Troubleshooting and FAQ
Purpose and Scope
This page collects troubleshooting patterns that are useful when working with Rust developer tools in this repository. The supplied source evidence comes from small bench-cargo-miri programs under src/tools/miri, so the source-backed guidance here focuses on how maintainers turn confusing behavior into runnable reproductions, regressions, and performance signals. For a rust-analyzer user or editor integrator, the same discipline is useful before filing an issue: isolate the workload, decide whether the problem is diagnostics, performance, concurrency, project loading, or output noise, and preserve enough context that another maintainer can run the case locally.
A “troubleshooting signal” is an observable symptom that can be reduced into code. In these files, the signals include stack-frame resolution, very large allocation behavior, allocation-id garbage-collection pressure, large static data, JSON deserialization workloads, and two-thread execution. They are not UI settings or language-server protocol options; instead, they show how this repository captures subtle runtime and performance questions as executable programs. That matters for any Rust tool because a good FAQ answer usually depends on a reproducible case rather than a prose-only description. Sources: src/tools/miri/bench-cargo-miri/backtraces/src/main.rs, src/tools/miri/bench-cargo-miri/big-allocs/src/main.rs, src/tools/miri/bench-cargo-miri/range-iteration/src/main.rs
Relevant Source Files
src/tools/miri/bench-cargo-miri/backtraces/src/main.rs— exercises backtrace frame collection, conversion, resolution, and formatting without emitting benchmark output to stdout or stderr.src/tools/miri/bench-cargo-miri/big-allocs/src/main.rs— records a regression-style benchmark for large zeroed allocations and documents why the loop count and allocation size are chosen for CI practicality.src/tools/miri/bench-cargo-miri/mse/src/main.rs— supplies a large static byte expectation array, representing the kind of fixed data set that can make a reproduction deterministic.src/tools/miri/bench-cargo-miri/range-iteration/src/main.rs— creates many simple iterations specifically to generate work for allocation-id garbage collection.src/tools/miri/bench-cargo-miri/serde1/src/main.rs— embeds a large JSON string for a serialization workload benchmark.src/tools/miri/bench-cargo-miri/serde2/src/main.rs— mirrors the serialization workload in a concurrent, two-thread form and explicitly avoids printing.
Troubleshooting Signals
The backtrace benchmark is a useful model for reports involving stack traces, symbol lookup, or noisy diagnostic output. It calls backtrace::trace, converts captured frames into BacktraceFrame values, builds a Backtrace, resolves it, and formats fields such as instruction pointer, symbol address, module base address, and symbols into a String. The comment explains that the benchmark intentionally avoids printing even though it still wants to exercise interesting code paths. For tool troubleshooting, this is the difference between “I saw output in my editor” and “this is the minimal code path that triggers symbol resolution while keeping logs quiet.” Sources: src/tools/miri/bench-cargo-miri/backtraces/src/main.rs
The large-allocation benchmark demonstrates how a regression note can encode both the bug being guarded and the environmental constraint that shaped the test. Its comment names a Miri issue, explains that allocations are backed by Box<[u8]>, and states the expected performance property: alloc_zeroed should make very large allocations cheap, but cloning allocations would still be slow. The program then repeats Vec::<u8>::with_capacity(512 * 1024 * 1024) twenty times and immediately drops the vector. This pattern is valuable for FAQ answers about slowness because it separates the workload, the suspected mechanism, and the CI-safe bound. Sources: src/tools/miri/bench-cargo-miri/big-allocs/src/main.rs
The range-iteration benchmark shows the opposite kind of reduction: the code is intentionally tiny, but the loop count creates enough internal work to make one subsystem visible. Its file-level comment says it generates work for the AllocId part of garbage collection, and the body simply iterates from 0 to 50_000. When a user reports that a tool feels slow only after a project has been open for a while, this kind of reproduction suggests a diagnostic question: can the behavior be triggered by many small operations rather than one complex program? The answer guides whether to investigate caching, garbage collection, indexing, or repeated request handling. Sources: src/tools/miri/bench-cargo-miri/range-iteration/src/main.rs
FAQ Patterns for Users and Integrators
A practical FAQ entry should start by classifying the symptom. If the issue is about stack traces or symbol information, reduce it toward the backtrace-style program: collect frames, resolve them, and keep output controlled. If the issue is about memory pressure or slow startup, reduce it toward the large-allocation pattern: explain the expected fast path, pick a bound that will not fail in CI, and repeat the operation enough times to rise above startup overhead. If the issue is about repeated background work, the range-iteration pattern is a good reminder that a tiny loop can be a better reproduction than a large project snapshot.
