Takeshi Yamamuro created SPARK-20460:
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Summary: 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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