liyunzhang_intel created HIVE-16840:
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Summary: 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.
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