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[BUG] Unable to retrieve Decimal types correctly when read via pandas.read_parquet #10172

Closed
galipremsagar opened this issue Jan 31, 2022 · 3 comments · Fixed by #10224
Closed

[BUG] Unable to retrieve Decimal types correctly when read via pandas.read_parquet #10172

galipremsagar opened this issue Jan 31, 2022 · 3 comments · Fixed by #10224
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@devavret
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bug Something isn't working cuIO cuIO issue libcudf Affects libcudf (C++/CUDA) code.

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@galipremsagar
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Describe the bug
It first appears to be a pandas bug but after a detailed comparison with pyarrow parquet writer, it appears to be a cudf parquet writer issue.

Steps/Code to reproduce bug

>>> import cudf
>>> import pandas as pd
>>> df = cudf.DataFrame({'a':[1, 2, 3, None, 4]})
>>> df
      a
0     1
1     2
2     3
3  <NA>
4     4
>>> df['a'] = df['a'].astype(cudf.Decimal32Dtype(5, 2))
>>> df
      a
0  1.00
1  2.00
2  3.00
3  <NA>
4  4.00
>>> df.to_parquet('p_gpu')
>>> pd.read_parquet('p_gpu')
      a
0  1.00
1  2.00
2  3.00
3  <NA>
4  4.00
>>> pd.read_parquet('p_gpu')['a']
0    1.00
1    2.00
2    3.00
3    <NA>
4    4.00
Name: a, dtype: string
>>> pd.read_parquet('p_gpu')['a'][0]
'1.00'    # This should be a Decimal object as opposed to `string`, see next one for reference.
>>> df.to_pandas()['a'][0]
Decimal('1.00')
>>> df
      a
0  1.00
1  2.00
2  3.00
3  <NA>
4  4.00



>>> pd.read_parquet('p_gpu', use_nullable_dtypes=True)['a'][0]  # Still the same issue with `use_nullable_dtypes=True` too.
'1.00'


# Pandas(via pyarrow) writer appears to be working fine too:
>>> pdf = df.to_pandas()
>>> pdf.to_parquet('p_cpu')
>>> pd.read_parquet('p_cpu')
      a
0  1.00
1  2.00
2  3.00
3  None
4  4.00
>>> pd.read_parquet('p_cpu')['a'][0]
Decimal('1.00')
>>> cudf.read_parquet('p_cpu')
      a
0  1.00
1  2.00
2  3.00
3  <NA>
4  4.00
>>> cudf.read_parquet('p_cpu').dtypes
a    decimal32
dtype: object




# Pyarrow writer directly also seems working fine:

>>> table = df.to_arrow()
>>> pa.parquet.write_table(table, 'p_t')
>>> pa.parquet.read_table('p_t')['a'][0]
<pyarrow.Decimal128Scalar: Decimal('1.00')>
>>> pd.read_parquet('p_t')
      a
0  1.00
1  2.00
2  3.00
3  None
4  4.00
>>> pd.read_parquet('p_t')['a'][0]
Decimal('1.00')

# The issue doesn't just seem to be metadata specific, as both of them appear identical.
>>> pa.parquet.read_metadata('p_t').metadata[b'pandas']
b'{"index_columns": [{"kind": "range", "name": null, "start": 0, "stop": 5, "step": 1}], "column_indexes": [{"name": null, "field_name": null, "pandas_type": "unicode", "numpy_type": "object", "metadata": {"encoding": "UTF-8"}}], "columns": [{"name": "a", "field_name": "a", "pandas_type": "decimal", "numpy_type": "object", "metadata": {"precision": 5, "scale": 2}}], "creator": {"library": "pyarrow", "version": "5.0.0"}, "pandas_version": "1.3.5"}'
>>> pa.parquet.read_metadata('p_gpu').metadata[b'pandas']
b'{"index_columns": [{"kind": "range", "name": null, "start": 0, "stop": 5, "step": 1}], "column_indexes": [{"name": null, "field_name": null, "pandas_type": "unicode", "numpy_type": "object", "metadata": {"encoding": "UTF-8"}}], "columns": [{"name": "a", "field_name": "a", "pandas_type": "decimal", "numpy_type": "string", "metadata": {"precision": 5, "scale": 2}}], "creator": {"library": "pyarrow", "version": "5.0.0"}, "pandas_version": "1.3.5"}'

