tokio_metrics/

runtime.rs

use crate::derived_metrics::derived_metrics;
#[cfg(tokio_unstable)]
use std::ops::Range;
use std::time::{Duration, Instant};
use tokio::runtime;

// `PollTimeHistogram` and `HistogramBucket` are plain data types (they only
// hold `Duration`s and counts), so they are always available with the `rt`
// feature. Only *populating* the histogram requires `tokio_unstable`. Keeping
// the types ungated ensures `RuntimeMetrics::poll_time_histogram` resolves even
// when `tokio_unstable` is not set (e.g. when a derive macro re-emits the
// struct and drops the field's cfg gate). See
// https://github.com/tokio-rs/tokio-metrics/issues/128.
mod poll_time_histogram;
pub use poll_time_histogram::{HistogramBucket, PollTimeHistogram};

#[cfg(feature = "metrics-rs-integration")]
pub(crate) mod metrics_rs_integration;

/// Monitors key metrics of the tokio runtime.
///
/// ### Usage
/// ```
/// use std::time::Duration;
/// use tokio_metrics::RuntimeMonitor;
///
/// #[tokio::main]
/// async fn main() -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
///     let handle = tokio::runtime::Handle::current();
///
///     // print runtime metrics every 500ms
///     {
///         let runtime_monitor = RuntimeMonitor::new(&handle);
///         tokio::spawn(async move {
///             for interval in runtime_monitor.intervals() {
///                 // pretty-print the metric interval
///                 println!("{:?}", interval);
///                 // wait 500ms
///                 tokio::time::sleep(Duration::from_millis(500)).await;
///             }
///         });
///     }
///
///     // await some tasks
///     tokio::join![
///         do_work(),
///         do_work(),
///         do_work(),
///     ];
///
///     Ok(())
/// }
///
/// async fn do_work() {
///     for _ in 0..25 {
///         tokio::task::yield_now().await;
///         tokio::time::sleep(Duration::from_millis(100)).await;
///     }
/// }
/// ```
#[derive(Debug)]
pub struct RuntimeMonitor {
    /// Handle to the runtime
    runtime: runtime::RuntimeMetrics,
}

macro_rules! define_runtime_metrics {
    (
    stable {
        $(
            $(#[$($attributes:tt)*])*
            $vis:vis $name:ident: $ty:ty
        ),*
        $(,)?
    }
    unstable {
        $(
            $(#[$($unstable_attributes:tt)*])*
            $unstable_vis:vis $unstable_name:ident: $unstable_ty:ty
        ),*
        $(,)?
    }
    ) => {
        /// Key runtime metrics.
        #[non_exhaustive]
        #[cfg_attr(feature = "metrique-integration", metrique::unit_of_work::metrics(subfield_owned))]
        #[derive(Default, Debug, Clone)]
        pub struct RuntimeMetrics {
            $(
                $(#[$($attributes)*])*
                #[cfg_attr(docsrs, doc(cfg(feature = "rt")))]
                $vis $name: $ty,
            )*
            $(
                $(#[$($unstable_attributes)*])*
                #[cfg(tokio_unstable)]
                #[cfg_attr(docsrs, doc(cfg(all(feature = "rt", tokio_unstable))))]
                $unstable_vis $unstable_name: $unstable_ty,
            )*
        }
    };
}

define_runtime_metrics! {
    stable {
        /// The number of worker threads used by the runtime.
        ///
        /// This metric is static for a runtime.
        ///
        /// This metric is always equal to [`tokio::runtime::RuntimeMetrics::num_workers`].
        /// When using the `current_thread` runtime, the return value is always `1`.
        ///
        /// The number of workers is set by configuring
        /// [`worker_threads`][`tokio::runtime::Builder::worker_threads`] with
        /// [`tokio::runtime::Builder`], or by parameterizing [`tokio::main`].
        ///
        /// ##### Examples
        /// In the below example, the number of workers is set by parameterizing [`tokio::main`]:
        /// ```
        /// use tokio::runtime::Handle;
        ///
        /// #[tokio::main(flavor = "multi_thread", worker_threads = 10)]
        /// async fn main() {
        ///     let handle = tokio::runtime::Handle::current();
        ///     let monitor = tokio_metrics::RuntimeMonitor::new(&handle);
        ///     let mut intervals = monitor.intervals();
        ///     let mut next_interval = || intervals.next().unwrap();
        ///
        ///     assert_eq!(next_interval().workers_count, 10);
        /// }
        /// ```
        ///
        /// [`tokio::main`]: https://docs.rs/tokio/latest/tokio/attr.main.html
        ///
        /// When using the `current_thread` runtime, the return value is always `1`; e.g.:
        /// ```
        /// use tokio::runtime::Handle;
        ///
        /// #[tokio::main(flavor = "current_thread")]
        /// async fn main() {
        ///     let handle = tokio::runtime::Handle::current();
        ///     let monitor = tokio_metrics::RuntimeMonitor::new(&handle);
        ///     let mut intervals = monitor.intervals();
        ///     let mut next_interval = || intervals.next().unwrap();
        ///
        ///     assert_eq!(next_interval().workers_count, 1);
        /// }
        /// ```
        ///
        /// This metric is always equal to [`tokio::runtime::RuntimeMetrics::num_workers`]; e.g.:
        /// ```
        /// use tokio::runtime::Handle;
        ///
        /// #[tokio::main]
        /// async fn main() {
        ///     let handle = Handle::current();
        ///     let monitor = tokio_metrics::RuntimeMonitor::new(&handle);
        ///     let mut intervals = monitor.intervals();
        ///     let mut next_interval = || intervals.next().unwrap();
        ///
        ///     assert_eq!(next_interval().workers_count, handle.metrics().num_workers());
        /// }
        /// ```
        pub workers_count: usize,

        /// The current number of alive tasks in the runtime.
        ///
        /// ##### Definition
        /// This metric is derived from [`tokio::runtime::RuntimeMetrics::num_alive_tasks`].
        pub live_tasks_count: usize,

        /// The number of times worker threads parked.
        ///
        /// The worker park count increases by one each time the worker parks the thread waiting for
        /// new inbound events to process. This usually means the worker has processed all pending work
        /// and is currently idle.
        ///
        /// ##### Definition
        /// This metric is derived from the sum of [`tokio::runtime::RuntimeMetrics::worker_park_count`]
        /// across all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::max_park_count`]
        /// - [`RuntimeMetrics::min_park_count`]
        ///
        /// ##### Examples
        /// ```
        /// #[tokio::main(flavor = "multi_thread", worker_threads = 2)]
        /// async fn main() {
        ///     let handle = tokio::runtime::Handle::current();
        ///     let monitor = tokio_metrics::RuntimeMonitor::new(&handle);
        ///     let mut intervals = monitor.intervals();
        ///     let mut next_interval = || intervals.next().unwrap();
        ///
        ///     let interval = next_interval(); // end of interval 1
        ///     assert_eq!(interval.total_park_count, 0);
        ///
        ///     induce_parks().await;
        ///
        ///     let interval = next_interval(); // end of interval 2
        ///     assert!(interval.total_park_count >= 1); // usually 1 or 2 parks
        /// }
        ///
        /// async fn induce_parks() {
        ///     let _ = tokio::time::timeout(std::time::Duration::ZERO, async {
        ///         loop { tokio::task::yield_now().await; }
        ///     }).await;
        /// }
        /// ```
        pub total_park_count: u64,

        /// The maximum number of times any worker thread parked.
        ///
        /// ##### Definition
        /// This metric is derived from the maximum of
        /// [`tokio::runtime::RuntimeMetrics::worker_park_count`] across all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::total_park_count`]
        /// - [`RuntimeMetrics::min_park_count`]
        pub max_park_count: u64,

        /// The minimum number of times any worker thread parked.
        ///
        /// ##### Definition
        /// This metric is derived from the maximum of
        /// [`tokio::runtime::RuntimeMetrics::worker_park_count`] across all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::total_park_count`]
        /// - [`RuntimeMetrics::max_park_count`]
        pub min_park_count: u64,

