# rmarc Rust Speedup Code Review and Architecture Tour

This document is for contributors who already know Python, know very little Rust, and want to answer four practical
questions:

1. What code is inherited from `pymarc`, and what code is new in `rmarc`?
2. How does a MARC record move through the Python and Rust layers?
3. Why is the Rust-backed path faster?
4. What should we trust already, and what should we still review carefully?

The short version is:

- `rmarc` keeps the public `pymarc` object model in Python: `Record`, `Field`, `RawField`, `MARCReader`, writers, JSON,
  XML, and convenience properties.
- `rmarc` moves the hot byte-crunching work into Rust:
    - binary MARC decode
    - binary MARC encode
    - MARC-8 to Unicode conversion
- Python still owns the user-facing API, error classes, and most object construction.
- Rust is used as an implementation detail, not as a new public API model.

That design choice matters. It keeps compatibility high, reduces PyO3 complexity, and explains both the speedups and the
remaining bottlenecks.

## Repository map

The parts that matter most for the speedup work are:

```text
python/rmarc/
├── __init__.py
├── reader.py
├── record.py
├── field.py
├── marc8.py
├── writer.py
└── _rmarc.pyi

src/
├── lib.rs
├── marc_codec.rs
├── marc8.rs
└── marc8_mapping.rs

bench/
├── bench_core.py
└── generate_data.py

spec/
├── phase3_rustification.md
├── phase4_performance.md
└── benchmarks.md
```

Use this mental model:

- `python/rmarc` is the compatibility layer and public surface area.
- `src/` is the acceleration engine.
- `bench/` tells you what the author considered performance-critical.
- `spec/` tells you the intended migration strategy and how closely the current code matches that plan.

## Big-picture architecture

The core idea is not "rewrite pymarc in Rust."

The real idea is:

1. Preserve the `pymarc` Python API.
2. Identify the small number of loops that do most of the CPU work.
3. Move only those loops into Rust.
4. Return simple Python-friendly structures.
5. Let Python wrap those structures back into familiar `Record` and `Field` objects.

That split is visible in [`python/rmarc/record.py`](../python/rmarc/record.py) and [
`src/marc_codec.rs`](../src/marc_codec.rs).

The most important boundary looks like this:

```text
MARC bytes
  -> Python Record.decode_marc()
  -> Rust decode_marc_raw()
  -> Python wraps returned tuples/lists into Field / RawField / Subfield objects
  -> user gets a normal Record
```

And the write path is the mirror image:

```text
Python Record / Field objects
  -> Python asks each field for bytes
  -> Rust encode_marc_raw() assembles directory + leader + record bytes
  -> user gets MARC transmission bytes
```

## What is still basically pymarc

Large parts of `rmarc` are very close to `pymarc`, sometimes almost line-for-line.

That is not a criticism. For this project, it is a strategy.

The following modules are mostly compatibility-preserving ports:

- `python/rmarc/reader.py`
- `python/rmarc/record.py`
- `python/rmarc/field.py`
- `python/rmarc/marc8.py`
- `python/rmarc/writer.py`
- `python/rmarc/marcjson.py`
- `python/rmarc/marcxml.py`

This matters for trust:

- it lowers behavioral risk, because most semantics are inherited rather than re-invented
- it makes diffs against upstream `pymarc` meaningful
- it also means many existing bugs or oddities may be preserved intentionally for compatibility

If you want to review `rmarc`, one very effective method is "compare to upstream, then focus on the inserted Rust fast
path."

## The Python side, module by module

## `python/rmarc/__init__.py`

This is almost pure re-export glue. It makes `rmarc` feel like `pymarc`.

One notable line is:

```python
from rmarc._rmarc import MarcRecord, version
```

`MarcRecord` is still a stub class from the early Rust scaffolding. It is exported, tested by a smoke test, but not part
of the main decoding pipeline.

