How to read a logical DOUBLE value stored in Avro format using Spark?

I have existing Hive data stored in Avro format. For whatever reason reading these data by executing SELECT is very slow. I didn't figure out yet why. So I decided to read the data directly by navigating to the partition path and using Spark SQLContext. This works much faster. However, the problem I have is reading the DOUBLE values which are stored as logical double type. In the schema file they are defined as:

{"name":"ENDING_NET_RECEIVABLES_LOCAL","type":["null",{"type":"bytes","logicalType":"decimal","precision":38,"scale":18}],"doc":"Ending Net Receivables Local","default":null}

Somewhere I found a recommendation of using the following approach to convert my Avro schema into a Spark SQL schema:

def getSparkSchemaForAvro(sqc: SQLContext, avroSchema: Schema): StructType = 
{ val tmpPath = "/tmp/avro_dummy" 
val dummyFIle = File.createTempFile(tmpPath, "avro") 
val datumWriter = new GenericDatumWriter[Any]() datumWriter.setSchema(avroSchema) 
val writer = new DataFileWriter(datumWriter).create(avroSchema, dummyFIle) 
writer.flush() 
writer.close() 
val df = sqc.read.format("com.databricks.spark.avro").load("file://" + dummyFIle.getAbsolutePath) 
val sparkSchema = df.schema 
sparkSchema 
}

However, it converts the above type into
StructField(ENDING_NET_RECEIVABLES_LOCAL,BinaryType,true) instead of StructField(ENDING_NET_RECEIVABLES_LOCAL,DecimalType,true)

I this conversion was correct I could have read the file like this:

val df = sqlContext.read.schema(CRDataUtils.getSparkSchemaForAvro(sqlContext, avroSchema)).avro(path)

I also looked at com.databricks.spark.avro.SchemaConverters but the conversion method deftoSqlType(avroSchema: Schema):SchemaType returns SchemaType instead of StructType required by the above approach.

Can anyone help how to read Avro files with logical types in Spark.