#memory #memory-leaks #alloc #segmentation #leak #trace


Track memory allocations by backtrace or originating thread

9 releases

0.3.0 Oct 10, 2023
0.2.3 Jan 3, 2023
0.2.2 Dec 30, 2022
0.1.3 Dec 30, 2022

#193 in Memory management

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513 lines


This project allows per-thread and per-backtrace realtime memory profiling.

Use Cases

  • Diagnosing memory fragmentation (in the form of volatile allocations)
  • Diagnosing memory leaks
  • Profiling memory consumption of individual components


  1. Add the following dependency to your project: alloc-track = "0.2.3"

  2. Set a global allocator wrapped by alloc_track::AllocTrack

    Default rust allocator:

    use alloc_track::{AllocTrack, BacktraceMode};
    use std::alloc::System;
    static GLOBAL_ALLOC: AllocTrack<System> = AllocTrack::new(System, BacktraceMode::Short);

    Jemallocator allocator:

    use alloc_track::{AllocTrack, BacktraceMode};
    use jemallocator::Jemalloc;
    static GLOBAL_ALLOC: AllocTrack<Jemalloc> = AllocTrack::new(Jemalloc, BacktraceMode::Short);
  3. Call alloc_track::thread_report() or alloc_track::backtrace_report() to generate a report. Note that backtrace_report requires the backtrace feature and the BacktraceMode::Short or BacktraceMode::Full flag to be passed to AllocTrack::new.


In BacktraceMode::None or without the backtrace feature enabled, the thread memory profiling is reasonably performant. It is not something you would want to run in a production environment though, so feature-gating is a good idea.

When backtrace logging is enabled, the performance will degrade substantially depending on the number of allocations and stack depth. Symbol resolution is delaying, but a lot of allocations means a lot of backtraces. backtrace_report takes a single argument, which is a filter for individual backtrace records. Filtering out uninteresting backtraces is both easier to read, and substantially faster to generate a report as symbol resolution can be skipped. See examples/example.rs for an example.

Real World Example

At LeakSignal, we had extreme memory segmentation in a high-bandwidth/high-concurrency gRPC service. We suspected a known hyper issue with high concurrency, but needed to confirm the cause and fix the issue ASAP. Existing tooling (bpftrace, valgrind) wasn't able to give us a concrete cause. I had created a prototype of this project back in 2019 or so, and it's time had come to shine. In a staging environment, I added an HTTP endpoint to generate a thread and backtrace report. I was able to identify a location where a large multi-allocation object was being cloned and dropped very often. A quick fix there solved our memory segmentation issue.


~760K SLoC