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https://issues.apache.org/jira/browse/SPARK-45879?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Liangcai li updated SPARK-45879:
--------------------------------
Environment: I tried on Spark 323 and Spark 341, and both reproduced this
issue. (was: I tried on Spark 323 and Spark 341, both reproduced this issue.)
> Number check for InputFileBlockSources is missing for V2 source (BatchScan) ?
> -----------------------------------------------------------------------------
>
> Key: SPARK-45879
> URL: https://issues.apache.org/jira/browse/SPARK-45879
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.5.0
> Environment: I tried on Spark 323 and Spark 341, and both reproduced
> this issue.
> Reporter: Liangcai li
> Priority: Major
>
> When doing a join with the "input_file_name()" function, it will blow up with
> a
> *AnalysisException* if using the v1 data source (FileSourceScan). That's ok.
>
> But if we change to use the v2 data source (BatchScan), the expected
> exception is gone, the join passed.
> Is this number check for InputFileDataSources mssing for V2 data source ? or
> it is by design ?
>
> Repro steps:
> {code:java}
> scala> spark.range(100).withColumn("const1",
> lit("from_t1")).write.parquet("/data/tmp/t1")
>
> scala> spark.range(100).withColumn("const2",
> lit("from_t2")).write.parquet("/data/tmp/t2")
>
> scala> spark.conf.set("spark.sql.sources.useV1SourceList", "parquet")
>
> scala>
> spark.read.parquet("/data/tmp/t1").join(spark.read.parquet("/data/tmp/t2"),
> "id", "inner").selectExpr("*", "input_file_name()").show(5, false)
> org.apache.spark.sql.AnalysisException: 'input_file_name' does not support
> more than one sources.; line 1 pos 0;
> Project id#376L, const1#377, const2#381, input_file_name() AS
> input_file_name()#389
> +- Project id#376L, const1#377, const2#381
> +- Join Inner, (id#376L = id#380L)
> :- Relation id#376L,const1#377 parquet
> +- Relation id#380L,const2#381 parquet
>
> at
> org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:52)
> at
> org.apache.spark.sql.execution.datasources.PreReadCheck$.org$apache$spark$sql$execution$datasources$PreReadCheck$$checkNumInputFileBlockSources(rules.scala:476)
> at
> org.apache.spark.sql.execution.datasources.PreReadCheck$.$anonfun$checkNumInputFileBlockSources$2(rules.scala:472)
> at
> org.apache.spark.sql.execution.datasources.PreReadCheck$.$anonfun$checkNumInputFileBlockSources$2$adapted(rules.scala:472)
> at
> scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:286)
> at scala.collection.Iterator.foreach(Iterator.scala:943)
> at scala.collection.Iterator.foreach$(Iterator.scala:943)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1431)
> at scala.collection.IterableLike.foreach(IterableLike.scala:74)
> at scala.collection.IterableLike.foreach$(IterableLike.scala:73)
> scala> spark.conf.set("spark.sql.sources.useV1SourceList", "")
>
> scala>
> spark.read.parquet("/data/tmp/t1").join(spark.read.parquet("/data/tmp/t2"),
> "id", "inner").selectExpr("*", "input_file_name()").show(5, false)
> +---+-------+-------+---------------------------------------------------------------------------------------+
> |id |const1 |const2 |input_file_name()
> |
> +---+-------+-------+---------------------------------------------------------------------------------------+
> |91
> |from_t1|from_t2|file:///data/tmp/t1/part-00011-a52b9990-4463-447c-9cdf-7a84542de2f7-c000.snappy.parquet|
> |92
> |from_t1|from_t2|file:///data/tmp/t1/part-00011-a52b9990-4463-447c-9cdf-7a84542de2f7-c000.snappy.parquet|
> |93
> |from_t1|from_t2|file:///data/tmp/t1/part-00011-a52b9990-4463-447c-9cdf-7a84542de2f7-c000.snappy.parquet|
> |94
> |from_t1|from_t2|file:///data/tmp/t1/part-00011-a52b9990-4463-447c-9cdf-7a84542de2f7-c000.snappy.parquet|
> |95
> |from_t1|from_t2|file:///data/tmp/t1/part-00011-a52b9990-4463-447c-9cdf-7a84542de2f7-c000.snappy.parquet|
> +---+-------+-------+---------------------------------------------------------------------------------------+
> only showing top 5 rows{code}
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