Expected behavior

>>> pd.read_parquet('p_gpu')['a'][0]
Decimal('1.00')

Environment overview (please complete the following information)

  • Environment location: [Bare-metal]
  • Method of cuDF install: [from source]

Environment details

Click here to see environment details
 **git***
 commit b217d7ea9fc77d4ff8eee41460f4aa657046268a (HEAD -> branch-22.04)
 Author: Alfred Xu <[email protected]>
 Date:   Mon Jan 31 23:06:55 2022 +0800
 
 JNI: Rewrite growBuffersAndRows to accelerate the HostColumnBuilder (#10025)
 
 According to https://github.com/NVIDIA/spark-rapids/issues/4393, current PR takes several measures to speed up the buffer growing during the build of `HostColumnVector`:
 1. Introduce `rowCapacity` to cache the maximum number of rows/bytes
 2. Introduce pura Java method `byteSizeOfNullMask` to get the size of the validity buffer
 3. Reorganize the code structure to reduce the number of method calls
 
 I have tested this PR with the spark-rapids tests locally.
 BTW, shall we clean up the `HostColumnVector.Builder` and replace all the usages of `Builder` with `ColumnBuilder`?
 
 Authors:
 - Alfred Xu (https://github.com/sperlingxx)
 
 Approvers:
 - Robert (Bobby) Evans (https://github.com/revans2)
 
 URL: https://github.com/rapidsai/cudf/pull/10025
 **git submodules***
 
 ***OS Information***
 DISTRIB_ID=Ubuntu
 DISTRIB_RELEASE=18.04
 DISTRIB_CODENAME=bionic
 DISTRIB_DESCRIPTION="Ubuntu 18.04.4 LTS"
 NAME="Ubuntu"
 VERSION="18.04.4 LTS (Bionic Beaver)"
 ID=ubuntu
 ID_LIKE=debian
 PRETTY_NAME="Ubuntu 18.04.4 LTS"
 VERSION_ID="18.04"
 HOME_URL="https://www.ubuntu.com/"
 SUPPORT_URL="https://help.ubuntu.com/"
 BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"
 PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"
 VERSION_CODENAME=bionic
 UBUNTU_CODENAME=bionic
 Linux dt07 4.15.0-76-generic #86-Ubuntu SMP Fri Jan 17 17:24:28 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux
 
 ***GPU Information***
 Mon Jan 31 11:17:25 2022
 +-----------------------------------------------------------------------------+
 | NVIDIA-SMI 495.29.05    Driver Version: 495.29.05    CUDA Version: 11.5     |
 |-------------------------------+----------------------+----------------------+
 | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
 | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
 |                               |                      |               MIG M. |
 |===============================+======================+======================|
 |   0  Tesla T4            On   | 00000000:3B:00.0 Off |                    0 |
 | N/A   50C    P0    27W /  70W |   4080MiB / 15109MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 |   1  Tesla T4            On   | 00000000:5E:00.0 Off |                    0 |
 | N/A   37C    P8     9W /  70W |      3MiB / 15109MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 |   2  Tesla T4            On   | 00000000:AF:00.0 Off |                    0 |
 | N/A   32C    P8     9W /  70W |      3MiB / 15109MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 |   3  Tesla T4            On   | 00000000:D8:00.0 Off |                    0 |
 | N/A   32C    P8     9W /  70W |      3MiB / 15109MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 
 +-----------------------------------------------------------------------------+
 | Processes:                                                                  |
 |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
 |        ID   ID                                                   Usage      |
 |=============================================================================|
 |    0   N/A  N/A      3501      C   python                            425MiB |
 |    0   N/A  N/A      3784      C   python                            425MiB |
 |    0   N/A  N/A      4028      C   python                            425MiB |
 |    0   N/A  N/A      4963      C   python                            425MiB |
 |    0   N/A  N/A      6602      C   python                            425MiB |
 |    0   N/A  N/A      7314      C   python                            425MiB |
 +-----------------------------------------------------------------------------+
 
 ***CPU***
 Architecture:        x86_64
 CPU op-mode(s):      32-bit, 64-bit
 Byte Order:          Little Endian
 CPU(s):              64
 On-line CPU(s) list: 0-63
 Thread(s) per core:  2
 Core(s) per socket:  16
 Socket(s):           2
 NUMA node(s):        2
 Vendor ID:           GenuineIntel
 CPU family:          6
 Model:               85
 Model name:          Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz
 Stepping:            4
 CPU MHz:             1122.944
 BogoMIPS:            4200.00
 Virtualization:      VT-x
 L1d cache:           32K
 L1i cache:           32K
 L2 cache:            1024K
 L3 cache:            22528K
 NUMA node0 CPU(s):   0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62
 NUMA node1 CPU(s):   1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,53,55,57,59,61,63
 Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke md_clear flush_l1d
 
 ***CMake***
 /nvme/0/pgali/envs/cudfdev/bin/cmake
 cmake version 3.21.3
 
 CMake suite maintained and supported by Kitware (kitware.com/cmake).
 
