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[SPARK-16975][SQL] Column-partition path starting '_' should be handled correctly - #14585
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dongjoon-hyun wants to merge 1 commit into
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[SPARK-16975][SQL] Column-partition path starting '_' should be handled correctly#14585dongjoon-hyun wants to merge 1 commit into
dongjoon-hyun wants to merge 1 commit into
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dongjoon-hyun
commented
Aug 10, 2016
MemberAuthor
cc @rxin |
SparkQA
commented
Aug 10, 2016
Test build #63556 has finished for PR 14585 at commit
|
dongjoon-hyun
commented
Aug 11, 2016
MemberAuthor
Hi, @rxin . |
liancheng
commented
Aug 12, 2016
Contributor
LGTM, merging to master and branch-2.0. Thanks! |
rxin
commented
Aug 12, 2016
Contributor
Did this make it into branch-2.0? |
liancheng
commented
Aug 12, 2016
Contributor
@rxin The test code conflicts with branch-2.0, I'm resolving it manually. |
asfgit pushed a commit
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Aug 12, 2016
…ed correctly
Currently, Spark ignores path names starting with underscore `_` and `.`. This causes read-failures for the column-partitioned file data sources whose partition column names starts from '_', e.g. `_col`.
**Before**
```scala
scala> spark.range(10).withColumn("_locality_code", $"id").write.partitionBy("_locality_code").save("/tmp/parquet")
scala> spark.read.parquet("/tmp/parquet")
org.apache.spark.sql.AnalysisException: Unable to infer schema for ParquetFormat at /tmp/parquet20. It must be specified manually;
```
**After**
```scala
scala> spark.range(10).withColumn("_locality_code", $"id").write.partitionBy("_locality_code").save("/tmp/parquet")
scala> spark.read.parquet("/tmp/parquet")
res2: org.apache.spark.sql.DataFrame = [id: bigint, _locality_code: int]
```
Pass the Jenkins with a new test case.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14585 from dongjoon-hyun/SPARK-16975-PARQUET.
(cherry picked from commit abff92b)
Signed-off-by: Cheng Lian <lian@databricks.com>liancheng
commented
Aug 12, 2016
Contributor
OK, resolved the conflict manually and got it merged into branch-2.0. |
| val jsonFiles = files.filterNot { status => | ||
| val name = status.getPath.getName | ||
| name.startsWith("_") || name.startsWith(".") | ||
| (name.startsWith("_") && !name.contains("=")) || name.startsWith(".") |
Member
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Hm.. @liancheng@dongjoon-hyun Do you mind if I ask a question please?
If my understanding is correct, the name will be part-.. file whether the parent directory contains _ or not. So, it would be unnecessary extra checking. It is happening in ParquetFileFormat as well.
Do you mind if I open a small follow-up to clean up those?
Member
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Oh yea, you're right. Here files only contains leaf files and this check is redundant. Please feel free to clean it up. Thanks!
dongjoon-hyun
commented
Aug 12, 2016
MemberAuthor
Thank you for review and merging, @liancheng and @rxin . |
ghost
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to dbtsai/spark
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Dec 22, 2016
…ata sources implementing FileFormat ## What changes were proposed in this pull request? This PR cleans up duplicated checking for file paths in implemented data sources and prevent to attempt to list twice in ORC data source. apache#14585 handles a problem for the partition column name having `_` and the issue itself is resolved correctly. However, it seems the data sources implementing `FileFormat` are validating the paths duplicately. Assuming from the comment in `CSVFileFormat`, `// TODO: Move filtering.`, I guess we don't have to check this duplicately. Currently, this seems being filtered in `PartitioningAwareFileIndex.shouldFilterOut` and`PartitioningAwareFileIndex.isDataPath`. So, `FileFormat.inferSchema` will always receive leaf files. For example, running to codes below: ``` scala spark.range(10).withColumn("_locality_code", $"id").write.partitionBy("_locality_code").save("/tmp/parquet") spark.read.parquet("/tmp/parquet") ``` gives the paths below without directories but just valid data files: ``` bash /tmp/parquet/_col=0/part-r-00000-094a8efa-bece-4b50-b54c-7918d1f7b3f8.snappy.parquet /tmp/parquet/_col=1/part-r-00000-094a8efa-bece-4b50-b54c-7918d1f7b3f8.snappy.parquet /tmp/parquet/_col=2/part-r-00000-25de2b50-225a-4bcf-a2bc-9eb9ed407ef6.snappy.parquet ... ``` to `FileFormat.inferSchema`. ## How was this patch tested? Unit test added in `HadoopFsRelationTest` and related existing tests. Author: hyukjinkwon <gurwls223@gmail.com> Closesapache#14627 from HyukjinKwon/SPARK-16975.
