I'm trying to switch from reading csv flat files to avro files on spark. following https://github.com/databricks/spark-avro I use:
import com.databricks.spark.avro._ val sqlContext = new org.apache.spark.sql.SQLContext(sc) val df = sqlContext.read.avro("gs://logs.xyz.com/raw/2016/04/20/div1/div2/2016-04-20-08-28-35.UTC.blah-blah.avro") and get
java.lang.UnsupportedOperationException: This mix of union types is not supported (see README): ArrayBuffer(STRING) the readme file states clearly:
This library supports reading all Avro types, with the exception of complex union types. It uses the following mapping from Avro types to Spark SQL types:
when i try to textread the same file I can see the schema
val df = sc.textFile("gs://logs.xyz.com/raw/2016/04/20/div1/div2/2016-04-20-08-28-35.UTC.blah-blah.avro") df.take(2).foreach(println) {"name":"log_record","type":"record","fields":[{"name":"request","type":{"type":"record","name":"request_data","fields":[{"name":"datetime","type":"string"},{"name":"ip","type":"string"},{"name":"host","type":"string"},{"name":"uri","type":"string"},{"name":"request_uri","type":"string"},{"name":"referer","type":"string"},{"name":"useragent","type":"string"}]}}
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since I have little control on the format I'm getting these files in, my question here is - is there a workaround someone tested and can recommend?
I use gc dataproc with
MASTER=yarn-cluster spark-shell --num-executors 4 --executor-memory 4G --executor-cores 4 --packages com.databricks:spark-avro_2.10:2.0.1,com.databricks:spark-csv_2.11:1.3.0
any help would be greatly appreciated.....
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