Github user chutium commented on a diff in the pull request:
https://github.com/apache/spark/pull/1959#discussion_r16530668
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala ---
@@ -373,9 +373,11 @@ private[parquet] object ParquetTypesConverter extends
Logging {
}
ParquetRelation.enableLogForwarding()
+ // NOTE: Explicitly list "_temporary" because hadoop 0.23 removed the
variable TEMP_DIR_NAME
+ // from FileOutputCommitter. Check MAPREDUCE-5229 for the detail.
val children = fs.listStatus(path).filterNot { status =>
val name = status.getPath.getName
- name(0) == '.' || name == FileOutputCommitter.SUCCEEDED_FILE_NAME
+ name(0) == '.' || name == FileOutputCommitter.SUCCEEDED_FILE_NAME ||
name == "_temporary"
}
// NOTE (lian): Parquet "_metadata" file can be very slow if the file
consists of lots of row
--- End diff --
hmm, a better solution for all of this could be:
no ```val children = fs.listStatus(path)...``` any more
then:
```
val metafile = fs.listStatus(path).find(_.getPath.getName ==
ParquetFileWriter.PARQUET_METADATA_FILE)
val datafile = fs.listStatus(path).find(isNotHiddenFile(_.getPath.getName))
```
this ```isNotHiddenFile``` simply check like this ```(name(0) != '.' &&
name(0) != '_')```
then something like:
```
if datafile is not null
return ParquetFileReader.readFooter(conf, datafile)
else
return ParquetFileReader.readFooter(conf, metafile)
```
and moreover, @liancheng, after reading carefully following comments,
finally i know what you mean "complete Parquet file on HDFS should be
directory" https://github.com/apache/spark/pull/2044#issuecomment-52733594
you mean the whole directory is "a single parquet file", and the files in
it are "data"? but such a definition is really very very confusing... are you
sure about this definition? i just googled, but found noting, only some like
"Parquet files are self-describing so the schema is preserved"
so, since they are self-describing, in my mind, each "data-file" in a
parquet file folder is also valid parquet-format-file, it should also be able
to take as an input source for parquet reader like our Spark SQLContext...
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