That tells us something historically useful:

- the Rust extension started as a proof that PyO3 packaging worked
- the serious work later moved into standalone functions instead of a full Rust object model

## `python/rmarc/reader.py`

`MARCReader` is still fully Python.

Its job is intentionally small:

1. read 5 bytes
2. parse record length
3. read the remaining record bytes
4. validate that the record is complete and ends with `END_OF_RECORD`
5. call `Record(...)`

The important point is this:

- `MARCReader` is not fast because it is clever
- `MARCReader` is fast enough because the expensive part happens after it calls `Record(chunk, ...)`

This file is still very close to `pymarc`, including permissive behavior:

- invalid records become `None`
- the actual exception is stored on `reader.current_exception`
- the bad bytes are stored on `reader.current_chunk`

That behavior is heavily tested and is part of the compatibility contract.

## `python/rmarc/field.py`

This file defines the Python object model contributors will spend most of their time with:

- `Subfield`: a `NamedTuple(code, value)`
- `Indicators`: a `NamedTuple(first, second)`
- `Field`
- `RawField`

Important design choices:

- control fields are tags below `"010"` and store `data`
- data fields store indicators plus a list of subfields
- `RawField` preserves byte strings instead of decoded text

Why this file matters for performance:

- even after Rust parsing, Python still constructs these objects
- `phase4_performance.md` correctly identifies `Field.__init__()` and `Subfield` creation as a major remaining cost

Notice the optimization already applied in `Record._decode_marc_rust()`:

- it bypasses `Field.__init__()`
- it uses `FieldClass.__new__(FieldClass)`
- then assigns attributes directly

That is a classic "keep the Python API, skip the expensive constructor path" optimization.

## `python/rmarc/record.py`

This is the most important Python file in the project.

It does three jobs:

1. define the `Record` API
2. decide whether to use Rust or pure Python
3. translate Rust return values back into Python objects

The key switch is here conceptually:

```python
if _HAS_RUST_CODEC:
    self._decode_marc_rust(...)
else:
    self._decode_marc_python(...)
```

That fallback structure is excellent for maintainability:

- the pure Python implementation remains a readable reference implementation
- Rust can be benchmarked against a known-good baseline
- unsupported build environments still work

This file is also where trust questions should be concentrated, because it contains the semantic bridge between Python
and Rust.

We will come back to that in the code review section.

## `python/rmarc/marc8.py`

This file follows the same pattern as `record.py`:

- try Rust first
- fall back to the original Python implementation otherwise

The public function is:

```python
marc8_to_unicode(marc8, hide_utf8_warnings=False)
```

The pure Python implementation is `MARC8ToUnicode.translate()`, a state machine that:

- tracks G0 and G1 character sets
- interprets escape sequences
- handles multi-byte CJK mode
- buffers combining characters
- normalizes the final string to NFC

This is exactly the kind of code that Rust usually accelerates well:

- lots of byte-by-byte logic
- many table lookups
- many branches
- very little need for Python objects during the hot loop

## The Rust side, module by module

If you are new to Rust, here is the most important mindset shift:

- Python code usually manipulates rich dynamic objects directly.
- Rust code usually works with very explicit types and byte slices.

That explicitness is a big reason this code can be faster.

## `src/lib.rs`

This file is the extension module entry point.

It does three simple things:

1. declares submodules
2. defines `version()`
3. registers Python-callable functions in the `_rmarc` module

The key PyO3 line is:

```rust
#[pymodule]
fn _rmarc(m: &Bound<'_, PyModule>) -> PyResult<()> { ... }
```

You can read that as:

"When Python imports `rmarc._rmarc`, build a module object and add exported functions/classes to it."

The important exports are:

- `marc_codec::decode_marc_raw`
- `marc_codec::encode_marc_raw`
- `marc8::marc8_to_unicode_rs`

### What `#[pyfunction]` means

When you see:

```rust
#[pyfunction]
pub fn decode_marc_raw(...) -> PyResult<...>
```

it means:

- PyO3 will expose this Rust function to Python
- Python arguments will be converted into Rust values
- the Rust return value will be converted back into Python objects
- `PyResult<T>` means "either return `T` or return a Python exception"

That is the main FFI pattern in this project.

