hotpath - find and profile bottlenecks in Rust

A lightweight, easy-to-configure Rust profiler that shows exactly where your code spends time and allocates memory. Instrument any function or code block to quickly spot bottlenecks, and focus your optimizations where they matter most.

Features

• Zero-cost when disabled — fully gated by a feature flag.
• Low-overhead profiling for both sync and async code.
• Memory allocation tracking — track bytes allocated or allocation counts per function.
• Detailed stats: avg, total time, call count, % of total runtime, and configurable percentiles (p95, p99, etc.).
• Background processing for minimal profiling impact.

Quick Start

Add to your Cargo.toml:

[dependencies]
hotpath = { version = "0.2", optional = true }

[features]
hotpath = ["dep:hotpath", "hotpath/hotpath"]
hotpath-alloc-bytes-total = ["hotpath/hotpath-alloc-bytes-total"]
hotpath-alloc-bytes-max = ["hotpath/hotpath-alloc-bytes-max"]
hotpath-alloc-count-total= ["hotpath/hotpath-alloc-count-total"]
hotpath-alloc-count-max= ["hotpath/hotpath-alloc-count-max"]

This config ensures that the lib has zero overhead unless explicitly enabled via a hotpath feature.

Usage

use std::time::Duration;

#[cfg_attr(feature = "hotpath", hotpath::measure)]
fn sync_function(sleep: u64) {
    std::thread::sleep(Duration::from_nanos(sleep));
}

#[cfg_attr(feature = "hotpath", hotpath::measure)]
async fn async_function(sleep: u64) {
    tokio::time::sleep(Duration::from_nanos(sleep)).await;
}

// When using with tokio, place the #[tokio::main] first
#[tokio::main]
// You can configure any percentile between 0 and 100
#[cfg_attr(feature = "hotpath", hotpath::main(percentiles = [99]))]
async fn main() {
    for i in 0..100 {
        // Measured functions will automatically send metrics
        sync_function(i);
        async_function(i * 2).await;

        // Measure code blocks with static labels
        #[cfg(feature = "hotpath")]
        hotpath::measure_block!("custom_block", {
            std::thread::sleep(Duration::from_nanos(i * 3))
        });
    }
}

Run your program with a hotpath feature:

cargo run --features=hotpath

Output:

[hotpath] Performance summary from basic::main (Total time: 122.13ms):
+-----------------------+-------+---------+---------+----------+---------+
| Function              | Calls | Avg     | P99     | Total    | % Total |
+-----------------------+-------+---------+---------+----------+---------+
| basic::async_function | 100   | 1.16ms  | 1.20ms  | 116.03ms | 95.01%  |
+-----------------------+-------+---------+---------+----------+---------+
| custom_block          | 100   | 17.09µs | 39.55µs | 1.71ms   | 1.40%   |
+-----------------------+-------+---------+---------+----------+---------+
| basic::sync_function  | 100   | 16.99µs | 35.42µs | 1.70ms   | 1.39%   |
+-----------------------+-------+---------+---------+----------+---------+

Allocation Tracking

In addition to time-based profiling, hotpath can track memory allocations. This feature uses a custom global allocator from allocation-counter crate to intercept all memory allocations and provides detailed statistics about memory usage per function.

Available alloc profiling modes:

• hotpath-alloc-bytes-total - Tracks total bytes allocated during each function call
• hotpath-alloc-bytes-max - Tracks peak memory usage during each function call
• hotpath-alloc-count-total - Tracks total number of allocations per function call
• hotpath-alloc-count-max - Tracks peak number of live allocations per function call

Run your program with a selected flag to print a similar report:

cargo run --features='hotpath,hotpath-alloc-bytes-max'

Profiling memory allocations for async functions

To profile memory usage of async functions you have to use a similar config:

#[cfg(any(
    feature = "hotpath-alloc-bytes-total",
    feature = "hotpath-alloc-bytes-max",
    feature = "hotpath-alloc-count-total",
    feature = "hotpath-alloc-count-max",
))]
#[tokio::main(flavor = "current_thread")]
async fn main() {
    _ = inner_main().await;
}

#[cfg(not(any(
    feature = "hotpath-alloc-bytes-total",
    feature = "hotpath-alloc-bytes-max",
    feature = "hotpath-alloc-count-total",
    feature = "hotpath-alloc-count-max",
)))]
#[tokio::main]
async fn main() {
    _ = inner_main().await;
}

#[cfg_attr(feature = "hotpath", hotpath::main)]
async fn inner_main() {
    // ...
}

It ensures that tokio runs in a current_thread runtime mode if any of the allocation profiling flags is enabled.

Why this limitation exists: The allocation tracking uses thread-local storage to track memory usage. In multi-threaded runtimes, async tasks can migrate between threads, making it impossible to accurately attribute allocations to specific function calls.

