Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

dupekit

Raison d'être: Home for the Rust code used for text deduplication.

Install

  • Locally: This code is auto-magically built by uv via Cargo and Maturin. You might need to install them (e.g., brew install maturin rust on macOS).
  • Cluster: This code is compiled as part of the Docker build (uv pip install -e ... step): Maturin builds the Rust code and places it in the system site-packages (e.g., /home/ray/anaconda3/lib/python3.11/site-packages/dupekit/dupekit.abi3.so).

[!NOTE] What about making dupekit a hybrid Python/Rust Maturin workspace? We tried and experienced issues getting the Docker build to work while keeping it simple—a simple Rust workspace helps keep the setup clean.

[!NOTE] Building from source requires a Rust toolchain (Cargo). Pre-built wheels are available from GitHub Releases for users who don't want to compile locally.

Benchmarking

The goal of these benchmarks is to test different ways of marshaling large text content between Python and Rust "foreign function interface" (wiki:FFI). These tests are designed to isolate the overhead of marshaling from the actual Rust computation (by doing minimal processing in Rust).

Dataset: 1 shard of HuggingFaceFW/fineweb-edu/sample/10BT (2.15 GB Parquet file, benchmarked on 250k out of 726k documents)

Install:

uv sync --all-packages --extra=benchmark --group dev

Benchmark (Takes a few minutes):

uv run pytest rust/dupekit/tests/bench/test_dedupe.py --run-benchmark --benchmark-min-rounds=20
uv run pytest rust/dupekit/tests/bench/test_marshaling.py --run-benchmark
uv run pytest rust/dupekit/tests/bench/test_batch_tuning.py --run-benchmark
uv run pytest rust/dupekit/tests/bench/test_io.py --run-benchmark
uv run pytest rust/dupekit/tests/bench/test_hashing.py --run-benchmark
uv run pytest rust/dupekit/tests/bench/test_minhash.py --run-benchmark

Note: Run separated by type of benchmark (otherwise results are mixed within one table)

Footprint (Note: sampling the stack might taint the mem measurements, so we disable benchmarking):

uv run pytest rust/dupekit/tests/bench/test_marshaling.py \
  --run-benchmark \
  --benchmark-disable \
  --memray \
  --native \
  --most-allocations=0

Results

Dedup: Rust vs. Python

---------------------------------------------------------------------------- benchmark 'Documents: Exact Deduplication': 2 tests ----------------------------------------------------------------------------
Name (time in ms)                             Min                 Max                Mean            StdDev              Median               IQR            Outliers       OPS            Rounds  Iterations
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_deduplication[rust-documents]         3.9872 (1.0)        5.2516 (1.0)        4.2341 (1.0)      0.1949 (1.0)        4.2247 (1.0)      0.2845 (1.0)          52;2  236.1805 (1.0)         188           1
test_deduplication[python-documents]     133.8747 (33.58)    157.3844 (29.97)    139.6233 (32.98)    7.7842 (39.94)    135.3300 (32.03)    9.6717 (34.00)         5;0    7.1621 (0.03)         20           1
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

--------------------------------------------------------------------------- benchmark 'Documents: Hash Generation': 2 tests ---------------------------------------------------------------------------
Name (time in ms)                       Min                 Max                Mean            StdDev              Median               IQR            Outliers       OPS            Rounds  Iterations
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_hashing[rust-documents]         2.2755 (1.0)        2.4842 (1.0)        2.3041 (1.0)      0.0301 (1.0)        2.2938 (1.0)      0.0381 (1.0)          50;9  434.0169 (1.0)         375           1
test_hashing[python-documents]     130.0445 (57.15)    132.3783 (53.29)    130.7795 (56.76)    0.6663 (22.13)    130.5910 (56.93)    0.6259 (16.44)         5;3    7.6465 (0.02)         20           1
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

