[jira] [Assigned] (ARROW-1974) [Python] Segfault when working with Arrow tables with duplicate columns

2018-03-08 Thread Wes McKinney (JIRA) 

 [ 
https://issues.apache.org/jira/browse/ARROW-1974?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Wes McKinney reassigned ARROW-1974:
---

Assignee: Wes McKinney  (was: Antoine Pitrou)

> [Python] Segfault when working with Arrow tables with duplicate columns
> ---
>
> Key: ARROW-1974
> URL: https://issues.apache.org/jira/browse/ARROW-1974
> Project: Apache Arrow
>  Issue Type: Bug
>  Components: C++, Python
>Affects Versions: 0.8.0
> Environment: Linux Mint 18.2
> Anaconda Python distribution + pyarrow installed from the conda-forge channel
>Reporter: Alexey Strokach
>Assignee: Wes McKinney
>Priority: Minor
>  Labels: pull-request-available
> Fix For: 0.9.0
>
>
> I accidentally created a large number of Parquet files with two 
> __index_level_0__ columns (through a Spark SQL query).
> PyArrow can read these files into tables, but it segfaults when converting 
> the resulting tables to Pandas DataFrames or when saving the tables to 
> Parquet files.
> {code:none}
> # Duplicate columns cause segmentation faults
> table = pq.read_table('/path/to/duplicate_column_file.parquet')
> table.to_pandas()  # Segmentation fault
> pq.write_table(table, '/some/output.parquet') # Segmentation fault
> {code}
> If I remove the duplicate column using table.remove_column(...) everything 
> works without segfaults.
> {code:none}
> # After removing duplicate columns, everything works fine
> table = pq.read_table('/path/to/duplicate_column_file.parquet')
> table.remove_column(34)
> table.to_pandas()  # OK
> pq.write_table(table, '/some/output.parquet')  # OK
> {code}
> For more concrete examples, see `test_segfault_1.py` and `test_segfault_2.py` 
> here: https://gitlab.com/ostrokach/pyarrow_duplicate_column_errors.



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[jira] [Assigned] (ARROW-1974) [Python] Segfault when working with Arrow tables with duplicate columns

2018-03-08 Thread Wes McKinney (JIRA) 

 [ 
https://issues.apache.org/jira/browse/ARROW-1974?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Wes McKinney reassigned ARROW-1974:
---

Assignee: Antoine Pitrou  (was: Phillip Cloud)

> [Python] Segfault when working with Arrow tables with duplicate columns
> ---
>
> Key: ARROW-1974
> URL: https://issues.apache.org/jira/browse/ARROW-1974
> Project: Apache Arrow
>  Issue Type: Bug
>  Components: C++, Python
>Affects Versions: 0.8.0
> Environment: Linux Mint 18.2
> Anaconda Python distribution + pyarrow installed from the conda-forge channel
>Reporter: Alexey Strokach
>Assignee: Antoine Pitrou
>Priority: Minor
>  Labels: pull-request-available
> Fix For: 0.9.0
>
>
> I accidentally created a large number of Parquet files with two 
> __index_level_0__ columns (through a Spark SQL query).
> PyArrow can read these files into tables, but it segfaults when converting 
> the resulting tables to Pandas DataFrames or when saving the tables to 
> Parquet files.
> {code:none}
> # Duplicate columns cause segmentation faults
> table = pq.read_table('/path/to/duplicate_column_file.parquet')
> table.to_pandas()  # Segmentation fault
> pq.write_table(table, '/some/output.parquet') # Segmentation fault
> {code}
> If I remove the duplicate column using table.remove_column(...) everything 
> works without segfaults.
> {code:none}
> # After removing duplicate columns, everything works fine
> table = pq.read_table('/path/to/duplicate_column_file.parquet')
> table.remove_column(34)
> table.to_pandas()  # OK
> pq.write_table(table, '/some/output.parquet')  # OK
> {code}
> For more concrete examples, see `test_segfault_1.py` and `test_segfault_2.py` 
> here: https://gitlab.com/ostrokach/pyarrow_duplicate_column_errors.



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[jira] [Assigned] (ARROW-1974) [Python] Segfault when working with Arrow tables with duplicate columns

2018-01-24 Thread Phillip Cloud (JIRA) 

 [ 
https://issues.apache.org/jira/browse/ARROW-1974?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Phillip Cloud reassigned ARROW-1974:


Assignee: Phillip Cloud

> [Python] Segfault when working with Arrow tables with duplicate columns
> ---
>
> Key: ARROW-1974
> URL: https://issues.apache.org/jira/browse/ARROW-1974
> Project: Apache Arrow
>  Issue Type: Bug
>  Components: C++, Python
>Affects Versions: 0.8.0
> Environment: Linux Mint 18.2
> Anaconda Python distribution + pyarrow installed from the conda-forge channel
>Reporter: Alexey Strokach
>Assignee: Phillip Cloud
>Priority: Minor
> Fix For: 0.9.0
>
>
> I accidentally created a large number of Parquet files with two 
> __index_level_0__ columns (through a Spark SQL query).
> PyArrow can read these files into tables, but it segfaults when converting 
> the resulting tables to Pandas DataFrames or when saving the tables to 
> Parquet files.
> {code:none}
> # Duplicate columns cause segmentation faults
> table = pq.read_table('/path/to/duplicate_column_file.parquet')
> table.to_pandas()  # Segmentation fault
> pq.write_table(table, '/some/output.parquet') # Segmentation fault
> {code}
> If I remove the duplicate column using table.remove_column(...) everything 
> works without segfaults.
> {code:none}
> # After removing duplicate columns, everything works fine
> table = pq.read_table('/path/to/duplicate_column_file.parquet')
> table.remove_column(34)
> table.to_pandas()  # OK
> pq.write_table(table, '/some/output.parquet')  # OK
> {code}
> For more concrete examples, see `test_segfault_1.py` and `test_segfault_2.py` 
> here: https://gitlab.com/ostrokach/pyarrow_duplicate_column_errors.



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