ultravin

The complete NHTSA VIN decoder, thousands of times faster. Fully offline.

_{VINs decoded per second — ultravin uses automatic batching over twenty million unique VINs.}

• ⚡️ ~258,000 VIN/s on one core with automatic batching — ~11,450× the NHTSA SQL Server baseline (22.5 VIN/s)
• 🦀 Pure Rust core, shipped as a Python library and a Rust crate
• 📦 The entire vPIC vehicle database baked into the wheel
• 🔌 Fully offline — no network, no database, no data files at runtime
• 🎯 Full-field vPIC parity, tested across decoding rules and their interactions — with documented upstream defects corrected (accuracy policy, evidence)
• 🐍 Installable via pip, with a CLI and a library API
• 🧵 Batches in parallel to ~935,000 VIN/s on 4 cores and ~1.85 million VIN/s on all 12 — ~41,558× and ~82,371× that same baseline
• 🗃️ Parquet in, parquet out — decodes a dataset of any size in the memory of one chunk

ultravin brings the complete spVinDecode algorithm into your process: vehicle attributes, model-year resolution, VIN correction, errors, and provenance. It checks every output field against NHTSA's unmodified Postgres procedure and corrects documented defects in the upstream data and procedures. You get vPIC fidelity with better answers on those defective cases, at over 11,400× the NHTSA SQL Server baseline on one core. Benchmarks and reproduction.

The complete vehicle database ships inside the binary. No network, no database server, no runtime data files. Install it and decode.

Getting Started

Installation

uv add ultravin

Prebuilt wheels require Python 3.10+ and nothing else — the data ships inside the wheel.

Usage

From Python:

import ultravin

r = ultravin.decode("1HGCM82633A004352")

r["model_year"]  # 2003
r["wmi"]  # '1HG'
r["check_digit_valid"]  # True
r["error_codes"]  # [0]

# `attributes` is the decoded vehicle, one entry per vPIC variable:
r["attributes"]["Make"]  # 'HONDA'
r["attributes"]["Model"]  # 'Accord'

decode(vin) returns a dict with keys vin, wmi, descriptor, model_year, error_codes, check_digit_valid, corrected_vin, and attributes — a single variable -> value mapping. Values are str, except the free-text note fields listed in ultravin.MULTI_VALUED, which are always list[str]: those are the only vPIC elements allowed to repeat within one decode, and each row is a separate note rather than a competing value.

Decode many at once with decode_batch:

results = ultravin.decode_batch(["1HGCM82633A004352", "5YJ3E1EA7JF000000"])

If you already know a vehicle's model year, pass it — the same optional hint the vPIC API calls modelyear. It matters for pre-2010 vehicles, where the VIN's year character is ambiguous (A means 1980 or 2010): the hinted year gets its own decode pass that competes against the VIN-derived one, and a hint that contradicts the decoded year adds error code 12.

ultravin.decode("1HGCM82633A004352", year=1995)  # decodes as a 1995
ultravin.decode_batch(vins, years=[2011, None, 1987])  # one entry per VIN

Provenance: full=True

The default keeps the value and drops the provenance. If you need to know where a value came from — source, over half of all rows being vehicle-type defaults rather than something the VIN encodes — or the raw vPIC attribute_id, pass full=True:

r = ultravin.decode("1HGCM82633A004352", full=True)

next(e for e in r["elements"] if e["variable"] == "Make")
# {'group_name': 'General', 'variable': 'Make', 'value': 'HONDA', 'source': 'pattern - model', …}

full=True replaces attributes with elements, a list of per-attribute dicts (group_name, variable, value, element_id, attribute_id, source, pattern_id, …), and works the same on decode_batch, decode_json and decode_batch_json. It is ~2× slower end to end: decoding is not the expensive part, and elements costs ~615 dict entries per VIN against the default's ~41.

ultravin.ELEMENTS maps each variable name to its static metadata (element_id, group_name, data_type, …). Pin to element_id if you need a key that survives NHTSA renaming a variable between data releases.