----------------------------------------------------------------------------- benchmark 'Paragraphs: Exact Deduplication': 2 tests ----------------------------------------------------------------------------
Name (time in ms)                              Min                 Max                Mean             StdDev              Median                IQR            Outliers      OPS            Rounds  Iterations
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_deduplication[rust-paragraphs]        85.4666 (1.0)      109.3652 (1.0)       90.2916 (1.0)       6.8294 (1.0)       87.3405 (1.0)       2.0275 (1.0)           4;4  11.0752 (1.0)          20           1
test_deduplication[python-paragraphs]     303.0885 (3.55)     342.9836 (3.14)     321.3022 (3.56)     13.8377 (2.03)     329.4886 (3.77)     25.1111 (12.39)         9;0   3.1123 (0.28)         20           1
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

--------------------------------------------------------------------------- benchmark 'Paragraphs: Hash Generation': 2 tests ---------------------------------------------------------------------------
Name (time in ms)                        Min                 Max                Mean             StdDev              Median               IQR            Outliers      OPS            Rounds  Iterations
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_hashing[rust-paragraphs]        23.9739 (1.0)       26.9860 (1.0)       25.3823 (1.0)       0.4419 (1.0)       25.3160 (1.0)      0.2099 (1.0)           5;5  39.3975 (1.0)          38           1
test_hashing[python-paragraphs]     247.5415 (10.33)    321.4654 (11.91)    255.3421 (10.06)    19.0653 (43.15)    249.1948 (9.84)     1.7899 (8.53)          2;2   3.9163 (0.10)         20           1
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Marshaling

-------------------------------------------------------------------------------------------- benchmark: 7 tests -------------------------------------------------------------------------------------------
Name (time in ms)                    Min                   Max                  Mean             StdDev                Median                IQR            Outliers      OPS            Rounds  Iterations
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_arrow_giant                 86.4414 (1.0)         96.0537 (1.01)        90.0259 (1.0)       2.8582 (31.54)       90.4363 (1.0)       4.1787 (27.96)         3;0  11.1079 (1.0)          11           1
test_arrow_small                 94.4010 (1.09)        94.6679 (1.0)         94.5616 (1.05)      0.0906 (1.0)         94.5570 (1.05)      0.1494 (1.0)           5;0  10.5751 (0.95)         11           1
test_dicts_batched_stream     3,975.1581 (45.99)    3,979.7102 (42.04)    3,977.7639 (44.18)     1.8357 (20.26)    3,978.3399 (43.99)     2.8370 (18.98)         2;0   0.2514 (0.02)          5           1
test_dicts_batch              4,398.7191 (50.89)    4,421.9632 (46.71)    4,410.0489 (48.99)     8.7694 (96.78)    4,411.2232 (48.78)    12.0295 (80.50)         2;0   0.2268 (0.02)          5           1
test_dicts_loop               4,411.8727 (51.04)    4,457.0985 (47.08)    4,431.9081 (49.23)    19.8323 (218.86)   4,430.5465 (48.99)    35.6846 (238.78)        2;0   0.2256 (0.02)          5           1
test_rust_structs             4,449.5728 (51.47)    4,479.8173 (47.32)    4,465.2999 (49.60)    14.1041 (155.65)   4,472.5336 (49.46)    24.8971 (166.60)        3;0   0.2239 (0.02)          5           1
test_arrow_tiny               7,023.5789 (81.25)    7,064.2094 (74.62)    7,044.9691 (78.25)    19.4414 (214.55)   7,047.1538 (77.92)    37.8036 (252.96)        1;0   0.1419 (0.01)          5           1
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

