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https://issues.apache.org/jira/browse/SPARK-20460?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15983079#comment-15983079
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Apache Spark commented on SPARK-20460:
--------------------------------------
User 'maropu' has created a pull request for this issue:
https://github.com/apache/spark/pull/17758
> Make it more consistent to handle column name duplication
> ---------------------------------------------------------
>
> Key: SPARK-20460
> URL: https://issues.apache.org/jira/browse/SPARK-20460
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.1.0
> Reporter: Takeshi Yamamuro
> Priority: Trivial
>
> In the current master, error handling is different when hitting column name
> duplication.
> {code}
> // json
> scala> val schema = StructType(StructField("a", IntegerType) ::
> StructField("a", IntegerType) :: Nil)
> scala> Seq("""{"a":1,
> "a":1}"""""").toDF().coalesce(1).write.mode("overwrite").text("/tmp/data")
> scala> spark.read.format("json").schema(schema).load("/tmp/data").show
> org.apache.spark.sql.AnalysisException: Reference 'a' is ambiguous, could be:
> a#12, a#13.;
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:287)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:181)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1.apply(LogicalPlan.scala:153)
> scala> spark.read.format("json").load("/tmp/data").show
> org.apache.spark.sql.AnalysisException: Duplicate column(s) : "a" found,
> cannot save to JSON format;
> at
> org.apache.spark.sql.execution.datasources.json.JsonDataSource.checkConstraints(JsonDataSource.scala:81)
> at
> org.apache.spark.sql.execution.datasources.json.JsonDataSource.inferSchema(JsonDataSource.scala:63)
> at
> org.apache.spark.sql.execution.datasources.json.JsonFileFormat.inferSchema(JsonFileFormat.scala:57)
> at
> org.apache.spark.sql.execution.datasources.DataSource$$anonfun$7.apply(DataSource.scala:176)
> at
> org.apache.spark.sql.execution.datasources.DataSource$$anonfun$7.apply(DataSource.scala:176)
> // csv
> scala> val schema = StructType(StructField("a", IntegerType) ::
> StructField("a", IntegerType) :: Nil)
> scala> Seq("a,a",
> "1,1").toDF().coalesce(1).write.mode("overwrite").text("/tmp/data")
> scala> spark.read.format("csv").schema(schema).option("header",
> false).load("/tmp/data").show
> org.apache.spark.sql.AnalysisException: Reference 'a' is ambiguous, could be:
> a#41, a#42.;
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:287)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:181)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1.apply(LogicalPlan.scala:153)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1.apply(LogicalPlan.scala:152)
> // If `inferSchema` is true, a CSV format is duplicate-safe (See SPARK-16896)
> scala> spark.read.format("csv").option("header", true).load("/tmp/data").show
> +---+---+
> | a0| a1|
> +---+---+
> | 1| 1|
> +---+---+
> // parquet
> scala> val schema = StructType(StructField("a", IntegerType) ::
> StructField("a", IntegerType) :: Nil)
> scala> Seq((1, 1)).toDF("a",
> "b").coalesce(1).write.mode("overwrite").parquet("/tmp/data")
> scala> spark.read.format("parquet").schema(schema).option("header",
> false).load("/tmp/data").show
> org.apache.spark.sql.AnalysisException: Reference 'a' is ambiguous, could be:
> a#110, a#111.;
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:287)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:181)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1.apply(LogicalPlan.scala:153)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolve$1.apply(LogicalPlan.scala:152)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> {code}
> To make this error reason clearer, IMO we'd better to make it more consistent
> to handle column name duplication.
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