Github user tedyu commented on a diff in the pull request:
https://github.com/apache/spark/pull/12361#discussion_r60339109
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/fileSourceInterfaces.scala
---
@@ -0,0 +1,530 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources
+
+import scala.collection.mutable
+import scala.util.Try
+
+import org.apache.hadoop.conf.Configuration
+import org.apache.hadoop.fs.{FileStatus, FileSystem, Path}
+import org.apache.hadoop.mapred.{FileInputFormat, JobConf}
+import org.apache.hadoop.mapreduce.{Job, TaskAttemptContext}
+
+import org.apache.spark.SparkContext
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.internal.Logging
+import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.{expressions, CatalystTypeConverters,
InternalRow}
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.execution.FileRelation
+import org.apache.spark.sql.sources.{BaseRelation, Filter}
+import org.apache.spark.sql.types.{StringType, StructType}
+import org.apache.spark.util.SerializableConfiguration
+
+/**
+ * ::Experimental::
+ * A factory that produces [[OutputWriter]]s. A new
[[OutputWriterFactory]] is created on driver
+ * side for each write job issued when writing to a [[HadoopFsRelation]],
and then gets serialized
+ * to executor side to create actual [[OutputWriter]]s on the fly.
+ *
+ * @since 1.4.0
+ */
+@Experimental
+abstract class OutputWriterFactory extends Serializable {
+ /**
+ * When writing to a [[HadoopFsRelation]], this method gets called by
each task on executor side
+ * to instantiate new [[OutputWriter]]s.
+ *
+ * @param path Path of the file to which this [[OutputWriter]] is
supposed to write. Note that
+ * this may not point to the final output file. For example,
`FileOutputFormat` writes to
+ * temporary directories and then merge written files back to the
final destination. In
+ * this case, `path` points to a temporary output file under the
temporary directory.
+ * @param dataSchema Schema of the rows to be written. Partition columns
are not included in the
+ * schema if the relation being written is partitioned.
+ * @param context The Hadoop MapReduce task context.
+ * @since 1.4.0
+ */
+ private[sql] def newInstance(
+ path: String,
+ bucketId: Option[Int], // TODO: This doesn't belong here...
+ dataSchema: StructType,
+ context: TaskAttemptContext): OutputWriter
+}
+
+/**
+ * ::Experimental::
+ * [[OutputWriter]] is used together with [[HadoopFsRelation]] for
persisting rows to the
+ * underlying file system. Subclasses of [[OutputWriter]] must provide a
zero-argument constructor.
+ * An [[OutputWriter]] instance is created and initialized when a new
output file is opened on
+ * executor side. This instance is used to persist rows to this single
output file.
+ *
+ * @since 1.4.0
+ */
+@Experimental
+abstract class OutputWriter {
+ /**
+ * Persists a single row. Invoked on the executor side. When writing
to dynamically partitioned
+ * tables, dynamic partition columns are not included in rows to be
written.
+ *
+ * @since 1.4.0
+ */
+ def write(row: Row): Unit
+
+ /**
+ * Closes the [[OutputWriter]]. Invoked on the executor side after all
rows are persisted, before
+ * the task output is committed.
+ *
+ * @since 1.4.0
+ */
+ def close(): Unit
+
+ private var converter: InternalRow => Row = _
+
+ protected[sql] def initConverter(dataSchema: StructType) = {
+ converter =
+
CatalystTypeConverters.createToScalaConverter(dataSchema).asInstanceOf[InternalRow
=> Row]
+ }
+
+ protected[sql] def writeInternal(row: InternalRow): Unit = {
+ write(converter(row))
+ }
+}
+
+/**
+ * Acts as a container for all of the metadata required to read from a
datasource. All discovery,
+ * resolution and merging logic for schemas and partitions has been
removed.
+ *
+ * @param location A [[FileCatalog]] that can enumerate the locations of
all the files that comprise
+ * this relation.