How It Works

1. #[cfg_attr(feature = "hotpath", hotpath::main)] - Macro that initializes the background measurement processing
2. #[cfg_attr(feature = "hotpath", hotpath::measure)] - Macro that wraps functions with profiling code
3. Background thread - Measurements are sent to a dedicated worker thread via bounded channel
4. Statistics aggregation - Worker thread maintains running statistics for each function/code block
5. Automatic reporting - Performance summary displayed when the program exits

API

#[cfg_attr(feature = "hotpath", hotpath::main]

Attribute macro that initializes the background measurement processing when applied.

#[cfg_attr(feature = "hotpath", hotpath::measure)]

An opt-in attribute macro that instruments functions to send timing measurements to the background processor.

hotpath::measure_block!(label, expr)

Macro that measures the execution time of a code block with a static string label.

Using hotpath::main macro vs hotpath::init guard

The #[hotpath::main] macro is convenient for most use cases, but hotpath::init() provides more control over when profiling starts and stops.

Key differences:

• #[hotpath::main] - Automatic initialization and cleanup, report printed at program exit
• let _guard = hotpath::init() - Manual control, report printed when guard is dropped, so you can fine-tune the measured scope.

Only one hotpath guard may be alive at a time, regardless of whether it was created by the main macro or by init(). If a second guard is created, the library will panic.

Using hotpath::init() for more control

use std::time::Duration;

#[cfg_attr(feature = "hotpath", hotpath::measure)]
fn example_function() {
    std::thread::sleep(Duration::from_millis(10));
}

fn main() {
    #[cfg(feature = "hotpath")]
    let _guard = hotpath::init(
        "my_program".to_string(),
        &[50, 95, 99],
        hotpath::Format::Table
    );

    example_function();

    // This will print the report.
    #[cfg(feature = "hotpath")]
    drop(_hotpath);

    // Immediate exit (no drops); `#[hotpath::main]` wouldn't print.
    std::process::exit(1);
}

Using in unit tests

In unit tests you can profile each individual test case:

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_sync_function() {
        #[cfg(feature = "hotpath")]
        let _hotpath = hotpath::init(
            "test_sync_function".to_string(),
            &[50, 90, 95],
            hotpath::Format::Table,
        );
        sync_function();
    }

    #[tokio::test(flavor = "current_thread")]
    async fn test_async_function() {
        #[cfg(feature = "hotpath")]
        let _hotpath = hotpath::init(
            "test_async_function".to_string(),
            &[50, 90, 95],
            hotpath::Format::Table,
        );

        async_function().await;
    }
}

Run tests with profiling enabled:

cargo test --features hotpath -- --test-threads=1

Note: Use --test-threads=1 to ensure tests run sequentially, as only one hotpath guard can be active at a time.

Percentiles Support

By default, hotpath displays P95 percentile in the performance summary. You can customize which percentiles to display using the percentiles parameter:

#[tokio::main]
#[cfg_attr(feature = "hotpath", hotpath::main(percentiles = [50, 75, 90, 95, 99]))]
async fn main() {
    // Your code here
}

For multiple measurements of the same function or code block, percentiles help identify performance distribution patterns. You can use percentile 0 to display min value and 100 to display max.

Output Formats

By default, hotpath displays results in a human-readable table format. You can also output results in JSON format for programmatic processing:

#[tokio::main]
#[cfg_attr(feature = "hotpath", hotpath::main(format = "json-pretty"))]
async fn main() {
    // Your code here
}

Supported format options:

• "table" (default) - Human-readable table format
• "json" - Compact, oneline JSON format
• "json-pretty" - Pretty-printed JSON format

Example JSON output:

{
  "hotpath_profiling_mode": "timing",
  "output": {
    "basic::async_function": {
      "calls": "100",
      "avg": "1.16ms",
      "p95": "1.26ms",
      "total": "116.41ms",
      "percent_total": "96.18%"
    },
    "basic::sync_function": {
      "calls": "100",
      "avg": "23.10µs",
      "p95": "37.89µs",
      "total": "2.31ms",
      "percent_total": "1.87%"
    }
  }
}

You can combine both percentiles and format parameters:

#[cfg_attr(feature = "hotpath", hotpath::main(percentiles = [50, 90, 99], format = "json"))]

hotpath-off feature

Profiling features are mutually exclusive. To make the lib work with --all-features config, there's an additional hotpath-off flag, which disables profiling.

Benchmarking

Measure overhead of profiling 100k method calls with hyperfine:

Timing:

cargo build --example benchmark --features hotpath --release
hyperfine --warmup 3 './target/release/examples/benchmark'

Allocations:

cargo build --example benchmark --features='hotpath,hotpath-alloc-count-max' --release
hyperfine --warmup 3 './target/release/examples/benchmark'