First Question is when we should use parallel hints ??
Without parallel it makes the query wait on the indexes data to be read. So we are just waiting for data to be read , so parallel hint makes sense.
Link to Article on Understanding Parallel Explain plans
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)
When you write parallel hint your session becomes coordinator processor and there are several slave processes which are created. Slave read data and give to coordinator which does the loading.Below is a scenario in which Parallel hint was actually slowing the query .Below analysis is for DML statements. Inserts were taking time
select sid, state,event,SECONDS_IN_WAIT,BLOCKING_SESSION,BLOCKING_INSTANCE,
row_wait_obj#,
row_wait_file#,
row_wait_block#,
row_wait_row#,p1,p2
from v$session
where username = 'ABC' and
OSUSER='kkk'
and event = 'enq: TX - row lock contention'
Note -- There are 66 parallel session on Measure val
select event, total_waits, (time_waited*10)/1000 tw_ms,
average_wait*10 aw_ms, max_wait*10 mw_ms
from v$session_event
where sid in (
select sid from v$session
where username = 'ABC' and
OSUSER='kkk' )
order by 3 desc
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)
There are two events which point highly in the favour of this .
1) PX Deq Credit: send blkd---- This means the slave are waiting for coordinator process to be free to supply their data
2) PX Deq: Execution Msg---This means once slave processes have supplied their data they are waiting for more request from coordinator process.
3) So both of them point that the coordinator is not able to handle the data provided by slaves, which means there are more slave processes that the coordinator can handle
Note -- On average every parallel read wait quarter of second to read
select name, sum(value)
from v$sesstat s, v$statname n
where n.statistic# = s.statistic# and
sid in (
select sid from v$session
where username = 'ABC' and
OSUSER='kkk' )
group by name
order by 2 desc
Note - The interconnnect bytes seems to be a large number
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To do insert in parallel we need to have parallel DML enabled which enables the slaves to do the insert other wise only coordinator processor does the insert and it will be bottleneck you will all the slaves waiting for one coordinator process do the load
Below is analysis for Report query which was taking time due to degree of parallelism
Analysis for logic in report
1) Changing the parallel degree in report logic from parallel(8,1) to parallel. Brings down the query time from 100 to 11 seconds
1. PX Deq: Execution Msg---This means once slave processes have supplied their data they are waiting for more request from coordinator process.
2. So both of them point that the coordinator is not able to handle the data provided by slaves, which means there are more slave processes that the coordinator can handle
select sid, state,event,SECONDS_IN_WAIT,BLOCKING_SESSION,BLOCKING_INSTANCE,
row_wait_obj#,
row_wait_file#,
row_wait_block#,
row_wait_row#,p1,p2
from v$session
where username = 'ABC' and
OSUSER='kkk'
Without parallel it makes the query wait on the indexes data to be read. So we are just waiting for data to be read , so parallel hint makes sense.
Link to Article on Understanding Parallel Explain plans
When you write parallel hint your session becomes coordinator processor and there are several slave processes which are created. Slave read data and give to coordinator which does the loading.Below is a scenario in which Parallel hint was actually slowing the query .Below analysis is for DML statements. Inserts were taking time
select sid, state,event,SECONDS_IN_WAIT,BLOCKING_SESSION,BLOCKING_INSTANCE,
row_wait_obj#,
row_wait_file#,
row_wait_block#,
row_wait_row#,p1,p2
from v$session
where username = 'ABC' and
OSUSER='kkk'
and event = 'enq: TX - row lock contention'
Note -- There are 66 parallel session on Measure val
select event, total_waits, (time_waited*10)/1000 tw_ms,
average_wait*10 aw_ms, max_wait*10 mw_ms
from v$session_event
where sid in (
select sid from v$session
where username = 'ABC' and
OSUSER='kkk' )
order by 3 desc
There are two events which point highly in the favour of this .
1) PX Deq Credit: send blkd---- This means the slave are waiting for coordinator process to be free to supply their data
2) PX Deq: Execution Msg---This means once slave processes have supplied their data they are waiting for more request from coordinator process.
3) So both of them point that the coordinator is not able to handle the data provided by slaves, which means there are more slave processes that the coordinator can handle
Note -- On average every parallel read wait quarter of second to read
select name, sum(value)
from v$sesstat s, v$statname n
where n.statistic# = s.statistic# and
sid in (
select sid from v$session
where username = 'ABC' and
OSUSER='kkk' )
group by name
order by 2 desc
Note - The interconnnect bytes seems to be a large number
To do insert in parallel we need to have parallel DML enabled which enables the slaves to do the insert other wise only coordinator processor does the insert and it will be bottleneck you will all the slaves waiting for one coordinator process do the load
Below is analysis for Report query which was taking time due to degree of parallelism
Analysis for logic in report
1) Changing the parallel degree in report logic from parallel(8,1) to parallel. Brings down the query time from 100 to 11 seconds
1. PX Deq: Execution Msg---This means once slave processes have supplied their data they are waiting for more request from coordinator process.
2. So both of them point that the coordinator is not able to handle the data provided by slaves, which means there are more slave processes that the coordinator can handle
select sid, state,event,SECONDS_IN_WAIT,BLOCKING_SESSION,BLOCKING_INSTANCE,
row_wait_obj#,
row_wait_file#,
row_wait_block#,
row_wait_row#,p1,p2
from v$session
where username = 'ABC' and
OSUSER='kkk'
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