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https://issues.apache.org/jira/browse/HIVE-16840?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16060835#comment-16060835
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Rui Li commented on HIVE-16840:
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Hi [~xuefuz], I doubt Spark can take much advantage of it because the reducer
fetches all the data anyway. But I agree limit with large number is rare so
it's OK to leave it aside at the moment.
Another thing I'm not sure is, hive should have already pushed down the limit
to the upstream of shuffle. Looking at the RS code, it uses a TopN hash to
track the top N keys in input. Ideally, each RS will only output N records. I
tried some simple query to verify how this saves shuffled data.
[~kellyzly], do you know why it's not working as expected in your case?
> 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
> Attachments: HIVE-16840.patch
>
>
> 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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