Either use Tanel's snapper to find out what work session is doing, or
connect
run the query
select sn.name, ms.value
from v$mystat ms, v$statname sn
where ms.value != 0
and sn.statistic# = ms.statistic#
Differences in the stats might give you some clue why the two systems behave
differently.
If it weren't Exadata I think one detail I'd have checked almost immediately is
that no-one has set the db_file_multiblock_read_count to different values for
the two databases.
And then I'd have checked any other parameters, just in case.
Regards
Jonathan Lewis
________________________________________
From: William Beldman <wbeldma@xxxxxx>
Sent: 15 December 2017 17:07
To: Jonathan Lewis; tanel@xxxxxxxxxxxxxx
Cc: ORACLE-L
Subject: RE: Comparing apples to apples on Exadata
I have no doubt that Database 2 will always be a little bit slower since it is
certainly busier. So the I/O and CPU WILL have a small affect. Just not by this
much.
What I'd like to see is some way to force that table in Database 2 to be
treated the same way it is treated in Database 1 (ie. This table is important
so make sure you cache it, either in SGA or Flash cache or something). You
would think that regular queries in Database 2 would do this but it doesn't
appear to be the case. Every query, no matter how often I run it, causes the
storage cells to go off and retrieve the data off disk which, for some reason,
it doesn't have to do for Database 1.
-----Original Message-----
From: oracle-l-bounce@xxxxxxxxxxxxx [mailto:oracle-l-bounce@xxxxxxxxxxxxx] On ;
Behalf Of Jonathan Lewis
Sent: December 15, 2017 10:30 AM
To: William Beldman <wbeldma@xxxxxx>; tanel@xxxxxxxxxxxxxx
Cc: ORACLE-L <oracle-l@xxxxxxxxxxxxx>
Subject: Re: Comparing apples to apples on Exadata
Tanel,
The top set both show Cell Offload, so I don't think it's (purely) a smart scan
thing.
I'd like to see the session stats for the executions, since that will probably
explain what the I/O and CPU are spent on, but from the top set one thing I
note is that the cell physical reads are single block, and the slow system does
62,900 compared to 6,000 on the fast system. To me this suggests either a
chained rows or a read-consistency/cleanout/commit time effect on the slow
system.
With repeated executions and offload disabled the slow query does 4M buffer
gets (which is remarkably similar to the number of rows is that a coincidence,
or a comment on the number of migrated rows?) and only 324MB read in 9,000 read
requests against 3GB in 6,000 requests - that suggests a lot of table blocks
cached on the tablescan - perhaps because so many of them have been pre-read
and got into the hot cache area thanks to "continued row fetches" in the
initial runs.
Regards
Jonathan Lewis
________________________________________
From: oracle-l-bounce@xxxxxxxxxxxxx <oracle-l-bounce@xxxxxxxxxxxxx> on behalf
of Tanel Poder <tanel@xxxxxxxxxxxxxx>
Sent: 14 December 2017 23:06:37
To: wbeldma@xxxxxx
Cc: ORACLE-L
Subject: Re: Comparing apples to apples on Exadata
In the fast case, the direct path read (thus also smart scan) has kicked in. In
the slow case it hasn't and regular buffered IO via buffer cache is used.
One indicator of that is the fast query having a Cell offload column in its
stats, the slow query doesn't (therefore there was no smart scan).
The other indicator is that you have "cell...physical read" wait events showing
up in the slow scenario. Smart scan can be so fast asynchronously feeding data
for your query, so that in the fast case you see only CPU usage and no IO wait
events at all.
There are quite a few different inputs that affect the direct path read
decision, affecting Oracle's estimation of how much IO it would have to do in
either case. Usually bigger buffer cache and smaller segment sizes (partitioned
vs non-partitioned tables!) cause more buffered reads and bigger segments &
smaller buffer cache end up favoring direct path reads/smart scans more.
Also, there are more reasons why smart scan doesn't get used even if direct
path reads have kicked in.
I have some high level explanations and a list of reasons affecting the
decision here:
https://blog.tanelpoder.com/2012/09/03/optimizer-statistics-driven-direct-path-read-decision-for-full-table-scans-_direct_read_decision_statistics_driven/
And there's plenty of low level geekery & details straight from the source:
https://blogs.oracle.com/smartscan-deep-dive/when-bloggers-get-it-wrong-part-1
https://blogs.oracle.com/smartscan-deep-dive/when-bloggers-get-it-wrong-part-2
--
Tanel Poder
https://blog.tanelpoder.com
On Thu, Dec 14, 2017 at 11:14 PM, Will Beldman
<wbeldma@xxxxxx<mailto:wbeldma@xxxxxx>> wrote:
I have two (nearly) identical databases on Exadata.
