ProofFrame

Relational Arrow contracts, exact dataset rules, and verifiable evidence.

ProofFrame is a Rust-native data quality engine for PyArrow, Pandas, Polars, CSV, Parquet, and Arrow streams. It compiles strict contracts against the physical schema, scans record batches without turning rows into Python objects, and produces evidence that can be stored, compared, and signed.

Why ProofFrame

Most data checks answer one question: did the table pass? Production systems usually need three:

1. What exactly was checked? Versioned BLAKE3 fingerprints identify the ordered dataset.
2. Why did it fail? Exact violation counts and bounded row-level findings explain the verdict.
3. What changed? Keyed diffs report added, removed, and changed records without loading both datasets into memory.

ProofFrame keeps these answers deterministic and resource-bounded. Exact uniqueness, diff, and leakage operations have explicit memory, temporary-storage, sample, and output limits. Corrupt temporary data, incompatible schemas, ambiguous contracts, and exceeded limits fail closed.

Current release — 0.6.0

0.6.0 adds review-required contract suggestions, complete contract and API guides, runnable workflows, and practical integration examples. Existing V1/V2 contract and fingerprint protocols remain compatible.

Install

Python 3.10–3.13:

pip install proofframe==0.6.0

Rust 1.85 or newer:

cargo add proofframe@0.6.0

The 30-second demo

import pyarrow as pa
import proofframe as pf

orders = pa.table({
    "order_id": [101, 102, 103],
    "subtotal": [12.50, 8.00, 10.00],
    "total": [12.50, 7.50, 10.00],
})

contract = {
    "version": "proofframe.contract.v2",
    "columns": {},
    "row_rules": [{
        "name": "total_covers_subtotal",
        "compare": {
            "left": {"column": "total"},
            "op": "gte",
            "right": {"column": "subtotal"},
        },
    }],
    "dataset_rules": {
        "row_count": {"min": 1},
        "distinct_ratio": {"order_id": {"min": 1.0}},
    },
}

report = pf.check(
    orders,
    contract,
    max_memory=64 << 20,
    max_temp=512 << 20,
    max_samples=20,
)

assert report["valid"] is False
assert report["violation_count"] == 1

The contract is compiled before scanning. Unknown fields, missing required columns, invalid bounds, and rules that do not match the Arrow type are rejected before the first row is processed. violation_count remains exact even when the retained findings sample is truncated.

Start from a reviewable draft

Use the native suggestion scanner to create a V2 draft, then inspect it before activation:

proofframe suggest data.parquet > contract.json
# Review contract.json and set "status" to "active" before checking it.
proofframe check data.parquet --contract contract.json

suggest performs its own Arrow scan so it can preserve exact integer bounds. It infers types and non-null columns by default; uniqueness, required columns, and category allowlists are explicit opt-ins. Timestamp and monotonically increasing numeric ranges are deliberately omitted and recorded in suggested_from.review. A draft is rejected with PF_DRAFT_CONTRACT until a reviewer changes its status to active. See the five-minute guide and contract reference.

Cross-column and conditional rules

V2 compares Arrow values in their physical type. It does not cast through Python objects or parse an expression language at runtime.

shipments = pa.table({
    "ordered_at": [1, 3],
    "delivered_at": [2, 2],
    "status": ["delivered", "pending"],
    "tracking_id": ["TR-1", None],
})

contract = {
    "version": "proofframe.contract.v2",
    "columns": {},
    "row_rules": [
        {
            "name": "delivery_window",
            "compare": {
                "left": {"column": "ordered_at"},
                "op": "lte",
                "right": {"column": "delivered_at"},
            },
        },
        {
            "name": "delivered_has_tracking",
            "when": {
                "left": {"column": "status"},
                "op": "eq",
                "right": {"literal": "delivered"},
            },
            "assert": {"column": "tracking_id", "not_null": True},
        },
    ],
}

report = pf.check(shipments, contract)

Comparisons support signed and unsigned integers, floats, booleans, UTF-8, dates, timestamps, and decimal128 where the Arrow types are compatible. Null behavior is explicit. Conditional assertions cover nullability, numeric bounds, allowlists, patterns, and NaN policy without building a row mask.

Dataset-level rules and partitions

Dataset rules keep exact state across record-batch and partition boundaries. Distinct and composite keys use canonical values, not hash-only identity. When the memory budget is reached, sorted, checksummed runs spill under the configured temporary-storage limit.

partitions = [
    pa.table({"order_id": [101, 101], "line_id": [1, 2]}),
    pa.table({"order_id": [102], "line_id": [1]}),
]

contract = {
    "version": "proofframe.contract.v2",
    "columns": {},
    "dataset_rules": {
        "row_count": {"min": 3},
        "distinct_ratio": {"order_id": {"min": 0.5}},
        "composite_unique": [{
            "name": "line_key",
            "columns": ["order_id", "line_id"],
        }],
    },
}

report = pf.check_partitions(partitions, contract, threads=2)
assert report["valid"] is True

pf.check_partitions_with_evidence additionally returns an ordered manifest binding every partition's V2 fingerprint, row count, schema, contract, compiled plan, result contribution, global result, and resource settings. Reordering, omission, duplication, or mixed identities fails verification.