 ***g++***
 /usr/bin/g++
 g++ (Ubuntu 9.4.0-1ubuntu1~18.04) 9.4.0
 Copyright (C) 2019 Free Software Foundation, Inc.
 This is free software; see the source for copying conditions.  There is NO
 warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
 
 
 ***nvcc***
 /usr/local/cuda/bin/nvcc
 nvcc: NVIDIA (R) Cuda compiler driver
 Copyright (c) 2005-2021 NVIDIA Corporation
 Built on Mon_Sep_13_19:13:29_PDT_2021
 Cuda compilation tools, release 11.5, V11.5.50
 Build cuda_11.5.r11.5/compiler.30411180_0
 
 ***Python***
 /nvme/0/pgali/envs/cudfdev/bin/python
 Python 3.8.12
 
 ***Environment Variables***
 PATH                            : /nvme/0/pgali/envs/cudfdev/bin:~/anaconda3/bin:/nvme/0/pgali/envs/cudfdev/bin:/home/nfs/pgali/anaconda3/condabin:/home/nfs/pgali/.vscode-server/bin/899d46d82c4c95423fb7e10e68eba52050e30ba3/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/usr/local/cuda/bin
 LD_LIBRARY_PATH                 : /usr/local/cuda/lib64::/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64
 NUMBAPRO_NVVM                   :
 NUMBAPRO_LIBDEVICE              :
 CONDA_PREFIX                    : /nvme/0/pgali/envs/cudfdev
 PYTHON_PATH                     :
 
