Hi all,

It finished in 2 hours 18 minutes!

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I need to dig in more.

Dr Mich Talebzadeh

 

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On 29 June 2016 at 10:42, Mich Talebzadeh <mich.talebzadeh@gmail.com> wrote:
Focusing on Spark job, as I mentioned before Spark is running in local mode with 8GB of memory for both the driver and executor memory.

However, I still see this enormous Duration time which indicates something is wrong badly!

Also I got rid of groupBy

  val s2 = HiveContext.table("sales2").select("PROD_ID")
  val s = HiveContext.table("sales_staging").select("PROD_ID")
  val rs = s2.join(s,"prod_id").sort(desc("prod_id")).take(5).foreach(println)


Inline images 3



Dr Mich Talebzadeh

 

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On 29 June 2016 at 10:18, Jörn Franke <jornfranke@gmail.com> wrote:

I think the TEZ engine is much more maintained with respect to optimizations related to Orc , hive , vectorizing, querying than the mr engine. It will be definitely better to use it.
Mr is also deprecated in hive 2.0.
For me it does not make sense to use mr with hive larger than 1.1.

As I said, order by might be inefficient to use (not sure if this has changed). You may want to use sort by.

That being said there are many optimizations methods.

On 29 Jun 2016, at 00:27, Mich Talebzadeh <mich.talebzadeh@gmail.com> wrote:

That is a good point.

The ORC table property is as follows

TBLPROPERTIES ( "orc.compress"="SNAPPY",
"orc.stripe.size"="268435456",
"orc.row.index.stride"="10000")

which puts each stripe at 256MB

Just to clarify this is spark running on Hive tables. I don't think the use of TEZ, MR or Spark as execution engines is going to make any difference?

This is the same query with Hive on MR

select a.prod_id from sales2 a, sales_staging b where a.prod_id = b.prod_id order by a.prod_id;

2016-06-28 23:23:51,203 Stage-1 map = 0%,  reduce = 0%
2016-06-28 23:23:59,480 Stage-1 map = 50%,  reduce = 0%, Cumulative CPU 7.32 sec
2016-06-28 23:24:08,771 Stage-1 map = 55%,  reduce = 0%, Cumulative CPU 18.21 sec
2016-06-28 23:24:11,860 Stage-1 map = 58%,  reduce = 0%, Cumulative CPU 22.34 sec
2016-06-28 23:24:18,021 Stage-1 map = 62%,  reduce = 0%, Cumulative CPU 30.33 sec
2016-06-28 23:24:21,101 Stage-1 map = 64%,  reduce = 0%, Cumulative CPU 33.45 sec
2016-06-28 23:24:24,181 Stage-1 map = 66%,  reduce = 0%, Cumulative CPU 37.5 sec
2016-06-28 23:24:27,270 Stage-1 map = 69%,  reduce = 0%, Cumulative CPU 42.0 sec
2016-06-28 23:24:30,349 Stage-1 map = 70%,  reduce = 0%, Cumulative CPU 45.62 sec
2016-06-28 23:24:33,441 Stage-1 map = 73%,  reduce = 0%, Cumulative CPU 49.69 sec
2016-06-28 23:24:36,521 Stage-1 map = 75%,  reduce = 0%, Cumulative CPU 52.92 sec
2016-06-28 23:24:39,605 Stage-1 map = 77%,  reduce = 0%, Cumulative CPU 56.78 sec
2016-06-28 23:24:42,686 Stage-1 map = 80%,  reduce = 0%, Cumulative CPU 60.36 sec
2016-06-28 23:24:45,767 Stage-1 map = 81%,  reduce = 0%, Cumulative CPU 63.68 sec
2016-06-28 23:24:48,842 Stage-1 map = 83%,  reduce = 0%, Cumulative CPU 66.92 sec
2016-06-28 23:24:51,918 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 70.18 sec
2016-06-28 23:25:52,354 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 127.99 sec
2016-06-28 23:25:57,494 Stage-1 map = 100%,  reduce = 67%, Cumulative CPU 134.64 sec
2016-06-28 23:26:57,847 Stage-1 map = 100%,  reduce = 67%, Cumulative CPU 141.01 sec

which basically sits at 67% all day





Dr Mich Talebzadeh

 

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On 28 June 2016 at 23:07, Jörn Franke <jornfranke@gmail.com> wrote:


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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