PyArrow Batch Size

--------------------------------------------------------------------------------------------- benchmark: 11 tests ----------------------------------------------------------------------------------------------
Name (time in ms)                         Min                   Max                  Mean             StdDev                Median                IQR            Outliers      OPS            Rounds  Iterations
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_arrow_batch_sizes[8192]          28.6030 (1.0)         32.0178 (1.07)        29.2802 (1.0)       0.8846 (4.75)        28.9333 (1.0)       0.7970 (3.52)          5;3  34.1528 (1.0)          34           1
test_arrow_batch_sizes[16384]         28.7303 (1.00)        30.8987 (1.03)        29.3111 (1.00)      0.5447 (2.92)        29.1404 (1.01)      0.5907 (2.61)          9;2  34.1168 (1.00)         33           1
test_arrow_batch_sizes[4096]          28.8488 (1.01)        30.1474 (1.01)        29.2876 (1.00)      0.3776 (2.03)        29.2212 (1.01)      0.6339 (2.80)         12;0  34.1441 (1.00)         34           1
test_arrow_batch_sizes[2048]          29.1493 (1.02)        30.4442 (1.02)        29.5710 (1.01)      0.3013 (1.62)        29.5505 (1.02)      0.3483 (1.54)         10;1  33.8169 (0.99)         32           1
test_arrow_batch_sizes[32768]         29.2200 (1.02)        29.9410 (1.0)         29.5896 (1.01)      0.1863 (1.0)         29.5706 (1.02)      0.2423 (1.07)         11;0  33.7956 (0.99)         34           1
test_arrow_batch_sizes[65536]         30.3973 (1.06)        31.3805 (1.05)        30.9409 (1.06)      0.2453 (1.32)        30.9829 (1.07)      0.2263 (1.0)           9;3  32.3197 (0.95)         33           1
test_arrow_batch_sizes[131072]        30.7074 (1.07)        33.1845 (1.11)        31.4322 (1.07)      0.6799 (3.65)        31.1102 (1.08)      0.8739 (3.86)          6;1  31.8145 (0.93)         32           1
test_arrow_batch_sizes[1024]          30.7724 (1.08)        32.6049 (1.09)        31.6173 (1.08)      0.5506 (2.96)        31.6311 (1.09)      0.9233 (4.08)         13;0  31.6283 (0.93)         30           1
test_arrow_batch_sizes[512]           33.8866 (1.18)        36.2981 (1.21)        34.5224 (1.18)      0.6189 (3.32)        34.2960 (1.19)      0.5087 (2.25)          6;3  28.9667 (0.85)         29           1
test_arrow_batch_sizes[128]           51.0530 (1.78)        56.3190 (1.88)        53.5492 (1.83)      1.6124 (8.65)        53.7474 (1.86)      2.3557 (10.41)         7;0  18.6744 (0.55)         18           1
test_arrow_batch_sizes[1]          2,781.2088 (97.23)    2,812.2547 (93.93)    2,797.8572 (95.55)    11.6892 (62.74)    2,801.0024 (96.81)    15.3956 (68.03)         2;0   0.3574 (0.01)          5           1
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

I/O

------------------------------------------------------------------------------- benchmark: 4 tests ------------------------------------------------------------------------------
Name (time in s)          Min               Max              Mean            StdDev            Median               IQR            Outliers     OPS            Rounds  Iterations
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_rust_native       1.6757 (1.0)      1.6848 (1.0)      1.6794 (1.0)      0.0035 (1.26)     1.6783 (1.0)      0.0047 (1.73)          2;0  0.5955 (1.0)           5           1
test_arrow_giant       2.9501 (1.76)     2.9570 (1.76)     2.9521 (1.76)     0.0028 (1.0)      2.9511 (1.76)     0.0027 (1.0)           1;0  0.3387 (0.57)          5           1
test_arrow_small       3.3476 (2.00)     3.6588 (2.17)     3.5583 (2.12)     0.1241 (44.48)    3.5726 (2.13)     0.1289 (47.18)         1;0  0.2810 (0.47)          5           1
test_dicts_loop_io     7.3664 (4.40)     7.3913 (4.39)     7.3837 (4.40)     0.0101 (3.63)     7.3871 (4.40)     0.0113 (4.14)          1;0  0.1354 (0.23)          5           1
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Hashing

