Cudf: [BUG] datetime dtype not being inferred by cudf avro reader
Describe the bug
Looks like the timestamp values written into avro files are not being inferred by cudf while reading an avro file.
Steps/Code to reproduce bug
AVRO FILE(compressed with unzip for github attachment reasons): test_avro.avro.zip
Reading an avro file in pyspark.
>>> from pyspark.sql import SparkSession
>>> # initialise sparkContext
>>> spark = SparkSession.builder \
... .master('local') \
... .appName('myAppName') \
... .config('spark.executor.memory', '5gb') \
... .config("spark.cores.max", "6") \
... .getOrCreate()
>>> df = spark.read.format("avro").load("test_avro.avro")
>>> df
DataFrame[str: string, float: double, int: bigint, int8: int, time: timestamp, bool: boolean, unsigned: bigint]
>>> df.show()
+----+-----+----+----+--------------------+-----+--------+
| str|float| int|int8| time| bool|unsigned|
+----+-----+----+----+--------------------+-----+--------+
| a| 0.32| 1| 1|1970-01-10 18:00:...| null| 1|
|null| 0.32| 2| 2|1970-01-10 18:00:...| true| 2|
| v| 0.0| 3| 3|1970-01-10 18:00:...|false| 3|
| a| 0.23|null|null| null| true| null|
+----+-----+----+----+--------------------+-----+--------+
Observe the time column values, they are being inferred as int64 dtypes in cudf:
>>> import cudf
>>> cudf.read_avro('test_avro.avro')
str float int int8 time bool unsigned
0 a 0.32 1 1 864000234234 <NA> 1
1 <NA> 0.32 2 2 864000023423 True 2
2 v 0.00 3 3 864000234234 False 3
3 a 0.23 <NA> <NA> <NA> True <NA>
Expected behavior
We should be inferring the timestamp dtype correctly.
Environment overview (please complete the following information)
- Environment location: [Bare-metal]
- Method of cuDF install: [from source]
Environment details
Please run and paste the output of the cudf/print_env.sh script here, to gather any other relevant environment details
Click here to see environment details
**git***
commit 4465437d8ed487e2c2981e00007baa2192376575 (HEAD -> branch-0.17, upstream/branch-0.17)
Author: GALI PREM SAGAR <[email protected]>
Date: Thu Oct 8 18:31:07 2020 -0500
Remove deprecated `DataFrame.from_gpu_matrix`, `DataFrame.to_gpu_matrix`, `DataFrame.add_column` APIs and method parameters (#6442)
* Removes deprecated methods: DataFrame.from_gpu_matrix, DataFrame.to_gpu_matrix, DataFrame.add_column.
* Handles setting index correctly, and throwing an error while setting index instead of erroring when accessing later.
**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***
Fri Oct 9 16:04:00 2020
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.64.00 Driver Version: 440.64.00 CUDA Version: 10.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 Tesla T4 On | 00000000:3B:00.0 Off | 0 |
| N/A 50C P8 16W / 70W | 36MiB / 15109MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 1 Tesla T4 On | 00000000:5E:00.0 Off | 0 |
| N/A 37C P8 10W / 70W | 36MiB / 15109MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 2 Tesla T4 On | 00000000:AF:00.0 Off | 0 |
| N/A 33C P8 9W / 70W | 36MiB / 15109MiB | 0% E. Process |
+-------------------------------+----------------------+----------------------+
| 3 Tesla T4 On | 00000000:D8:00.0 Off | 0 |
| N/A 32C P8 9W / 70W | 36MiB / 15109MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 62160 C nvidia-cuda-mps-server 25MiB |
| 1 62160 C nvidia-cuda-mps-server 25MiB |
| 2 62160 C nvidia-cuda-mps-server 25MiB |
| 3 62160 C nvidia-cuda-mps-server 25MiB |
+-----------------------------------------------------------------------------+
***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: 1146.722
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 3.18.2
CMake suite maintained and supported by Kitware (kitware.com/cmake).
***g++***
/usr/bin/g++
g++ (Ubuntu 7.5.0-3ubuntu1~18.04) 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/bin/nvcc
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Wed_Oct_23_19:24:38_PDT_2019
Cuda compilation tools, release 10.2, V10.2.89
***Python***
/nvme/0/pgali/envs/cudfdev/bin/python
Python 3.7.8
***Environment Variables***
PATH : /usr/share/swift/usr/bin:/home/nfs/pgali/bin:/home/nfs/pgali/.local/bin:/nvme/0/pgali/envs/cudfdev/bin:/home/nfs/pgali/anaconda3/bin:/home/nfs/pgali/anaconda3/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/usr/lib/jvm/default-java/bin:/usr/share/sbt-launcher-packaging/bin/sbt-launch.jar/bin:/usr/lib/spark/bin:/usr/lib/spark/sbin:/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/bin/conda
# packages in environment at /nvme/0/pgali/envs/cudfdev:
#
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Additional context
Surfaced while implementing fuzz tests #6001
galipremsagar
All 3 comments
Interesting. There is no low-level timestamp type in avro, but there is apparently a "logicalType" json attribute (not sure if this is spark-specific, as it doesn't seem to appear in the official avro spec, but it shouldn't be too difficult to add):
{"type":"record","name":"topLevelRecord","fields":[
{"name":"str","type":["string","null"]},
{"name":"float","type":["double","null"]},
{"name":"int","type":["long","null"]},
{"name":"int8","type":["int","null"]},
{"name":"time","type":[{"type":"long","logicalType":"timestamp-micros"},"null"]},
{"name":"bool","type":["boolean","null"]},
{"name":"unsigned","type":["long","null"]}]}
OlivierNV
on 10 Oct 2020
👍1
From the Avro spec, looks like there is additional support for a few more derived types by using logicalTypes: https://avro.apache.org/docs/current/spec.html#Logical+Types
I think spark is utilizing the same as you pointed out @OlivierNV
galipremsagar
on 10 Oct 2020
👍1
It helps to read the spec all the way to the end :)
OlivierNV
on 10 Oct 2020
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