cmonkey pushed a commit
to cmonkey/spark
that referenced
this pull request
Dec 24, 2016
…ata sources implementing FileFormat ## What changes were proposed in this pull request? This PR cleans up duplicated checking for file paths in implemented data sources and prevent to attempt to list twice in ORC data source. apache#14585 handles a problem for the partition column name having `_` and the issue itself is resolved correctly. However, it seems the data sources implementing `FileFormat` are validating the paths duplicately. Assuming from the comment in `CSVFileFormat`, `// TODO: Move filtering.`, I guess we don't have to check this duplicately. Currently, this seems being filtered in `PartitioningAwareFileIndex.shouldFilterOut` and`PartitioningAwareFileIndex.isDataPath`. So, `FileFormat.inferSchema` will always receive leaf files. For example, running to codes below: ``` scala spark.range(10).withColumn("_locality_code", $"id").write.partitionBy("_locality_code").save("/tmp/parquet") spark.read.parquet("/tmp/parquet") ``` gives the paths below without directories but just valid data files: ``` bash /tmp/parquet/_col=0/part-r-00000-094a8efa-bece-4b50-b54c-7918d1f7b3f8.snappy.parquet /tmp/parquet/_col=1/part-r-00000-094a8efa-bece-4b50-b54c-7918d1f7b3f8.snappy.parquet /tmp/parquet/_col=2/part-r-00000-25de2b50-225a-4bcf-a2bc-9eb9ed407ef6.snappy.parquet ... ``` to `FileFormat.inferSchema`. ## How was this patch tested? Unit test added in `HadoopFsRelationTest` and related existing tests. Author: hyukjinkwon <gurwls223@gmail.com> Closesapache#14627 from HyukjinKwon/SPARK-16975.
uzadude pushed a commit
to uzadude/spark
that referenced
this pull request
Jan 27, 2017
…ata sources implementing FileFormat ## What changes were proposed in this pull request? This PR cleans up duplicated checking for file paths in implemented data sources and prevent to attempt to list twice in ORC data source. apache#14585 handles a problem for the partition column name having `_` and the issue itself is resolved correctly. However, it seems the data sources implementing `FileFormat` are validating the paths duplicately. Assuming from the comment in `CSVFileFormat`, `// TODO: Move filtering.`, I guess we don't have to check this duplicately. Currently, this seems being filtered in `PartitioningAwareFileIndex.shouldFilterOut` and`PartitioningAwareFileIndex.isDataPath`. So, `FileFormat.inferSchema` will always receive leaf files. For example, running to codes below: ``` scala spark.range(10).withColumn("_locality_code", $"id").write.partitionBy("_locality_code").save("/tmp/parquet") spark.read.parquet("/tmp/parquet") ``` gives the paths below without directories but just valid data files: ``` bash /tmp/parquet/_col=0/part-r-00000-094a8efa-bece-4b50-b54c-7918d1f7b3f8.snappy.parquet /tmp/parquet/_col=1/part-r-00000-094a8efa-bece-4b50-b54c-7918d1f7b3f8.snappy.parquet /tmp/parquet/_col=2/part-r-00000-25de2b50-225a-4bcf-a2bc-9eb9ed407ef6.snappy.parquet ... ``` to `FileFormat.inferSchema`. ## How was this patch tested? Unit test added in `HadoopFsRelationTest` and related existing tests. Author: hyukjinkwon <gurwls223@gmail.com> Closesapache#14627 from HyukjinKwon/SPARK-16975.
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What changes were proposed in this pull request?
Currently, Spark ignores path names starting with underscore
_and.. This causes read-failures for the column-partitioned file data sources whose partition column names starts from '_', e.g._col.Before
After
How was this patch tested?
Pass the Jenkins with a new test case.