 ***conda packages***
 /home/nfs/pgali/anaconda3/condabin/conda
 # packages in environment at /nvme/0/pgali/envs/cudfdev:
 #
 # Name                    Version                   Build  Channel
 _libgcc_mutex             0.1                 conda_forge    conda-forge
 _openmp_mutex             4.5                      1_llvm    conda-forge
 abseil-cpp                20210324.2           h9c3ff4c_0    conda-forge
 alabaster                 0.7.12                     py_0    conda-forge
 appdirs                   1.4.4              pyh9f0ad1d_0    conda-forge
 argon2-cffi               21.3.0             pyhd8ed1ab_0    conda-forge
 argon2-cffi-bindings      21.2.0           py38h497a2fe_1    conda-forge
 arrow-cpp                 5.0.0           py38h579a05f_22_cuda    conda-forge
 arrow-cpp-proc            3.0.0                      cuda    conda-forge
 asttokens                 2.0.5              pyhd8ed1ab_0    conda-forge
 attrs                     21.4.0             pyhd8ed1ab_0    conda-forge
 aws-c-cal                 0.5.11               h95a6274_0    conda-forge
 aws-c-common              0.6.2                h7f98852_0    conda-forge
 aws-c-event-stream        0.2.7               h3541f99_13    conda-forge
 aws-c-io                  0.10.5               hfb6a706_0    conda-forge
 aws-checksums             0.1.11               ha31a3da_7    conda-forge
 aws-sdk-cpp               1.8.186              hb4091e7_3    conda-forge
 babel                     2.9.1              pyh44b312d_0    conda-forge
 backcall                  0.2.0              pyh9f0ad1d_0    conda-forge
 backports                 1.0                        py_2    conda-forge
 backports-zoneinfo        0.2.1                    pypi_0    pypi
 backports.functools_lru_cache 1.6.4              pyhd8ed1ab_0    conda-forge
 beautifulsoup4            4.10.0             pyha770c72_0    conda-forge
 binutils_impl_linux-64    2.36.1               h193b22a_2    conda-forge
 black                     19.10b0                    py_4    conda-forge
 bleach                    4.1.0              pyhd8ed1ab_0    conda-forge
 bokeh                     2.4.2            py38h578d9bd_0    conda-forge
 brotlipy                  0.7.0           py38h497a2fe_1003    conda-forge
 bzip2                     1.0.8                h7f98852_4    conda-forge
 c-ares                    1.18.1               h7f98852_0    conda-forge
 ca-certificates           2021.10.8            ha878542_0    conda-forge
 cachetools                5.0.0              pyhd8ed1ab_0    conda-forge
 certifi                   2021.10.8        py38h578d9bd_1    conda-forge
 cffi                      1.15.0           py38h3931269_0    conda-forge
 cfgv                      3.3.1              pyhd8ed1ab_0    conda-forge
 charset-normalizer        2.0.10             pyhd8ed1ab_0    conda-forge
 clang                     11.1.0               ha770c72_1    conda-forge
 clang-11                  11.1.0          default_ha53f305_1    conda-forge
 clang-tools               11.1.0          default_ha53f305_1    conda-forge
 clangxx                   11.1.0          default_ha53f305_1    conda-forge
 click                     8.0.3            py38h578d9bd_1    conda-forge
 cloudpickle               2.0.0              pyhd8ed1ab_0    conda-forge
 cmake                     3.21.3               h8897547_0    conda-forge
 cmake_setuptools          0.1.3                      py_0    rapidsai
 colorama                  0.4.4              pyh9f0ad1d_0    conda-forge
 commonmark                0.9.1                      py_0    conda-forge
 cryptography              36.0.1           py38h3e25421_0    conda-forge
 cuda-python               11.6.0           py38h3fd9d12_0    nvidia
 cudatoolkit               11.5.1               hcf5317a_9    nvidia
 cudf                      22.4.0a0+92.gb217d7ea9f.dirty          pypi_0    pypi
 cupy                      10.1.0           py38h592bde7_0    conda-forge
 cyrus-sasl                2.1.27               h230043b_5    conda-forge
 cython                    0.29.27          py38h709712a_0    conda-forge
 cytoolz                   0.11.2           py38h497a2fe_1    conda-forge
 dask                      2022.1.1                 pypi_0    pypi
 dask-cudf                 22.4.0a0+92.gb217d7ea9f.dirty          pypi_0    pypi
 dataclasses               0.8                pyhc8e2a94_3    conda-forge
 debugpy                   1.5.1            py38h709712a_0    conda-forge
 decorator                 5.1.1              pyhd8ed1ab_0    conda-forge
 defusedxml                0.7.1              pyhd8ed1ab_0    conda-forge
 distlib                   0.3.4              pyhd8ed1ab_0    conda-forge
 distributed               2022.1.1+1.g7ed1f1a6          pypi_0    pypi
 dlpack                    0.5                  h9c3ff4c_0    conda-forge
 docutils                  0.17.1           py38h578d9bd_1    conda-forge
 double-conversion         3.2.0                h9c3ff4c_0    conda-forge
 entrypoints               0.3             pyhd8ed1ab_1003    conda-forge
 execnet                   1.9.0              pyhd8ed1ab_0    conda-forge
 executing                 0.8.2              pyhd8ed1ab_0    conda-forge
 expat                     2.4.3                h9c3ff4c_0    conda-forge
 fastavro                  1.4.9            py38h497a2fe_0    conda-forge
 fastrlock                 0.8              py38h709712a_1    conda-forge
 filelock                  3.4.2              pyhd8ed1ab_1    conda-forge
 flake8                    3.8.3                      py_1    conda-forge
 flit-core                 3.6.0              pyhd8ed1ab_0    conda-forge
 freetype                  2.10.4               h0708190_1    conda-forge
 fsspec                    2022.1.0           pyhd8ed1ab_0    conda-forge
 future                    0.18.2           py38h578d9bd_4    conda-forge
 gcc_impl_linux-64         11.2.0              h82a94d6_12    conda-forge
 gettext                   0.19.8.1          h73d1719_1008    conda-forge
 gflags                    2.2.2             he1b5a44_1004    conda-forge
