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https://issues.apache.org/jira/browse/SPARK-26801?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16760178#comment-16760178
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Dhruve Ashar commented on SPARK-26801:
--------------------------------------
I have given a short summary of the issue in the PR description. It would be
great if you could review it [~hyukjin.kwon]
Thanks.
> Spark unable to read valid avro types
> -------------------------------------
>
> Key: SPARK-26801
> URL: https://issues.apache.org/jira/browse/SPARK-26801
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.4.0
> Reporter: Dhruve Ashar
> Priority: Major
>
> Currently the external avro package reads avro schemas for type records only.
> This is probably because of representation of InternalRow in spark sql. As a
> result, if the avro file has anything other than a sequence of records it
> fails to read it.
> We faced this issue earlier while trying to read primitive types. We
> encountered this again while trying to read an array of records. Below are
> code examples trying to read valid avro data showing the stack traces.
> {code:java}
> spark.read.format("avro").load("avroTypes/randomInt.avro").show
> java.lang.RuntimeException: Avro schema cannot be converted to a Spark SQL
> StructType:
> "int"
> at
> org.apache.spark.sql.avro.AvroFileFormat.inferSchema(AvroFileFormat.scala:95)
> at
> org.apache.spark.sql.execution.datasources.DataSource$$anonfun$6.apply(DataSource.scala:180)
> at
> org.apache.spark.sql.execution.datasources.DataSource$$anonfun$6.apply(DataSource.scala:180)
> at scala.Option.orElse(Option.scala:289)
> at
> org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:179)
> at
> org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:373)
> at
> org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:223)
> at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:211)
> at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178)
> ... 49 elided
> ======================================================================
> scala> spark.read.format("avro").load("avroTypes/randomEnum.avro").show
> java.lang.RuntimeException: Avro schema cannot be converted to a Spark SQL
> StructType:
> {
> "type" : "enum",
> "name" : "Suit",
> "symbols" : [ "SPADES", "HEARTS", "DIAMONDS", "CLUBS" ]
> }
> at
> org.apache.spark.sql.avro.AvroFileFormat.inferSchema(AvroFileFormat.scala:95)
> at
> org.apache.spark.sql.execution.datasources.DataSource$$anonfun$6.apply(DataSource.scala:180)
> at
> org.apache.spark.sql.execution.datasources.DataSource$$anonfun$6.apply(DataSource.scala:180)
> at scala.Option.orElse(Option.scala:289)
> at
> org.apache.spark.sql.execution.datasources.DataSource.getOrInferFileFormatSchema(DataSource.scala:179)
> at
> org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:373)
> at
> org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:223)
> at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:211)
> at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:178)
> ... 49 elided
> {code}
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