Bzip2 is splittable for text files.

Btw in Orc the question of splittable does not matter because each stripe is compressed individually.

Have you tried tez? As far as I recall (at least it was in the first version of Hive) mr uses for order by a single reducer which is a bottleneck.

Do you see some errors in the log file?

On 28 Jun 2016, at 23:53, Mich Talebzadeh <mich.talebzadeh@gmail.com> wrote:

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.png>


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

 

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