[BUG] Merging on categorical variables with mismatched ordering is ambiguous

[jangorecki]

Describe the bug
Grouping query fails with "Merging on categorical variables with mismatched ordering is ambiguous" error.

Steps/Code to reproduce bug

• generate data

wget https://raw.githubusercontent.com/h2oai/db-benchmark/master/_data/groupby-datagen.R
## install data.table if needed, then generate data using script
Rscript groupby-datagen.R 1e7 1e2 0 0

• run code

import cudf as cu
import dask_cudf as dc

x = dc.read_csv("G1_1e7_1e2_0_0.csv", header=0, dtype=['str','str','str','int32','int32','int32','int32','int32','float64'])
x['id1'] = x['id1'].astype('category')
x['id2'] = x['id2'].astype('category')
x['id3'] = x['id3'].astype('category')
x = x.persist()
ans = x.groupby(['id1','id2','id3','id4','id5','id6'], as_index=False, dropna=False).agg({'v3':'sum', 'v1':'size'}).compute()

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/dask/base.py", line 284, in compute
    (result,) = compute(self, traverse=False, **kwargs)
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/dask/base.py", line 566, in compute
    results = schedule(dsk, keys, **kwargs)
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/dask/local.py", line 565, in get_sync
    **kwargs
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/dask/local.py", line 503, in get_async
    for key, res_info, failed in queue_get(queue).result():
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/concurrent/futures/_base.py", line 428, in result
    return self.__get_result()
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/concurrent/futures/_base.py", line 384, in __get_result
    raise self._exception
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/dask/local.py", line 545, in submit
    fut.set_result(fn(*args, **kwargs))
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/dask/local.py", line 237, in batch_execute_tasks
    return [execute_task(*a) for a in it]
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/dask/local.py", line 237, in <listcomp>
    return [execute_task(*a) for a in it]
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/dask/local.py", line 228, in execute_task
    result = pack_exception(e, dumps)
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/dask/local.py", line 223, in execute_task
    result = _execute_task(task, data)
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/dask/core.py", line 121, in _execute_task
    return func(*(_execute_task(a, cache) for a in args))
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/dask/utils.py", line 34, in apply
    return func(*args, **kwargs)
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/dask/dataframe/groupby.py", line 930, in _groupby_apply_funcs
    return type(df)(result)
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/contextlib.py", line 74, in inner
    return func(*args, **kwds)
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/cudf/core/dataframe.py", line 301, in __init__
    self._init_from_dict_like(data, index=index, columns=columns)
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/cudf/core/dataframe.py", line 459, in _init_from_dict_like
    data, index = self._align_input_series_indices(data, index=index)
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/cudf/core/dataframe.py", line 531, in _align_input_series_indices
    input_series
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/cudf/core/series.py", line 7207, in _align_indices
    for sr in series_list
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/cudf/core/series.py", line 7207, in <listcomp>
    for sr in series_list
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/cudf/core/series.py", line 6381, in _align_to_index
    result = lhs.join(rhs, how=how, sort=sort)
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/contextlib.py", line 74, in inner
    return func(*args, **kwds)
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/cudf/core/dataframe.py", line 4564, in join
    sort=sort,
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/contextlib.py", line 74, in inner
    return func(*args, **kwds)
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/cudf/core/dataframe.py", line 4514, in merge
    suffixes=suffixes,
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/cudf/core/frame.py", line 3164, in _merge
    suffixes=suffixes,
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/cudf/core/join/join.py", line 54, in merge
    return mergeobj.perform_merge()
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/cudf/core/join/join.py", line 168, in perform_merge
    lhs, rhs = self._match_key_dtypes(self.lhs, self.rhs)
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/cudf/core/join/join.py", line 447, in _match_key_dtypes
    lcol, rcol, how=self.how
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/cudf/core/join/_join_helpers.py", line 95, in _match_join_keys
    return _match_categorical_dtypes(lcol, rcol, how)
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/cudf/core/join/_join_helpers.py", line 143, in _match_categorical_
dtypes
    return _match_categorical_dtypes_both(lcol, rcol, how)
  File "/home/jan/anaconda3/envs/cudf/lib/python3.7/site-packages/cudf/core/join/_join_helpers.py", line 171, in _match_categorical_
dtypes_both
    "Merging on categorical variables with mismatched"
TypeError: Merging on categorical variables with mismatched ordering is ambiguous

Expected behavior
Query should complete successfully.