I have a simple query and can force it to use the same execution plan on both
databases.
On database 1, the query finishes consistently around 1 second. The cost
analyzer shows
Global Stats
Elapsed
Time(s)
Cpu
Time(s)
IO
Waits(s)
Other
Waits(s)
Fetch
Calls
Buffer
Gets
Read
Reqs
Read
Bytes
Cell
Offload
0.97
0.94
0.02
0.01
1
380K
6026
3GB
95.13%
SQL Plan Monitoring Details (Plan Hash Value=1373192284)
Id
Operation
Name
Rows
(Estim)
Cost
Time
Active(s)
Start
Active
Execs
Rows
(Actual)
Read
Reqs
Read
Bytes
Cell
Offload
Mem
(Max)
Activity
(%)
Activity Detail
(# samples)
0
SELECT STATEMENT
1
+1
1
3
1
. SORT GROUP BY
3
105K
2
+0
1
3
2048
100.00
Cpu (1)
2
.. TABLE ACCESS STORAGE FULL
##TABLE_NAME##
4M
105K
1
+1
1
4M
6026
3GB
95.13%
3M
On database 2, the query finishes anywhere between 10 seconds to 60 seconds.
The cost analyzer shows Global Stats
Elapsed
Time(s)
Cpu
Time(s)
IO
Waits(s)
Application
Waits(s)
Cluster
Waits(s)
Fetch
Calls
Buffer
Gets
Read
Reqs
Read
Bytes
Cell
Offload
21
10
12
0.00
0.00
1
4M
62926
4GB
81.24%
SQL Plan Monitoring Details (Plan Hash Value=1373192284)
Id
Operation
Name
Rows
(Estim)
Cost
Time
Active(s)
Start
Active
Execs
Rows
(Actual)
Read
Reqs
Read
Bytes
Cell
Offload
Mem
(Max)
Activity
(%)
Activity Detail
(# samples)
0
SELECT STATEMENT
19
+2
1
3
1
. SORT GROUP BY
3
118K
19
+2
1
3
2048
2
.. TABLE ACCESS STORAGE FULL
##TABLE_NAME##
4M
118K
20
+1
1
4M
62926
4GB
81.24%
3M
100.00
Cpu (9)
cell single block physical read (11)
I can't explain why one database spends so little time on the data retrieval
while the other one spends almost all it's time trying to retrieve the data.
I am guessing it is due to smart scan offloading. If I force data reads by
adding the hint +OPT_PARAM('cell_offload_processing' 'false') and run the query
a few times, I can get similar execution times on both databases.
Database 1:
Global Stats
Elapsed
Time(s)
Cpu
Time(s)
IO
Waits(s)
Fetch
Calls
Buffer
Gets
Read
Reqs
Read
Bytes
3.86
2.96
0.91
1
380K
6015
3GB
SQL Plan Monitoring Details (Plan Hash Value=1373192284)
Id
Operation
Name
Rows
(Estim)
Cost
Time
Active(s)
Start
Active
Execs
Rows
(Actual)
Read
Reqs
Read
Bytes
Mem
(Max)
Activity
(%)
Activity Detail
(# samples)
0
SELECT STATEMENT
2
+2
1
3
1
. SORT GROUP BY
3
105K
2
+2
1
3
2048
2
.. TABLE ACCESS STORAGE FULL
##TABLE_NAME##
4M
105K
4
+0
1
4M
6015
3GB
100.00
Cpu (4)
Database 2:
Global Stats
Elapsed
Time(s)
Cpu
Time(s)
IO
Waits(s)
Cluster
Waits(s)
Fetch
Calls
Buffer
Gets
Read
Reqs
Read
Bytes
9.40
7.11
2.28
0.00
1
4M
9546
324MB
SQL Plan Monitoring Details (Plan Hash Value=1373192284)
Id
Operation
Name
Rows
(Estim)
Cost
Time
Active(s)
Start
Active
Execs
Rows
(Actual)
Read
Reqs
Read
Bytes
Mem
(Max)
Activity
(%)
Activity Detail
(# samples)
0
SELECT STATEMENT
8
+2
1
3
1
. SORT GROUP BY
3
118K
8
+2
1
3
2048
2
.. TABLE ACCESS STORAGE FULL
##TABLE_NAME##
4M
118K
9
+2
1
4M
9546
324MB
100.00
Cpu (7)
cell multiblock physical read (1)
cell single block physical read (1)
Is Smart Scan really the culprit here and if so, why isn't database 2 using it
as well as database 1? If not, how else can I account for such wild
discrepancies for such similar data?
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