[SPARK-30091][SQL][Python] Document mergeSchema option directly in the PySpark Parquet APIs #26730
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nchammas
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Test build #114693 has finished for PR 26730 at commit
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Seems fine but mind filing a JIRA please @nchammas? |
nchammas
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nchammas
commented
Dec 2, 2019
python/pyspark/sql/readwriter.py
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| @@ -300,18 +300,20 @@ def table(self, tableName): | |||
| return self._df(self._jreader.table(tableName)) | |||
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| @since(1.4) | |||
| def parquet(self, *paths): | |||
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Side question for you @HyukjinKwon: The *paths parameter bothers me a bit. None of the other load methods use this pattern, and the streaming version of parquet() doesn't use it either. How would you feel about a separate PR changing this to paths? I suppose the 3.0 release would be our chance to do it, since it changes the API.
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I think this *paths allows a consistent way with Scala and Java side (def parquet(paths: String*)). So I think technically it's more correct to support *paths.
In case of streaming, it's also matched to Scala / Java side def parquet(path: String).
Maybe we should introduce keyword-only argument (as you said earlier somewhere) after completely dropping Python 2 in Spark 3.1. ... I am not sure about this yet.
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Ah .. conflict. .. can you resolve it @nchammas please? |
HyukjinKwon
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Dec 4, 2019
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Test build #114812 has finished for PR 26730 at commit
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nchammas
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Merged to master. |
HyukjinKwon
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Dec 4, 2019
… APIs ### What changes were proposed in this pull request? This PR is a follow-up to #24043 and cousin of #26730. It exposes the `mergeSchema` option directly in the ORC APIs. ### Why are the changes needed? So the Python API matches the Scala API. ### Does this PR introduce any user-facing change? Yes, it adds a new option directly in the ORC reader method signatures. ### How was this patch tested? I tested this manually as follows: ``` >>> spark.range(3).write.orc('test-orc') >>> spark.range(3).withColumnRenamed('id', 'name').write.orc('test-orc/nested') >>> spark.read.orc('test-orc', recursiveFileLookup=True, mergeSchema=True) DataFrame[id: bigint, name: bigint] >>> spark.read.orc('test-orc', recursiveFileLookup=True, mergeSchema=False) DataFrame[id: bigint] >>> spark.conf.set('spark.sql.orc.mergeSchema', True) >>> spark.read.orc('test-orc', recursiveFileLookup=True) DataFrame[id: bigint, name: bigint] >>> spark.read.orc('test-orc', recursiveFileLookup=True, mergeSchema=False) DataFrame[id: bigint] ``` Closes #26755 from nchammas/SPARK-30113-ORC-mergeSchema. Authored-by: Nicholas Chammas <[email protected]> Signed-off-by: HyukjinKwon <[email protected]>
attilapiros
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Dec 6, 2019
…e PySpark Parquet APIs
### What changes were proposed in this pull request?
This change properly documents the `mergeSchema` option directly in the Python APIs for reading Parquet data.
### Why are the changes needed?
The docstring for `DataFrameReader.parquet()` mentions `mergeSchema` but doesn't show it in the API. It seems like a simple oversight.
Before this PR, you'd have to do this to use `mergeSchema`:
```python
spark.read.option('mergeSchema', True).parquet('test-parquet').show()
```
After this PR, you can use the option as (I believe) it was intended to be used:
```python
spark.read.parquet('test-parquet', mergeSchema=True).show()
```
### Does this PR introduce any user-facing change?
Yes, this PR changes the signatures of `DataFrameReader.parquet()` and `DataStreamReader.parquet()` to match their docstrings.
### How was this patch tested?
Testing the `mergeSchema` option directly seems to be left to the Scala side of the codebase. I tested my change manually to confirm the API works.
I also confirmed that setting `spark.sql.parquet.mergeSchema` at the session does not get overridden by leaving `mergeSchema` at its default when calling `parquet()`:
```
>>> spark.conf.set('spark.sql.parquet.mergeSchema', True)
>>> spark.range(3).write.parquet('test-parquet/id')
>>> spark.range(3).withColumnRenamed('id', 'name').write.parquet('test-parquet/name')
>>> spark.read.option('recursiveFileLookup', True).parquet('test-parquet').show()
+----+----+
| id|name|
+----+----+
|null| 1|
|null| 2|
|null| 0|
| 1|null|
| 2|null|
| 0|null|
+----+----+
>>> spark.read.option('recursiveFileLookup', True).parquet('test-parquet', mergeSchema=False).show()
+----+
| id|
+----+
|null|
|null|
|null|
| 1|
| 2|
| 0|
+----+
```
Closes apache#26730 from nchammas/parquet-merge-schema.
Authored-by: Nicholas Chammas <[email protected]>
Signed-off-by: HyukjinKwon <[email protected]>
attilapiros
pushed a commit
to attilapiros/spark
that referenced
this pull request
Dec 6, 2019
… APIs ### What changes were proposed in this pull request? This PR is a follow-up to apache#24043 and cousin of apache#26730. It exposes the `mergeSchema` option directly in the ORC APIs. ### Why are the changes needed? So the Python API matches the Scala API. ### Does this PR introduce any user-facing change? Yes, it adds a new option directly in the ORC reader method signatures. ### How was this patch tested? I tested this manually as follows: ``` >>> spark.range(3).write.orc('test-orc') >>> spark.range(3).withColumnRenamed('id', 'name').write.orc('test-orc/nested') >>> spark.read.orc('test-orc', recursiveFileLookup=True, mergeSchema=True) DataFrame[id: bigint, name: bigint] >>> spark.read.orc('test-orc', recursiveFileLookup=True, mergeSchema=False) DataFrame[id: bigint] >>> spark.conf.set('spark.sql.orc.mergeSchema', True) >>> spark.read.orc('test-orc', recursiveFileLookup=True) DataFrame[id: bigint, name: bigint] >>> spark.read.orc('test-orc', recursiveFileLookup=True, mergeSchema=False) DataFrame[id: bigint] ``` Closes apache#26755 from nchammas/SPARK-30113-ORC-mergeSchema. Authored-by: Nicholas Chammas <[email protected]> Signed-off-by: HyukjinKwon <[email protected]>
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What changes were proposed in this pull request?
This change properly documents the
mergeSchemaoption directly in the Python APIs for reading Parquet data.Why are the changes needed?
The docstring for
DataFrameReader.parquet()mentionsmergeSchemabut doesn't show it in the API. It seems like a simple oversight.Before this PR, you'd have to do this to use
mergeSchema:After this PR, you can use the option as (I believe) it was intended to be used:
Does this PR introduce any user-facing change?
Yes, this PR changes the signatures of
DataFrameReader.parquet()andDataStreamReader.parquet()to match their docstrings.How was this patch tested?
Testing the
mergeSchemaoption directly seems to be left to the Scala side of the codebase. I tested my change manually to confirm the API works.I also confirmed that setting
spark.sql.parquet.mergeSchemaat the session does not get overridden by leavingmergeSchemaat its default when callingparquet():