        /// The amount of time worker threads were busy.
        ///
        /// The worker busy duration increases whenever the worker is spending time processing work.
        /// Using this value can indicate the total load of workers.
        ///
        /// ##### Definition
        /// This metric is derived from the sum of
        /// [`tokio::runtime::RuntimeMetrics::worker_total_busy_duration`] across all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::min_busy_duration`]
        /// - [`RuntimeMetrics::max_busy_duration`]
        ///
        /// ##### Examples
        /// In the below example, tasks spend a total of 3s busy:
        /// ```
        /// use tokio::time::Duration;
        ///
        /// fn main() {
        ///     let start = tokio::time::Instant::now();
        ///
        ///     let rt = tokio::runtime::Builder::new_current_thread()
        ///         .enable_all()
        ///         .build()
        ///         .unwrap();
        ///
        ///     let handle = rt.handle();
        ///     let monitor = tokio_metrics::RuntimeMonitor::new(&handle);
        ///     let mut intervals = monitor.intervals();
        ///     let mut next_interval = || intervals.next().unwrap();
        ///
        ///     let delay_1s = Duration::from_secs(1);
        ///     let delay_3s = Duration::from_secs(3);
        ///
        ///     rt.block_on(async {
        ///         // keep the main task busy for 1s
        ///         spin_for(delay_1s);
        ///
        ///         // spawn a task and keep it busy for 2s
        ///         let _ = tokio::spawn(async move {
        ///             spin_for(delay_3s);
        ///         }).await;
        ///     });
        ///
        ///     // flush metrics
        ///     drop(rt);
        ///
        ///     let elapsed = start.elapsed();
        ///
        ///     let interval =  next_interval(); // end of interval 2
        ///     assert!(interval.total_busy_duration >= delay_1s + delay_3s);
        ///     assert!(interval.total_busy_duration <= elapsed);
        /// }
        ///
        /// fn time<F>(task: F) -> Duration
        /// where
        ///     F: Fn() -> ()
        /// {
        ///     let start = tokio::time::Instant::now();
        ///     task();
        ///     start.elapsed()
        /// }
        ///
        /// /// Block the current thread for a given `duration`.
        /// fn spin_for(duration: Duration) {
        ///     let start = tokio::time::Instant::now();
        ///     while start.elapsed() <= duration {}
        /// }
        /// ```
        ///
        /// Busy times may not accumulate as the above example suggests (FIXME: Why?); e.g., if we
        /// remove the three second delay, the time spent busy falls to mere microseconds:
        /// ```should_panic
        /// use tokio::time::Duration;
        ///
        /// fn main() {
        ///     let rt = tokio::runtime::Builder::new_current_thread()
        ///         .enable_all()
        ///         .build()
        ///         .unwrap();
        ///
        ///     let handle = rt.handle();
        ///     let monitor = tokio_metrics::RuntimeMonitor::new(&handle);
        ///     let mut intervals = monitor.intervals();
        ///     let mut next_interval = || intervals.next().unwrap();
        ///
        ///     let delay_1s = Duration::from_secs(1);
        ///
        ///     let elapsed = time(|| rt.block_on(async {
        ///         // keep the main task busy for 1s
        ///         spin_for(delay_1s);
        ///     }));
        ///
        ///     // flush metrics
        ///     drop(rt);
        ///
        ///     let interval =  next_interval(); // end of interval 2
        ///     assert!(interval.total_busy_duration >= delay_1s); // FAIL
        ///     assert!(interval.total_busy_duration <= elapsed);
        /// }
        ///
        /// fn time<F>(task: F) -> Duration
        /// where
        ///     F: Fn() -> ()
        /// {
        ///     let start = tokio::time::Instant::now();
        ///     task();
        ///     start.elapsed()
        /// }
        ///
        /// /// Block the current thread for a given `duration`.
        /// fn spin_for(duration: Duration) {
        ///     let start = tokio::time::Instant::now();
        ///     while start.elapsed() <= duration {}
        /// }
        /// ```
        pub total_busy_duration: Duration,

        /// The maximum amount of time a worker thread was busy.
        ///
        /// ##### Definition
        /// This metric is derived from the maximum of
        /// [`tokio::runtime::RuntimeMetrics::worker_total_busy_duration`] across all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::total_busy_duration`]
        /// - [`RuntimeMetrics::min_busy_duration`]
        pub max_busy_duration: Duration,

        /// The minimum amount of time a worker thread was busy.
        ///
        /// ##### Definition
        /// This metric is derived from the minimum of
        /// [`tokio::runtime::RuntimeMetrics::worker_total_busy_duration`] across all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::total_busy_duration`]
        /// - [`RuntimeMetrics::max_busy_duration`]
        pub min_busy_duration: Duration,

        /// The number of tasks currently scheduled in the runtime's global queue.
        ///
        /// Tasks that are spawned or notified from a non-runtime thread are scheduled using the
        /// runtime's global queue. This metric returns the **current** number of tasks pending in
        /// the global queue. As such, the returned value may increase or decrease as new tasks are
        /// scheduled and processed.
        ///
        /// ##### Definition
        /// This metric is derived from [`tokio::runtime::RuntimeMetrics::global_queue_depth`].
        ///
        /// ##### Example
        /// ```
        /// # let current_thread = tokio::runtime::Builder::new_current_thread()
        /// #     .enable_all()
        /// #     .build()
        /// #     .unwrap();
        /// #
        /// # let multi_thread = tokio::runtime::Builder::new_multi_thread()
        /// #     .worker_threads(2)
        /// #     .enable_all()
        /// #     .build()
        /// #     .unwrap();
        /// #
        /// # for runtime in [current_thread, multi_thread] {
        /// let handle = runtime.handle().clone();
        /// let monitor = tokio_metrics::RuntimeMonitor::new(&handle);
        /// let mut intervals = monitor.intervals();
        /// let mut next_interval = || intervals.next().unwrap();
        ///
        /// let interval = next_interval(); // end of interval 1
        /// # #[cfg(tokio_unstable)]
        /// assert_eq!(interval.num_remote_schedules, 0);
        ///
        /// // spawn a system thread outside of the runtime
        /// std::thread::spawn(move || {
        ///     // spawn two tasks from this non-runtime thread
        ///     handle.spawn(async {});
        ///     handle.spawn(async {});
        /// }).join().unwrap();
        ///
        /// // flush metrics
        /// drop(runtime);
        ///
        /// let interval = next_interval(); // end of interval 2
        /// # #[cfg(tokio_unstable)]
        /// assert_eq!(interval.num_remote_schedules, 2);
        /// # }
        /// ```
        pub global_queue_depth: usize,

        /// Total amount of time elapsed since observing runtime metrics.
        pub elapsed: Duration,
    }
    unstable {
        /// The average duration of a single invocation of poll on a task.
        ///
        /// This average is an exponentially-weighted moving average of the duration
        /// of task polls on all runtime workers.
        ///
        /// ##### Examples
        /// ```
        /// #[tokio::main(flavor = "multi_thread", worker_threads = 2)]
        /// async fn main() {
        ///     let handle = tokio::runtime::Handle::current();
        ///     let monitor = tokio_metrics::RuntimeMonitor::new(&handle);
        ///     let mut intervals = monitor.intervals();
        ///     let mut next_interval = || intervals.next().unwrap();
        ///
        ///     let interval = next_interval();
        ///     println!("mean task poll duration is {:?}", interval.mean_poll_duration);
        /// }
        /// ```
        pub mean_poll_duration: Duration,

        /// The average duration of a single invocation of poll on a task on the
        /// worker with the lowest value.
        ///
        /// This average is an exponentially-weighted moving average of the duration
        /// of task polls on the runtime worker with the lowest value.
        ///
        /// ##### Examples
        /// ```
        /// #[tokio::main(flavor = "multi_thread", worker_threads = 2)]
        /// async fn main() {
        ///     let handle = tokio::runtime::Handle::current();
        ///     let monitor = tokio_metrics::RuntimeMonitor::new(&handle);
        ///     let mut intervals = monitor.intervals();
        ///     let mut next_interval = || intervals.next().unwrap();
        ///
        ///     let interval = next_interval();
        ///     println!("min mean task poll duration is {:?}", interval.mean_poll_duration_worker_min);
        /// }
        /// ```
        pub mean_poll_duration_worker_min: Duration,