 glog                      0.5.0                h48cff8f_0    conda-forge
 gmp                       6.2.1                h58526e2_0    conda-forge
 grpc-cpp                  1.42.0               ha1441d3_1    conda-forge
 heapdict                  1.0.1                      py_0    conda-forge
 huggingface_hub           0.4.0              pyhd8ed1ab_0    conda-forge
 hypothesis                6.36.1             pyhd8ed1ab_0    conda-forge
 identify                  2.4.6              pyhd8ed1ab_0    conda-forge
 idna                      3.3                pyhd8ed1ab_0    conda-forge
 imagesize                 1.3.0              pyhd8ed1ab_0    conda-forge
 importlib-metadata        4.10.1           py38h578d9bd_0    conda-forge
 importlib_metadata        4.10.1               hd8ed1ab_0    conda-forge
 importlib_resources       5.4.0              pyhd8ed1ab_0    conda-forge
 iniconfig                 1.1.1              pyh9f0ad1d_0    conda-forge
 ipykernel                 6.7.0            py38he5a9106_0    conda-forge
 ipython                   8.0.1            py38h578d9bd_0    conda-forge
 ipython_genutils          0.2.0                      py_1    conda-forge
 isort                     5.6.4                      py_0    conda-forge
 jbig                      2.1               h7f98852_2003    conda-forge
 jedi                      0.18.1           py38h578d9bd_0    conda-forge
 jinja2                    3.0.3              pyhd8ed1ab_0    conda-forge
 joblib                    1.1.0              pyhd8ed1ab_0    conda-forge
 jpeg                      9e                   h7f98852_0    conda-forge
 jsonschema                4.4.0              pyhd8ed1ab_0    conda-forge
 jupyter_client            7.1.2              pyhd8ed1ab_0    conda-forge
 jupyter_core              4.9.1            py38h578d9bd_1    conda-forge
 jupyterlab_pygments       0.1.2              pyh9f0ad1d_0    conda-forge
 kernel-headers_linux-64   2.6.32              he073ed8_15    conda-forge
 krb5                      1.19.2               hcc1bbae_3    conda-forge
 lcms2                     2.12                 hddcbb42_0    conda-forge
 ld_impl_linux-64          2.36.1               hea4e1c9_2    conda-forge
 lerc                      3.0                  h9c3ff4c_0    conda-forge
 libblas                   3.9.0            13_linux64_mkl    conda-forge
 libbrotlicommon           1.0.9                h7f98852_6    conda-forge
 libbrotlidec              1.0.9                h7f98852_6    conda-forge
 libbrotlienc              1.0.9                h7f98852_6    conda-forge
 libcblas                  3.9.0            13_linux64_mkl    conda-forge
 libclang-cpp11.1          11.1.0          default_ha53f305_1    conda-forge
 libcurl                   7.81.0               h2574ce0_0    conda-forge
 libdeflate                1.8                  h7f98852_0    conda-forge
 libedit                   3.1.20191231         he28a2e2_2    conda-forge
 libev                     4.33                 h516909a_1    conda-forge
 libevent                  2.1.10               h9b69904_4    conda-forge
 libffi                    3.4.2                h7f98852_5    conda-forge
 libgcc-devel_linux-64     11.2.0              h0952999_12    conda-forge
 libgcc-ng                 11.2.0              h1d223b6_12    conda-forge
 libgcrypt                 1.9.4                h7f98852_0    conda-forge
 libgomp                   11.2.0              h1d223b6_12    conda-forge
 libgpg-error              1.42                 h9c3ff4c_0    conda-forge
 libgsasl                  1.10.0               h5b4c23d_0    conda-forge
 liblapack                 3.9.0            13_linux64_mkl    conda-forge
 libllvm11                 11.1.0               hf817b99_2    conda-forge
 libnghttp2                1.46.0               h812cca2_0    conda-forge
 libnsl                    2.0.0                h7f98852_0    conda-forge
 libntlm                   1.4               h7f98852_1002    conda-forge
 libpng                    1.6.37               h21135ba_2    conda-forge
 libprotobuf               3.19.3               h780b84a_0    conda-forge
 librdkafka                1.7.0                hc49e61c_1    conda-forge
 librmm                    22.04.00a220131 cuda11_g81d523a_15    rapidsai-nightly
 libsanitizer              11.2.0              he4da1e4_12    conda-forge
 libsodium                 1.0.18               h36c2ea0_1    conda-forge
 libssh2                   1.10.0               ha56f1ee_2    conda-forge
 libstdcxx-ng              11.2.0              he4da1e4_12    conda-forge
 libthrift                 0.15.0               he6d91bd_1    conda-forge
 libtiff                   4.3.0                h6f004c6_2    conda-forge
 libutf8proc               2.7.0                h7f98852_0    conda-forge
 libuv                     1.43.0               h7f98852_0    conda-forge
 libwebp-base              1.2.2                h7f98852_1    conda-forge
 libzlib                   1.2.11            h36c2ea0_1013    conda-forge
 llvm-openmp               12.0.1               h4bd325d_1    conda-forge
 llvmlite                  0.38.0           py38h4630a5e_0    conda-forge
 locket                    0.2.0                      py_2    conda-forge
 lz4-c                     1.9.3                h9c3ff4c_1    conda-forge
 markdown                  3.3.6              pyhd8ed1ab_0    conda-forge
 markupsafe                2.0.1            py38h497a2fe_1    conda-forge
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@galipremsagar galipremsagar added bug Something isn't working Needs Triage Need team to review and classify libcudf Affects libcudf (C++/CUDA) code. cuIO cuIO issue labels Jan 31, 2022
@galipremsagar galipremsagar changed the title [BUG] Unable to retrieve Decimal types correct when read via pandas.read_parquet [BUG] Unable to retrieve Decimal types correctly when read via pandas.read_parquet Jan 31, 2022
@vuule vuule removed the Needs Triage Need team to review and classify label Feb 3, 2022
@devavret
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devavret commented Feb 4, 2022

