Hi,
I have a simple join between table sales2 a compressed (snappy) ORC with 22 million rows and another simple table sales_staging under a million rows stored as a text file with no compression. The join is very simple val s2 = HiveContext.table("sales2").select("PROD_ID") val s = HiveContext.table("sales_staging").select("PROD_ID") val rs = s2.join(s,"prod_id").orderBy("prod_id").sort(desc("prod_id")).take(5).foreach(println) Now what is happening is it is sitting on SortMergeJoin operation on ZippedPartitionRDD as shown in the DAG diagram below [image: Inline images 1] And at this rate only 10% is done and will take for ever to finish :( Stage 3:==> (10 + 2) / 200] Ok I understand that zipped files cannot be broken into blocks and operations on them cannot be parallelized. Having said that what are the alternatives? Never use compression and live with it. I emphasise that any operation on the compressed table itself is pretty fast as it is a simple table scan. However, a join between two tables on a column as above suggests seems to be problematic? Thanks P.S. the same is happening using Hive with MR select a.prod_id from sales2 a inner join sales_staging b on a.prod_id = b.prod_id order by a.prod_id; Dr Mich Talebzadeh LinkedIn * https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw <https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw>* http://talebzadehmich.wordpress.com *Disclaimer:* Use it at your own risk. Any and all responsibility for any loss, damage or destruction of data or any other property which may arise from relying on this email's technical content is explicitly disclaimed. The author will in no case be liable for any monetary damages arising from such loss, damage or destruction.