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Original file line number Diff line number Diff line change
Expand Up @@ -292,7 +292,10 @@ object FileFormatWriter extends Logging {
override def execute(iter: Iterator[InternalRow]): Set[String] = {
var fileCounter = 0
var recordsInFile: Long = 0L
newOutputWriter(fileCounter)
// Skip the empty partition to avoid creating a mass of 'empty' files.
if (iter.hasNext) {
newOutputWriter(fileCounter)

@HyukjinKwon HyukjinKwon Mar 23, 2017

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I proposed the similar PR before (about a year ago?) but got reverted. In this case, Parquet would not write out the footer and schema information. Namely, this will break the case below:

spark.range(100).filter("id > 100").write.parquet("/tmp/abc")
spark.read.parquet("/tmp/abc").show()

Up to my knowledge, we don't have test cases for them if I haven't missed related PRs it seems now there is.

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@HyukjinKwon IIUC, this case should fail as expected, as there is no output. Am i missing something?

spark.range(100).filter("id > 100").write.parquet("/tmp/abc")
spark.read.parquet("/tmp/abc").show()

@HyukjinKwon HyukjinKwon Mar 23, 2017

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Reading empty data should be fine too. It should preserve the schema. I am pretty sure that we want this case because mine was reverted due to the case above.

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Thanks for your prompt. How about just left one empty file containing the metadata when df has empty partition? Furthmore, we may just left one metadata file?

@HyukjinKwon HyukjinKwon Mar 24, 2017

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Yes, I was thinking in that way. I remember I did several tries at that time but failed to make a confident fix, and could not have some time to work on that further.

Another problem is, it might be related with a datasource-specific issue because, for example, ORC does not write out empty df. For example,

scala> spark.range(100).filter("id > 100").write.orc("/tmp/abc1")

scala> spark.read.orc("/tmp/abc1").show()
org.apache.spark.sql.AnalysisException: Unable to infer schema for ORC. It must be specified manually.;
  at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$8.apply(DataSource.scala:182)
  at org.apache.spark.sql.execution.datasources.DataSource$$anonfun$8.apply(DataSource.scala:182)

This issue is described in https://issues.apache.org/jira/browse/SPARK-15474.

FWIW, I happened to see https://issues.apache.org/jira/browse/SPARK-15693 around that time and I kind of felt we may be able to consolidate this issue with it although it is a rough idea.

}
while (iter.hasNext) {
if (description.maxRecordsPerFile > 0 && recordsInFile >= description.maxRecordsPerFile) {
fileCounter += 1
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Original file line number Diff line number Diff line change
Expand Up @@ -140,10 +140,10 @@ class ParquetFileFormat
// initialized.
private val parquetLogRedirector = ParquetLogRedirector.INSTANCE

override def newInstance(
path: String,
dataSchema: StructType,
context: TaskAttemptContext): OutputWriter = {
override def newInstance(
path: String,
dataSchema: StructType,
context: TaskAttemptContext): OutputWriter = {
new ParquetOutputWriter(path, context)
}

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