Github user liancheng commented on a diff in the pull request:
https://github.com/apache/spark/pull/5526#discussion_r28425682
--- 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
--- End diff --
@rxin I decided to keep this method. For data sources like Hive, Parquet,
and ORC, although the driver side preparation work done before issuing the
write job can be somehow converted to executor side, we still need another
similar hook to do the work. Constructor of `OutputWriter` instances is not a
proper place to do the executor side preparation. Because in case of dynamic
partitioning, a single task may create multiple `OutputWriter`, while the
preparation should be done only once. Another reason is that, for traditional
Hadoop users, driver side setup code can be pretty conventional and familiar.
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