[jira] [Updated] (ARROW-2659) [Python] More graceful reading of empty String columns in ParquetDataset
[
https://issues.apache.org/jira/browse/ARROW-2659?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wes McKinney updated ARROW-2659:
Fix Version/s: 1.0.0
> [Python] More graceful reading of empty String columns in ParquetDataset
>
>
> Key: ARROW-2659
> URL: https://issues.apache.org/jira/browse/ARROW-2659
> Project: Apache Arrow
> Issue Type: Bug
> Components: C++, Python
>Affects Versions: 0.9.0
>Reporter: Uwe Korn
>Priority: Major
> Labels: dataset, dataset-parquet-read, parquet
> Fix For: 1.0.0
>
> Attachments: read_parquet_dataset.error.read_table.novalidation.txt,
> read_parquet_dataset.error.read_table.txt
>
>
> When currently saving a {{ParquetDataset}} from Pandas, we don't get
> consistent schemas, even if the source was a single DataFrame. This is due to
> the fact that in some partitions object columns like string can become empty.
> Then the resulting Arrow schema will differ. In the central metadata, we will
> store this column as {{pa.string}} whereas in the partition file with the
> empty columns, this columns will be stored as {{pa.null}}.
> The two schemas are still a valid match in terms of schema evolution and we
> should respect that in
> https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L754
> Instead of doing a {{pa.Schema.equals}} in
> https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L778
> we should introduce a new method {{pa.Schema.can_evolve_to}} that is more
> graceful and returns {{True}} if a dataset piece has a null column where the
> main metadata states a nullable column of any type.
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[jira] [Updated] (ARROW-2659) [Python] More graceful reading of empty String columns in ParquetDataset
[
https://issues.apache.org/jira/browse/ARROW-2659?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Joris Van den Bossche updated ARROW-2659:
-
Labels: dataset dataset-parquet-read parquet (was: dataset parquet)
> [Python] More graceful reading of empty String columns in ParquetDataset
>
>
> Key: ARROW-2659
> URL: https://issues.apache.org/jira/browse/ARROW-2659
> Project: Apache Arrow
> Issue Type: Bug
> Components: C++, Python
>Affects Versions: 0.9.0
>Reporter: Uwe Korn
>Priority: Major
> Labels: dataset, dataset-parquet-read, parquet
> Attachments: read_parquet_dataset.error.read_table.novalidation.txt,
> read_parquet_dataset.error.read_table.txt
>
>
> When currently saving a {{ParquetDataset}} from Pandas, we don't get
> consistent schemas, even if the source was a single DataFrame. This is due to
> the fact that in some partitions object columns like string can become empty.
> Then the resulting Arrow schema will differ. In the central metadata, we will
> store this column as {{pa.string}} whereas in the partition file with the
> empty columns, this columns will be stored as {{pa.null}}.
> The two schemas are still a valid match in terms of schema evolution and we
> should respect that in
> https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L754
> Instead of doing a {{pa.Schema.equals}} in
> https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L778
> we should introduce a new method {{pa.Schema.can_evolve_to}} that is more
> graceful and returns {{True}} if a dataset piece has a null column where the
> main metadata states a nullable column of any type.
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[jira] [Updated] (ARROW-2659) [Python] More graceful reading of empty String columns in ParquetDataset
[
https://issues.apache.org/jira/browse/ARROW-2659?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wes McKinney updated ARROW-2659:
Fix Version/s: (was: 0.16.0)
> [Python] More graceful reading of empty String columns in ParquetDataset
>
>
> Key: ARROW-2659
> URL: https://issues.apache.org/jira/browse/ARROW-2659
> Project: Apache Arrow
> Issue Type: Bug
> Components: C++, Python
>Affects Versions: 0.9.0
>Reporter: Uwe Korn
>Priority: Major
> Labels: dataset, parquet
> Attachments: read_parquet_dataset.error.read_table.novalidation.txt,
> read_parquet_dataset.error.read_table.txt
>
>
> When currently saving a {{ParquetDataset}} from Pandas, we don't get
> consistent schemas, even if the source was a single DataFrame. This is due to
> the fact that in some partitions object columns like string can become empty.
> Then the resulting Arrow schema will differ. In the central metadata, we will
> store this column as {{pa.string}} whereas in the partition file with the
> empty columns, this columns will be stored as {{pa.null}}.
> The two schemas are still a valid match in terms of schema evolution and we
> should respect that in
> https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L754
> Instead of doing a {{pa.Schema.equals}} in
> https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L778
> we should introduce a new method {{pa.Schema.can_evolve_to}} that is more
> graceful and returns {{True}} if a dataset piece has a null column where the
> main metadata states a nullable column of any type.
