[SPARK-8501] [SQL] Avoids reading schema from empty ORC files #7199
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
|
Test build #36437 has finished for PR 7199 at commit
|
|
Test build #36456 has finished for PR 7199 at commit
|
|
Test build #36459 has finished for PR 7199 at commit
|
|
Merging to master. This PR is backported to branch-1.4 by #7200. |
asfgit
pushed a commit
that referenced
this pull request
Jul 3, 2015
…rt to 1.4) This PR backports #7199 to branch-1.4 Author: Cheng Lian <[email protected]> Closes #7200 from liancheng/spark-8501-for-1.4 and squashes the following commits: 725e9e3 [Cheng Lian] Addresses comments 0fa25af [Cheng Lian] Avoids reading schema from empty ORC files
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
ORC writes empty schema (
struct<>) to ORC files containing zero rows. This is OK for Hive since the table schema is managed by the metastore. But it causes trouble when reading raw ORC files via Spark SQL since we have to discover the schema from the files.Notice that the ORC data source always avoids writing empty ORC files, but it's still problematic when reading Hive tables which contain empty part-files.