A quick, modular metrics toolkit for Rust applications; similar to popular logging frameworks, but with counters, markers, gauges and timers.

Dipstick builds on stable Rust with minimal dependencies and is published as a crate.

Features

• Send metrics to stdout, log, statsd or graphite (one or many)
• Synchronous, asynchronous or mixed operation
• Optional fast random statistical sampling
• Immediate propagation or local aggregation of metrics (count, sum, average, min/max)
• Periodic or programmatic publication of aggregated metrics
• Customizable output statistics and formatting
• Global or scoped (e.g. per request) metrics
• Per-application and per-output metric namespaces
• Predefined or ad-hoc metrics

Cookbook

Dipstick is easy to add to your code: rust use dipstick::*; let app_metrics = metrics(to_graphite("host.com:2003")); app_metrics.counter("my_counter").count(3);

Metrics can be sent to multiple outputs at the same time: rust let app_metrics = metrics((to_stdout(), to_statsd("localhost:8125", "app1.host."))); Since instruments are decoupled from the backend, outputs can be swapped easily.

Metrics can be aggregated and scheduled to be published periodically in the background: rust use std::time::Duration; let (to_aggregate, from_aggregate) = aggregate(); publish_every(Duration::from_secs(10), from_aggregate, to_log("last_ten_secs:"), all_stats); let app_metrics = metrics(to_aggregate); Aggregation is performed locklessly and is very fast. Count, sum, min, max and average are tracked where they make sense. Published statistics can be selected with presets such as all_stats (see previous example), summary, average.

For more control over published statistics, a custom filter can be provided: rust let (_to_aggregate, from_aggregate) = aggregate(); publish(from_aggregate, to_log("my_custom_stats:"), |metric_kind, metric_name, metric_score| match metric_score { HitCount(hit_count) => Some((Counter, vec![metric_name, ".per_thousand"], hit_count / 1000)), _ => None });

Metrics can be statistically sampled: rust let app_metrics = metrics(sample(0.001, to_statsd("server:8125", "app.sampled."))); A fast random algorithm is used to pick samples. Outputs can use sample rate to expand or format published data.

Metrics can be recorded asynchronously: rust let app_metrics = metrics(async(48, to_stdout())); The async queue uses a Rust channel and a standalone thread. The current behavior is to block when full.

Metric definitions can be cached to make using ad-hoc metrics faster: rust let app_metrics = metrics(cache(512, to_log())); app_metrics.gauge(format!("my_gauge_{}", 34)).value(44);

The preferred way is to predefine metrics, possibly in a lazy_static! block: ```rust

[macrouse] external crate lazystatic;

lazystatic! { pub static ref METRICS: AppMetrics> = metrics(tostdout()); pub static ref COUNTERA: Counter = METRICS.counter("countera"); } COUNTER_A.count(11); ```

Timers can be used multiple ways: ```rust let timer = appmetrics.timer("mytimer"); time!(timer, {/* slow code here /} ); timer.time(|| {/ slow code here */} );

let start = timer.start(); /* slow code here */ timer.stop(start);

timer.intervalus(123456); ```

Related metrics can share a namespace: rust let db_metrics = app_metrics.with_prefix("database."); let db_timer = db_metrics.timer("db_timer"); let db_counter = db_metrics.counter("db_counter");

Design

Dipstick's design goals are to: - support as many metrics backends as possible while favoring none - support all types of applications, from embedded to servers - promote metrics conventions that facilitate app monitoring and maintenance - stay out of the way in the code and at runtime (ergonomic, fast, resilient)

Performance

Predefined timers use a bit more code but are generally faster because their initialization cost is is only paid once. Ad-hoc timers are redefined "inline" on each use. They are more flexible, but have more overhead because their init cost is paid on each use. Defining a metric cache() reduces that cost for recurring metrics.

Run benchmarks with cargo +nightly bench --features bench.

TODO

Although already usable, Dipstick is still under heavy development and makes no guarantees of any kind at this point. See the following list for any potential caveats : - META turn TODOs into GitHub issues - generic publisher / sources - feature flags - time measurement units in metric kind (us, ms, etc.) for naming & scaling - heartbeat metric on publish - logger templates - configurable aggregation (?) - non-aggregating buffers - framework glue (rocket, iron, gotham, indicatif, etc.) - more tests & benchmarks - complete doc / inline samples - more example apps - A cool logo - method annotation processors #[timer("name")] - fastsinks (M / &M) vs. safesinks (Arc) - static_metric! macro to replace lazy_static! blocks and handle generics boilerplate.

License: MIT/Apache-2.0

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