## `src/marc_codec.rs`

This is the core Rust acceleration module.

It implements:

- `decode_marc_raw`
- `encode_marc_raw`

### What `decode_marc_raw` receives

The function signature is:

```rust
pub fn decode_marc_raw<'py>(
    py: Python<'py>,
    data: &[u8],
    to_unicode: bool,
    force_utf8: bool,
    encoding: &str,
    utf8_handling: &str,
    quiet: bool,
) -> PyResult<(Bound<'py, PyString>, Bound<'py, PyList>)>
```

A translation into plain English:

- `data: &[u8]` means "borrowed bytes"; Rust can inspect the record without copying it first
- `encoding: &str` means "borrowed string slice"
- `py: Python<'py>` is a token proving we are allowed to create Python objects
- the return type is a Python string plus a Python list

Why borrowed slices matter:

- they are lightweight views into existing memory
- they avoid many intermediate allocations
- they make the inner parser mostly operate on offsets and slices rather than Python objects

### How `decode_marc_raw` works

The function does this, in order:

1. validate that there are at least 24 bytes for a leader
2. treat the leader bytes as ASCII
3. decide the effective encoding
4. decide a `DecodeMode`
5. parse base address and record length
6. slice out the directory
7. iterate over fixed-width 12-byte directory entries
8. for each entry:
    - read tag
    - read length
    - read offset
    - slice the field payload
    - decide control field vs data field
9. build Python tuples/lists describing fields

The important speed trick is that the parser does not create `Field` objects in Rust.

Instead, it returns simple structured data shaped like:

```text
(leader_str, [
  (tag, ("control", value)),
  (tag, ("data", ind1, ind2, [(code, value), ...])),
])
```

This is a deliberate compromise:

- simpler than exporting full Python classes from Rust
- much faster than doing all parsing in Python
- still compatible with the existing Python object model

### Why `DecodeMode` exists

`DecodeMode` is a small Rust enum:

```rust
enum DecodeMode {
    Raw,
    Utf8,
    Marc8,
}
```

This is a very Rust-like choice.

Instead of sprinkling conditionals everywhere, the parser picks one decode strategy once, then uses it consistently for
field values.

That improves:

- readability
- branch locality
- confidence that all field-value paths are handled the same way

### Rust tuples and Python tuples are not the same thing

This file creates real Python objects using PyO3 helpers such as:

- `PyList::empty(py)`
- `PyTuple::new(...)`
- `PyString::new(...)`
- `PyBytes::new(...)`

So even though Rust does most of the parsing work, the boundary still creates Python-owned results before returning.

This explains an important performance truth:

- Rust removes the expensive Python byte-parsing loop
- but it does not remove all Python allocation costs

That is why the speedup is large but not infinite.

### Why `PyString::intern` is used

This code interns repeated strings such as tags and sentinel values:

- `"245"`
- `"a"`
- `"control"`
- `"data"`

Interning means:

- Python can reuse a single string object for repeated identical values
- fewer allocations happen
- repeated comparisons may be cheaper

That is a small optimization, but in record parsing small repeated costs add up fast.

### How `encode_marc_raw` works

`encode_marc_raw` is simpler than decode.

Python has already asked each field to serialize itself. Rust just assembles:

1. the directory
2. the field area
3. the trailing record terminator
4. the updated leader with correct lengths

This function is faster than the Python version mainly because:

- it builds byte vectors efficiently
- it avoids repeated Python bytes concatenation
- it calculates lengths and offsets in a compiled loop

But notice the limit:

- field-level encoding still mostly happens in Python `Field.as_marc()`
- so encode acceleration is helpful, but not as complete as decode acceleration

That matches the performance notes in `phase4_performance.md`.

## `src/marc8.rs`

This is the Rust port of the Python `MARC8ToUnicode.translate()` state machine.