Serialization and data-shape problems need a different style of evidence. The serde1 benchmark embeds a very large JSON string directly in the program, while serde2 uses the same kind of workload but documents that it runs in two threads and does not print. For troubleshooting, this distinction matters because it separates three variables that are often conflated: the input payload, the parser or serializer workload, and concurrency. A language-server issue that appears only with generated code, macro-expanded data, or large configuration files should be reduced in the same way: keep the input fixed, then vary whether the workload is single-threaded or concurrent. Sources: src/tools/miri/bench-cargo-miri/serde1/src/main.rs, src/tools/miri/bench-cargo-miri/serde2/src/main.rs
Use this compact checklist when preparing a report: identify whether the problem is output noise, memory use, repeated background work, fixed large input, or concurrency; create a runnable program or project that isolates one of those categories; include comments explaining why sizes, loop counts, or thread counts were chosen; and avoid unnecessary printing when the benchmark is meant to measure execution rather than terminal I/O. The official Rust documentation style also favors direct, question-shaped guidance and runnable examples, so a good issue report should read like an answerable FAQ question backed by a small reproduction.
Privacy and Security Considerations
Privacy and security troubleshooting starts by minimizing what a reproduction contains. The supplied benchmark files are useful examples because their interesting inputs are synthetic or embedded workload data, not user-specific projects, credentials, editor logs, or private source trees. The backtrace benchmark formats technical addresses and symbols into an in-memory string, then drops that string, and the serde concurrent benchmark explicitly avoids printing. Those choices are not a complete privacy policy, but they do show a repository practice that is relevant to tool reports: exercise the code path without emitting more data than the test needs. Sources: src/tools/miri/bench-cargo-miri/backtraces/src/main.rs, src/tools/miri/bench-cargo-miri/serde2/src/main.rs
For rust-analyzer users and integrators, the source-backed lesson is to sanitize before sharing. Replace proprietary modules with small public examples, reduce large data to synthetic constants when possible, and describe the editor or build-system action separately from private project contents. If a report requires paths, environment variables, or logs, review them before posting. If it requires concurrent behavior, prefer a small two-thread reproduction over a full workspace archive. This keeps the report actionable while reducing accidental disclosure of local file names, source code, or organization-specific configuration.
Testing Signals and Next Steps
These files are also a reminder that troubleshooting is part of testing, not just support. Each program is small enough to serve as a focused benchmark and descriptive enough to explain the intended signal. Comments capture why a workload exists, why output is suppressed, why allocation sizes are bounded, and why a loop count was chosen. When investigating a Rust tooling issue, aim for the same shape: executable evidence first, explanation next, and only then broader diagnosis. That order helps maintainers decide whether the issue belongs in a language server, compiler, interpreter, library, build system, or editor integration layer.
Next, read the rust-analyzer overview and configuration pages for the public editor-facing surface, then use the patterns here when turning a troubleshooting report into a minimal reproduction. If the problem is Cargo-related, consult the Cargo FAQ for ecosystem-level package and registry questions. If the problem is best explained by runnable code, Rust By Example is a useful model for reducing concepts into small examples. For repository work, keep reproductions deterministic, quiet by default, and explicit about the subsystem they are intended to exercise.