        /// The average duration of a single invocation of poll on a task on the
        /// worker with the highest value.
        ///
        /// This average is an exponentially-weighted moving average of the duration
        /// of task polls on the runtime worker with the highest value.
        ///
        /// ##### Examples
        /// ```
        /// #[tokio::main(flavor = "multi_thread", worker_threads = 2)]
        /// async fn main() {
        ///     let handle = tokio::runtime::Handle::current();
        ///     let monitor = tokio_metrics::RuntimeMonitor::new(&handle);
        ///     let mut intervals = monitor.intervals();
        ///     let mut next_interval = || intervals.next().unwrap();
        ///
        ///     let interval = next_interval();
        ///     println!("max mean task poll duration is {:?}", interval.mean_poll_duration_worker_max);
        /// }
        /// ```
        pub mean_poll_duration_worker_max: Duration,

        /// A histogram of task polls since the previous probe grouped by poll
        /// times.
        ///
        /// Each bucket contains the configured [`Duration`] range and the count
        /// of task polls that fell into that range during the interval. Use
        /// [`PollTimeHistogram::as_counts`] to get just the raw counts as a
        /// `Vec<u64>`.
        ///
        /// This metric must be explicitly enabled when creating the runtime with
        /// [`enable_metrics_poll_time_histogram`][tokio::runtime::Builder::enable_metrics_poll_time_histogram];
        /// if it is not enabled, the histogram will contain no buckets. Bucket
        /// sizes are fixed and configured at the runtime level. See
        /// configuration options on
        /// [`runtime::Builder`][tokio::runtime::Builder::enable_metrics_poll_time_histogram].
        ///
        /// ##### Examples
        /// ```
        /// use tokio::runtime::HistogramConfiguration;
        /// use std::time::Duration;
        ///
        /// let config = HistogramConfiguration::linear(Duration::from_micros(50), 12);
        ///
        /// let rt = tokio::runtime::Builder::new_multi_thread()
        ///     .enable_metrics_poll_time_histogram()
        ///     .metrics_poll_time_histogram_configuration(config)
        ///     .build()
        ///     .unwrap();
        ///
        /// rt.block_on(async {
        ///     let handle = tokio::runtime::Handle::current();
        ///     let monitor = tokio_metrics::RuntimeMonitor::new(&handle);
        ///     let mut intervals = monitor.intervals();
        ///     let mut next_interval = || intervals.next().unwrap();
        ///
        ///     let interval = next_interval();
        ///     for bucket in interval.poll_time_histogram.buckets() {
        ///         println!("{:?}..{:?} => {} polls", bucket.range_start(), bucket.range_end(), bucket.count());
        ///     }
        /// });
        /// ```
        pub poll_time_histogram: PollTimeHistogram,

        /// The number of times worker threads unparked but performed no work before parking again.
        ///
        /// The worker no-op count increases by one each time the worker unparks the thread but finds
        /// no new work and goes back to sleep. This indicates a false-positive wake up.
        ///
        /// ##### Definition
        /// This metric is derived from the sum of [`tokio::runtime::RuntimeMetrics::worker_noop_count`]
        /// across all worker threads.
        ///
        /// ##### Examples
        /// Unfortunately, there isn't a great way to reliably induce no-op parks, as they occur as
        /// false-positive events under concurrency.
        ///
        /// The below example triggers fewer than two parks in the single-threaded runtime:
        /// ```
        /// #[tokio::main(flavor = "current_thread")]
        /// async fn main() {
        ///     let handle = tokio::runtime::Handle::current();
        ///     let monitor = tokio_metrics::RuntimeMonitor::new(&handle);
        ///     let mut intervals = monitor.intervals();
        ///     let mut next_interval = || intervals.next().unwrap();
        ///
        ///     assert_eq!(next_interval().total_park_count, 0);
        ///
        ///     async {
        ///         tokio::time::sleep(std::time::Duration::from_millis(1)).await;
        ///     }.await;
        ///
        ///     assert!(next_interval().total_park_count > 0);
        /// }
        /// ```
        ///
        /// The below example triggers fewer than two parks in the multi-threaded runtime:
        /// ```
        /// #[tokio::main(flavor = "multi_thread")]
        /// async fn main() {
        ///     let handle = tokio::runtime::Handle::current();
        ///     let monitor = tokio_metrics::RuntimeMonitor::new(&handle);
        ///     let mut intervals = monitor.intervals();
        ///     let mut next_interval = || intervals.next().unwrap();
        ///
        ///     async {
        ///         tokio::time::sleep(std::time::Duration::from_millis(1)).await;
        ///     }.await;
        ///
        ///     assert!(next_interval().total_noop_count > 0);
        /// }
        /// ```
        pub total_noop_count: u64,

        /// The maximum number of times any worker thread unparked but performed no work before parking
        /// again.
        ///
        /// ##### Definition
        /// This metric is derived from the maximum of
        /// [`tokio::runtime::RuntimeMetrics::worker_noop_count`] across all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::total_noop_count`]
        /// - [`RuntimeMetrics::min_noop_count`]
        pub max_noop_count: u64,

        /// The minimum number of times any worker thread unparked but performed no work before parking
        /// again.
        ///
        /// ##### Definition
        /// This metric is derived from the minimum of
        /// [`tokio::runtime::RuntimeMetrics::worker_noop_count`] across all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::total_noop_count`]
        /// - [`RuntimeMetrics::max_noop_count`]
        pub min_noop_count: u64,

        /// The number of tasks worker threads stole from another worker thread.
        ///
        /// The worker steal count increases by the amount of stolen tasks each time the worker
        /// has processed its scheduled queue and successfully steals more pending tasks from another
        /// worker.
        ///
        /// This metric only applies to the **multi-threaded** runtime and will always return `0` when
        /// using the current thread runtime.
        ///
        /// ##### Definition
        /// This metric is derived from the sum of [`tokio::runtime::RuntimeMetrics::worker_steal_count`] for
        /// all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::min_steal_count`]
        /// - [`RuntimeMetrics::max_steal_count`]
        ///
        /// ##### Examples
        /// In the below example, a blocking channel is used to backup one worker thread:
        /// ```
        /// #[tokio::main(flavor = "multi_thread", worker_threads = 2)]
        /// async fn main() {
        ///     let handle = tokio::runtime::Handle::current();
        ///     let monitor = tokio_metrics::RuntimeMonitor::new(&handle);
        ///     let mut intervals = monitor.intervals();
        ///     let mut next_interval = || intervals.next().unwrap();
        ///
        ///     let interval = next_interval(); // end of first sampling interval
        ///     assert_eq!(interval.total_steal_count, 0);
        ///     assert_eq!(interval.min_steal_count, 0);
        ///     assert_eq!(interval.max_steal_count, 0);
        ///
        ///     // induce a steal
        ///     async {
        ///         let (tx, rx) = std::sync::mpsc::channel();
        ///         // Move to the runtime.
        ///         tokio::spawn(async move {
        ///             // Spawn the task that sends to the channel
        ///             tokio::spawn(async move {
        ///                 tx.send(()).unwrap();
        ///             });
        ///             // Spawn a task that bumps the previous task out of the "next
        ///             // scheduled" slot.
        ///             tokio::spawn(async {});
        ///             // Blocking receive on the channel.
        ///             rx.recv().unwrap();
        ///             flush_metrics().await;
        ///         }).await.unwrap();
        ///         flush_metrics().await;
        ///     }.await;
        ///
        ///     let interval = { flush_metrics().await; next_interval() }; // end of interval 2
        ///     println!("total={}; min={}; max={}", interval.total_steal_count, interval.min_steal_count, interval.max_steal_count);
        ///
        ///     let interval = { flush_metrics().await; next_interval() }; // end of interval 3
        ///     println!("total={}; min={}; max={}", interval.total_steal_count, interval.min_steal_count, interval.max_steal_count);
        /// }
        ///
        /// async fn flush_metrics() {
        ///     let _ = tokio::time::sleep(std::time::Duration::ZERO).await;
        /// }
        /// ```
        pub total_steal_count: u64,