If you are new to Rust, this file is one of the best places to learn because it is mostly straightforward logic with a
few Rust idioms.

### Rust concepts used here

#### Constants

```rust
const BASIC_LATIN: u8 = 0x42;
```

This means:

- fixed value
- type is `u8` = unsigned 8-bit integer

Rust likes explicit integer sizes because it makes byte-level code precise.

#### Mutable local variables

```rust
let mut g0: u8 = BASIC_LATIN;
```

`let` creates a variable.

`mut` means it can change.

Without `mut`, Rust would treat it as immutable.

#### `Vec<char>`

```rust
let mut uni_list: Vec<char> = Vec::with_capacity(data.len());
```

This means:

- `Vec<T>` is Rust's growable array
- this vector will store Unicode scalar values as `char`
- `with_capacity` preallocates memory, reducing reallocations

That is a classic performance optimization.

### How the MARC-8 state machine works

Very roughly, the loop does this:

1. inspect the next byte
2. if it is `ESC`, update active character set state
3. decide whether we are in multibyte mode
4. build a code point from 1 or 3 bytes
5. look up the code point in the current mapping table
6. if the character is combining, buffer it
7. if it is a base character, append it and then flush buffered combinings
8. NFC-normalize at the end

The hardest conceptual part is this:

- MARC-8 combining marks come before the base character
- Unicode combining sequences are logically attached to the previous base character

So the algorithm buffers combining marks until a base character arrives, then emits them in the order Unicode expects.

That buffering logic is one of the most important correctness points in the whole Rust port.

## `src/marc8_mapping.rs`

This file is huge because it is generated character mapping data.

It uses `phf`, which stands for perfect hash function.

The practical meaning is:

- the mapping tables are compiled into the binary
- lookups are fast
- the tables are immutable

This is a strong design for a fixed standards table.

It avoids:

- Python dictionary lookup overhead
- runtime table-building cost
- accidental mutation

Trust note:

- generated table files are annoying to review manually
- but for this kind of project, generated immutable lookup tables are usually safer than hand-maintained dynamic ones

The review burden shifts from "inspect every entry" to:

- verify how the file was generated
- verify that tests cover representative mappings
- verify that the generated file is not edited by hand

## End-to-end decode walkthrough

Let us walk one record through the actual system.

### Step 1: `MARCReader` reads bytes

`python/rmarc/reader.py` reads:

- first 5 bytes for length
- remaining bytes for the record

It checks:

- enough bytes were read
- the final byte is the end-of-record marker

Then it calls:

```python
Record(chunk, ...)
```

### Step 2: `Record.__init__()` calls `decode_marc()`

If there is input data, `Record.__init__()` immediately calls `self.decode_marc(...)`.

That method chooses:

- Rust fast path when `_rmarc` is importable
- otherwise pure Python fallback

### Step 3: Python performs compatibility-critical validation

Before calling Rust, `_decode_marc_rust()` still performs some validation in Python:

- leader length
- leader decoding
- base address sanity
- truncated record check

Why do this in Python if Rust could do it?

Because Python needs to raise the same exception types users already expect:

- `BaseAddressNotFound`
- `BaseAddressInvalid`
- `TruncatedRecord`
- and so on

That is a good example of the overall architecture:

- keep compatibility-sensitive semantics in Python
- move byte-intensive parsing to Rust

### Step 4: Rust parses directory and fields

Rust receives the raw record bytes and returns a simple structure describing the record.

At this stage, Rust has already done most of the expensive work:

- slicing
- parsing fixed-width numeric fields
- splitting subfields
- decoding UTF-8 or MARC-8 when possible

### Step 5: Python wraps Rust output into `Field` objects

Back in `_decode_marc_rust()`, Python loops over `fields_raw` and creates either:

- `Field`
- or `RawField`

For control fields:

- the field stores `data`

For data fields:

- Python builds `Subfield(code, value)` objects
- then attaches `Indicators`

The constructor-bypass optimization is used here to avoid expensive repeated validation already handled upstream.

