spark HadoopFsRelation 源码
spark HadoopFsRelation 代码
文件路径:/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/HadoopFsRelation.scala
/*
* 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 org.apache.spark.sql.{SparkSession, SQLContext}
import org.apache.spark.sql.catalyst.catalog.BucketSpec
import org.apache.spark.sql.execution.FileRelation
import org.apache.spark.sql.sources.{BaseRelation, DataSourceRegister}
import org.apache.spark.sql.types.{StructField, StructType}
/**
* 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 [[FileIndex]] 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(
location: FileIndex,
partitionSchema: StructType,
// The top-level columns in `dataSchema` should match the actual physical file schema, otherwise
// the ORC data source may not work with the by-ordinal mode.
dataSchema: StructType,
bucketSpec: Option[BucketSpec],
fileFormat: FileFormat,
options: Map[String, String])(val sparkSession: SparkSession)
extends BaseRelation with FileRelation {
override def sqlContext: SQLContext = sparkSession.sqlContext
// When data and partition schemas have overlapping columns, the output
// schema respects the order of the data schema for the overlapping columns, and it
// respects the data types of the partition schema.
val (schema: StructType, overlappedPartCols: Map[String, StructField]) =
PartitioningUtils.mergeDataAndPartitionSchema(dataSchema,
partitionSchema, sparkSession.sessionState.conf.caseSensitiveAnalysis)
override def toString: String = {
fileFormat match {
case source: DataSourceRegister => source.shortName()
case _ => "HadoopFiles"
}
}
override def sizeInBytes: Long = {
val compressionFactor = sqlContext.conf.fileCompressionFactor
(location.sizeInBytes * compressionFactor).toLong
}
override def inputFiles: Array[String] = location.inputFiles
}
相关信息
相关文章
spark AggregatePushDownUtils 源码
0
赞
- 所属分类: 前端技术
- 本文标签:
热门推荐
-
2、 - 优质文章
-
3、 gate.io
-
8、 golang
-
9、 openharmony
-
10、 Vue中input框自动聚焦