From the command line:

ultravin decode 1HGCM82633A004352             # JSON object
ultravin decode 1HGCM82633A004352 --year 1995 # with a caller model-year hint
ultravin decode 1HGCM82633A004352 --full      # with per-element provenance
ultravin decode-batch vins.txt                # one VIN per line -> JSON array
ultravin decode-batch - --jsonl < vins.txt    # stdin -> streaming JSON Lines
ultravin decode-batch vins.txt --jsonl --batch-size 1000
ultravin info                                # decoder version + data identity as JSON
ultravin version

decode-batch accepts VIN,year on each input line. JSONL mode processes bounded chunks and emits complete JSON objects in input order, ready for shell pipelines. Its automatic default adapts under an 8 MiB working-buffer target; pass --batch-size N to pin a measured size or --batch-memory-mb N to change the automatic target. If a later input line is malformed, the command exits with an error; already-emitted lines remain valid results.

Reproducible results

Every batch automatically captures one clock for the whole job. That includes Python and Rust batches, CLI input loading, every JSONL chunk, and every file in a Parquet stream. A job that spans a date or year boundary keeps using its starting instant.

Pin the package version and freeze the decode clock when a job needs to produce the same answers on a later date:

from datetime import datetime, timezone

as_of = datetime(2026, 9, 1, tzinfo=timezone.utc)
r = ultravin.decode("1HGCM82633A004352", now=as_of)
results = ultravin.decode_batch(vins, now=as_of)
identity = ultravin.provenance()
# data_month, artifact_blake3, decoder_version

now= also works on the JSON APIs and decode_stream. It controls publication dates and year resolution; year= remains the vehicle's model-year hint. Naive datetimes mean UTC. Each stream captures one clock reading for the whole job, and its Arrow/Parquet metadata records the data identity and decode clock: ultravin.data_month, ultravin.artifact_blake3, ultravin.decoder_version, and ultravin.now_micros (Unix epoch microseconds).

Rust

The engine is its own crate, ultravin: cargo add ultravin, then

let r = ultravin::decode("1HGCM82633A004352", None);
assert_eq!(r.model_year, Some(2003));

The 83 MB vPIC database is too big for crates.io, so the first build fetches it from the matching GitHub release, verifies it, caches it per machine and bakes it into your binary — runtime stays fully offline. Offline builds and runtime loading: crates/ultravin/README.md.

Datasets

For bulk work there is decode_stream — a stream of decoded Arrow batches, without a single row ever becoming a Python object:

import ultravin

rows = ultravin.decode_stream("registrations.parquet").to_parquet("decoded.parquet")

rows  # 4812004 — the rows written, not the rows themselves

Parquet output replaces its destination only after every batch and the footer have been written successfully. A failed decode leaves an existing output intact.

The source is a parquet file, a directory of *.parquet read in sorted order, or anything speaking the Arrow C data interface — so the same call takes a pandas DataFrame (pandas ≥ 2.2, with pyarrow installed), a pyarrow Table, a polars DataFrame, a duckdb result, or a RecordBatchReader. A DecodeStream is itself an Arrow source, which is what lets it hand the decode straight to whatever you already use:

import pandas as pd, polars as pl, pyarrow as pa, duckdb

df = pd.DataFrame({"vin": ["1HGCM82633A004352", "5YJ3E1EA7KF328931"]})

pl.DataFrame(ultravin.decode_stream(df))  # -> polars
pa.table(ultravin.decode_stream(df))  # -> pyarrow
ultravin.decode_stream(df).to_pandas()  # -> pandas (needs pandas + pyarrow)

stream = ultravin.decode_stream(df)  # duckdb resolves the name from scope
duckdb.sql("select Make, count(*) from stream group by 1")

Each stream is single-use — note the fresh decode_stream(...) on every line above. It pulls from a source that has already moved on, so a second consumer would get a silently truncated result; consuming one twice raises RuntimeError rather than handing back a short answer. Call decode_stream again to re-read.

Picking columns

columns= takes vPIC variable names, element_ids, or both together; omit it for every publicly decodable element:

ultravin.decode_stream("registrations.parquet", columns=["Make", "Model", 13])

Pin to element_id for anything long-lived. The id is the one key NHTSA does not rename between monthly data releases.

