Fokko commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-2602221346
https://github.com/apache/iceberg-python/pull/1345 has been merged, closing
this one :)
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Fokko closed issue #208: Support partitioned writes
URL: https://github.com/apache/iceberg-python/issues/208
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Fokko commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-2448147351
@RLashofRegas Sorry for the long wait, @sungwy has been working on adding a
rust extension to efficiently run the bucketing transform 🥳 We're blocked on a
release on the rust sid
RLashofRegas commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-2308652459
@Fokko any expected timeline you can share on support for bucket transform?
Is there a separate issue I can follow for that? Thanks for all the hard work
so far!!
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mike-luabase commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-2156618883
Here's what I've been trying (sorry for long example, but thought the
context would help)
```python
iowa_sales_df =
pcsv.read_csv("/Users/mritchie712/blackbird/de
mike-luabase commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-2156617018
@Fokko I installed from source, but I'm [hitting this
error](https://github.com/apache/iceberg-python/blob/94e8a9835995e3b61f07f0dfb48d8a22a1e1d1b0/pyiceberg/table/__init__.
Fokko commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-2156424393
Hey everyone, the support for partioned writes are coming along pretty
nicely. We miss some of the transforms, such as the bucket transform. Most of
the stuff is on the main branc
ppasquet commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-2156311362
Curious as well as where you guys are standing on partitioned write.
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deepika094 commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-2151843836
hi, do we have any way to write to partitioned table so far?
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jaychia commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-2085981955
> Idea from @Fokko - support day/month/year transforms first
You can also try using the transforms that Daft has already implemented.
Full list of transforms:
* [Ex
syun64 commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-2085959258
Idea from @Fokko - support day/month/year transforms first
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jqin61 commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-2083999610
Updates for monthly sync:
1. Working on dynamic overwrite which gets unblocked by partial deletes
https://github.com/apache/iceberg-python/pull/569
2. For transforms functio
jqin61 commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-1922851143
Opened draft PR with working code samples (it supports partitioned append
with identity transform for now):
https://github.com/apache/iceberg-python/pull/353
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jqin61 commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-1921735727
I have an incoming PR with working code samples that conform to the design
above and cover identity transform + append as the first step of supporting
partitioned write. During i
syun64 commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-1919558503
The [Design
Document](https://docs.google.com/document/d/1TLIzxKJilvhAq4JDoGMWMZdkRZXvcrG5YrxLvJ5UXkQ/edit#heading=h.f84o4qaemlga)
on data file writes that was discussed during t
syun64 commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-1912742015
@jqin61 and I discussed this a great deal offline, and we just wanted to
follow up on step (2). If we wanted to use existing PyArrow functions, I think
we could use a 2 pass algo
asheeshgarg commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-1912738795
@Fokko @syun64 @syun64 another option I can think is use polars to do it
simple example below with hashing and partitioning sorting in partition. Where
all the partition is
asheeshgarg commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-1912636932
@jqin61 I have also seen this behavior pyarrow.dataset.write_dataset(), its
behavior removes the partition columns in the written-out parquet files.
@syun64 above approac
syun64 commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-1912560771
Maybe another approach we could take if we want to use existing PyArrow
functions is:
1. table.sort_by (all partitions)
2. figure out the row index for each permutation of p
syun64 commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-1912495464
Right, as @jqin61 mentioned, if we only had to support **Transformed
Partitions**, we could have employed some hack to add partition column to the
dataset, which gets consumed by
jqin61 commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-1912416306
@Fokko Thank you! These 2 points of supporting hidden partitioning and
extracting metrics efficiently during writing are very insightful!
For using pyarrow.dataset.write_da
asheeshgarg commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-1910802240
@Fokko thanks for pointing out the mismatch it worked. After modifying the
datatype it worked.
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Fokko commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-1910290987
Hey @jqin61
Thanks for the elaborate post, and sorry for my slow reply. I did want to
take the time to write a good answer.
Probably the following statement needs ano
asheeshgarg commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-1908955580
@Fokko @jqin61
Today I tried basic example on partition write
from pyiceberg.io.pyarrow import schema_to_pyarrow
import pyarrow as pa
from pyarrow import parquet
syun64 commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-1906879564
> @Fokko @jqin61 I am also interested in this to move forward as we also
deal with lot of write involves partitions. Happy to collaborate on to this.
For write_dataset() we might
jqin61 commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-1906856252
> @jqin61 just wondering if we can use this directly
https://arrow.apache.org/docs/python/generated/pyarrow.dataset.partitioning.html
Thank you Ashish! I overlooked it, as
asheeshgarg commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-1906088623
@jqin61 just wondering if we can use this directly
https://arrow.apache.org/docs/python/generated/pyarrow.dataset.partitioning.html
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jqin61 commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-1901345425
Based on the existing discussion, there are 3 major possible directions for
detecting partitions and writing each partition in a multi-threaded way to
maximize I/O. It seems ther
Fokko commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-1891664926
@jqin61 I did some more thinking over the weekend, and I think that the
approach that you suggested is the most flexible. I forgot about the sort-order
that we also want to add at
Fokko commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-1889891973
I currently see two approaches:
- First get the unique partitions, and then filter for each of the
partitions the relevant data. It is nice that we know the partition upfron
jqin61 commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-1889656103
>How are we going to fan out the writing of the data. We have an Arrow
table, what is an efficient way to compute the partitions and scale out the
work. For example, are we going
jqin61 commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-1885365903
> In Iceberg it can be that some files are still on an older partitioning,
we should make sure that we handle those correctly based on the that we provide.
It seems Spark's
Fokko commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-1879726210
Hey @jqin61 Thanks for replying here. I'm not aware of the fact that anyone
already started on this. It would be great if you can take a stab at it 🚀
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jqin61 commented on issue #208:
URL: https://github.com/apache/iceberg-python/issues/208#issuecomment-1879332455
Hi @Fokko and Iceberg community, I and @syun64 are continuing working on
testing the write capability in [Write support
pr](https://github.com/apache/iceberg-python/pull/41). We
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