Environment overview (please complete the following information)

• Environment location: Bare-metal
• Method of cuDF install: conda

Environment details

Click here to see environment details

 **git***
 commit de056ca926109569998c99b68213de04b2230977 (HEAD -> cudf-use-dask, upstream/cudf-use-dask)
 Author: jangorecki <j.gorecki@wit.edu.pl>
 Date:   Thu May 27 13:14:08 2021 +0200
 
 median not implemented in dask cudf?
 **git submodules***
 
 ***OS Information***
 DISTRIB_ID=Ubuntu
 DISTRIB_RELEASE=16.04
 DISTRIB_CODENAME=xenial
 DISTRIB_DESCRIPTION="Ubuntu 16.04.7 LTS"
 NAME="Ubuntu"
 VERSION="16.04.7 LTS (Xenial Xerus)"
 ID=ubuntu
 ID_LIKE=debian
 PRETTY_NAME="Ubuntu 16.04.7 LTS"
 VERSION_ID="16.04"
 HOME_URL="http://www.ubuntu.com/"
 SUPPORT_URL="http://help.ubuntu.com/"
 BUG_REPORT_URL="http://bugs.launchpad.net/ubuntu/"
 VERSION_CODENAME=xenial
 UBUNTU_CODENAME=xenial
 Linux mr-dl11 4.15.0-122-generic #124~16.04.1-Ubuntu SMP Thu Oct 15 16:08:36 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux
 
 ***GPU Information***
 Thu May 27 04:23:32 2021
 +-----------------------------------------------------------------------------+
 | NVIDIA-SMI 455.23.05    Driver Version: 455.23.05    CUDA Version: 11.1     |
 |-------------------------------+----------------------+----------------------+
 | 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  GeForce GTX 108...  On   | 00000000:02:00.0 Off |                  N/A |
 | 23%   34C    P8    16W / 250W |      1MiB / 11178MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 |   1  GeForce GTX 108...  On   | 00000000:81:00.0 Off |                  N/A |
 | 23%   38C    P8    12W / 250W |      1MiB / 11178MiB |      0%      Default |
 |                               |                      |                  N/A |
 +-------------------------------+----------------------+----------------------+
 
 +-----------------------------------------------------------------------------+
 | Processes:                                                                  |
 |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
 |        ID   ID                                                   Usage      |
 |=============================================================================|
 |  No running processes found                                                 |
 +-----------------------------------------------------------------------------+
 
 ***CPU***
 Architecture:          x86_64
 CPU op-mode(s):        32-bit, 64-bit
 Byte Order:            Little Endian
 CPU(s):                40
 On-line CPU(s) list:   0-39
 Thread(s) per core:    2
 Core(s) per socket:    10
 Socket(s):             2
 NUMA node(s):          2
 Vendor ID:             GenuineIntel
 CPU family:            6
 Model:                 79
 Model name:            Intel(R) Xeon(R) CPU E5-2630 v4 @ 2.20GHz
 Stepping:              1
 CPU MHz:               2595.848
 CPU max MHz:           3100.0000
 CPU min MHz:           1200.0000
 BogoMIPS:              4401.79
 Virtualization:        VT-x
 L1d cache:             32K
 L1i cache:             32K
 L2 cache:              256K
 L3 cache:              25600K
 NUMA node0 CPU(s):     0-9,20-29
 NUMA node1 CPU(s):     10-19,30-39
 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 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 ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a rdseed adx smap intel_pt xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts md_clear flush_l1d
 
 ***CMake***
 /usr/local/bin/cmake
 cmake version 3.13.2
 
 CMake suite maintained and supported by Kitware (kitware.com/cmake).
 
