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JIT deserialization overview (facet-format)

jit-overview

Orientation to facet-format JIT deserialization (tiering, fallbacks, key types/entry points) and where to look when changing or debugging JIT code

SKILL.md

Full skill instructions

JIT deserialization overview (facet-format)

Facet’s JIT lives in facet-format and is used by format crates like facet-json.

When to read this

  • You’re touching anything under facet-format/​src/​jit/ or enabling the jit feature.
  • You’re investigating performance changes in deserialization (especially “tier2”/​JIT benchmarks).
  • You’re debugging a JIT crash (SIGSEGV/​UB-ish symptoms).

Mental model

facet-format defines a format-neutral deserialization pipeline:

  • A FormatParser (implemented by each format crate) produces a stream of ParseEvents.
  • A shape-driven layer consumes those events and writes to an output value (often via facet-reflect).

The JIT accelerates this by compiling deserialization code specialized for:

  1. the target type (T / its Shape), and sometimes
  2. the format parser (P).

Two-tier architecture (high level)

Tier 1 (shape JIT)

  • Compiles code that consumes ParseEvents and writes directly into the output’s memory at known offsets.
  • Works with any format that implements FormatParser (JSON/​YAML/​TOML/…).

Tier 2 (format JIT)

  • For the “entire input slice is available” case, a format crate can provide a FormatJitParser + JitFormat implementation.
  • Tier 2 emits Cranelift IR to parse bytes directly, bypassing the ParseEvent stream for maximum throughput.

Fallbacks are part of the design

  • Tier 2 may return “unsupported” for shapes/​input it can’t handle, and must be side-effect-free in that case.
  • Callers typically try tier 2, then tier 1, then reflection.

Entry points & where to look

  • Main docs and contracts: facet-format/​src/​jit/​mod.rs
  • JIT-enabled parser trait: facet-format/​src/​parser.rs (FormatJitParser)
  • JIT usage in a format crate:
    • facet-json/​Cargo.toml feature jit = ["facet-format/​jit"]
    • Example: facet-json/​examples/​profile_jit_vec (requires jit)
  • Windows crash debugging notes: .claude/​skills/​windbg-jit.md
  • Memory debugging: .claude/​skills/​debug-with-valgrind/​SKILL.md (uses nextest profiles)

Debugging checklist (practical)

  1. Reproduce with a minimal type + input (see .claude/​skills/​reproduce-reduce-regress/​SKILL.md).
  2. Run the failing test under:
    • valgrind: cargo nextest run --profile valgrind … (configured in .config/​nextest.toml)
    • or Miri when applicable (just miri) for UB/​provenance issues outside the JIT itself.
  3. If the crash is in JIT codegen/​execution:
    • Prefer isolating the smallest shape that triggers tier selection and failure.
    • Look for tier selection diagnostics and caching behavior in facet-format/​src/​jit/​mod.rs.

Common pitfalls

  • Assuming tier 2 supports “all shapes”: it intentionally supports a performance-focused subset.
  • Forgetting that “unsupported” must not advance the parser cursor or partially initialize output.
  • Introducing new unsafe paths without tests that exercise drop/​cleanup on error paths.

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