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[jira] [Updated] (ARROW-2659) [Python] More graceful reading of empty String columns in ParquetDataset
[
https://issues.apache.org/jira/browse/ARROW-2659?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wes McKinney updated ARROW-2659:
Component/s: C++
> [Python] More graceful reading of empty String columns in ParquetDataset
>
>
> Key: ARROW-2659
> URL: https://issues.apache.org/jira/browse/ARROW-2659
> Project: Apache Arrow
> Issue Type: Bug
> Components: C++, Python
>Affects Versions: 0.9.0
>Reporter: Uwe L. Korn
>Priority: Major
> Labels: dataset, parquet
> Fix For: 1.0.0
>
> Attachments: read_parquet_dataset.error.read_table.novalidation.txt,
> read_parquet_dataset.error.read_table.txt
>
>
> When currently saving a {{ParquetDataset}} from Pandas, we don't get
> consistent schemas, even if the source was a single DataFrame. This is due to
> the fact that in some partitions object columns like string can become empty.
> Then the resulting Arrow schema will differ. In the central metadata, we will
> store this column as {{pa.string}} whereas in the partition file with the
> empty columns, this columns will be stored as {{pa.null}}.
> The two schemas are still a valid match in terms of schema evolution and we
> should respect that in
> https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L754
> Instead of doing a {{pa.Schema.equals}} in
> https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L778
> we should introduce a new method {{pa.Schema.can_evolve_to}} that is more
> graceful and returns {{True}} if a dataset piece has a null column where the
> main metadata states a nullable column of any type.
--
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[jira] [Updated] (ARROW-2659) [Python] More graceful reading of empty String columns in ParquetDataset
[
https://issues.apache.org/jira/browse/ARROW-2659?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wes McKinney updated ARROW-2659:
Fix Version/s: (was: 0.14.0)
0.15.0
> [Python] More graceful reading of empty String columns in ParquetDataset
>
>
> Key: ARROW-2659
> URL: https://issues.apache.org/jira/browse/ARROW-2659
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
>Affects Versions: 0.9.0
>Reporter: Uwe L. Korn
>Priority: Major
> Labels: dataset, parquet
> Fix For: 0.15.0
>
> Attachments: read_parquet_dataset.error.read_table.novalidation.txt,
> read_parquet_dataset.error.read_table.txt
>
>
> When currently saving a {{ParquetDataset}} from Pandas, we don't get
> consistent schemas, even if the source was a single DataFrame. This is due to
> the fact that in some partitions object columns like string can become empty.
> Then the resulting Arrow schema will differ. In the central metadata, we will
> store this column as {{pa.string}} whereas in the partition file with the
> empty columns, this columns will be stored as {{pa.null}}.
> The two schemas are still a valid match in terms of schema evolution and we
> should respect that in
> https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L754
> Instead of doing a {{pa.Schema.equals}} in
> https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L778
> we should introduce a new method {{pa.Schema.can_evolve_to}} that is more
> graceful and returns {{True}} if a dataset piece has a null column where the
> main metadata states a nullable column of any type.
--
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[jira] [Updated] (ARROW-2659) [Python] More graceful reading of empty String columns in ParquetDataset
[
https://issues.apache.org/jira/browse/ARROW-2659?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wes McKinney updated ARROW-2659:
Labels: dataset parquet (was: parquet)
> [Python] More graceful reading of empty String columns in ParquetDataset
>
>
> Key: ARROW-2659
> URL: https://issues.apache.org/jira/browse/ARROW-2659
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
>Affects Versions: 0.9.0
>Reporter: Uwe L. Korn
>Priority: Major
> Labels: dataset, parquet
> Fix For: 0.14.0
>
> Attachments: read_parquet_dataset.error.read_table.novalidation.txt,
> read_parquet_dataset.error.read_table.txt
>
>
> When currently saving a {{ParquetDataset}} from Pandas, we don't get
> consistent schemas, even if the source was a single DataFrame. This is due to
> the fact that in some partitions object columns like string can become empty.
> Then the resulting Arrow schema will differ. In the central metadata, we will
> store this column as {{pa.string}} whereas in the partition file with the
> empty columns, this columns will be stored as {{pa.null}}.
> The two schemas are still a valid match in terms of schema evolution and we
> should respect that in
> https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L754
> Instead of doing a {{pa.Schema.equals}} in
> https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L778
> we should introduce a new method {{pa.Schema.can_evolve_to}} that is more
> graceful and returns {{True}} if a dataset piece has a null column where the
> main metadata states a nullable column of any type.
