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https://issues.apache.org/jira/browse/SPARK-17101?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15818648#comment-15818648
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Shuai Lin commented on SPARK-17101:
-----------------------------------
Seems this issue has already been resolved by
https://github.com/apache/spark/pull/14680 ? cc [~rxin]
> Provide consistent format identifiers for TextFileFormat and ParquetFileFormat
> ------------------------------------------------------------------------------
>
> Key: SPARK-17101
> URL: https://issues.apache.org/jira/browse/SPARK-17101
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.1.0
> Reporter: Jacek Laskowski
> Priority: Trivial
>
> Define the format identifier that is used in {{Optimized Logical Plan}} in
> {{explain}} for {{text}} file format.
> {code}
> scala> spark.read.text("people.csv").cache.explain(extended = true)
> ...
> == Optimized Logical Plan ==
> InMemoryRelation [value#24], true, 10000, StorageLevel(disk, memory,
> deserialized, 1 replicas)
> +- *FileScan text [value#24] Batched: false, Format:
> org.apache.spark.sql.execution.datasources.text.TextFileFormat@262e2c8c,
> InputPaths: file:/Users/jacek/dev/oss/spark/people.csv, PartitionFilters: [],
> PushedFilters: [], ReadSchema: struct<value:string>
> == Physical Plan ==
> InMemoryTableScan [value#24]
> +- InMemoryRelation [value#24], true, 10000, StorageLevel(disk, memory,
> deserialized, 1 replicas)
> +- *FileScan text [value#24] Batched: false, Format:
> org.apache.spark.sql.execution.datasources.text.TextFileFormat@262e2c8c,
> InputPaths: file:/Users/jacek/dev/oss/spark/people.csv, PartitionFilters: [],
> PushedFilters: [], ReadSchema: struct<value:string>
> {code}
> When you {{explain}} csv format you can see {{Format: CSV}}.
> {code}
> scala> spark.read.csv("people.csv").cache.explain(extended = true)
> == Parsed Logical Plan ==
> Relation[_c0#39,_c1#40,_c2#41,_c3#42] csv
> == Analyzed Logical Plan ==
> _c0: string, _c1: string, _c2: string, _c3: string
> Relation[_c0#39,_c1#40,_c2#41,_c3#42] csv
> == Optimized Logical Plan ==
> InMemoryRelation [_c0#39, _c1#40, _c2#41, _c3#42], true, 10000,
> StorageLevel(disk, memory, deserialized, 1 replicas)
> +- *FileScan csv [_c0#39,_c1#40,_c2#41,_c3#42] Batched: false, Format:
> CSV, InputPaths: file:/Users/jacek/dev/oss/spark/people.csv,
> PartitionFilters: [], PushedFilters: [], ReadSchema:
> struct<_c0:string,_c1:string,_c2:string,_c3:string>
> == Physical Plan ==
> InMemoryTableScan [_c0#39, _c1#40, _c2#41, _c3#42]
> +- InMemoryRelation [_c0#39, _c1#40, _c2#41, _c3#42], true, 10000,
> StorageLevel(disk, memory, deserialized, 1 replicas)
> +- *FileScan csv [_c0#39,_c1#40,_c2#41,_c3#42] Batched: false,
> Format: CSV, InputPaths: file:/Users/jacek/dev/oss/spark/people.csv,
> PartitionFilters: [], PushedFilters: [], ReadSchema:
> struct<_c0:string,_c1:string,_c2:string,_c3:string>
> {code}
> The custom format is defined for JSON, too.
> {code}
> scala> spark.read.json("people.csv").cache.explain(extended = true)
> == Parsed Logical Plan ==
> Relation[_corrupt_record#93] json
> == Analyzed Logical Plan ==
> _corrupt_record: string
> Relation[_corrupt_record#93] json
> == Optimized Logical Plan ==
> InMemoryRelation [_corrupt_record#93], true, 10000, StorageLevel(disk,
> memory, deserialized, 1 replicas)
> +- *FileScan json [_corrupt_record#93] Batched: false, Format: JSON,
> InputPaths: file:/Users/jacek/dev/oss/spark/people.csv, PartitionFilters: [],
> PushedFilters: [], ReadSchema: struct<_corrupt_record:string>
> == Physical Plan ==
> InMemoryTableScan [_corrupt_record#93]
> +- InMemoryRelation [_corrupt_record#93], true, 10000, StorageLevel(disk,
> memory, deserialized, 1 replicas)
> +- *FileScan json [_corrupt_record#93] Batched: false, Format: JSON,
> InputPaths: file:/Users/jacek/dev/oss/spark/people.csv, PartitionFilters: [],
> PushedFilters: [], ReadSchema: struct<_corrupt_record:string>
> {code}
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