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https://issues.apache.org/jira/browse/HIVE-16840?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16043964#comment-16043964
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Xuefu Zhang commented on HIVE-16840:
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[~kellyzly], Thanks for sharing the info. This might not fit into HoS's design.
I reused your example just to demo the idea. Be more specifically, I was
thinking that for a query like select * from T order by id limit 10, instead of
generating a Spark plan like this:
{code}
STAGE PLANS:
Stage: Stage-1
Spark
Edges:
Reducer 2 <- Map 1 (SORT, 1)
{code}
we generate a plan like this:
{code}
STAGE PLANS:
Stage: Stage-1
Spark
Edges:
Reducer 2 <- Map 1 (SORT, 100)
Reducer 3 <- Reducer 2 (SORT, 1)
{code}
In {{Reducer2}} and {{Reducer3}} there is an limit operator with 10 as the
limit. In essence, we introduce one additional shuffle to sort, but only on
1000 rows of data.
> Investigate the performance of order by limit in HoS
> ----------------------------------------------------
>
> Key: HIVE-16840
> URL: https://issues.apache.org/jira/browse/HIVE-16840
> Project: Hive
> Issue Type: Bug
> Reporter: liyunzhang_intel
> Assignee: liyunzhang_intel
>
> We found that on 1TB data of TPC-DS, q17 of TPC-DS hanged.
> {code}
> select i_item_id
> ,i_item_desc
> ,s_state
> ,count(ss_quantity) as store_sales_quantitycount
> ,avg(ss_quantity) as store_sales_quantityave
> ,stddev_samp(ss_quantity) as store_sales_quantitystdev
> ,stddev_samp(ss_quantity)/avg(ss_quantity) as store_sales_quantitycov
> ,count(sr_return_quantity) as_store_returns_quantitycount
> ,avg(sr_return_quantity) as_store_returns_quantityave
> ,stddev_samp(sr_return_quantity) as_store_returns_quantitystdev
> ,stddev_samp(sr_return_quantity)/avg(sr_return_quantity) as
> store_returns_quantitycov
> ,count(cs_quantity) as catalog_sales_quantitycount ,avg(cs_quantity)
> as catalog_sales_quantityave
> ,stddev_samp(cs_quantity)/avg(cs_quantity) as
> catalog_sales_quantitystdev
> ,stddev_samp(cs_quantity)/avg(cs_quantity) as catalog_sales_quantitycov
> from store_sales
> ,store_returns
> ,catalog_sales
> ,date_dim d1
> ,date_dim d2
> ,date_dim d3
> ,store
> ,item
> where d1.d_quarter_name = '2000Q1'
> and d1.d_date_sk = store_sales.ss_sold_date_sk
> and item.i_item_sk = store_sales.ss_item_sk
> and store.s_store_sk = store_sales.ss_store_sk
> and store_sales.ss_customer_sk = store_returns.sr_customer_sk
> and store_sales.ss_item_sk = store_returns.sr_item_sk
> and store_sales.ss_ticket_number = store_returns.sr_ticket_number
> and store_returns.sr_returned_date_sk = d2.d_date_sk
> and d2.d_quarter_name in ('2000Q1','2000Q2','2000Q3')
> and store_returns.sr_customer_sk = catalog_sales.cs_bill_customer_sk
> and store_returns.sr_item_sk = catalog_sales.cs_item_sk
> and catalog_sales.cs_sold_date_sk = d3.d_date_sk
> and d3.d_quarter_name in ('2000Q1','2000Q2','2000Q3')
> group by i_item_id
> ,i_item_desc
> ,s_state
> order by i_item_id
> ,i_item_desc
> ,s_state
> limit 100;
> {code}
> the reason why the script hanged is because we only use 1 task to implement
> sort.
> {code}
> STAGE PLANS:
> Stage: Stage-1
> Spark
> Edges:
> Reducer 10 <- Reducer 9 (SORT, 1)
> Reducer 2 <- Map 1 (PARTITION-LEVEL SORT, 889), Map 11
> (PARTITION-LEVEL SORT, 889)
> Reducer 3 <- Map 12 (PARTITION-LEVEL SORT, 1009), Reducer 2
> (PARTITION-LEVEL SORT, 1009)
> Reducer 4 <- Map 13 (PARTITION-LEVEL SORT, 683), Reducer 3
> (PARTITION-LEVEL SORT, 683)
> Reducer 5 <- Map 14 (PARTITION-LEVEL SORT, 751), Reducer 4
> (PARTITION-LEVEL SORT, 751)
> Reducer 6 <- Map 15 (PARTITION-LEVEL SORT, 826), Reducer 5
> (PARTITION-LEVEL SORT, 826)
> Reducer 7 <- Map 16 (PARTITION-LEVEL SORT, 909), Reducer 6
> (PARTITION-LEVEL SORT, 909)
> Reducer 8 <- Map 17 (PARTITION-LEVEL SORT, 1001), Reducer 7
> (PARTITION-LEVEL SORT, 1001)
> Reducer 9 <- Reducer 8 (GROUP, 2)
> {code}
> The parallelism of Reducer 9 is 1. It is a orderby limit case so we use 1
> task to execute to ensure the correctness. But the performance is poor.
> the reason why we use 1 task to implement order by limit is
> [here|https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/optimizer/spark/SetSparkReducerParallelism.java#L207]
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