### Step 6: User sees a normal `Record`

At the end, user code receives an ordinary `Record` and does not need to know whether Rust was involved.

That is the success condition for this project.

## End-to-end encode walkthrough

The encode path is more Python-heavy than the decode path.

### Step 1: `Record.as_marc()` chooses an output encoding

If `to_unicode` is true, the leader gets updated to indicate UTF-8 output.

Then Python chooses:

- `utf-8` if leader position 9 says UTF-8, or `force_utf8` is true
- otherwise `iso8859-1`

### Step 2: each field serializes itself in Python

For each field:

- `RawField.as_marc()` preserves bytes
- `Field.as_marc(encoding=...)` constructs a string and encodes it

This is still a major remaining Python hotspot.

### Step 3: Rust assembles the record

Python builds:

```python
[(tag_str, field_data_bytes), ...]
```

and hands that to Rust.

Rust then:

- appends directory entries
- appends field bytes
- appends terminators
- recomputes base address and record length
- patches the leader

This means decode is more aggressively Rustified than encode.

That asymmetry shows up in the benchmark results.

## Why the Rust path is faster

The speedup is not "Rust is magic."

It comes from very specific changes in where work happens.

## 1. Byte slicing is cheaper in Rust than in Python object code

Binary MARC parsing is full of operations like:

- fixed-width numeric parsing
- slicing byte ranges
- scanning for delimiters
- building offsets

Python can do these, but every step tends to involve more interpreter overhead and more object creation.

Rust performs the same work in tight compiled loops over byte slices.

## 2. Rust reduces the number of temporary Python objects on the hot path

The pure Python decoder repeatedly creates:

- temporary decoded strings
- temporary split lists
- repeated Python-level loop frames

The Rust decoder still returns Python objects at the boundary, but it avoids creating many intermediate Python objects
during parsing itself.

## 3. MARC-8 conversion is a classic compiled-language win

The Python MARC-8 converter is a state machine with many dictionary lookups and per-byte operations.

The Rust version improves this by:

- using explicit byte indexing
- using compiled `phf` tables
- using preallocated vectors
- avoiding Python call overhead inside the inner loop

That is why the MARC-8 benchmark shows the largest speedup.

## 4. The optimized path avoids repeated constructor work

The project did not stop at "call Rust."

It also optimized the wrap-back path by bypassing `Field.__init__()` when the data is already normalized.

That is an important lesson for future contributors:

- the FFI boundary is not the only place performance matters
- a lot of speed was won by carefully removing redundant Python-side work too

## Why the speedup is not even larger

Two big costs still remain in Python:

1. `Field` / `Subfield` / `Indicators` object creation
2. field-level encoding in `Field.as_marc()`

So the current architecture is best described as:

- Rust for parsing and conversion
- Python for API objects and some serialization

That is a good compromise, but it is not a fully Rust-native pipeline.

## Code review: strengths

These are the main reasons I would take the current implementation seriously.

## 1. The design is intentionally narrow

The Rust code does not try to own the whole domain model.

That reduces complexity and review burden.

Instead of reviewing:

- Rust classes mirroring every Python class
- bidirectional object mutation
- lifetime-heavy PyO3 patterns

we mostly review:

- pure parsing functions
- pure conversion functions
- Python wrappers that map results into existing classes

That is the right tradeoff for trust.

## 2. The pure Python implementation still exists

This is a major safety feature.

It provides:

- a reference implementation
- a fallback for unsupported environments
- a direct way to compare semantics

Many accelerated projects become much harder to trust once the reference path disappears. `rmarc` has not made that
mistake.

## 3. The Rust code appears memory-safe

I did not see `unsafe` blocks in the Rust implementation.

That is important.

It means the main review concerns are semantic correctness and Python boundary behavior, not manual memory management
bugs.

## 4. Tests are broad and compatibility-oriented

The test suite is not tiny, and it is not only Rust-specific unit tests.

