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Spark1.6 with databricks 2.0.1 : Exception in thread "main" java.lang.UnsupportedOperationException: This mix of union types is not supported (see README): ArrayBuffer(RECORD) at
#290
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sagarsvp opened this issue
Aug 6, 2018
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For databricks 2.0.1 with spark 1.6, While Reading a Complex AVRO with below Code
**SparkConf conf = new SparkConf().setAppName("SearchAVRO").setMaster("local[*]");
JavaSparkContext sc = new JavaSparkContext(conf);
SQLContext sqlContext = new SQLContext(sc);
// Creates a DataFrame from a specified file
DataFrame df = sqlContext.read().format("com.databricks.spark.avro").load(inputPath);**
We get an exception:
Exception in thread "main" java.lang.UnsupportedOperationException: This mix of union types is not supported (see README): ArrayBuffer(RECORD) at com.databricks.spark.avro.SchemaConverters$.toSqlType(SchemaConverters.scala:88) at com.databricks.spark.avro.SchemaConverters$.toSqlType(SchemaConverters.scala:63) at com.databricks.spark.avro.SchemaConverters$.toSqlType(SchemaConverters.scala:79) at com.databricks.spark.avro.SchemaConverters$$anonfun$1.apply(SchemaConverters.scala:56)
==========================================================
Similar code works properly for databricks 4.0.0 with Spark 2.2
Can you please add support for the same in databricks 2.0.1??
The text was updated successfully, but these errors were encountered:
sagarsvp
changed the title
Exception in thread "main" java.lang.UnsupportedOperationException: This mix of union types is not supported (see README): ArrayBuffer(RECORD) at
Spark1.6 with databricks 2.0.1 : Exception in thread "main" java.lang.UnsupportedOperationException: This mix of union types is not supported (see README): ArrayBuffer(RECORD) at
Aug 6, 2018
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For databricks 2.0.1 with spark 1.6, While Reading a Complex AVRO with below Code
We get an exception:
Exception in thread "main" java.lang.UnsupportedOperationException: This mix of union types is not supported (see README): ArrayBuffer(RECORD) at com.databricks.spark.avro.SchemaConverters$.toSqlType(SchemaConverters.scala:88) at com.databricks.spark.avro.SchemaConverters$.toSqlType(SchemaConverters.scala:63) at com.databricks.spark.avro.SchemaConverters$.toSqlType(SchemaConverters.scala:79) at com.databricks.spark.avro.SchemaConverters$$anonfun$1.apply(SchemaConverters.scala:56)
==========================================================
Similar code works properly for databricks 4.0.0 with Spark 2.2
Can you please add support for the same in databricks 2.0.1??
The text was updated successfully, but these errors were encountered: