[
https://issues.apache.org/jira/browse/ARROW-12513?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
David Beach updated ARROW-12513:
--------------------------------
Description:
When writing a Table as Parquet, when the table contains columns represented as
dictionary-encoded arrays, those columns show an incorrect null_count of 0 in
the Parquet metadata. If the same data is saved without dictionary-encoding
the array, then the null_count is correct.
Confirmed bug with PyArrow 1.0.1, 2.0.0, and 3.0.0.
NOTE: I'm a PyArrow user, but I believe this bug is actually in the C++
implementation of the Arrow/Parquet writer.
h3. Setup
{code:python}
import pyarrow as pa
from pyarrow import parquet{code}
h3. Bug
(writes a dictionary encoded Arrow array to parquet)
{code:python}
array1 = pa.array([None, 'foo', 'bar'] * 5, type=pa.string())
assert array1.null_count == 5
array1dict = array1.dictionary_encode()
assert array1dict.null_count == 5
table = pa.Table.from_arrays([array1dict], ["mycol"])
parquet.write_table(table, "testtable.parquet")
meta = parquet.read_metadata("testtable.parquet")
meta.row_group(0).column(0).statistics.null_count # RESULT: 0 (WRONG!){code}
h3. Correct
(writes same data without dictionary encoding the Arrow array)
{code:python}
array1 = pa.array([None, 'foo', 'bar'] * 5, type=pa.string())
assert array1.null_count == 5
table = pa.Table.from_arrays([array1], ["mycol"])
parquet.write_table(table, "testtable.parquet")
meta = parquet.read_metadata("testtable.parquet")
meta.row_group(0).column(0).statistics.null_count # RESULT: 5 (CORRECT)
{code}
was:
When writing a Table as Parquet, when the table contains columns represented as
dictionary-encoded arrays, those columns show an incorrect null_count of 0 in
the Parquet metadata. If the same data is saved without dictionary-encoding
the array, then the null_count is correct.
Confirmed bug with PyArrow 1.0.1, 2.0.0, and 3.0.0.
NOTE: I'm a PyArrow user, but I believe this but is actually in the C++
implementation of the Arrow/Parquet writer.
h3. Setup
{code:python}
import pyarrow as pa
from pyarrow import parquet{code}
h3. Bug
(writes a dictionary encoded Arrow array to parquet)
{code:python}
array1 = pa.array([None, 'foo', 'bar'] * 5, type=pa.string())
assert array1.null_count == 5
array1dict = array1.dictionary_encode()
assert array1dict.null_count == 5
table = pa.Table.from_arrays([array1dict], ["mycol"])
parquet.write_table(table, "testtable.parquet")
meta = parquet.read_metadata("testtable.parquet")
meta.row_group(0).column(0).statistics.null_count # RESULT: 0 (WRONG!){code}
h3. Correct
(writes same data without dictionary encoding the Arrow array)
{code:python}
array1 = pa.array([None, 'foo', 'bar'] * 5, type=pa.string())
assert array1.null_count == 5
table = pa.Table.from_arrays([array1], ["mycol"])
parquet.write_table(table, "testtable.parquet")
meta = parquet.read_metadata("testtable.parquet")
meta.row_group(0).column(0).statistics.null_count # RESULT: 5 (CORRECT)
{code}
> Parquet Writer always puts null_count=0 in Parquet statistics for
> dictionary-encoded array with nulls
> -----------------------------------------------------------------------------------------------------
>
> Key: ARROW-12513
> URL: https://issues.apache.org/jira/browse/ARROW-12513
> Project: Apache Arrow
> Issue Type: Bug
> Components: C++, Parquet, Python
> Affects Versions: 1.0.1, 2.0.0, 3.0.0
> Environment: RHEL6
> Reporter: David Beach
> Priority: Critical
>
> When writing a Table as Parquet, when the table contains columns represented
> as dictionary-encoded arrays, those columns show an incorrect null_count of 0
> in the Parquet metadata. If the same data is saved without
> dictionary-encoding the array, then the null_count is correct.
> Confirmed bug with PyArrow 1.0.1, 2.0.0, and 3.0.0.
> NOTE: I'm a PyArrow user, but I believe this bug is actually in the C++
> implementation of the Arrow/Parquet writer.
> h3. Setup
> {code:python}
> import pyarrow as pa
> from pyarrow import parquet{code}
> h3. Bug
> (writes a dictionary encoded Arrow array to parquet)
> {code:python}
> array1 = pa.array([None, 'foo', 'bar'] * 5, type=pa.string())
> assert array1.null_count == 5
> array1dict = array1.dictionary_encode()
> assert array1dict.null_count == 5
> table = pa.Table.from_arrays([array1dict], ["mycol"])
> parquet.write_table(table, "testtable.parquet")
> meta = parquet.read_metadata("testtable.parquet")
> meta.row_group(0).column(0).statistics.null_count # RESULT: 0 (WRONG!){code}
> h3. Correct
> (writes same data without dictionary encoding the Arrow array)
> {code:python}
> array1 = pa.array([None, 'foo', 'bar'] * 5, type=pa.string())
> assert array1.null_count == 5
> table = pa.Table.from_arrays([array1], ["mycol"])
> parquet.write_table(table, "testtable.parquet")
> meta = parquet.read_metadata("testtable.parquet")
> meta.row_group(0).column(0).statistics.null_count # RESULT: 5 (CORRECT)
> {code}
>
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