AWR - Analyzing

This section is self explanatory which provides database name, id, instance, platform information and snap interval. (Database workload time duration in review).

DB Name	DB Id	Instance	Inst num	Startup Time	Release	RAC
evatdb	2393456878	evatdb	2	15-Aug-13 19:08	11.1.0.6.0	NO

Host Name	Platform	CPUs	Cores	Sockets	Memory (GB)
returns	Linux 64-bit	4	4	2	15.66

	Snap Id	Snap Time	Sessions	Cursors/Session
Begin Snap:	28566	14-Aug-13 03:00:21	130	4.8
End Snap:	28567	14-Aug-13 04:00:43	135	4.5
Elapsed:		60.35 (mins)
DB Time:		15.07 (mins)

	Begin	End
Buffer Cache:	5,666M	5,666M	Std Block Size:	8K
Shared Pool Size:	8,904M	8,904M	Log Buffer:	138,328K

DB Time(s):

DB CPU(s):

Redo size:

Logical reads:

Block changes:

Physical reads:

Physical writes:

User calls:

Parses:

Hard parses:

Soft parses:

Logons:

Executes:

Rollbacks:

Transactions:

Sorts:

It’s the amount of time oracle has spent performing database user calls. it does not include background processes

It’s the amount of CPU time spent on user calls. Same as DB time it does not include background process. The value is in microseconds

For example, the table below shows that an average transaction generates about 19,000 of redo data along with around 48,000 redo per second.

Consistent Gets+ DB blocks Gets = Logical reads

The number of block modified during the sample interval

No of block request causing I/O operation

Number of physical writes performed

Number of user queries generated

The total of all parses: both hard and soft.

The parses requiring a completely new parse of the SQL statement. These consume both latches and shared pool area.

Soft parses are not listed but derived by subtracting the hard parses from parses. A soft parse reuses a previous hard parse; hence it consumes far fewer resources.

No of logons during the interval

No of SQL Executes

No of rollback statements executed between the cumulative intervals

No of transactions per second

No of sorts performed

Instance efficiency should be close to 100 %

Buffer NoWait %:	99.99	Redo NoWait %:	100.00
Buffer Hit %:	93.06	In-memory Sort %:	100.00
Library Hit %:	98.67	Soft Parse %:	98.20
Execute to Parse %:	3.40	Latch Hit %:	99.98
Parse CPU to Parse Elapsd %:	0.01	% Non-Parse CPU:	96.21

Execute to Parse % and Parse CPU to Parse Elapsed %:

If the values are low like in the above case of 3.40 and 0.01 means that there could be a parsing problem. You may need to look at bind variable issues or shared pool sizing issue.

Redo NoWait%: Usually this stats is 99 or greater

In-memory Sort %:

This can tell you how efficient is you sort_area_size, hash_area_size or pga_aggrigate_target are. If you don’t have adequate sizes of sort, hash and pga parameters, then you in-memory sort per cent will go down

Soft parse %:

With 98.20 % for the soft parse meaning that about 1.72 % (100 -soft parse) is happening for hard parsing. You might want to look at you bind variables issues.

Latch Hit %: should be close to 100.

% Non-Parse CPU:

Most of our statements were already parsed so we weren't doing a lot of re parsing. Re parsing is high on CPU and should be avoided.

Shared Pool Statistics

	Begin	End
Memory Usage %:	73.86	75.42
% SQL with executions>1:	92.61	93.44
% Memory for SQL w/exec>1:	94.33	94.98

Memory Usage % is the shared pool usage. So here we have use 73.86 per cent of our shared pool and out of that almost 94 percent is being re-used. If Memory Usage % is too large like 90 % it could mean that your shared pool is too small and if the percent is in 50 for example then this could mean that you shared pool is too large

Top 5 timed events

Event	Waits	Time(s)	Avg wait (ms)	% DB time	Wait Class
DB CPU		1,019		112.73
log file sync	25,642	43	2	4.73	Commit
db file scattered read	3,064	40	13	4.43	User I/O
library cache pin	136,267	27	0	2.98	Concurrency
db file sequential read	7,608	24	3	2.71	User I/O

It’s critical to look into this section. If you turn off the statistic parameter, then the Time(s) won’t appear. Wait analysis should be done with respect to Time(s) as there could be millions of waits but if that happens for a second or so then who cares. Therefore, time is very important component.