+ * @param partitionSchema The schema of the columns (if any) that are used
to partition the relation
+ * @param dataSchema The schema of any remaining columns. Note that if
any partition columns are
+ * present in the actual data files as well, they are
preserved.
+ * @param bucketSpec Describes the bucketing (hash-partitioning of the
files by some column values).
+ * @param fileFormat A file format that can be used to read and write the
data in files.
+ * @param options Configuration used when reading / writing data.
+ */
+case class HadoopFsRelation(
+ sqlContext: SQLContext,
+ location: FileCatalog,
+ partitionSchema: StructType,
+ dataSchema: StructType,
+ bucketSpec: Option[BucketSpec],
+ fileFormat: FileFormat,
+ options: Map[String, String]) extends BaseRelation with FileRelation {
+
+ val schema: StructType = {
+ val dataSchemaColumnNames = dataSchema.map(_.name.toLowerCase).toSet
+ StructType(dataSchema ++ partitionSchema.filterNot { column =>
+ dataSchemaColumnNames.contains(column.name.toLowerCase)
+ })
+ }
+
+ def partitionSchemaOption: Option[StructType] =
+ if (partitionSchema.isEmpty) None else Some(partitionSchema)
+ def partitionSpec: PartitionSpec = location.partitionSpec()
+
+ def refresh(): Unit = location.refresh()
+
+ override def toString: String =
+ s"HadoopFiles"
+
+ /** Returns the list of files that will be read when scanning this
relation. */
+ override def inputFiles: Array[String] =
+ location.allFiles().map(_.getPath.toUri.toString).toArray
+
+ override def sizeInBytes: Long = location.allFiles().map(_.getLen).sum
+}
+
+/**
+ * Used to read and write data stored in files to/from the [[InternalRow]]
format.
+ */
+trait FileFormat {
+ /**
+ * When possible, this method should return the schema of the given
`files`. When the format
+ * does not support inference, or no valid files are given should return
None. In these cases
+ * Spark will require that user specify the schema manually.
+ */
+ def inferSchema(
+ sqlContext: SQLContext,
+ options: Map[String, String],
+ files: Seq[FileStatus]): Option[StructType]
+
+ /**
+ * Prepares a read job and returns a potentially updated data source
option [[Map]]. This method
+ * can be useful for collecting necessary global information for
scanning input data.
+ */
+ def prepareRead(
+ sqlContext: SQLContext,
+ options: Map[String, String],
+ files: Seq[FileStatus]): Map[String, String] = options
+
+ /**
+ * Prepares a write job and returns an [[OutputWriterFactory]]. Client
side job preparation can
+ * be put here. For example, user defined output committer can be
configured here
+ * by setting the output committer class in the conf of
spark.sql.sources.outputCommitterClass.
+ */
+ def prepareWrite(
+ sqlContext: SQLContext,
+ job: Job,
+ options: Map[String, String],
+ dataSchema: StructType): OutputWriterFactory
+
+ /**
+ * Returns whether this format support returning columnar batch or not.
+ *
+ * TODO: we should just have different traits for the different formats.
+ */
+ def supportBatch(sqlContext: SQLContext, dataSchema: StructType):
Boolean = {
+ false
+ }
+
+ /**
+ * Returns a function that can be used to read a single file in as an
Iterator of InternalRow.
+ *
+ * @param dataSchema The global data schema. It can be either specified
by the user, or
+ * reconciled/merged from all underlying data files.
If any partition columns
+ * are contained in the files, they are preserved in
this schema.
+ * @param partitionSchema The schema of the partition column row that
will be present in each
+ * PartitionedFile. These columns should be
appended to the rows that
+ * are produced by the iterator.
+ * @param requiredSchema The schema of the data that should be output
for each row. This may be a
+ * subset of the columns that are present in the
file if column pruning has
+ * occurred.
+ * @param filters A set of filters than can optionally be used to reduce
the number of rows output
+ * @param options A set of string -> string configuration options.
+ * @return
+ */
+ def buildReader(
+ sqlContext: SQLContext,
--- End diff --
Do we need @param for this ?
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