        /// The maximum number of tasks any worker thread stole from another worker thread.
        ///
        /// ##### Definition
        /// This metric is derived from the maximum of [`tokio::runtime::RuntimeMetrics::worker_steal_count`]
        /// across all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::total_steal_count`]
        /// - [`RuntimeMetrics::min_steal_count`]
        pub max_steal_count: u64,

        /// The minimum number of tasks any worker thread stole from another worker thread.
        ///
        /// ##### Definition
        /// This metric is derived from the minimum of [`tokio::runtime::RuntimeMetrics::worker_steal_count`]
        /// across all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::total_steal_count`]
        /// - [`RuntimeMetrics::max_steal_count`]
        pub min_steal_count: u64,

        /// The number of times worker threads stole tasks from another worker thread.
        ///
        /// The worker steal operations increases by one each time the worker has processed its
        /// scheduled queue and successfully steals more pending tasks from another worker.
        ///
        /// This metric only applies to the **multi-threaded** runtime and will always return `0` when
        /// using the current thread runtime.
        ///
        /// ##### Definition
        /// This metric is derived from the sum of [`tokio::runtime::RuntimeMetrics::worker_steal_operations`]
        /// for all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::min_steal_operations`]
        /// - [`RuntimeMetrics::max_steal_operations`]
        ///
        /// ##### Examples
        /// In the below example, a blocking channel is used to backup one worker thread:
        /// ```
        /// #[tokio::main(flavor = "multi_thread", worker_threads = 2)]
        /// async fn main() {
        ///     let handle = tokio::runtime::Handle::current();
        ///     let monitor = tokio_metrics::RuntimeMonitor::new(&handle);
        ///     let mut intervals = monitor.intervals();
        ///     let mut next_interval = || intervals.next().unwrap();
        ///
        ///     let interval = next_interval(); // end of first sampling interval
        ///     assert_eq!(interval.total_steal_operations, 0);
        ///     assert_eq!(interval.min_steal_operations, 0);
        ///     assert_eq!(interval.max_steal_operations, 0);
        ///
        ///     // induce a steal
        ///     async {
        ///         let (tx, rx) = std::sync::mpsc::channel();
        ///         // Move to the runtime.
        ///         tokio::spawn(async move {
        ///             // Spawn the task that sends to the channel
        ///             tokio::spawn(async move {
        ///                 tx.send(()).unwrap();
        ///             });
        ///             // Spawn a task that bumps the previous task out of the "next
        ///             // scheduled" slot.
        ///             tokio::spawn(async {});
        ///             // Blocking receive on the channe.
        ///             rx.recv().unwrap();
        ///             flush_metrics().await;
        ///         }).await.unwrap();
        ///         flush_metrics().await;
        ///     }.await;
        ///
        ///     let interval = { flush_metrics().await; next_interval() }; // end of interval 2
        ///     println!("total={}; min={}; max={}", interval.total_steal_operations, interval.min_steal_operations, interval.max_steal_operations);
        ///
        ///     let interval = { flush_metrics().await; next_interval() }; // end of interval 3
        ///     println!("total={}; min={}; max={}", interval.total_steal_operations, interval.min_steal_operations, interval.max_steal_operations);
        /// }
        ///
        /// async fn flush_metrics() {
        ///     let _ = tokio::time::sleep(std::time::Duration::ZERO).await;
        /// }
        /// ```
        pub total_steal_operations: u64,

        /// The maximum number of times any worker thread stole tasks from another worker thread.
        ///
        /// ##### Definition
        /// This metric is derived from the maximum of [`tokio::runtime::RuntimeMetrics::worker_steal_operations`]
        /// across all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::total_steal_operations`]
        /// - [`RuntimeMetrics::min_steal_operations`]
        pub max_steal_operations: u64,

        /// The minimum number of times any worker thread stole tasks from another worker thread.
        ///
        /// ##### Definition
        /// This metric is derived from the minimum of [`tokio::runtime::RuntimeMetrics::worker_steal_operations`]
        /// across all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::total_steal_operations`]
        /// - [`RuntimeMetrics::max_steal_operations`]
        pub min_steal_operations: u64,

        /// The number of tasks scheduled from **outside** of the runtime.
        ///
        /// The remote schedule count increases by one each time a task is woken from **outside** of
        /// the runtime. This usually means that a task is spawned or notified from a non-runtime
        /// thread and must be queued using the Runtime's global queue, which tends to be slower.
        ///
        /// ##### Definition
        /// This metric is derived from [`tokio::runtime::RuntimeMetrics::remote_schedule_count`].
        ///
        /// ##### Examples
        /// In the below example, a remote schedule is induced by spawning a system thread, then
        /// spawning a tokio task from that system thread:
        /// ```
        /// #[tokio::main(flavor = "multi_thread", worker_threads = 2)]
        /// async fn main() {
        ///     let handle = tokio::runtime::Handle::current();
        ///     let monitor = tokio_metrics::RuntimeMonitor::new(&handle);
        ///     let mut intervals = monitor.intervals();
        ///     let mut next_interval = || intervals.next().unwrap();
        ///
        ///     let interval = next_interval(); // end of first sampling interval
        ///     assert_eq!(interval.num_remote_schedules, 0);
        ///
        ///     // spawn a non-runtime thread
        ///     std::thread::spawn(move || {
        ///         // spawn two tasks from this non-runtime thread
        ///         async move {
        ///             handle.spawn(async {}).await;
        ///             handle.spawn(async {}).await;
        ///         }
        ///     }).join().unwrap().await;
        ///
        ///     let interval = next_interval(); // end of second sampling interval
        ///     assert_eq!(interval.num_remote_schedules, 2);
        ///
        ///     let interval = next_interval(); // end of third sampling interval
        ///     assert_eq!(interval.num_remote_schedules, 0);
        /// }
        /// ```
        pub num_remote_schedules: u64,

        /// The number of tasks scheduled from worker threads.
        ///
        /// The local schedule count increases by one each time a task is woken from **inside** of the
        /// runtime. This usually means that a task is spawned or notified from within a runtime thread
        /// and will be queued on the worker-local queue.
        ///
        /// ##### Definition
        /// This metric is derived from the sum of
        /// [`tokio::runtime::RuntimeMetrics::worker_local_schedule_count`] across all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::min_local_schedule_count`]
        /// - [`RuntimeMetrics::max_local_schedule_count`]
        ///
        /// ##### Examples
        /// ###### With `current_thread` runtime
        /// In the below example, two tasks are spawned from the context of a third tokio task:
        /// ```
        /// #[tokio::main(flavor = "current_thread")]
        /// async fn main() {
        ///     let handle = tokio::runtime::Handle::current();
        ///     let monitor = tokio_metrics::RuntimeMonitor::new(&handle);
        ///     let mut intervals = monitor.intervals();
        ///     let mut next_interval = || intervals.next().unwrap();
        ///
        ///     let interval = { flush_metrics().await; next_interval() }; // end interval 2
        ///     assert_eq!(interval.total_local_schedule_count, 0);
        ///
        ///     let task = async {
        ///         tokio::spawn(async {}); // local schedule 1
        ///         tokio::spawn(async {}); // local schedule 2
        ///     };
        ///
        ///     let handle = tokio::spawn(task); // local schedule 3
        ///
        ///     let interval = { flush_metrics().await; next_interval() }; // end interval 2
        ///     assert_eq!(interval.total_local_schedule_count, 3);
        ///
        ///     let _ = handle.await;
        ///
        ///     let interval = { flush_metrics().await; next_interval() }; // end interval 3
        ///     assert_eq!(interval.total_local_schedule_count, 0);
        /// }
        ///
        /// async fn flush_metrics() {
        ///     tokio::task::yield_now().await;
        /// }
        /// ```
        ///
        /// ###### With `multi_thread` runtime
        /// In the below example, 100 tasks are spawned:
        /// ```
        /// #[tokio::main(flavor = "multi_thread", worker_threads = 2)]
        /// async fn main() {
        ///     let handle = tokio::runtime::Handle::current();
        ///     let monitor = tokio_metrics::RuntimeMonitor::new(&handle);
        ///     let mut intervals = monitor.intervals();
        ///     let mut next_interval = || intervals.next().unwrap();
        ///
        ///     let interval = next_interval(); // end of interval 1
        ///     assert_eq!(interval.total_local_schedule_count, 0);
        ///
        ///     use std::sync::atomic::{AtomicBool, Ordering};
        ///     static SPINLOCK: AtomicBool = AtomicBool::new(true);
        ///
        ///     // block the other worker thread
        ///     tokio::spawn(async {
        ///         while SPINLOCK.load(Ordering::SeqCst) {}
        ///     });
        ///
        ///     // FIXME: why does this need to be in a `spawn`?
        ///     let _ = tokio::spawn(async {
        ///         // spawn 100 tasks
        ///         for _ in 0..100 {
        ///             tokio::spawn(async {});
        ///         }
        ///         // this spawns 1 more task
        ///         flush_metrics().await;
        ///     }).await;
        ///
        ///     // unblock the other worker thread
        ///     SPINLOCK.store(false, Ordering::SeqCst);
        ///
        ///     let interval = { flush_metrics().await; next_interval() }; // end of interval 2
        ///     assert_eq!(interval.total_local_schedule_count, 100 + 1);
        /// }
        ///
        /// async fn flush_metrics() {
        ///     let _ = tokio::time::sleep(std::time::Duration::ZERO).await;
        /// }
        /// ```
        pub total_local_schedule_count: u64,