So you have several different types of waits. So you may see the different waits on your AWR report. So let’s discuss the most common waits.

DB file type waits:
db file sequential read:
Is the wait that comes from the physical side of the database. it related to memory starvation and non selective index use. Sequential read is an index read followed by table read because it is doing index lookups which tells exactly which block to go to
db file scattered read:

Caused due to full table scans may be because of insufficient indexes or un-availability of updated statistics

direct Path writes:
You won’t see them unless you are doing some appends or data loads
direct Path reads:
could happen if you are doing a lot of parallel query activity
db file parallel writes / read: 
if you are doing a lot of partition activity then expect to see that wait even. it could be a table or index partition
db file single write:
if you see this event than probably you have a lot of data files in your database.
direct path read temp or direct path write temp:
this wait event shows Temp file activity (sort,hashes,temp tables, bitmap)
check pga parameter or sort area or hash area parameters. You might want to increase them

Buffer type waits

So what's going on in your memory
latch cache buffer chains:

 Check hot objects,

Free buffer waits: Insufficient buffers, Process holding buffers too long or i/o subsystem is over loaded.

Also check you db writes may be getting clogged up.

Buffer busy waits:
see what is causing them further along in report. Most of the time its data block related.
Gc buffer busy:
It’s in the RAC environment. Caused may be because of not enough memory on your nodes, overloaded interconnect. Also look RAC specific section of the report latch:
Cache buffers chain – Free list issues, hot blocks latch:

Cache buffer handles – Freelist issues, hot blocks
buffer busy - See what is causing them further along in report
no free buffers – Insufficient buffers, dbwr contention

Log Type Waits

log file parallel write – Look for log file contention
log buffer space – Look at increasing log buffer size
log file switch (checkpoint incomplete) – May indicate excessive db files or slow IO subsystem
log file switch (archiving needed) – Indicates archive files are written too slowly
log file switch completion – May need more log files per
log file sync – Could indicate excessive commits

GC Events

gccr multi block request – Full table or index scans
gc current multi block request – Full table or index scans
gccr block 2-way – Blocks are busy in another instance, check for block level contention or hot blocks
gccr block 3-way – Blocks are busy in another instance, check for block level contention or hot blocks
gccr block busy – Blocks are busy in another instance, check for block level contention or hot blocks
gccr block congested – cr block congestion, check for hot blocks or busy interconnect
gccr block lost – Indicates interconnect issues and contention
gc current block 2-way – Blocks are busy in another instance, check for block level contention or hot blocks
gc current block 3-way – Blocks are busy in another instance, check for block level contention or hot blocks
gc current block busy – Block is already involved in GC operation, shows hot blocks or congestion
gc current block congested – current block congestion, check for hot blocks or busy interconnect
gc current block lost - Indicates interconnect issues and contention

Log Type Waits

log file parallel write – Look for log file contention
log buffer space – Look at increasing log buffer size
log file switch (checkpoint incomplete) – May indicate excessive db files or slow IO subsystem
log file switch (archiving needed) – Indicates archive files are written too slowly
log file switch completion – May need more log files per
log file sync – Could indicate excessive commits

Undo Events

undo segment extension – If excessive, tune undo
latch: In memory undo latch – If excessive could be bug, check for your version, may have to turn off in memory undo
wait for a undo record – Usually only during recovery of large transactions, look at turning off parallel undo recovery.

What Next?

Determine wait events of concern
Drill down to specific sections of report for deeper analysis
Use custom scripts, ADDM and Ash to investigate issues