Column naming and schema drift

Naming output columns after vPIC variables means a data refresh can silently change your table's shape — NHTSA renames variables, and Displacement (L) becoming something else takes every downstream query with it. column_names="id" labels each projected column attr_<element_id> instead, which never moves:

ultravin.decode_stream(src, columns=[26, 13], column_names="id")
# -> vin, decoded_model_year, attr_26, attr_13

Passthrough columns (the source VIN column, the source year column, and decoded_model_year) keep their own names in both modes; only the projection is renamed. The default is "variable" — reach for "id" when the output feeds a persisted schema rather than a human.

You never lose the other name. Every projected column carries both keys as Arrow field metadata, in both modes, and they survive the parquet round-trip:

table = pa.table(ultravin.decode_stream(src, columns=[26, 13], column_names="id"))
{f.name: dict(f.metadata) for f in table.schema if f.metadata}
# {'attr_26': {b'element_id': b'26', b'variable': b'Make'},
#  'attr_13': {b'element_id': b'13', b'variable': b'Displacement (L)'}}

Columns and layout

The VIN column is found by name (vin, case-insensitively) and then, for a parquet source, by sniffing the leading rows — as is the optional caller-year column (year, model_year, …); pass vin_column=/year_column= to name them outright. Any text encoding works: Utf8, LargeUtf8, Utf8View, or the dictionary a pandas categorical arrives as.

The output holds the VIN and caller year passed through, then decoded_model_year (named so it cannot collide with an input column called model_year), then one column per projected element — string, int64 or float64 following vPIC's own data_type, with an empty value written as null. Row order and row count always equal the input's: an undecodable VIN is a row of nulls, never a raise and never a dropped row.

By default, batch_size="auto" adapts Parquet and Arrow work chunks to measured throughput under a 64 MiB working-buffer budget. A shipped predictor uses the worker count and a short single-core measurement on real input rows to choose the starting size; runtime feedback then refines it. See the predictor and heatmaps, or call ultravin.predict_batch_size(workers=12, single_core_rows_per_second=100_000) to inspect an estimate. Change the working-buffer target with batch_memory_mb=. It is a budget for buffers ultravin builds for the current batch, not a process-RSS limit, and it does not include buffers retained by an upstream Arrow producer. Adaptation respects RAYON_NUM_THREADS; it never changes the process-global worker pool.

Pass an integer for fixed-size Parquet chunks. With an Arrow source an explicit integer preserves the producer's batches and only sets the parquet row-group size of to_parquet. In either mode the GIL is released while Rust decodes, and memory stays bounded independently of total source rows. Reading and writing parquet is the same Rust as the decoding, so this path needs no pyarrow and no other install; only the pyarrow/polars/pandas hand-offs need those libraries.

From the command line:

ultravin decode-parquet registrations.parquet decoded.parquet --columns Make,Model
ultravin decode-parquet parts/ decoded.parquet --columns 26,28 --vin-column chassis_no
ultravin decode-parquet registrations.parquet decoded.parquet --column-names id

Benchmarks

How many VINs each engine decodes per second, measured against the NHTSA MSSQL spVinDecode baseline of 22.5 VIN/s.

Commit 5750746, September 23, 2026, Apple M2 Max (eight performance and four efficiency cores), 20,000,000 unique synthetic VINs, automatic batching on one, four, or twelve workers, medians of two fresh-process trials. Across worker counts ultravin scales to ~3.6× and ~7.2× its own single-core rate.

ultravin runs in-process with the database embedded — no server, no round-trip. The corgi rows are derived from that project's published per-VIN latency, and the vPIC web API row is its published request rate limit rather than a decode time; neither was re-measured here. Methodology, inputs, caveats, reproduction, and prior sessions.

Documentation

• Vision — what this is, and what it deliberately is not
• Benchmarks — the numbers, the methodology, how to reproduce them
• End-to-end performance — throughput, startup, and memory across all output paths
• Performance improvements — the optimization history, measured gains, tradeoffs, and rejected experiments
• Acceptance — the parity policy: what counts as passing, how a divergence is adjudicated
• Known deviations — the vPIC defects ultravin does not reproduce, with evidence
• Corpora — generate, cover_vins, sweep: hitting every decode behaviour with the fewest VINs
• Data refresh — how the monthly NHTSA dump is integrated behind parity gates
• Release — tags, wheels, the embedded artifact, crates.io

License

MIT. The embedded NHTSA vPIC data has its own provenance — see NOTICE.