--------------------------------------------------------------------------------------- benchmark: 6 tests ---------------------------------------------------------------------------------------
Name (time in ms)                     Min                Max               Mean            StdDev             Median               IQR            Outliers       OPS            Rounds  Iterations
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_hash_rust_xxh3_64_batch       4.4886 (1.0)       4.9466 (1.0)       4.5860 (1.0)      0.0616 (1.57)      4.5939 (1.0)      0.0957 (2.52)         74;1  218.0558 (1.0)         210           1
test_hash_rust_xxh3_64_scalar      5.0276 (1.12)      5.3367 (1.08)      5.1276 (1.12)     0.0393 (1.0)       5.1307 (1.12)     0.0379 (1.0)         41;12  195.0244 (0.89)        190           1
test_hash_rust_xxh3_128            6.1686 (1.37)      6.5772 (1.33)      6.2901 (1.37)     0.1098 (2.79)      6.2334 (1.36)     0.1731 (4.56)         37;0  158.9811 (0.73)        160           1
test_hash_rust_blake3             28.7743 (6.41)     29.0392 (5.87)     28.8919 (6.30)     0.0593 (1.51)     28.8799 (6.29)     0.0709 (1.87)         10;1   34.6118 (0.16)         35           1
test_hash_rust_blake2             54.1043 (12.05)    55.0271 (11.12)    54.4180 (11.87)    0.3711 (9.43)     54.1916 (11.80)    0.7337 (19.34)         5;0   18.3763 (0.08)         19           1
test_hash_python_blake2b          84.0109 (18.72)    84.1698 (17.02)    84.0611 (18.33)    0.0465 (1.18)     84.0469 (18.30)    0.0595 (1.57)          3;0   11.8961 (0.05)         12           1
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Mem Footprin (sorted from high to low):

Allocation results for rust/dupekit/tests/bench/test_marshaling.py::test_rust_structs at the high watermark

	 📦 Total memory allocated: 4.3GiB
	 📏 Total allocations: 21
	 📊 Histogram of allocation sizes: | ▃█▁▃|

Allocation results for rust/dupekit/tests/bench/test_marshaling.py::test_dicts_batch at the high watermark

	 📦 Total memory allocated: 3.3GiB
	 📏 Total allocations: 20
	 📊 Histogram of allocation sizes: |  ▁█▂|

Allocation results for rust/dupekit/tests/bench/test_marshaling.py::test_dicts_loop at the high watermark

	 📦 Total memory allocated: 3.3GiB
	 📏 Total allocations: 19
	 📊 Histogram of allocation sizes: |  ▁█▂|

Allocation results for rust/dupekit/tests/bench/test_marshaling.py::test_arrow_giant at the high watermark

	 📦 Total memory allocated: 64.9MiB
	 📏 Total allocations: 36
	 📊 Histogram of allocation sizes: |▅█   |

Allocation results for rust/dupekit/tests/bench/test_marshaling.py::test_dicts_batched_stream at the high watermark

	 📦 Total memory allocated: 28.1MiB
	 📏 Total allocations: 7
	 📊 Histogram of allocation sizes: |█▄▄▄▄|

Allocation results for rust/dupekit/tests/bench/test_marshaling.py::test_arrow_tiny at the high watermark

	 📦 Total memory allocated: 22.0MiB
	 📏 Total allocations: 37
	 📊 Histogram of allocation sizes: |█▇   |

Allocation results for rust/dupekit/tests/bench/test_marshaling.py::test_arrow_small at the high watermark

	 📦 Total memory allocated: 551.7KiB
	 📏 Total allocations: 42
	 📊 Histogram of allocation sizes: |▂█▁  |

Statement of attribution:

  • This code was seeded from nelson-liu/rbloom-gcs.
  • Bloom filters were originally proposed in (Bloom, 1970). Furthermore, this implementation makes use of a constant recommended by (L'Ecuyer, 1999) for redistributing the entropy of a single hash over multiple integers using a linear congruential generator.

Metadata

Release files for marin-dupekit 0.1.2.dev202605310835

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for marin-dupekit 0.1.2.dev202605310835
File Size Uploaded
marin_dupekit-0.1.2.dev202605310835.tar.gz 73.9 kB Details

Built distributions (wheels)

