kevinzwang commented on PR #1208:
URL: https://github.com/apache/iceberg-python/pull/1208#issuecomment-2379885390
Perhaps let's just wait on the response on the devlist first.
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kevinjqliu commented on PR #1208:
URL: https://github.com/apache/iceberg-python/pull/1208#issuecomment-2378222143
> Basically, Spark stores day transformed partition values incorrectly in
the metadata
Thats an interesting find... The core Iceberg library is using `DateType` as
the Re
kevinjqliu commented on PR #1208:
URL: https://github.com/apache/iceberg-python/pull/1208#issuecomment-2378238683
devlist https://lists.apache.org/thread/2gq7b54nvc9q6f1j08l9lnzgm5onkmx5
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kevinzwang commented on PR #1208:
URL: https://github.com/apache/iceberg-python/pull/1208#issuecomment-2377884626
I played around with Spark and inspected the generated metadata. I believe
the partition value is actually stored in the metadata as date type for day
transformed partitions.
kevinjqliu commented on PR #1208:
URL: https://github.com/apache/iceberg-python/pull/1208#issuecomment-237771
The partition value is stored in the metadata as int, but the "partition
metadata table"
(https://iceberg.apache.org/docs/latest/spark-queries/#partitions) shows the
partition
kevinzwang commented on PR #1208:
URL: https://github.com/apache/iceberg-python/pull/1208#issuecomment-2377581839
It's more readable sure, but just does not conform to the spec. I'm not
entirely sure what you mean by your question @kevinjqliu, I believe partition
values are only stored in m
kevinjqliu commented on PR #1208:
URL: https://github.com/apache/iceberg-python/pull/1208#issuecomment-2375810823
I like that its converted, its more readable! Do you know where the
transform happens? Is it only for the metadata table?
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kevinzwang commented on PR #1208:
URL: https://github.com/apache/iceberg-python/pull/1208#issuecomment-2375801600
> Im not 100% sure, perhaps the metadata table does the transformation.
>
> https://iceberg.apache.org/docs/latest/spark-queries/#partitions
I think you are correct
kevinjqliu commented on PR #1208:
URL: https://github.com/apache/iceberg-python/pull/1208#issuecomment-2375566385
Im not 100% sure, perhaps the metadata table does the transformation.
https://iceberg.apache.org/docs/latest/spark-queries/#partitions
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kevinzwang commented on PR #1208:
URL: https://github.com/apache/iceberg-python/pull/1208#issuecomment-2375530341
Ok so interesting... Spark actually does store day transforms as date type
in the metadata, which is why the integration test is failing. This is probably
why this library had t
kevinjqliu commented on code in PR #1208:
URL: https://github.com/apache/iceberg-python/pull/1208#discussion_r1776147728
##
pyiceberg/transforms.py:
##
@@ -517,9 +517,6 @@ def day_func(v: Any) -> int:
def can_transform(self, source: IcebergType) -> bool:
return isi
kevinzwang commented on code in PR #1208:
URL: https://github.com/apache/iceberg-python/pull/1208#discussion_r1776140899
##
pyiceberg/transforms.py:
##
@@ -517,9 +517,6 @@ def day_func(v: Any) -> int:
def can_transform(self, source: IcebergType) -> bool:
return isi
kevinjqliu commented on code in PR #1208:
URL: https://github.com/apache/iceberg-python/pull/1208#discussion_r1776111931
##
pyiceberg/transforms.py:
##
@@ -517,9 +517,6 @@ def day_func(v: Any) -> int:
def can_transform(self, source: IcebergType) -> bool:
return isi
kevinzwang commented on PR #1208:
URL: https://github.com/apache/iceberg-python/pull/1208#issuecomment-2375442625
> is this the source of truth?
https://iceberg.apache.org/spec/#partition-transforms
Yup, precisely
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kevinjqliu commented on PR #1208:
URL: https://github.com/apache/iceberg-python/pull/1208#issuecomment-2375337828
is this the source of truth?
https://iceberg.apache.org/spec/#partition-transforms
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