Struct telemetry::plain::Linear

pub struct Linear<T> where T: Flatten {
    // some fields omitted
}

Linear histograms.

Linear histograms classify numeric integer values into same-sized buckets. This type is typically used for percentages, or to store a relatively precise approximation of the amount of resources (time, memory) used by a section or a data structure.

With SerializationFormat::SimpleJson, these histograms are serialized as an array of numbers, one per bucket, in the numeric order of buckets.

Methods

impl<T> Linear<T> where T: Flatten

fn new(service: &Service, name: String, min: u32, max: u32, buckets: usize) -> Linear<T>

Create a new Linear histogram with a given name.

• name is used as key when processing and exporting the data. Each name must be unique to the Service.

• min is the minimal value expected to be entered in this histogram. Any value lower than min is rounded up to min.

• max is the maximal value expected to be entered in this histogram. Any value higher than max is rounded up to max.

• buckets is the number of buckets in this histogram. For highest possible precision, use buckets = max - min + 1. In most cases, however, such precision is not needed, so you should use a lower number of buckets.

Performance

Increasing the number of buckets increases the memory usage on the client by a few bytes per bucket. More importantly, it also increases the size of the payload, hence the total amount of data that the application will eventually upload to a central server. If your application has many clients and you wish to keep your server happy and your bandwidth costs manageable, don't use too many buckets.

Panics

If name is already used by another histogram in service.

If min >= max.

If buckets < max - min + 1.

Trait Implementations

impl<T> Histogram<T> for Linear<T> where T: Flatten

fn record_cb<F>(&self, cb: F) where F: FnOnce() -> Option<T>

fn record(&self, value: T)

impl<T> Clone for Linear<T> where T: Flatten

fn clone(&self) -> Self

fn clone_from(&mut self, source: &Self)