dipstick

A fast and modular metrics toolkit for all Rust applications. Similar to popular logging frameworks, but with counters and timers.

• Does not bind application code to a single metrics implementation.
• Builds on stable Rust with minimal dependencies.

use dipstick::*;
let app_metrics = metrics(to_stdout());
app_metrics.counter("my_counter").count(3);

Metrics can be sent to multiple outputs at the same time.

let app_metrics = metrics((to_log("log_this:"), to_statsd("localhost:8125")));

Since instruments are decoupled from the backend, outputs can be swapped easily.

Metrics can be aggregated and sent periodically in the background.

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);

Use predefined publishing strategies all_stats, summary, average or roll your own.

Metrics can be statistically sampled.

let app_metrics = metrics(sample(0.001, to_statsd("localhost:8125")));

Metrics can be recorded asynchronously.

let app_metrics = metrics(async(to_stdout()));

Metric definitions can be cached to make using ad-hoc metrics faster.

let app_metrics = metrics(cache(512, to_log()));
app_metrics.gauge(format!("my_gauge_{}", 34)).value(44);

Timers can be used multiple ways.

let timer =  app_metrics.timer("my_timer");
time!(timer, {/* slow code here */} );
timer.time(|| {/* slow code here */} );

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

timer.interval_us(123_456);

Related metrics can share a namespace.

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
• dispatch scopes
• feature flags
• derive stats
• 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)