Fast, ergonomic metrics for Rust!
Metered helps you measure the performance of your programs in production. Inspired by Coda Hale's Java metrics library, Metered makes live measurements easy by providing measurement declarative and procedural macros, and a variety of useful metrics ready out-of-the-box:
HitCount
: a counter tracking how much a piece of code was hit.ErrorCount
: a counter tracking how many errors were returned -- (works on any expression returning a stdResult
)InFlight
: a gauge tracking how many requests are activeResponseTime
: statistics backed by an HdrHistogram of the duration of an expressionThroughput
: statistics backed by an HdrHistogram of how many times an expression is called per second.
These metrics are usually applied to methods, with provided procedural macros that generate the boilerplate for us.
For better performance, these stock metrics can be customized to use non-thread safe (!Sync
/!Send
) datastructures. For ergonomy reasons, stock metrics default to thread-safe datastructures, implemented using lock-free strategies where possible.
Metered is designed as a zero-overhead abstraction -- in the sense that the higher-level ergonomics should not cost over manually adding metrics. Stock metrics will not allocate memory after they're initialized the first time. However, they are triggered at every method call and it can be interesting to use lighter metrics (e.g HitCount
) in very hot code paths and favour heavier metrics (Throughput
, ResponseTime
) in entry points.
If a metric you need is missing, or if you want to customize a metric (for instance, to track how many times a specific error occurs, or react depending on your return type), it is possible to implement your own metrics simply by implementing the Metric
trait .
Metered does not use statics or shared global state. Instead, it lets you either build your own metric registry using the metrics you need, or can generate a metric registry for you using method attributes. Metered will generate one registry per impl
block annotated with the metered
attribute, under the name provided as the registry
parameter. By default, Metered will expect the registry to be accessed as self.metrics
but the expression can be overridden with the registry_expr
attribute parameter. See the demo for more examples.
Metered will generate metric registries that derive Debug
and serde::Serialize
to extract your metrics easily. Adapters for metric storage and monitoring systems are planned (contributions welcome!). Metered generates one sub-registry per method annotated with the measure
attribute, hence organizing metrics hierarchically. This ensures access time to metrics in generated registries is always constant (and, when possible, cache-friendly), without any overhead other than the metric itself.
Metered will happily measure any method, whether it is async
or not, and the metrics will work as expected (e.g, ResponseTime
will return the completion time across await!
invocations).
Example using procedural macros (recommended)
# extern crate metered;
# extern crate rand;
use metered::{metered, Throughput, HitCount};
#[derive(Default, Debug)]
pub struct Biz {
metrics: BizMetrics,
}
#[metered::metered(registry = BizMetrics)]
impl Biz {
#[measure([HitCount, Throughput])]
pub fn biz(&self) {
let delay = std::time::Duration::from_millis(rand::random::<u64>() % 200);
std::thread::sleep(delay);
}
}
# fn main() {
# }
In the snippet above, we will measure the HitCount
and Throughput
of the biz
method.
This works by first annotating the impl
block with the metered
annotation and specifying the name Metered should give to the metric registry (here BizMetrics
). Later, Metered will assume the expression to access that repository is self.metrics
, hence we need a metrics
field with the BizMetrics
type in Biz
. It would be possible to use another field name by specificying another registry expression, such as #[metered(registry = BizMetrics, registry_expr = self.my_custom_metrics)]
.
Then, we must annotate which methods we wish to measure using the measure
attribute, specifying the metrics we wish to apply: the metrics here are simply types of structures implementing the Metric
trait, and you can define your own. Since there is no magic, we must ensure self.metrics
can be accessed, and this will only work on methods with a &self
or &mut self
receiver.
Example of manually using metrics
use metered::{measure, HitCount, ErrorCount};
#[derive(Default, Debug)]
struct TestMetrics {
hit_count: HitCount,
error_count: ErrorCount,
}
fn test(should_fail: bool, metrics: &TestMetrics) -> Result<u32, &'static str> {
let hit_count = &metrics.hit_count;
let error_count = &metrics.error_count;
measure!(hit_count, {
measure!(error_count, {
if should_fail {
Err("Failed!")
} else {
Ok(42)
}
})
})
}
The code above shows how different metrics compose, and in general the kind of boilerplate generated by the #[metered]
procedural macro.