--
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[jira] [Updated] (ARROW-2659) [Python] More graceful reading of empty String columns in ParquetDataset
[
https://issues.apache.org/jira/browse/ARROW-2659?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wes McKinney updated ARROW-2659:
Fix Version/s: (was: 0.13.0)
0.14.0
> [Python] More graceful reading of empty String columns in ParquetDataset
>
>
> Key: ARROW-2659
> URL: https://issues.apache.org/jira/browse/ARROW-2659
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
>Affects Versions: 0.9.0
>Reporter: Uwe L. Korn
>Priority: Major
> Labels: parquet
> Fix For: 0.14.0
>
> Attachments: read_parquet_dataset.error.read_table.novalidation.txt,
> read_parquet_dataset.error.read_table.txt
>
>
> When currently saving a {{ParquetDataset}} from Pandas, we don't get
> consistent schemas, even if the source was a single DataFrame. This is due to
> the fact that in some partitions object columns like string can become empty.
> Then the resulting Arrow schema will differ. In the central metadata, we will
> store this column as {{pa.string}} whereas in the partition file with the
> empty columns, this columns will be stored as {{pa.null}}.
> The two schemas are still a valid match in terms of schema evolution and we
> should respect that in
> https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L754
> Instead of doing a {{pa.Schema.equals}} in
> https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L778
> we should introduce a new method {{pa.Schema.can_evolve_to}} that is more
> graceful and returns {{True}} if a dataset piece has a null column where the
> main metadata states a nullable column of any type.
--
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[jira] [Updated] (ARROW-2659) [Python] More graceful reading of empty String columns in ParquetDataset
[
https://issues.apache.org/jira/browse/ARROW-2659?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wes McKinney updated ARROW-2659:
Fix Version/s: (was: 0.12.0)
0.13.0
> [Python] More graceful reading of empty String columns in ParquetDataset
>
>
> Key: ARROW-2659
> URL: https://issues.apache.org/jira/browse/ARROW-2659
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
>Affects Versions: 0.9.0
>Reporter: Uwe L. Korn
>Priority: Major
> Labels: parquet
> Fix For: 0.13.0
>
> Attachments: read_parquet_dataset.error.read_table.novalidation.txt,
> read_parquet_dataset.error.read_table.txt
>
>
> When currently saving a {{ParquetDataset}} from Pandas, we don't get
> consistent schemas, even if the source was a single DataFrame. This is due to
> the fact that in some partitions object columns like string can become empty.
> Then the resulting Arrow schema will differ. In the central metadata, we will
> store this column as {{pa.string}} whereas in the partition file with the
> empty columns, this columns will be stored as {{pa.null}}.
> The two schemas are still a valid match in terms of schema evolution and we
> should respect that in
> https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L754
> Instead of doing a {{pa.Schema.equals}} in
> https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L778
> we should introduce a new method {{pa.Schema.can_evolve_to}} that is more
> graceful and returns {{True}} if a dataset piece has a null column where the
> main metadata states a nullable column of any type.
--
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[jira] [Updated] (ARROW-2659) [Python] More graceful reading of empty String columns in ParquetDataset
[
https://issues.apache.org/jira/browse/ARROW-2659?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wes McKinney updated ARROW-2659:
Labels: parquet (was: beginner)
> [Python] More graceful reading of empty String columns in ParquetDataset
>
>
> Key: ARROW-2659
> URL: https://issues.apache.org/jira/browse/ARROW-2659
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
>Affects Versions: 0.9.0
>Reporter: Uwe L. Korn
>Priority: Major
> Labels: parquet
> Fix For: 0.12.0
>
> Attachments: read_parquet_dataset.error.read_table.novalidation.txt,
> read_parquet_dataset.error.read_table.txt
>
>
> When currently saving a {{ParquetDataset}} from Pandas, we don't get
> consistent schemas, even if the source was a single DataFrame. This is due to
> the fact that in some partitions object columns like string can become empty.
> Then the resulting Arrow schema will differ. In the central metadata, we will
> store this column as {{pa.string}} whereas in the partition file with the
> empty columns, this columns will be stored as {{pa.null}}.
> The two schemas are still a valid match in terms of schema evolution and we
> should respect that in
> https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L754
> Instead of doing a {{pa.Schema.equals}} in
> https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L778
> we should introduce a new method {{pa.Schema.can_evolve_to}} that is more
> graceful and returns {{True}} if a dataset piece has a null column where the
> main metadata states a nullable column of any type.
