ----------- | ----------- |
| `env:production` | ~3-5 | Yes |
| `method:GET` | ~7 | Yes |
| `status_code:200` | ~20-50 | Yes |
| `endpoint:/api/users` | ~50-200 | Caution |
| `user_id:12345` | Unbounded | No |
## Aggregation and Flush
The flush cycle determines metric resolution. Default: 10 seconds.
- Counters reset to 0 after flush; gauges are sticky (retain last value)
- If no counter values received during flush: behavior depends on `deleteCounters` config (default: send 0)
- Enable client-side aggregation for high-throughput applications (Go v5.0+, Java v3.0+, .NET v7.0+) — pre-aggregates
before sending to Agent
See `${CLAUDE_SKILL_DIR}/references/aggregation.md` for flush mechanics, Graphite downsampling rules, DogStatsD
aggregation details, and pre-aggregated timestamps.
## Client Patterns
### Initialization
- **One client instance per application** — do not create per-request
- **Set namespace prefix** — auto-prepends to all metric names
- **Set global/constant tags** — env, service, version set once
- **Close/flush on shutdown** — buffered metrics lost otherwise
### Buffering
Enable client-side buffering — packs multiple metrics into single UDP packets. Reduces syscall overhead in hot paths.
Most modern DogStatsD clients buffer by default. Call `flush()` before shutdown.
### Sampling
Client randomly decides whether to send each metric based on sample rate. Datagram includes `|@<rate>` so server
corrects the count.
| Volume | Recommendation |
| ------------------ | --------------------------------------------------- |
| < 1000 metrics/sec | `rate=1.0` (no sampling) |
| 1000-10000/sec | `rate=0.5` to `0.1` for counters/timers |
| > 10000/sec | `rate=0.1` or lower; enable client-side aggregation |
**Never sample gauges or sets** — server cannot correct for these types.
See `${CLAUDE_SKILL_DIR}/references/client-patterns.md` for high-throughput tuning steps, error handling, and Kubernetes
deployment patterns.
## Backends
| Need | Backend |
| -------------------------------- | -------------------------- |
| Simple, self-hosted graphing | Graphite |
| Cloud monitoring + APM | Datadog (DogStatsD) |
| Prometheus ecosystem integration | statsd_exporter |
| Flexible multi-output pipeline | Telegraf |
| Migrating StatsD to Prometheus | statsd_exporter with relay |
See `${CLAUDE_SKILL_DIR}/references/backends.md` for statsd_exporter configuration, Telegraf setup, and migration
guides.
## Application
When **writing** StatsD instrumentation:
- Choose the metric type based on what the value represents, not convenience.
- Apply naming conventions silently — don't narrate each rule.
- If an existing codebase contradicts a convention, follow the codebase pattern and flag the divergence once.
- Always configure client-side buffering for production use.
When **reviewing** StatsD instrumentation:
- Check metric type correctness first — most common and most damaging mistake.
- Verify tag cardinality is bounded.
- Cite the specific issue and show the fix inline.
## Integration
The coding skill governs workflow; this skill governs StatsD instrumentation choices.