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From "Bikas Saha (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (TEZ-2496) Consider scheduling tasks in ShuffleVertexManager based on the partition sizes from the source
Date Mon, 06 Jul 2015 23:21:04 GMT

    [ https://issues.apache.org/jira/browse/TEZ-2496?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14615866#comment-14615866
] 

Bikas Saha commented on TEZ-2496:
---------------------------------

Yes. That is what I am suggesting because I think going the full fledged partition route is
risky in its current form. Not sure if you disagree with that risk assessment. The other features
mentioned would need new information to be passed to the reducer inputs right? That could
be done via new events sent from shufflevertexmanager to the tasks (eg. input data information
event being used to send splits today).

> Consider scheduling tasks in ShuffleVertexManager based on the partition sizes from the
source
> ----------------------------------------------------------------------------------------------
>
>                 Key: TEZ-2496
>                 URL: https://issues.apache.org/jira/browse/TEZ-2496
>             Project: Apache Tez
>          Issue Type: Improvement
>            Reporter: Rajesh Balamohan
>            Assignee: Rajesh Balamohan
>         Attachments: TEZ-2496.1.patch, TEZ-2496.2.patch, TEZ-2496.3.patch, TEZ-2496.4.patch,
TEZ-2496.5.patch, TEZ-2496.6.patch
>
>
> Consider scheduling tasks in ShuffleVertexManager based on the partition sizes from the
source.  This would be helpful in scenarios, where there is limited resources (or concurrent
jobs running or multiple waves) with dataskew and the task which gets large amount of data
gets sceheduled much later.
> e.g Consider the following hive query running in a queue with limited capacity (42 slots
in total) @ 200 GB scale
> {noformat}
> CREATE TEMPORARY TABLE sampleData AS
>   SELECT CASE
>            WHEN ss_sold_time_sk IS NULL THEN 70429
>            ELSE ss_sold_time_sk
>        END AS ss_sold_time_sk,
>        ss_item_sk,
>        ss_customer_sk,
>        ss_cdemo_sk,
>        ss_hdemo_sk,
>        ss_addr_sk,
>        ss_store_sk,
>        ss_promo_sk,
>        ss_ticket_number,
>        ss_quantity,
>        ss_wholesale_cost,
>        ss_list_price,
>        ss_sales_price,
>        ss_ext_discount_amt,
>        ss_ext_sales_price,
>        ss_ext_wholesale_cost,
>        ss_ext_list_price,
>        ss_ext_tax,
>        ss_coupon_amt,
>        ss_net_paid,
>        ss_net_paid_inc_tax,
>        ss_net_profit,
>        ss_sold_date_sk
>   FROM store_sales distribute by ss_sold_time_sk;
> {noformat}
> This generated 39 maps and 134 reduce slots (3 reduce waves). When lots of nulls are
there for ss_sold_time_sk, it would tend to have data skew towards 70429.  If the reducer
which gets this data gets scheduled much earlier (i.e in first wave itself), entire job would
finish fast.



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