[BUG] Bools written by cuIO ORC writer don't match when read by pyarrow/pyorc

[devavret]

When writing a large dataframe with bool column using cuIO ORC writer, the result of reading the file back using pyarrow does not match the input dataframe. However when reading back from cudf's ORC reader it matches.

import pandas as pd
import numpy as np
import pyarrow as pa
import pyorc
import cudf

np.random.seed(0)
from cudf._lib.null_mask import bitmask_allocation_size_bytes

def random_bitmask(size):
    sz = bitmask_allocation_size_bytes(size)
    data = np.random.randint(0, 255, dtype="u1", size=sz)
    return data.view("i1")

size = 6000000
arr = np.random.randint(low=0, high=2, size=size).astype(np.bool)
s = cudf.Series.from_masked_array(arr, random_bitmask(size))
gdf = cudf.DataFrame({"col_bool": s})

# write with cuIO
fname = "brokenbool.orc"
gdf.to_orc(fname)

# read with pyarrow
pdf = pa.orc.ORCFile(fname).read().to_pandas()

# the sum doesn't match
print(gdf.col_bool.sum(), pdf.col_bool.sum())

# read with pyorc
file = open(fname, 'rb')
data = pyorc.Reader(file).read()
pdf = pd.DataFrame(data, columns=["col_bool"])

# sum matches pyarrow but not original df
print(gdf.col_bool.sum(), pdf.col_bool.sum())

# reading with cuIO gives the correct result
print(gdf.col_bool.sum(), cudf.read_orc(fname).col_bool.sum())

Note that this doesn't occur when there are no nulls in the input.

[vuule]

Based on the offline discussion with @rgsl888prabhu , this is potentially a Pyarrow issue, as they don't handle the way our writer splits boolean data streams into stripes. Keeping the priority for now.

[rgsl888prabhu]

For the reference, https://issues.apache.org/jira/browse/ARROW-10635

And assumption in cudf ORC writer https://github.com/rapidsai/cudf/blob/01b8b5c5d0735b5a1c1df4e967fc929b337a9926/cpp/src/io/orc/orc.cpp#L210

[vuule]

We got confirmation that the issue also repros with Spark reader, so treating this as a cuIO bug (not Pyarrow bug).

[rgsl888prabhu]

bool_pq.zip

code to reproduce

import cudf
import pandas as pd
df = cudf.read_parquet("bool_pq.parquet")
df.to_orc("broken_bool.orc")
pdf = pd.read_orc("broken_bool.orc")
gdf = cudf.read_orc("broken_bool.orc")
# test pandas and cudf orc read
pdf.dropna()[gdf.dropna().to_pandas()['col_bool'] != pdf.dropna()['col_bool']]
# Compare parquet and orc cudf read
gdf.dropna()[df.dropna().to_pandas()['col_bool'] != gdf.dropna().to_pandas()['col_bool']]

[vuule]

Root cause: ORC encodes bools as bits, where null values are omitted from the data stream. Row groups have 10k elements, so when there are no nulls they fill 1250 bytes completely. When nulls are present, the last byte might be incomplete. Other readers (pyarrow, Spark) expect all bits in encoded column to be valid (with the exception of last byte in the stripe).

Thus, we need to encode bool values from the next row group into the incomplete byte and set the next row group starting offset to the correct bit within the data encoded as part of the current row group. This offsets the encoding of the next row groups and the effect ripples over the entire stripe. Significant changes are needed to the current implementation to be able to support this.

[vuule]

Suggested approach:

1. Gather row ranges that need to be encoded together (new step/code):
   1. Gather null counts per row group.
   2. Apply thrust::inclusive_scan on the null counts.
   3. Modulo 8 each element to get the number of bits that this row group borrow from the next one (as part of inclusive_scan?).
   4. Find the row number of the last row each row group encoder needs to encode (might be larger than the number of borrowed bits, as nulls are not encoded in the data stream) - thread per row group.
2. Encode kernel changes:
   1. Pass the information about bool row ranges to the encode kernel.
   2. Encode the bool column rows in the range, instead of rows in the row group.
   3. Save the location of the last batch of encoded (Byte RLE) values .
3. Row group offset computation: use information about last encoded batch and the number of borrowed bits.

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