        /// The maximum number of tasks scheduled from any one worker thread.
        ///
        /// ##### Definition
        /// This metric is derived from the maximum of
        /// [`tokio::runtime::RuntimeMetrics::worker_local_schedule_count`] for all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::total_local_schedule_count`]
        /// - [`RuntimeMetrics::min_local_schedule_count`]
        pub max_local_schedule_count: u64,

        /// The minimum number of tasks scheduled from any one worker thread.
        ///
        /// ##### Definition
        /// This metric is derived from the minimum of
        /// [`tokio::runtime::RuntimeMetrics::worker_local_schedule_count`] for all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::total_local_schedule_count`]
        /// - [`RuntimeMetrics::max_local_schedule_count`]
        pub min_local_schedule_count: u64,

        /// The number of times worker threads saturated their local queues.
        ///
        /// The worker steal count increases by one each time the worker attempts to schedule a task
        /// locally, but its local queue is full. When this happens, half of the
        /// local queue is moved to the global queue.
        ///
        /// This metric only applies to the **multi-threaded** scheduler.
        ///
        /// ##### Definition
        /// This metric is derived from the sum of
        /// [`tokio::runtime::RuntimeMetrics::worker_overflow_count`] across all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::min_overflow_count`]
        /// - [`RuntimeMetrics::max_overflow_count`]
        ///
        /// ##### Examples
        /// ```
        /// #[tokio::main(flavor = "multi_thread", worker_threads = 1)]
        /// async fn main() {
        ///     let handle = tokio::runtime::Handle::current();
        ///     let monitor = tokio_metrics::RuntimeMonitor::new(&handle);
        ///     let mut intervals = monitor.intervals();
        ///     let mut next_interval = || intervals.next().unwrap();
        ///
        ///     let interval = next_interval(); // end of interval 1
        ///     assert_eq!(interval.total_overflow_count, 0);
        ///
        ///     use std::sync::atomic::{AtomicBool, Ordering};
        ///
        ///     // spawn a ton of tasks
        ///     let _ = tokio::spawn(async {
        ///         // we do this in a `tokio::spawn` because it is impossible to
        ///         // overflow the main task
        ///         for _ in 0..300 {
        ///             tokio::spawn(async {});
        ///         }
        ///     }).await;
        ///
        ///     let interval = { flush_metrics().await; next_interval() }; // end of interval 2
        ///     assert_eq!(interval.total_overflow_count, 1);
        /// }
        ///
        /// async fn flush_metrics() {
        ///     let _ = tokio::time::sleep(std::time::Duration::from_millis(1)).await;
        /// }
        /// ```
        pub total_overflow_count: u64,

        /// The maximum number of times any one worker saturated its local queue.
        ///
        /// ##### Definition
        /// This metric is derived from the maximum of
        /// [`tokio::runtime::RuntimeMetrics::worker_overflow_count`] across all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::total_overflow_count`]
        /// - [`RuntimeMetrics::min_overflow_count`]
        pub max_overflow_count: u64,

        /// The minimum number of times any one worker saturated its local queue.
        ///
        /// ##### Definition
        /// This metric is derived from the maximum of
        /// [`tokio::runtime::RuntimeMetrics::worker_overflow_count`] across all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::total_overflow_count`]
        /// - [`RuntimeMetrics::max_overflow_count`]
        pub min_overflow_count: u64,

        /// The number of tasks that have been polled across all worker threads.
        ///
        /// The worker poll count increases by one each time a worker polls a scheduled task.
        ///
        /// ##### Definition
        /// This metric is derived from the sum of
        /// [`tokio::runtime::RuntimeMetrics::worker_poll_count`] across all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::min_polls_count`]
        /// - [`RuntimeMetrics::max_polls_count`]
        ///
        /// ##### Examples
        /// In the below example, 42 tasks are spawned and polled:
        /// ```
        /// #[tokio::main(flavor = "current_thread")]
        /// async fn main() {
        ///     let handle = tokio::runtime::Handle::current();
        ///     let monitor = tokio_metrics::RuntimeMonitor::new(&handle);
        ///     let mut intervals = monitor.intervals();
        ///     let mut next_interval = || intervals.next().unwrap();
        ///
        ///     let interval = { flush_metrics().await; next_interval() }; // end of interval 1
        ///     assert_eq!(interval.total_polls_count, 0);
        ///     assert_eq!(interval.min_polls_count, 0);
        ///     assert_eq!(interval.max_polls_count, 0);
        ///
        ///     const N: u64 = 42;
        ///
        ///     for _ in 0..N {
        ///         let _ = tokio::spawn(async {}).await;
        ///     }
        ///
        ///     let interval = { flush_metrics().await; next_interval() }; // end of interval 2
        ///     assert_eq!(interval.total_polls_count, N);
        ///     assert_eq!(interval.min_polls_count, N);
        ///     assert_eq!(interval.max_polls_count, N);
        /// }
        ///
        /// async fn flush_metrics() {
        ///     let _ = tokio::task::yield_now().await;
        /// }
        /// ```
        pub total_polls_count: u64,

        /// The maximum number of tasks that have been polled in any worker thread.
        ///
        /// ##### Definition
        /// This metric is derived from the maximum of
        /// [`tokio::runtime::RuntimeMetrics::worker_poll_count`] across all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::total_polls_count`]
        /// - [`RuntimeMetrics::min_polls_count`]
        pub max_polls_count: u64,

        /// The minimum number of tasks that have been polled in any worker thread.
        ///
        /// ##### Definition
        /// This metric is derived from the minimum of
        /// [`tokio::runtime::RuntimeMetrics::worker_poll_count`] across all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::total_polls_count`]
        /// - [`RuntimeMetrics::max_polls_count`]
        pub min_polls_count: u64,