Check a foreign key across datasets

orders = pa.table({"customer_id": [1, 99, 2]})
customers = pa.table({"id": [1, 2]})

contract = {
    "version": "proofframe.contract.v2",
    "dataset_rules": {
        "references": [{
            "name": "orders_customer_fk",
            "columns": ["customer_id"],
            "reference": "customers",
            "reference_columns": ["id"],
        }],
    },
}

report = pf.check(orders, contract, references={"customers": customers})
assert report["valid"] is False
assert report["findings"][0]["row"] == 1
assert report["references"][0]["reference_fingerprint"].startswith("pf-fp-v2:")

The contract names the reference; the caller supplies it. A declared reference with no bound dataset, and a bound dataset no rule uses, are both errors: a foreign key that is never evaluated would otherwise report as one that held. The report records the fingerprint of the dataset the keys resolved against, because "the key held" is not verifiable without saying against what.

One engine, several proof operations

Fingerprint a dataset

legacy = pf.fingerprint(orders, version="v1")
current = pf.fingerprint(orders, version="v2")

Fingerprints bind schema, row and column order, nulls, type tags, and canonical values. They do not depend on Arrow display formatting and remain stable across record-batch boundaries. V1 is frozen for existing proofs; V2 is a separate protocol for new evidence.

Diff by business key

changes = pf.diff(
    before,
    after,
    keys="order_id",
    max_memory=256 << 20,
    max_temp=2 << 30,
    max_samples=100,
    output="changes.jsonl",
    spill="auto",
)

Counts are exact. Samples stay bounded, while full change records can be written atomically as JSON Lines or Arrow IPC. Duplicate keys and schema mismatches fail loudly.

Create verifiable evidence

checked = pf.check_with_evidence(orders, contract, max_samples=20)
report = checked["report"]
evidence = checked["evidence"]

assert report["valid"] is False
assert evidence["schema"] == "proofframe.evidence.v2"

Evidence V2 binds the dataset fingerprint, canonical contract source, compiled plan, Arrow schema, engine version, resource limits, and result. Signed receipts use Ed25519 and separate cryptographic validity from signer trust. Keep signing material in a secret manager and verify against a public key obtained independently from the receipt.

Find PII and train/test leakage

pii = pf.scan_pii(customers)
overlap = pf.detect_leakage(train, test, keys="user_id")

PII findings contain the class, column, row, confidence, and a keyed fingerprint—not the matched value. Leakage reports support business keys or full-row identity and expose only bounded hashed samples.

Arrow-native by design

Pandas / Polars / PyArrow / Arrow C Stream / CSV / Parquet
                           |
                           v
                  Arrow record batches
                           |
          +----------------+----------------+
          |                |                |
       contracts       fingerprints      keyed diff
          |                |                |
          +----------------+----------------+
                           |
                           v
              deterministic JSON evidence

Known DataFrame containers provide exact row and logical-byte hints. Stream-only inputs remain streaming. The Rust scan releases the Python GIL, and the ProofFrame crate itself uses #![forbid(unsafe_code)].

Where ProofFrame fits

ProofFrame is strongest when you need Arrow-native, exact checks with bounded resources and evidence that records both the contract source and executable plan. It is deliberately smaller than established data-quality platforms.

These tools can coexist: use ProofFrame at an Arrow boundary when repeatable checks, resource limits, and independently verifiable evidence matter.

CLI

proofframe check data.parquet --contract contract.json --max-memory 256MiB --max-temp 2GiB
proofframe fingerprint data.csv --fingerprint-version v2
proofframe diff old.parquet new.parquet --key order_id --output changes.jsonl
proofframe evidence data.parquet --contract contract.json --output evidence.json
proofframe verify receipt.json --expected-public-key "$PROOFFRAME_PUBLIC_KEY"

JSON is emitted only after a successful operation. File outputs use same-directory temporary files, fsync, and atomic replacement.

Rust core

The default crate has no Python dependency. Rust users get the same compiled contracts, typed Arrow kernels, fingerprints, evidence, receipt verification, resource accounting, and spill engine used by the Python wheels. See the crate guide and API documentation.

Compatibility and performance evidence

Version 0.6.0 preserves V1 fingerprints and the established compatibility entry points. New work should use check, explicit fingerprint versions, Evidence V2, and Receipt V2.

Performance claims are tied to raw samples, dataset hashes, compiler and package versions, and machine metadata. The committed smoke harness is deterministic; the pinned 7,645,034-row Bitcoin comparison remains a dedicated-runner gate rather than a published benchmark claim. See testing and benchmark methodology.

Release integrity

The release workflow packages the exact wheel, sdist, and crate subjects before publication. It emits deterministic SHA-256 checksums, SPDX JSON SBOMs, GitHub build-provenance attestations, and SBOM attestations. PyPI uses trusted publishing; crates.io publication is gated by the same tagged commit and CI evidence. These controls support provenance verification, but they are not a claim of formal SLSA certification.

Development

cargo test --locked --all-targets --all-features
cargo clippy --locked --all-targets --all-features -- -D warnings
maturin develop --release --locked
python -m pytest -q

CI covers Rust 1.85, Python 3.10–3.13 on Linux, macOS, and Windows, portable wheels, release-mode allocation contracts, Miri-compatible state machines, fuzz targets, source-package hygiene, coverage, DeepSource, and SonarCloud.

License and security

ProofFrame is licensed under Apache-2.0. Report vulnerabilities through the process in SECURITY.md. Sponsorship is available through GitHub Sponsors.