#arrow #hash #digest

arrow-digest

Stable hashes for Apache Arrow

7 releases (breaking)

Uses new Rust 2021

0.7.0 Feb 20, 2022
0.6.1 Dec 13, 2021
0.4.0 Dec 12, 2021
0.3.0 Dec 12, 2021
0.1.0 Dec 9, 2021

#489 in Cryptography

Download history 40/week @ 2022-06-07 31/week @ 2022-06-14 12/week @ 2022-06-21 8/week @ 2022-06-28 62/week @ 2022-07-05 63/week @ 2022-07-12 38/week @ 2022-07-19 41/week @ 2022-07-26 125/week @ 2022-08-02 36/week @ 2022-08-16 14/week @ 2022-08-23 6/week @ 2022-08-30 28/week @ 2022-09-06 17/week @ 2022-09-13 4/week @ 2022-09-20

55 downloads per month

Apache-2.0

50KB
986 lines

arrow-digest

Crates.io

Unofficial Apache Arrow crate that aims to standardize stable hashing of structured data.

Motivation

Today, structured data formats like Parquet are binary-unstable / non-reproducible - writing the same logical data may result in different files on binary level depending on which writer implementation and you use and may vary with each version.

This crate provides a method and implementation for computing stable hashes of structured data (logical hash) based on Apache Arrow in-memory format.

Benefits:

  • Fast way to check for equality / equivalence of large datasets
  • Two parties can compare data without needing to transfer it or reveal its contents
  • A step towards content addressability of structured data (e.g. when storing dataset chunks in DHTs like IPFS)

Use

// Hash single array
let array = Int32Array::from(vec![1, 2, 3]);
let digest = ArrayDigestV0::<Sha3_256>::digest(&array);
println!("{:x}", digest);

// Alternatively: Use `.update(&array)` to hash multiple arrays of the same type

// Hash record batches
let schema = Arc::new(Schema::new(vec![
    Field::new("a", DataType::Int32, false),
    Field::new("b", DataType::Utf8, false),
]));

let record_batch = RecordBatch::try_new(Arc::new(schema), vec![
    Arc::new(Int32Array::from(vec![1, 2, 3])), 
    Arc::new(StringArray::from(vec!["a", "b", "c"])),
]).unwrap();

let digest = RecordsDigestV0::<Sha3_256>::digest(&record_batch);
println!("{:x}", digest);

// Alternatively: Use `.update(&batch)` to hash multiple batches with same schema

Status

While we're working towards v1 we reserve the right to break the hash stability. Create an issue if you're planning to use this crate.

  • Schema hashing
  • Fixed size types
  • Nullability: primitive types
  • Binaries
  • Uft8 variants
  • Structs
  • Nested structs
  • Nullability: nested structs
  • Lists
  • Lists of structs
  • Dictionaries
  • Intervals
  • Unions
  • Maps
  • Metadata endianness check
  • Better test coverage + fuzzing

Design Goals

  • Be reasonably fast
  • Same hash no matter how many batches the input was split into
  • Same hash no matter if dictionary encoding is used

Drawbacks

  • Logical hasing stops short of perfect content addressibility
    • Logical hashing would need to be supported by IPFS and the likes, but this is a stretch as this is not a general-purpose hashing algo
    • A fully deterministic binary encoding with Parquet compatibility may be a better approach
  • Proposed method is order-dependent - it will produce different hashes if records are reordered
  • Boolean hashing could be more efficient

Hashing Process

Starting from primitives and building up:

  • Endinanness - always assume little endian
  • Fixed Size Types
    • Int, FloatingPoint, Decimal, Date, Time, Timestamp - hashed using their in-memory binary representation
    • Bool - hash the individual values as byte-sized values 1 for false and 2 for true
  • Variable Size Types
    • Binary, LargeBinary, FixedSizeBinary, Utf8, LargeUtf8 - hash length (as u64) followed by in-memory representation of the value
    • List, LargeList, FixedSizeList - hash length of the list (as u64) followed by the hash of the sub-array list according to its data type
  • Nullability - every null value is represented by a 0 (zero) byte
    • Arrays without validity bitmap have same hashes as arrays that do and all items are valid
  • Array Data
    • (once per hashing session) Hash data type according to the table below
    • Hash items sequentially using the above rules
  • Record Batch Data
    • (once per hashing session) For every field hash filed_name as utf8, nesting_level (zero-based) as u64 recursively traversing the schema in the depth-first order
    • For every leaf column:
      • Produce a combined nullability bitmap from nullability of every parent
      • Update corresponding column's hasher using above rules
    • (final step) Digests of every array are fed into the combined hasher to produce the final digest
Type (in Schema.fb) TypeID (as u16) Followed by
Null 0
Int 1 unsigned/signed (0/1) as u8, bitwidth as u64
FloatingPoint 2 bitwidth as u64
Binary 3
Utf8 4
Bool 5
Decimal 6 bitwidth as u64, precision as u64, scale as u64
Date 7 bitwidth as u64, DateUnitID
Time 8 bitwidth as u64, TimeUnitID
Timestamp 9 TimeUnitID, timeZone as nullable Utf8
Interval 10
List 11 items data type
Struct 12
Union 13
FixedSizeBinary 3
FixedSizeList 11 items data type
Map 16
Duration 17
LargeBinary 3
LargeUtf8 4
LargeList 11 items data type

Note that some types (Utf8 and LargeUtf8, Binary FixedSizeBinary and LargeBinary, List FixedSizeList and LargeList) are represented in the hash the same, as the difference between them is purely an encoding concern.

DateUnit (in Schema.fb) DateUnitID (as u16)
DAY 0
MILLISECOND 1
TimeUnit (in Schema.fb) TimeUnitID (as u16)
SECOND 0
MILLISECOND 1
MICROSECOND 2
NANOSECOND 3

References

Dependencies

~290KB