 ***g++***
 /usr/bin/g++
 g++ (Ubuntu 7.5.0-3ubuntu1~16.04) 7.5.0
 Copyright (C) 2017 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-9.2/bin/nvcc
 nvcc: NVIDIA (R) Cuda compiler driver
 Copyright (c) 2005-2018 NVIDIA Corporation
 Built on Tue_Jun_12_23:07:04_CDT_2018
 Cuda compilation tools, release 9.2, V9.2.148
 
 ***Python***
 /usr/bin/python
 Python 2.7.12
 
 ***Environment Variables***
 PATH                            : /usr/local/cuda-9.2/bin:/home/jan/bin:/home/jan/.local/bin:/home/jan/bin:/home/jan/.local/bin:/home/jan/anaconda3/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/opt/mapd:/snap/bin:/usr/lib/jvm/java-8-oracle/bin:/usr/lib/jvm/java-8-oracle/db/bin:/usr/lib/jvm/java-8-oracle/jre/bin:/opt/julia-1.5.3/bin:/opt/julia-1.6.0/bin
 LD_LIBRARY_PATH                 : /usr/local/cuda-9.2/lib64:/usr/lib/jvm/java-8-oracle/jre/lib/amd64/server
 NUMBAPRO_NVVM                   :
 NUMBAPRO_LIBDEVICE              :
 CONDA_PREFIX                    :
 PYTHON_PATH                     :
 
 ***conda packages***
 /home/jan/anaconda3/condabin/conda
 # packages in environment at /home/jan/anaconda3:
 #
 # Name                    Version                   Build  Channel
 _ipyw_jlab_nb_ext_conf    0.1.0                    py36_0
 _libgcc_mutex             0.1                        main
 alabaster                 0.7.12                     py_0    conda-forge
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 anaconda-navigator        1.9.2                    py36_0
 anaconda-project          0.8.2                      py_1    conda-forge
 appdirs                   1.4.3                      py_1    conda-forge
 arrow-cpp                 0.10.0           py36h70250a7_0    conda-forge
 asn1crypto                0.24.0                py36_1003    conda-forge
 astroid                   2.0.3                 py36_1000    conda-forge
 astropy                   3.0.5            py36h470a237_0    conda-forge
 atomicwrites              1.2.1                      py_0    conda-forge
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 automat                   0.7.0                      py_1    conda-forge
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 backports.os              0.1.1                 py36_1000    conda-forge
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 distributed               1.24.0                py36_1000    conda-forge
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Additional context
Query completed fine when not using dask_cudf but only cudf.

[quasiben]

I believe this should be resolved by #8332 . Can you try with latest nightly ?

[jangorecki]

@quasiben I am not able to try nightly.
I hit this error as well but when doing merge on categorical variable, documented in h2oai/db-benchmark#218.
This report is strictly about grouping, not merging. Also the error I got during merging was "not yet implemented" error, which is different from error during group by here. So it is possibly a different root cause of it.

[shwina]

Looking into it.

[shwina]

@jangorecki Thanks for reporting.

Are you able to provide a more minimal reproducer? I get a 404 on the URL for groupby-datagen.R. I also don't have a lot of experience using R.

Testing with the nightly build, I can do a similar groupby on dask_cudf without issue:

import cudf
import dask_cudf

gdf = cudf.datasets.randomdata(
    nrows=100,
    dtypes={'k1': str, 'k2': str, 'k3': int, 'v1': float}
)
dgdf = dask_cudf.from_cudf(gdf, npartitions=2)

dgdf['k1'] = dgdf['k1'].astype('category')
dgdf['k2'] = dgdf['k2'].astype('category')
dgdf['k3'] = dgdf['k3'].astype('category')

dgdf = dgdf.persist()
ans = dgdf.groupby(['k1', 'k2', 'k3'], as_index=False, dropna=False).agg({'v1':'size'}).compute()
print(ans)

[jangorecki]

Sorry, link was broken. I corrected it. Unfortunately I am tracking down other issue in cudf and doesn't really have much time to simplify this now.

[github-actions]

This issue has been labeled inactive-90d due to no recent activity in the past 90 days. Please close this issue if no further response or action is needed. Otherwise, please respond with a comment indicating any updates or changes to the original issue and/or confirm this issue still needs to be addressed.

[RitikParmar]

I am getting this issue following the same procedures as described above in this bug for version 22.06.01 of cudf and using rapids 22.06. Does the fix described in #8332 still work with this version of cudf?

[vyasr]

This no longer fails for me using the same procedure above for data generation.