We have a full ACID hive managed table that we need to access from spark ETL. We used the documentation provided to connect to Hive WareHouse connector ->
https://docs.cloudera.com/HDPDocuments/HDP3/HDP-3.0.1/integrating-hive/content/hive_hivewarehousecon...
In addition to using hive warehouse connector to access the acid tables, what spark execution mode differences are there when using JDBC hwc connector and HiveWareHouseSession vs SparkContext without hwc connector. We don't see any information spark ui/ spark history server and the query takes far too long (x3) than a similar query from SQLContext using a non-acid managed table.
from pyspark_llap import HiveWarehouseSession
hive = HiveWarehouseSession.session(spark).build()
df= hive.sql("select * from incidents LIMIT 100")
...
df.show(10)
#additional spark transformation code..
# NO DAG in spark history server, slower, takes higher memory
__________________________
The same pattern using SQLContext
from pyspark.sql import SQLContext
sqlSparkContext = SQLContext(spark.sparkContext)
df = sqlSparkContext.sql("select * from incidents LIMIT 100")
...
df.show(10)
#additional spark transformation code..
# SHOWS DAG in spark ui/ spark history server, faster
Can someone please explain the difference apart from hive table access where the HiveWarehouseSession spark code gets executed, engines in play, optimization, memory usage etc. vs spark code using SQLContext.
Does "spark.sql.hive.hwc.execution.mode"=spark change the spark map reduce execution?