This is a metadata problem. The pandas json metadata for the column says "numpy_type": "string" for cudf written file but "numpy_type": "object" for pandas written file. Manually editing the former to also have "object" fixed it.

Need to figure out why generate_pandas_metadata creates the erroneous metadata in cudf.

@galipremsagar
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This is a metadata problem. The pandas json metadata for the column says "numpy_type": "string" for cudf written file but "numpy_type": "object" for pandas written file. Manually editing the former to also have "object" fixed it.

Need to figure out why generate_pandas_metadata creates the erroneous metadata in cudf.

I think we need to add decimal to the special case here:

https://github.com/rapidsai/cudf/blob/branch-22.04/python/cudf/cudf/_lib/utils.pyx#L197-L198

@devavret
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devavret commented Feb 4, 2022 •
edited
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pa.pandas_compat.construct_metadata does the right thing. We then mess it up with

cudf/python/cudf/cudf/_lib/utils.pyx

Lines 189 to 196 in 05dd541

if (
col_meta["name"] in table._column_names
and table._data[col_meta["name"]].nullable
and col_meta["numpy_type"] in PARQUET_META_TYPE_MAP
):
col_meta["numpy_type"] = PARQUET_META_TYPE_MAP[
col_meta["numpy_type"]
]
where we translate as per the map

cudf/python/cudf/cudf/utils/dtypes.py

Lines 40 to 51 in 8d7330f

np_dtypes_to_pandas_dtypes = {
np.dtype("uint8"): pd.UInt8Dtype(),
np.dtype("uint16"): pd.UInt16Dtype(),
np.dtype("uint32"): pd.UInt32Dtype(),
np.dtype("uint64"): pd.UInt64Dtype(),
np.dtype("int8"): pd.Int8Dtype(),
np.dtype("int16"): pd.Int16Dtype(),
np.dtype("int32"): pd.Int32Dtype(),
np.dtype("int64"): pd.Int64Dtype(),
np.dtype("bool_"): pd.BooleanDtype(),
np.dtype("object"): pd.StringDtype(),
}

A better fix would be to fix that map but I didn't do it for "list" so we can do the quick fix now also

@galipremsagar galipremsagar mentioned this issue Feb 4, 2022
Merged
rapids-bot bot pushed a commit that referenced this issue Feb 4, 2022
@galipremsagar
Fix decimal metadata in parquet writer (#10224)
e5ba292 
Fixes: #10172 

`pa.pandas_compat.construct_metadata` constructs the correct metadata but is being overridden by special `list` & `struct` handling logic as `string`, rather than retaining it as `object`. This PR fixes the issue and modifies existing tests to validate the issue.

Authors:
  - GALI PREM SAGAR (https://github.com/galipremsagar)

Approvers:
  - https://github.com/brandon-b-miller
  - Devavret Makkar (https://github.com/devavret)

URL: #10224
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bug Something isn't working cuIO cuIO issue libcudf Affects libcudf (C++/CUDA) code.
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[REVIEW] Fix decimal metadata in parquet writer galipremsagar/cudf
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@devavret @galipremsagar @vuule