It includes:

- inherited `pymarc` behavior tests
- malformed record tests
- MARC-8 conversion tests
- encoding edge cases
- reader permissiveness tests

I also ran a focused slice covering the critical reader and encoding paths:

```text
.venv\Scripts\python -m pytest test_pymarc\test_reader.py test_pymarc\test_marc8.py test_pymarc\test_utf8.py -q
43 passed
```

That does not prove everything, but it does support the claim that the core compatibility path is actively exercised.

## 5. The performance claims are backed by realistic benchmarks

The benchmark suite includes:

- single-record decode
- round-trip
- iterator workloads
- MARC-8 conversion
- bulk file processing

That is much better than only publishing a tiny microbenchmark.

## Code review: important risks and caveats

This is the section contributors should read most carefully if they are deciding how much trust to place in the Rust
path.

## Finding 1: Rust UTF-8 `"strict"` mode is not actually strict

This is the most important semantic issue I found.

In Python, UTF-8 decoding does this:

```python
data.decode("utf-8", utf8_handling)
```

When `utf8_handling == "strict"`, invalid UTF-8 should raise `UnicodeDecodeError`.

In Rust, the helper is:

```rust
fn decode_utf8(data: &[u8], handling: &str) -> String {
    match handling {
        "strict" => String::from_utf8(data.to_vec())
            .unwrap_or_else(|_| String::from_utf8_lossy(data).into_owned()),
        ...
    }
}
```

That means:

- `"strict"` does not raise
- it silently falls back to lossy decoding with replacement characters

Why this matters:

- it is a real semantic divergence from the Python fallback
- bad UTF-8 data may be accepted instead of surfacing as an error
- because the reader is permissive, this may change `current_exception` behavior in edge cases

This is the first place I would tighten if the goal is "can contributors trust the Rust path as behaviorally identical?"

## Finding 2: Rust error mapping still depends on parsing exception message text

`Record._decode_marc_rust()` catches `ValueError` from Rust and then does message matching:

- `"DirectoryInvalid"`
- `"NoFieldsFound"`
- `"not valid ASCII"`

That works, but it is brittle.

The problem is not speed. The problem is maintainability.

If Rust error strings change, Python exception mapping may silently drift.

A more trustworthy design would return structured error codes instead of parsing strings.

## Finding 3: decode semantics are more trustworthy than encode semantics

Decode has a strong story:

- well-isolated hot path
- clear fallback
- lots of tests

Encode is somewhat less complete because:

- field serialization still happens in Python
- Rust only assembles the final record structure

This is not wrong, but it means the encode speedup story is less "Rust owns encoding" and more "Rust owns the record
assembly part."

Contributors should understand that before assuming encode and decode are equally reviewed or equally accelerated.

## Finding 4: generated mapping tables are trustworthy only if the generation path is documented

`src/marc8_mapping.rs` is generated and huge.

That is normal, but it changes the trust question.

You should not try to line-review the whole file manually.

You should instead ask:

1. what generated it?
2. is generation reproducible?
3. do tests validate representative entries and behavior?

Right now the file says it is auto-generated from `python/rmarc/marc8_mapping.py`, which is good, but a documented
regeneration workflow would make contributor trust much stronger.

## Finding 5: there is still Python boundary overhead by design

This is not a correctness problem, but it is important for future optimization discussions.

The architecture deliberately pays for:

- Python tuple/list creation in Rust
- Python object wrapping in `record.py`

That means future "why not 20x faster?" questions have an architectural answer:

- because this project chose compatibility and simplicity over a full Rust object graph

That is probably the right choice, but contributors should know it is a choice.

## How to read the Rust code if you are coming from Python

If you know Python and little Rust, read the Rust in this order:

1. `src/lib.rs`
2. `src/marc_codec.rs`
3. `src/marc8.rs`
4. only then skim `src/marc8_mapping.rs`

And use these translation rules.