        /// The total number of tasks currently scheduled in workers' local queues.
        ///
        /// Tasks that are spawned or notified from within a runtime thread are scheduled using that
        /// worker's local queue. This metric returns the **current** number of tasks pending in all
        /// workers' local queues. As such, the returned value may increase or decrease as new tasks
        /// are scheduled and processed.
        ///
        /// ##### Definition
        /// This metric is derived from [`tokio::runtime::RuntimeMetrics::worker_local_queue_depth`].
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::min_local_queue_depth`]
        /// - [`RuntimeMetrics::max_local_queue_depth`]
        ///
        /// ##### Example
        ///
        /// ###### With `current_thread` runtime
        /// The below example spawns 100 tasks:
        /// ```
        /// #[tokio::main(flavor = "current_thread")]
        /// async fn main() {
        ///     const N: usize = 100;
        ///
        ///     let handle = tokio::runtime::Handle::current();
        ///     let monitor = tokio_metrics::RuntimeMonitor::new(&handle);
        ///     let mut intervals = monitor.intervals();
        ///     let mut next_interval = || intervals.next().unwrap();
        ///
        ///     let interval =  next_interval(); // end of interval 1
        ///     assert_eq!(interval.total_local_queue_depth, 0);
        ///
        ///
        ///     for _ in 0..N {
        ///         tokio::spawn(async {});
        ///     }
        ///     let interval =  next_interval(); // end of interval 2
        ///     assert_eq!(interval.total_local_queue_depth, N);
        /// }
        /// ```
        ///
        /// ###### With `multi_thread` runtime
        /// The below example spawns 100 tasks and observes them in the
        /// local queue:
        /// ```
        /// #[tokio::main(flavor = "multi_thread", worker_threads = 2)]
        /// async fn main() {
        ///     use std::sync::mpsc;
        ///     use tokio::sync::oneshot;
        ///
        ///     const N: usize = 100;
        /// 
        ///     let handle = tokio::runtime::Handle::current();
        ///
        ///     // block one worker so the other is the only one running
        ///     let (block_tx, block_rx) = mpsc::channel::<()>();
        ///     let (started_tx, started_rx) = oneshot::channel();
        ///     tokio::spawn(async move {
        ///         let _ = started_tx.send(());
        ///         let _ = block_rx.recv();
        ///     });
        ///     let _ = started_rx.await;
        ///
        ///     // spawn + sample from the free worker thread
        ///     let (depth_tx, depth_rx) = oneshot::channel();
        ///     tokio::spawn(async move {
        ///         let monitor = tokio_metrics::RuntimeMonitor::new(&handle);
        ///         let mut intervals = monitor.intervals();
        ///         let _ = intervals.next().unwrap(); // baseline
        ///
        ///         for _ in 0..N {
        ///             tokio::spawn(async {});
        ///         }
        ///
        ///         let depth = intervals.next().unwrap().total_local_queue_depth;
        ///         let _ = depth_tx.send(depth);
        ///     });
        ///
        ///     let depth = depth_rx.await.unwrap();
        ///
        ///     // Tokio may place one spawned task in a LIFO slot rather than the
        ///     // local queue, which may not be reflected in `worker_local_queue_depth`,
        ///     // so accept N or N - 1.
        ///     assert!(depth == N || depth == N - 1, "depth = {depth}");
        ///
        ///     let _ = block_tx.send(());
        /// }
        /// ```
        pub total_local_queue_depth: usize,

        /// The maximum number of tasks currently scheduled any worker's local queue.
        ///
        /// ##### Definition
        /// This metric is derived from the maximum of
        /// [`tokio::runtime::RuntimeMetrics::worker_local_queue_depth`] across all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::total_local_queue_depth`]
        /// - [`RuntimeMetrics::min_local_queue_depth`]
        pub max_local_queue_depth: usize,

        /// The minimum number of tasks currently scheduled any worker's local queue.
        ///
        /// ##### Definition
        /// This metric is derived from the minimum of
        /// [`tokio::runtime::RuntimeMetrics::worker_local_queue_depth`] across all worker threads.
        ///
        /// ##### See also
        /// - [`RuntimeMetrics::total_local_queue_depth`]
        /// - [`RuntimeMetrics::max_local_queue_depth`]
        pub min_local_queue_depth: usize,

        /// The number of tasks currently waiting to be executed in the runtime's blocking threadpool.
        ///
        /// ##### Definition
        /// This metric is derived from [`tokio::runtime::RuntimeMetrics::blocking_queue_depth`].
        pub blocking_queue_depth: usize,

        /// The number of additional threads spawned by the runtime.
        ///
        /// ##### Definition
        /// This metric is derived from [`tokio::runtime::RuntimeMetrics::num_blocking_threads`].
        pub blocking_threads_count: usize,

        /// The number of idle threads, which have spawned by the runtime for `spawn_blocking` calls.
        ///
        /// ##### Definition
        /// This metric is derived from [`tokio::runtime::RuntimeMetrics::num_idle_blocking_threads`].
        pub idle_blocking_threads_count: usize,

        /// Returns the number of times that tasks have been forced to yield back to the scheduler after exhausting their task budgets.
        ///
        /// This count starts at zero when the runtime is created and increases by one each time a task yields due to exhausting its budget.
        ///
        /// The counter is monotonically increasing. It is never decremented or reset to zero.
        ///
        /// ##### Definition
        /// This metric is derived from [`tokio::runtime::RuntimeMetrics::budget_forced_yield_count`].
        pub budget_forced_yield_count: u64,

        /// Returns the number of ready events processed by the runtime’s I/O driver.
        ///
        /// ##### Definition
        /// This metric is derived from [`tokio::runtime::RuntimeMetrics::io_driver_ready_count`].
        pub io_driver_ready_count: u64,
    }
}

macro_rules! define_semi_stable {
    (
    $(#[$($attributes:tt)*])*
    $vis:vis struct $name:ident {
        stable {
            $($stable_name:ident: $stable_ty:ty),*
            $(,)?
        }
        $(,)?
        unstable {
            $($unstable_name:ident: $unstable_ty:ty),*
            $(,)?
        }
    }
    ) => {
        $(#[$($attributes)*])*
        $vis struct $name {
            $(
                $stable_name: $stable_ty,
            )*
            $(
                #[cfg(tokio_unstable)]
                #[cfg_attr(docsrs, doc(cfg(all(feature = "rt", tokio_unstable))))]
                $unstable_name: $unstable_ty,
            )*
        }
    };
}

define_semi_stable! {
    /// Snapshot of per-worker metrics
    #[derive(Debug, Default)]
    struct Worker {
        stable {
            worker: usize,
            total_park_count: u64,
            total_busy_duration: Duration,
        }
        unstable {
            total_noop_count: u64,
            total_steal_count: u64,
            total_steal_operations: u64,
            total_local_schedule_count: u64,
            total_overflow_count: u64,
            total_polls_count: u64,
            poll_time_histogram: Vec<u64>,
        }
    }
}

define_semi_stable! {
    /// Iterator returned by [`RuntimeMonitor::intervals`].
    ///
    /// See that method's documentation for more details.
    #[derive(Debug)]
    pub struct RuntimeIntervals {
        stable {
            runtime: runtime::RuntimeMetrics,
            started_at: Instant,
            workers: Vec<Worker>,
        }
        unstable {
            // Number of tasks scheduled from *outside* of the runtime
            num_remote_schedules: u64,
            budget_forced_yield_count: u64,
            io_driver_ready_count: u64,
            // Cached bucket ranges, static config that doesn't change after runtime creation.
            bucket_ranges: Vec<Range<Duration>>,
        }
    }
}

impl RuntimeIntervals {
    fn probe(&mut self) -> RuntimeMetrics {
        let now = Instant::now();

        let mut metrics = RuntimeMetrics {
            workers_count: self.runtime.num_workers(),
            live_tasks_count: self.runtime.num_alive_tasks(),
            elapsed: now.saturating_duration_since(self.started_at),
            global_queue_depth: self.runtime.global_queue_depth(),
            min_park_count: u64::MAX,
            min_busy_duration: Duration::from_secs(1000000000),
            ..Default::default()
        };

