Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/10604#discussion_r49810260
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala ---
@@ -683,7 +714,20 @@ abstract class HadoopFsRelation private[sql](
}
}
- buildInternalScan(requiredColumns, filters, inputStatuses,
broadcastedConf)
+ groupBucketFiles(inputStatuses).map { groupedBucketFiles =>
+ // For each bucket id, firstly we get all files belong to this
bucket, by detecting bucket
+ // id from file name. Then read these files into a RDD(use
one-partition empty RDD for empty
+ // bucket), and coalesce it to one partition. Finally union all
bucket RDDs to one result.
+ val perBucketRows = (0 until maybeBucketSpec.get.numBuckets).map {
bucketId =>
+ groupedBucketFiles.get(bucketId).map { inputStatuses =>
+ buildInternalScan(requiredColumns, filters, inputStatuses,
broadcastedConf).coalesce(1)
--- End diff --
using coalesce and then Union here doesn't seem like a great way to do it.
can we just create a single RDD, and each partition of the RDD just reads all
the files? We would likely need to create a variant of the hadooprdd, but I
think it's worth doing it.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
