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From 俊杰陈 <cjjnj...@gmail.com>
Subject Re: performance issue on big table join
Date Fri, 27 Oct 2017 05:38:05 GMT
Thanks, let me put here.

Yes, the query is intended to verify parallelism of partitioned join. We
want to know how many fragment instances started for a hash join on a
single node, and how many threads in a fragment instance perform the join.
I'm not sure whether there is only one thread participate in hash join,
since thus it can not maximize the CPU utilization.

The compute stats met following error:

Query: compute stats store_sales
WARNINGS: ImpalaRuntimeException: Error making
'updateTableColumnStatistics' RPC to Hive Metastore:
CAUSED BY: MetaException: Insert of object
"org.apache.hadoop.hive.metastore.model.MTableColumnStatistics@49fe55a1"
using statement "INSERT INTO `TAB_COL_STATS`
(`CS_ID`,`AVG_COL_LEN`,`BIT_VECTOR`,`COLUMN_NAME`,`COLUMN_TYPE`,`DB_NAME`,`BIG_DECIMAL_HIGH_VALUE`,`BIG_DECIMAL_LOW_VALUE`,`DOUBLE_HIGH_VALUE`,`DOUBLE_LOW_VALUE`,`LAST_ANALYZED`,`LONG_HIGH_VALUE`,`LONG_LOW_VALUE`,`MAX_COL_LEN`,`NUM_DISTINCTS`,`NUM_FALSES`,`NUM_NULLS`,`NUM_TRUES`,`TBL_ID`,`TABLE_NAME`)
VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)" failed : Unknown column
'BIT_VECTOR' in 'field list'


2017-10-27 12:02 GMT+08:00 Mostafa Mokhtar <mmokhtar@cloudera.com>:

> Hi,
>
> Looks like you are joining store_sales with catalog_sales on item_sk, this
> kind of join condition is a many to many, which means the output number of
> rows will be much larger then input number of rows, not sure if this is
> intended.
>
> Also did you run "compute stats [TABLE_NAME]" on both tables?
>
> For a more comprehensive query try TPCDS Q17
>
> 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;
>
>
> I recommend moving this kind of discussion on
> user@impala.incubator.apache.org.
>
> On Thu, Oct 26, 2017 at 7:25 PM, 俊杰陈 <cjjnjust@gmail.com> wrote:
>
> > The profile file is damaged. Here is a screenshot for exec summary
> >
> > ​
> >
> > 2017-10-27 10:04 GMT+08:00 俊杰陈 <cjjnjust@gmail.com>:
> >
> >> Hi Devs
> >>
> >> I met a performance issue on big table join. The query takes more than 3
> >> hours on Impala and only 3 minutes on Spark SQL on the same 5 nodes
> >> cluster. when running query,  the left scanner and exchange node are
> very
> >> slow.  Did I miss some key arguments?
> >>
> >> you can see profile file in attachment.
> >>
> >>
> >> ​
> >> --
> >> Thanks & Best Regards
> >>
> >
> >
> >
> > --
> > Thanks & Best Regards
> >
>



-- 
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