Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/5526#discussion_r28451657
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala ---
@@ -197,3 +233,69 @@ trait InsertableRelation {
trait CatalystScan {
def buildScan(requiredColumns: Seq[Attribute], filters:
Seq[Expression]): RDD[Row]
}
+
+/**
+ * ::Experimental::
+ * [[OutputWriter]] is used together with [[FSBasedRelation]] for
persisting rows to the
+ * underlying file system. An [[OutputWriter]] instance is created when a
new output file is
+ * opened. This instance is used to persist rows to this single output
file.
+ */
+@Experimental
+trait OutputWriter {
+ /**
+ * Persists a single row. Invoked on the executor side.
+ */
+ def write(row: Row): Unit
+
+ /**
+ * Closes the [[OutputWriter]]. Invoked on the executor side after all
rows are persisted, before
+ * the task output is committed.
+ */
+ def close(): Unit
+}
+
+/**
+ * ::Experimental::
+ * A [[BaseRelation]] that abstracts file system based data sources.
+ *
+ * For the read path, similar to [[PrunedFilteredScan]], it can eliminate
unneeded columns and
+ * filter using selected predicates before producing an RDD containing all
matching tuples as
+ * [[Row]] objects.
+ *
+ * In addition, when reading from Hive style partitioned tables stored in
file systems, it's able to
+ * discover partitioning information from the paths of input directories,
and perform partition
+ * pruning before start reading the data.
+ *
+ * For the write path, it provides the ability to write to both
non-partitioned and partitioned
+ * tables. Directory layout of the partitioned tables is compatible with
Hive.
+ */
+@Experimental
+trait FSBasedRelation extends BaseRelation {
+ /**
+ * Builds an `RDD[Row]` containing all rows within this relation.
+ *
+ * @param requiredColumns Required columns.
+ * @param filters Candidate filters to be pushed down. The actual filter
should be the conjunction
+ * of all `filters`. The pushed down filters are currently
purely an optimization as they
+ * will all be evaluated again. This means it is safe to use them
with methods that produce
+ * false positives such as filtering partitions based on a bloom
filter.
+ * @param inputPaths Data files to be read. If the underlying relation
is partitioned, only data
+ * files within required partition directories are included.
+ */
+ def buildScan(
+ requiredColumns: Array[String],
+ filters: Array[Filter],
+ inputPaths: Array[String]): RDD[Row]
+
+ /**
+ * When writing rows to this relation, this method is invoked on the
driver side before the actual
+ * write job is issued. It provides an opportunity to configure the
write job to be performed.
+ */
+ def prepareForWrite(conf: Configuration): Unit
+
+ /**
+ * This method is responsible for producing a new [[OutputWriter]] for
each newly opened output
+ * file on the executor side.
+ */
+ def newOutputWriter(path: String): OutputWriter
--- End diff --
does this mean now BaseRelation needs to be serializable? I don't think
this was the case before, was it?
I think it'd make more sense to have OutputWriter to have a zero-arg ctor,
and the relation simply returns the class or the class name, and then we create
the OutputWriter on each executor using reflection, and then call init(path:
String, schema: StructType, options: Map[...]) on those.
---
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 infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