Table of built distributions (wheels) for marin-dupekit 0.1.2.dev202605310835
File
marin_dupekit-0.1.2.dev202605310835-cp311-abi3-manylinux_2_28_x86_64.whl CPython 3.11 abi3 Linux glibc 2.28+ x86-64 Details
marin_dupekit-0.1.2.dev202605310835-cp311-abi3-manylinux_2_28_aarch64.whl CPython 3.11 abi3 Linux glibc 2.28+ ARM64 Details
marin_dupekit-0.1.2.dev202605310835-cp311-abi3-macosx_11_0_arm64.whl CPython 3.11 abi3 macOS 11.0+ ARM64 Details
marin_dupekit-0.1.2.dev202605310835-cp311-abi3-macosx_10_12_x86_64.whl CPython 3.11 abi3 macOS 10.12+ x86-64 Details

Total release size: 17.1 MB

Release files / marin_dupekit-0.1.2.dev202605310835.tar.gz

Download URL marin_dupekit-0.1.2.dev202605310835.tar.gz
Size 73.9 kB
Tags Source
SHA-256 checksum
How to use checksums
76ebf4927bad6ddb28c441182fa4f7e14bd6dc6413a21bbece15ae1b4b3c6d06
BLAKE2b-256 checksum
How to use checksums
faeb26af99dfeb8ce3354d59d6117cc5c090816a013ca0bf00de6f51bf2f4f6e
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.13

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on May 31, 2026.

Transparency log

Release files / marin_dupekit-0.1.2.dev202605310835-cp311-abi3-manylinux_2_28_x86_64.whl

Download URL marin_dupekit-0.1.2.dev202605310835-cp311-abi3-manylinux_2_28_x86_64.whl
Size 4.5 MB
Tags CPython 3.11 Linux glibc 2.28+ x86-64 abi3
SHA-256 checksum
How to use checksums
65b8bd62172f901fd1f7bfaf01422d383f38056a7ee0291faedadc4e33735f49
BLAKE2b-256 checksum
How to use checksums
22bf79fc4b87938f42e6709bdff2e53cab167f76fddf4c44a6ecc8f5f83ef4ba
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.13

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on May 31, 2026.

Transparency log

Release files / marin_dupekit-0.1.2.dev202605310835-cp311-abi3-manylinux_2_28_aarch64.whl

Download URL marin_dupekit-0.1.2.dev202605310835-cp311-abi3-manylinux_2_28_aarch64.whl
Size 4.1 MB
Tags CPython 3.11 Linux glibc 2.28+ ARM64 abi3
SHA-256 checksum
How to use checksums
79a5ae8161d705d7f266548bc032b0a8cb4ff12926536fa55f354d65beb673b9
BLAKE2b-256 checksum
How to use checksums
61bcd7a873e28dce744c02a5129c05aa9d755573e6482df76b12bd7381b2cc27
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.13

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on May 31, 2026.

Transparency log

Release files / marin_dupekit-0.1.2.dev202605310835-cp311-abi3-macosx_11_0_arm64.whl

Download URL marin_dupekit-0.1.2.dev202605310835-cp311-abi3-macosx_11_0_arm64.whl
Size 4.0 MB
Tags CPython 3.11 abi3 macOS 11.0+ ARM64
SHA-256 checksum
How to use checksums
8ec29610cbde49ce8baf583abcb246806ee7b94dfd386e93a1969481f4b88279
BLAKE2b-256 checksum
How to use checksums
2695f483edd4f6ca915a174eff6d3a8db2e74347c8e0eafa12c57648cd35aecc
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.13

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on May 31, 2026.

Transparency log

Release files / marin_dupekit-0.1.2.dev202605310835-cp311-abi3-macosx_10_12_x86_64.whl

Download URL marin_dupekit-0.1.2.dev202605310835-cp311-abi3-macosx_10_12_x86_64.whl
Size 4.4 MB
Tags CPython 3.11 abi3 macOS 10.12+ x86-64
SHA-256 checksum
How to use checksums
6dfa0bba672727e160166c59ab23ea245a7ba5eb237aecbcf13f5db4a8d1acfb
BLAKE2b-256 checksum
How to use checksums
d87170905f8a22e1a6cbe1db0991605c3a1857070fde0eab30a9feecb17664c3
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.13

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on May 31, 2026.

Transparency log

Release history Release notifications | RSS feed

This release

0.1.1

5 release files

0.1.0

2 release files

0.0.1

1 release file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page