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https://issues.apache.org/jira/browse/SPARK-25126?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16581578#comment-16581578
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Apache Spark commented on SPARK-25126:
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User 'raofu' has created a pull request for this issue:
https://github.com/apache/spark/pull/22113
> OrcFileOperator.getFileReader: avoid creating OrcFile.Reader for all orc files
> ------------------------------------------------------------------------------
>
> Key: SPARK-25126
> URL: https://issues.apache.org/jira/browse/SPARK-25126
> Project: Spark
> Issue Type: Bug
> Components: Input/Output
> Affects Versions: 2.3.1
> Reporter: Rao Fu
> Priority: Major
>
> https://github.com/apache/spark/blob/master/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFileOperator.scala#L73
> Where `basePath` passed to getFileReader is a directory, a OrcFile.Reader is
> created for every file under the directory although only the first one with a
> non-empty schema is returned. It consumes a lot of memory when there are many
> files under the directory as the metadata for the orc file is loaded into
> memory during the Reader creation.
> I tried the following workaround and the OOM issue went away,
> 1) create a DataSet<Row> from a single orc file.
> Dataset<Row> rowsForFirstFile = spark.read().format("orc").load(oneFile);
> 2) when creating DataSet<Row> from all files under the directory, use the
> schema from the previous DataSet.
> Dataset<Row> rows =
> spark.read().schema(rowsForFirstFile.schema()).format("orc").load(path);
>
>
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