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https://issues.apache.org/jira/browse/TEZ-2496?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14565558#comment-14565558
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TezQA commented on TEZ-2496:
----------------------------
{color:green}+1 overall{color}. Here are the results of testing the latest
attachment
http://issues.apache.org/jira/secure/attachment/12736054/TEZ-2496.3.patch
against master revision 87f26a2.
{color:green}+1 @author{color}. The patch does not contain any @author
tags.
{color:green}+1 tests included{color}. The patch appears to include 4 new
or modified test files.
{color:green}+1 javac{color}. The applied patch does not increase the
total number of javac compiler warnings.
{color:green}+1 javadoc{color}. There were no new javadoc warning messages.
{color:green}+1 findbugs{color}. The patch does not introduce any new
Findbugs (version 3.0.1) warnings.
{color:green}+1 release audit{color}. The applied patch does not increase
the total number of release audit warnings.
{color:green}+1 core tests{color}. The patch passed unit tests in .
Test results: https://builds.apache.org/job/PreCommit-TEZ-Build/768//testReport/
Console output: https://builds.apache.org/job/PreCommit-TEZ-Build/768//console
This message is automatically generated.
> 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
>
>
> 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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