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Posted to issues@impala.apache.org by "Henry Robinson (JIRA)" <ji...@apache.org> on 2017/05/20 20:41:04 UTC
[jira] [Resolved] (IMPALA-5136) Running 48 concurrent Q17 queries
against TPC-DS 1TB queries fail with Cannot process row that is bigger than
the IO size (row_size=1.55 GB, null_indicators_size=0)
[ https://issues.apache.org/jira/browse/IMPALA-5136?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Henry Robinson resolved IMPALA-5136.
------------------------------------
Resolution: Duplicate
Fix Version/s: Impala 2.10.0
Seems to be same root cause as IMPALA-5093.
> Running 48 concurrent Q17 queries against TPC-DS 1TB queries fail with Cannot process row that is bigger than the IO size (row_size=1.55 GB, null_indicators_size=0)
> --------------------------------------------------------------------------------------------------------------------------------------------------------------------
>
> Key: IMPALA-5136
> URL: https://issues.apache.org/jira/browse/IMPALA-5136
> Project: IMPALA
> Issue Type: Sub-task
> Components: Distributed Exec
> Affects Versions: Impala 2.9.0
> Reporter: Mostafa Mokhtar
> Assignee: Henry Robinson
> Fix For: Impala 2.10.0
>
>
> Running 48 concurrent queries from TPC-DS Q17 against a 16 node cluster queries failed with
> Cannot process row that is bigger than the IO size (row_size=1.02 GB, null_indicators_size=0). To run this query, increase the IO size (--read_size option).
> Cannot process row that is bigger than the IO size (row_size=1.55 GB, null_indicators_size=0). To run this query, increase the IO size (--read_size option).
> Other iterations of the query failed with
> {code}
> Remote error: Service unavailable: ReportExecStatus request on impala.ExecControlService from 10.17.193.20:55530 dropped due to backpressure. The service queue is full; it has 1024 items.
> Timed out: ReportExecStatus RPC to 10.17.193.10:22000 timed out after 10.000s (SENT)
> {code}
> {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) 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 = ss_sold_date_sk and i_item_sk = ss_item_sk and s_store_sk = ss_store_sk and ss_customer_sk = sr_customer_sk and ss_item_sk = sr_item_sk and ss_ticket_number = sr_ticket_number and sr_returned_date_sk = d2.d_date_sk and d2.d_quarter_name in ('2000Q1','2000Q2','2000Q3') and sr_customer_sk = cs_bill_customer_sk and sr_item_sk = cs_item_sk and 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}
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