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From Mich Talebzadeh <mich.talebza...@gmail.com>
Subject Re: Joining a compressed ORC table with a non compressed text table
Date Wed, 29 Jun 2016 09:42:47 GMT
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)


[image: 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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>
>
>
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>
>
> *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.
>
>
>
> 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
>>
>>
>>
>> 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.
>>
>>
>>
>>
>

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