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https://issues.apache.org/jira/browse/DRILL-7578?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17039096#comment-17039096
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ASF GitHub Bot commented on DRILL-7578:
---------------------------------------
cgivre commented on pull request #1978: DRILL-7578: HDF5 Metadata Queries Fail
with Large Files
URL: https://github.com/apache/drill/pull/1978#discussion_r380681160
##########
File path:
contrib/format-hdf5/src/test/java/org/apache/drill/exec/store/hdf5/TestHDF5Format.java
##########
@@ -98,9 +98,9 @@ public void testStarQuery() throws Exception {
testBuilder()
.sqlQuery("SELECT * FROM dfs.`hdf5/dset.h5`")
- .unOrdered()
- .baselineColumns("path", "data_type", "file_name", "int_data")
- .baselineValues("/dset", "DATASET", "dset.h5", finalList)
+ .ordered()
Review comment:
Hey @paul-rogers
I'd agree that this is a bit of a hack, but I think you misunderstood how
this plugin works. There is a config variable called `defaultPath` which when
`null` returns metadata. If this variable is set to an HDF5 path, you get the
data (and no metadata).
The issue is the rather unique nature of HDF5, as a file system within a
file. I do like the idea of treating the file as directory however, the
metadata is really useful to the user in that some file paths are shortcuts,
some are groups etc. Also, datasets have attributes which can be useful.
IMHO, this really blends the lines between storage and format plugin, so
it's quite challenging to design this. I am curious as to whether this
actually gets used in the scientific community.
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> HDF5 Metadata Queries Fail with Large Files
> -------------------------------------------
>
> Key: DRILL-7578
> URL: https://issues.apache.org/jira/browse/DRILL-7578
> Project: Apache Drill
> Issue Type: Bug
> Affects Versions: 1.18.0
> Reporter: Charles Givre
> Assignee: Charles Givre
> Priority: Major
> Fix For: 1.18.0
>
>
> With large files, Drill runs out of memory when attempting to project large
> datasets in the metadata.
> This PR adds a configuration option which removes the dataset projection from
> metadata queries and fixes this issue.
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