## Rust translation cheatsheet

### `&[u8]`

Borrowed byte slice.

Python equivalent idea:

```python
memoryview(data)
```

Not identical, but close as a mental model.

### `&str`

Borrowed UTF-8 string slice.

Think "read-only string view."

### `Vec<T>`

Growable array.

Closest Python idea:

```python
list[T]
```

but typed and contiguous in memory.

### `Option<T>`

Either `Some(value)` or `None`.

Closest Python idea:

```python
T | None
```

### `Result<T, E>` / `PyResult<T>`

Either success or error.

Closest Python idea:

- return a value
- or raise an exception

except Rust encodes that possibility in the type system.

### `match`

Like a stricter, exhaustive `if/elif`.

Rust uses it heavily for:

- enum dispatch
- pattern-based control flow
- making sure every case is handled

### Lifetimes like `'py`

You do not need to master this project-wide.

In this code, read `'py` mostly as:

"this Python-owned object cannot outlive the Python interpreter context it came from."

That is how PyO3 keeps Rust from returning Python references with invalid lifetimes.

## What to review first if you want to audit trustworthiness

If I were onboarding a new contributor to audit this code, I would review in this order.

## 1. Compare `Record._decode_marc_python()` and `Record._decode_marc_rust()`

This is the highest-value review because it tells you whether the fast path preserves the slow path's semantics.

Pay special attention to:

- encoding selection
- exception types
- malformed indicators
- non-ASCII subfield codes
- unknown encodings such as `cp1251`

## 2. Review `src/marc_codec.rs` with the MARC21 record structure next to you

You want to verify:

- leader parsing
- base address handling
- directory slicing
- fixed-width numeric parsing
- field offset math

Most correctness bugs in this kind of parser are offset bugs.

## 3. Review `src/marc8.rs` against the Python implementation

This code is more stateful and therefore easier to get subtly wrong.

Pay special attention to:

- escape handling
- G0/G1 switching
- multibyte mode
- combining character buffering
- normalization

## 4. Review the benchmark claims only after semantic review

Fast code is only interesting if it is still correct.

The repository does a decent job keeping correctness and benchmark work side by side; keep following that discipline.

## Why I think this architecture is a good fit for the project

Because the project goal is not "write the most Rust."

The goal is:

- preserve `pymarc` ergonomics
- keep contributor approachability high
- speed up large real-world MARC workflows

This implementation supports that goal well.

It gives contributors a layered system they can reason about:

- Python for public behavior
- Rust for hot loops
- tests to compare both

That is much easier to maintain than a total rewrite.

## Suggested next improvements

If the team wants to strengthen trust rather than chase raw speed, I would prioritize these in order:

1. Make Rust UTF-8 `"strict"` truly strict and add explicit regression tests.
2. Replace message-string-based error mapping with structured error codes.
3. Document or script regeneration of `src/marc8_mapping.rs`.
4. Add direct parity tests that force Python fallback and Rust path on the same malformed inputs.
5. Add a small contributor doc explaining how to diff `rmarc` behavior against upstream `pymarc`.

If the team wants to chase more speed after that, the best next targets are probably:

1. move more field encoding into Rust
2. reduce Python object creation cost further
3. possibly batch more work across the Python/Rust boundary

## Final trust assessment

My overall assessment is:

- the architecture is sound
- the Rust acceleration targets are the right ones
- the code is readable enough to audit
- the fallback structure is a major trust advantage
- the current implementation looks materially faster for the right reasons

But I would not call it behaviorally perfect yet.

The most important reason is the UTF-8 strictness mismatch in the Rust helper, plus the more general fact that Python
exception semantics are still being reconstructed from Rust-side messages.

So the practical conclusion is:

- yes, this looks like a serious and mostly careful acceleration layer
- yes, contributors can learn and review it without becoming Rust experts
- no, we should not treat the fast path as beyond suspicion
- and yes, there is a clear, manageable list of things to audit next

That is a healthy place for a contributor-oriented performance rewrite to be.