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[jira] [Updated] (ARROW-2659) [Python] More graceful reading of empty String columns in ParquetDataset
[
https://issues.apache.org/jira/browse/ARROW-2659?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wes McKinney updated ARROW-2659:
Fix Version/s: (was: 0.11.0)
0.12.0
> [Python] More graceful reading of empty String columns in ParquetDataset
>
>
> Key: ARROW-2659
> URL: https://issues.apache.org/jira/browse/ARROW-2659
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
>Affects Versions: 0.9.0
>Reporter: Uwe L. Korn
>Priority: Major
> Labels: beginner
> Fix For: 0.12.0
>
> Attachments: read_parquet_dataset.error.read_table.novalidation.txt,
> read_parquet_dataset.error.read_table.txt
>
>
> When currently saving a {{ParquetDataset}} from Pandas, we don't get
> consistent schemas, even if the source was a single DataFrame. This is due to
> the fact that in some partitions object columns like string can become empty.
> Then the resulting Arrow schema will differ. In the central metadata, we will
> store this column as {{pa.string}} whereas in the partition file with the
> empty columns, this columns will be stored as {{pa.null}}.
> The two schemas are still a valid match in terms of schema evolution and we
> should respect that in
> https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L754
> Instead of doing a {{pa.Schema.equals}} in
> https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L778
> we should introduce a new method {{pa.Schema.can_evolve_to}} that is more
> graceful and returns {{True}} if a dataset piece has a null column where the
> main metadata states a nullable column of any type.
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[jira] [Updated] (ARROW-2659) [Python] More graceful reading of empty String columns in ParquetDataset
[
https://issues.apache.org/jira/browse/ARROW-2659?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Aldrin updated ARROW-2659:
--
Attachment: read_parquet_dataset.error.read_table.txt
read_parquet_dataset.error.read_table.novalidation.txt
> [Python] More graceful reading of empty String columns in ParquetDataset
>
>
> Key: ARROW-2659
> URL: https://issues.apache.org/jira/browse/ARROW-2659
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
>Affects Versions: 0.9.0
>Reporter: Uwe L. Korn
>Priority: Major
> Labels: beginner
> Fix For: 0.11.0
>
> Attachments: read_parquet_dataset.error.read_table.novalidation.txt,
> read_parquet_dataset.error.read_table.txt
>
>
> When currently saving a {{ParquetDataset}} from Pandas, we don't get
> consistent schemas, even if the source was a single DataFrame. This is due to
> the fact that in some partitions object columns like string can become empty.
> Then the resulting Arrow schema will differ. In the central metadata, we will
> store this column as {{pa.string}} whereas in the partition file with the
> empty columns, this columns will be stored as {{pa.null}}.
> The two schemas are still a valid match in terms of schema evolution and we
> should respect that in
> https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L754
> Instead of doing a {{pa.Schema.equals}} in
> https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L778
> we should introduce a new method {{pa.Schema.can_evolve_to}} that is more
> graceful and returns {{True}} if a dataset piece has a null column where the
> main metadata states a nullable column of any type.
--
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[jira] [Updated] (ARROW-2659) [Python] More graceful reading of empty String columns in ParquetDataset
[
https://issues.apache.org/jira/browse/ARROW-2659?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Uwe L. Korn updated ARROW-2659:
---
Labels: beginner (was: )
> [Python] More graceful reading of empty String columns in ParquetDataset
>
>
> Key: ARROW-2659
> URL: https://issues.apache.org/jira/browse/ARROW-2659
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
>Affects Versions: 0.9.0
>Reporter: Uwe L. Korn
>Priority: Major
> Labels: beginner
> Fix For: 0.11.0
>
>
> When currently saving a {{ParquetDataset}} from Pandas, we don't get
> consistent schemas, even if the source was a single DataFrame. This is due to
> the fact that in some partitions object columns like string can become empty.
> Then the resulting Arrow schema will differ. In the central metadata, we will
> store this column as {{pa.string}} whereas in the partition file with the
> empty columns, this columns will be stored as {{pa.null}}.
> The two schemas are still a valid match in terms of schema evolution and we
> should respect that in
> https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L754
> Instead of doing a {{pa.Schema.equals}} in
> https://github.com/apache/arrow/blob/79a22074e0b059a24c5cd45713f8d085e24f826a/python/pyarrow/parquet.py#L778
> we should introduce a new method {{pa.Schema.can_evolve_to}} that is more
> graceful and returns {{True}} if a dataset piece has a null column where the
> main metadata states a nullable column of any type.
--
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