        #[cfg(tokio_unstable)]
        {
            let num_remote_schedules = self.runtime.remote_schedule_count();
            let budget_forced_yields = self.runtime.budget_forced_yield_count();
            let io_driver_ready_events = self.runtime.io_driver_ready_count();

            metrics.num_remote_schedules = num_remote_schedules.saturating_sub(self.num_remote_schedules);
            metrics.min_noop_count = u64::MAX;
            metrics.min_steal_count = u64::MAX;
            metrics.min_local_schedule_count = u64::MAX;
            metrics.min_overflow_count = u64::MAX;
            metrics.min_polls_count = u64::MAX;
            metrics.min_local_queue_depth = usize::MAX;
            metrics.mean_poll_duration_worker_min = Duration::MAX;
            metrics.poll_time_histogram = PollTimeHistogram::new(
                self.bucket_ranges
                    .iter()
                    .map(|range| HistogramBucket::new(range.start, range.end, 0))
                    .collect(),
            );
            metrics.budget_forced_yield_count =
                budget_forced_yields.saturating_sub(self.budget_forced_yield_count);
            metrics.io_driver_ready_count = io_driver_ready_events.saturating_sub(self.io_driver_ready_count);

            self.num_remote_schedules = num_remote_schedules;
            self.budget_forced_yield_count = budget_forced_yields;
            self.io_driver_ready_count = io_driver_ready_events;
        }
        self.started_at = now;

        for worker in &mut self.workers {
            worker.probe(&self.runtime, &mut metrics);
        }

        #[cfg(tokio_unstable)]
        {
            if metrics.total_polls_count == 0 {
                debug_assert_eq!(metrics.mean_poll_duration, Duration::default());

                metrics.mean_poll_duration_worker_max = Duration::default();
                metrics.mean_poll_duration_worker_min = Duration::default();
            }
        }

        metrics
    }
}

impl Iterator for RuntimeIntervals {
    type Item = RuntimeMetrics;

    fn next(&mut self) -> Option<RuntimeMetrics> {
        Some(self.probe())
    }
}

impl RuntimeMonitor {
    /// Creates a new [`RuntimeMonitor`].
    pub fn new(runtime: &runtime::Handle) -> RuntimeMonitor {
        let runtime = runtime.metrics();

        RuntimeMonitor { runtime }
    }

    /// Produces an unending iterator of [`RuntimeMetrics`].
    ///
    /// Each sampling interval is defined by the time elapsed between advancements of the iterator
    /// produced by [`RuntimeMonitor::intervals`]. The item type of this iterator is [`RuntimeMetrics`],
    /// which is a bundle of runtime metrics that describe *only* changes occurring within that sampling
    /// interval.
    ///
    /// # Example
    ///
    /// ```
    /// use std::time::Duration;
    ///
    /// #[tokio::main]
    /// async fn main() -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
    ///     let handle = tokio::runtime::Handle::current();
    ///     // construct the runtime metrics monitor
    ///     let runtime_monitor = tokio_metrics::RuntimeMonitor::new(&handle);
    ///
    ///     // print runtime metrics every 500ms
    ///     {
    ///         tokio::spawn(async move {
    ///             for interval in runtime_monitor.intervals() {
    ///                 // pretty-print the metric interval
    ///                 println!("{:?}", interval);
    ///                 // wait 500ms
    ///                 tokio::time::sleep(Duration::from_millis(500)).await;
    ///             }
    ///         });
    ///     }
    ///
    ///     // await some tasks
    ///     tokio::join![
    ///         do_work(),
    ///         do_work(),
    ///         do_work(),
    ///     ];
    ///
    ///     Ok(())
    /// }
    ///
    /// async fn do_work() {
    ///     for _ in 0..25 {
    ///         tokio::task::yield_now().await;
    ///         tokio::time::sleep(Duration::from_millis(100)).await;
    ///     }
    /// }
    /// ```
    pub fn intervals(&self) -> RuntimeIntervals {
        let started_at = Instant::now();

        let workers = (0..self.runtime.num_workers())
            .map(|worker| Worker::new(worker, &self.runtime))
            .collect();

        RuntimeIntervals {
            runtime: self.runtime.clone(),
            started_at,
            workers,

            #[cfg(tokio_unstable)]
            num_remote_schedules: self.runtime.remote_schedule_count(),
            #[cfg(tokio_unstable)]
            budget_forced_yield_count: self.runtime.budget_forced_yield_count(),
            #[cfg(tokio_unstable)]
            io_driver_ready_count: self.runtime.io_driver_ready_count(),
            #[cfg(tokio_unstable)]
            bucket_ranges: (0..self.runtime.poll_time_histogram_num_buckets())
                .map(|i| self.runtime.poll_time_histogram_bucket_range(i))
                .collect(),
        }
    }
}

impl Worker {
    fn new(worker: usize, rt: &runtime::RuntimeMetrics) -> Worker {
        #[allow(unused_mut, clippy::needless_update)]
        let mut wrk = Worker {
            worker,
            total_park_count: rt.worker_park_count(worker),
            total_busy_duration: rt.worker_total_busy_duration(worker),
            ..Default::default()
        };

        #[cfg(tokio_unstable)]
        {
            let poll_time_histogram = if rt.poll_time_histogram_enabled() {
                vec![0; rt.poll_time_histogram_num_buckets()]
            } else {
                vec![]
            };
            wrk.total_noop_count = rt.worker_noop_count(worker);
            wrk.total_steal_count = rt.worker_steal_count(worker);
            wrk.total_steal_operations = rt.worker_steal_operations(worker);
            wrk.total_local_schedule_count = rt.worker_local_schedule_count(worker);
            wrk.total_overflow_count = rt.worker_overflow_count(worker);
            wrk.total_polls_count = rt.worker_poll_count(worker);
            wrk.poll_time_histogram = poll_time_histogram;
        };
        wrk
    }

    fn probe(&mut self, rt: &runtime::RuntimeMetrics, metrics: &mut RuntimeMetrics) {
        macro_rules! metric {
            ( $sum:ident, $max:ident, $min:ident, $probe:ident ) => {{
                let val = rt.$probe(self.worker);
                let delta = val - self.$sum;
                self.$sum = val;

                metrics.$sum += delta;

                if delta > metrics.$max {
                    metrics.$max = delta;
                }

                if delta < metrics.$min {
                    metrics.$min = delta;
                }
            }};
        }

        metric!(
            total_park_count,
            max_park_count,
            min_park_count,
            worker_park_count
        );
        metric!(
            total_busy_duration,
            max_busy_duration,
            min_busy_duration,
            worker_total_busy_duration
        );

        #[cfg(tokio_unstable)]
        {
            let mut worker_polls_count = self.total_polls_count;
            let total_polls_count = metrics.total_polls_count;

            metric!(
                total_noop_count,
                max_noop_count,
                min_noop_count,
                worker_noop_count
            );
            metric!(
                total_steal_count,
                max_steal_count,
                min_steal_count,
                worker_steal_count
            );
            metric!(
                total_steal_operations,
                max_steal_operations,
                min_steal_operations,
                worker_steal_operations
            );
            metric!(
                total_local_schedule_count,
                max_local_schedule_count,
                min_local_schedule_count,
                worker_local_schedule_count
            );
            metric!(
                total_overflow_count,
                max_overflow_count,
                min_overflow_count,
                worker_overflow_count
            );
            metric!(
                total_polls_count,
                max_polls_count,
                min_polls_count,
                worker_poll_count
            );

            // Get the number of polls since last probe
            worker_polls_count = self.total_polls_count.saturating_sub(worker_polls_count);

            // Update the mean task poll duration if there were polls
            if worker_polls_count > 0 {
                let val = rt.worker_mean_poll_time(self.worker);

                if val > metrics.mean_poll_duration_worker_max {
                    metrics.mean_poll_duration_worker_max = val;
                }

                if val < metrics.mean_poll_duration_worker_min {
                    metrics.mean_poll_duration_worker_min = val;
                }

                // First, scale the current value down
                let ratio = total_polls_count as f64 / metrics.total_polls_count as f64;
                let mut mean = metrics.mean_poll_duration.as_nanos() as f64 * ratio;

                // Add the scaled current worker's mean poll duration
                let ratio = worker_polls_count as f64 / metrics.total_polls_count as f64;
                mean += val.as_nanos() as f64 * ratio;

                metrics.mean_poll_duration = Duration::from_nanos(mean as u64);
            }

            // Update the histogram counts if there were polls since last count
            if worker_polls_count > 0 {
                for (bucket, entry) in metrics.poll_time_histogram.buckets_mut().iter_mut().enumerate() {
                    let new = rt.poll_time_histogram_bucket_count(self.worker, bucket);
                    let delta = new.saturating_sub(self.poll_time_histogram[bucket]);
                    self.poll_time_histogram[bucket] = new;

                    entry.add_count(delta);
                }
            }

            // Local scheduled tasks is an absolute value
            let local_scheduled_tasks = rt.worker_local_queue_depth(self.worker);
            metrics.total_local_queue_depth = metrics.total_local_queue_depth.saturating_add(local_scheduled_tasks);

            if local_scheduled_tasks > metrics.max_local_queue_depth {
                metrics.max_local_queue_depth = local_scheduled_tasks;
            }

            if local_scheduled_tasks < metrics.min_local_queue_depth {
                metrics.min_local_queue_depth = local_scheduled_tasks;
            }

            // Blocking queue depth is an absolute value too
            metrics.blocking_queue_depth = rt.blocking_queue_depth();

            metrics.blocking_threads_count = rt.num_blocking_threads();
            metrics.idle_blocking_threads_count = rt.num_idle_blocking_threads();
        }
    }
}

derived_metrics!(
    [RuntimeMetrics] {
        stable {
            /// Returns the ratio of the [`RuntimeMetrics::total_busy_duration`] to the [`RuntimeMetrics::elapsed`].
            pub fn busy_ratio(&self) -> f64 {
                self.total_busy_duration.as_nanos() as f64 / self.elapsed.as_nanos() as f64
            }
        }
        unstable {
            /// Returns the ratio of the [`RuntimeMetrics::total_polls_count`] to the [`RuntimeMetrics::total_noop_count`].
            pub fn mean_polls_per_park(&self) -> f64 {
                let total_park_count = self.total_park_count.saturating_sub(self.total_noop_count);
                if total_park_count == 0 {
                    0.0
                } else {
                    self.total_polls_count as f64 / total_park_count as f64
                }
            }
        }
    }
);

#[cfg(all(test, tokio_unstable, feature = "metrique-integration"))]
mod metrique_integration_tests {
    use super::*;
    use metrique::test_util::test_metric;

    /// Compile-time regression: if a field is added whose type doesn't
    /// implement `CloseValue`, this will fail to compile.
    #[test]
    fn metrique_integration_produces_expected_fields() {
        let metrics = RuntimeMetrics {
            workers_count: 4,
            total_park_count: 100,
            poll_time_histogram: PollTimeHistogram::new(vec![
                HistogramBucket::new(Duration::from_micros(0), Duration::from_micros(100), 10),
                HistogramBucket::new(Duration::from_micros(100), Duration::from_micros(200), 0),
                HistogramBucket::new(Duration::from_micros(200), Duration::from_micros(500), 3),
            ]),
            ..Default::default()
        };

        let entry = test_metric(metrics);

        // Stable fields
        assert_eq!(entry.metrics["workers_count"], 4);
        assert_eq!(entry.metrics["total_park_count"], 100);
        assert_eq!(entry.metrics["elapsed"].as_f64(), 0.0);
        assert_eq!(entry.metrics["total_busy_duration"].as_f64(), 0.0);
        assert_eq!(entry.metrics["global_queue_depth"].as_u64(), 0);

        // Unstable fields
        assert_eq!(entry.metrics["mean_poll_duration"].as_f64(), 0.0);
        assert_eq!(entry.metrics["total_steal_count"].as_u64(), 0);
        assert_eq!(entry.metrics["total_polls_count"].as_u64(), 0);

        // 2 non-zero buckets (count 10 and 3) should produce 2 observations
        let hist = &entry.metrics["poll_time_histogram"];
        assert_eq!(hist.distribution.len(), 2, "expected 2 non-zero buckets");

        // midpoint of 0..100µs = 50µs, count = 10
        match hist.distribution[0] {
            metrique::writer::Observation::Repeated { total, occurrences } => {
                assert_eq!(occurrences, 10);
                assert!((total - 500.0).abs() < 0.01, "expected 50 * 10 = 500, got {total}");
            }
            other => panic!("expected Repeated, got {other:?}"),
        }

        // midpoint of 200..500µs = 350µs, count = 3
        match hist.distribution[1] {
            metrique::writer::Observation::Repeated { total, occurrences } => {
                assert_eq!(occurrences, 3);
                assert!((total - 1050.0).abs() < 0.01, "expected 350 * 3 = 1050, got {total}");
            }
            other => panic!("expected Repeated, got {other:?}"),
        }
    }

    /// Collect `RuntimeMetrics` from a live Tokio runtime and verify the pipeline produces valid output.
    #[cfg(feature = "rt")]
    #[test]
    fn metrique_end_to_end() {
        let rt = tokio::runtime::Builder::new_current_thread()
            .enable_all()
            .enable_metrics_poll_time_histogram()
            .build()
            .unwrap();

        rt.block_on(async {
            let handle = tokio::runtime::Handle::current();
            let monitor = RuntimeMonitor::new(&handle);
            let mut intervals = monitor.intervals();

            let _ = intervals.next().unwrap();

            // Spawn tasks to create some work for the runtime to poll.
            let mut metrics_with_polls = None;
            for _ in 0..4 {
                for _ in 0..25 {
                    tokio::spawn(async {
                        tokio::task::yield_now().await;
                    })
                    .await
                    .unwrap();
                }
                // Slow poll (>900µs) to land in the last histogram bucket.
                tokio::spawn(async {
                    std::thread::sleep(Duration::from_millis(1));
                })
                .await
                .unwrap();

                let metrics = intervals.next().unwrap();
                let total_polls: u64 = metrics.poll_time_histogram.buckets().iter().map(|b| b.count()).sum();
                if total_polls > 0 {
                    metrics_with_polls = Some(metrics);
                    break;
                }
            }
            let metrics = metrics_with_polls.expect("expected polls to be recorded within 4 sampled intervals");

            let expected_workers_count = metrics.workers_count;
            let expected_non_zero_buckets = metrics
                .poll_time_histogram
                .buckets()
                .iter()
                .filter(|b| b.count() > 0)
                .count();

            let expected_total_polls: u64 = metrics.poll_time_histogram.buckets().iter().map(|b| b.count()).sum();
            assert!(expected_workers_count > 0);
            assert!(expected_total_polls > 0);

            let last_bucket = metrics.poll_time_histogram.buckets().last().unwrap();

            // Sanity check: Tokio's last histogram bucket ends at Duration::from_nanos(u64::MAX)
            assert_eq!(last_bucket.range_end(), Duration::from_nanos(u64::MAX));
            assert!(last_bucket.count() > 0, "expected slow poll to land in last bucket");
            let last_bucket_start_us = last_bucket.range_start().as_micros() as f64;
            let last_bucket_count = last_bucket.count();

            let entry = test_metric(metrics);

            assert_eq!(entry.metrics["workers_count"], expected_workers_count as u64);
            assert!(entry.metrics["elapsed"].as_f64() >= 0.0);
            assert!(entry.metrics["total_busy_duration"].as_f64() >= 0.0);

            let hist = &entry.metrics["poll_time_histogram"];
            assert_eq!(hist.distribution.len(), expected_non_zero_buckets);
            let observed_total_occurrences: u64 = hist
                .distribution
                .iter()
                .map(|obs| match obs {
                    metrique::writer::Observation::Repeated { occurrences, .. } => *occurrences,
                    other => panic!("expected Repeated, got {other:?}"),
                })
                .sum();
            assert_eq!(observed_total_occurrences, expected_total_polls);

            // The last observation corresponds to the last histogram bucket.
            // Verify it uses range_start as the representative value instead of a midpoint,
            // since the last bucket range_end is Duration::from_nanos(u64::MAX).
            let last_obs = hist.distribution.last().unwrap();
            match last_obs {
                metrique::writer::Observation::Repeated { total, occurrences } => {
                    assert_eq!(*occurrences, last_bucket_count);
                    let expected_total = last_bucket_start_us * last_bucket_count as f64;
                    assert!(
                        (total - expected_total).abs() < 0.01,
                        "last bucket should use range_start ({last_bucket_start_us}µs) as representative value, \
                         expected total={expected_total}, got {total}"
                    );
                }
                other => panic!("expected Repeated, got {other:?}"),
            }
        });
    }
}