|When viewing AWR report,
always check corresponding ADDM report for actionable
ADDM is a self diagnostic engine designed from the experience of Oracle’s best tuning experts and makes specific performance recommendations.
|REPORT NAME||SQL Script|
|Automatic Workload Repository Report||awrrpt.sql|
|Automatic Database Diagnostics Monitor Report||addmrpt.sql|
|AWR Diff Periods Report||awrddrpt.sql|
|AWR Single SQL Statement Report||awrsqrpt.sql|
|AWR Global Report||awrgrpt.sql|
|AWR Global Diff Report||awrgdrpt.sql|
AWR Automated Snapshots
Oracle uses a scheduled job, GATHER_STATS_JOB, to collect AWR statistics. This job is created, and enabled automatically, when you create a new Oracle database. To see this job, use the DBA_SCHEDULER_JOBS view as seen in this example:
a.enabled, c.window_name, c.schedule_name, c.start_date,
FROM dba_scheduler_jobs a, dba_scheduler_wingroup_members b, dba_scheduler_windows c
You can disable this job using the dbms_scheduler.disable
procedure as seen in this example:
And you can enable the job using the dbms_scheduler.enable
procedure as seen in this example:
|DB Name||DB Id||Instance||Inst num||Startup Time||Release||RAC|
|Host Name||Platform||CPUs||Cores||Sockets||Memory (GB)|
|atl-frauddb-04.fiservipo.com||Linux x86 64-bit||16||8||2||11.72|
|Snap Id||Snap Time||Sessions||Cursors/Session|
|Begin Snap:||20813||08-Jul-13 00:00:19||267||3.1|
|End Snap:||20854||09-Jul-13 15:54:14||278||3.6|
|DB Time:||4,689.46 (mins)|
|Buffer Cache:||1,520M||1,344M||Std Block Size:||8K|
|Shared Pool Size:||1,120M||1,296M||Log Buffer:||8,632K|
|Per Second||Per Transaction||Per Exec||Per Call|
|W/A MB processed:||4.6||2.1|
DB time(s): It's the amount of time
oracle has spent performing database user calls. It doesn't
include background processes. From the system view, the DB Time
is the sum of system usage, which is the CPU usage plus any
system call to lower systems (storage, network, etc).Application
waits (latches, locks, dbms_lock.sleep,... ) are implemented as
system calls as well.
DB CPU(s): It's
the amount of CPU time spent on user calls. It doesn't include
background process. The value is in microseconds.
Here are few important stats for a DBA to look into. Fist is "DB CPU(s)" per second. Before that let's understand how DB CUP's work.
Suppose you have 12 cores into the system. So, per wall clock second you have 12 seconds to work on CPU. So, if "DB CPU(s)" per second in this report > cores in (Host Configuration (#2)) it means env is CPU bound and either need more CPU's or need to further check is this happening all the time or just for a fraction of time. As per my experience there are very few cases, when system is CPU bound.
Redo size: This is the amount of DML happening in the DB. High redo figures mean that either lots of new data is being saved into the database, or existing data is undergoing lots of changes. For example, the table below shows that an average transaction generates about 12,000 bytes of redo data along with around 26,000 redo bytes per second.
This is calculated as Consistent Gets + DB Block Gets
= Logical Reads. Logical reads is simply the number of
blocks read by the database, including physical (i.e. disk)
Block Changes: The number of blocks modified during the sample interval. If you see an increase here then more DML statements are taking place (meaning your users are doing more INSERTs, UPDATEs, and DELETEs than before).
Physical reads: The number of requests for a block that caused a physical I/O.
Physical writes: Number of physical writes performed
User calls: Indicates how many user calls have occurred during the snapshot period. This value can give you some indication if usage has increased. The user calls is the database client requesting the server to to do something like login, parse,etc while SQL Execute is executing the sql. Depending on how the client is connecting, the numbers can be higher or lower. In particular, when the database is executing many times per a user call, this could be an indication of excessive context switching (e.g. a PL/SQL function in a SQL statement called too often because of a bad plan). In such cases looking into “SQL ordered by executions” will be the logical next step.
Parses: The total of all parses; both hard and soft.
Hard Parses: Those parses requiring a completely new parse of the SQL statement. A ‘hard parse’ rate of greater than 100 per second indicates there is a very high amount of hard parsing on the system. High hard parse rates cause serious performance issues, and must be investigated. A high hard parse rate is usually accompanied by latch contention on the shared pool and library cache latches. Check whether waits for ‘latch free’ appear in the top-5 wait events, and if so, examine the latching sections of the report. Of course, we want a low number here. Possible reasons for excessive hard parses may be a small shared pool or may be that bind variables are not being used.
As a rule of a thumb, anything below 1 hard parse per second is probably okay, and everything above 100 per second suggests a problem (if the database has a large number of CPUs, say, above 100, those numbers should be scaled up accordingly). It also helps to look at the number of hard parses as % of executions (especially if you’re in the grey zone).
If you suspect that excessive parsing is hurting your database’s performance:
1) check “time model statistics” section (hard parse elapsed
time, parse time elapsed etc.)
2) see if there are any signs of library cache contention in the top-5 events
3) see if CPU is an issue.
If that confirms your suspicions, then find the source of excessive parsing (for soft parsing, use “SQL by parse calls”
Soft Parses: Not listed but derived by subtracting the hard parses from parses. A soft parse reuses a previous hard parse and hence consumes far fewer resources. A high soft parse rate could be anywhere in the rate of 300 or more per second. Unnecessary soft parses also limit application scalability; optimally a SQL statement should be soft-parsed once per session, and executed many times.
Sorts: Number of sorts occurring in the database. Establishing a new database connection is also expensive (and even more expensive in case of audit or triggers). If you suspect that high number of logons is degrading your performance, check “connection management elapsed time” in “Time model statistics”.
Logons: No of logons during the interval.
Executes: how many statements we are executing per second / transaction
Transactions: How many transactions per second we process.
Logical and Physical Reads combined
shows measure of how many I/O the DB is performing. If this is
too high, go to section “SQL by Logical Reads” or “SQL by
Next stat to look at are Parses and Hard parses. If the ratio of hard parse to parse is high, this means Database is performing more hard parse. So, needs to look at parameters like cursor_sharing and application level for bind variables etc.
|Buffer Nowait %:||100.00||Redo NoWait %:||100.00|
|Buffer Hit %:||99.32||In-memory Sort %:||100.00|
|Library Hit %:||98.94||Soft Parse %:||97.25|
|Execute to Parse %:||75.00||Latch Hit %:||98.78|
|Parse CPU to Parse Elapsd %:||22.99||% Non-Parse CPU:||99.93|
(parse time CPU) / (parse time elapsed)
A low value for this ratio could mean that the non-CPU-related
parse time was spent waiting for latches, which might indicate a
parsing or latching problem. To investigate further, look at the
shared-pool and library-cache latches in the Latch sections of the
report for indications of contention on these latches.
Latch Hit Ratio. This is the ratio of the total number of latch misses to the number of latch gets for all latches. A low value for this ratio indicates a latching problem, whereas a high value is generally good. However, as the data is rolled up over all latches, a high latch hit ratio can artificially mask a low get rate on a specific latch. Cross-check this value with the Top 5 Wait Events to see if latch free is in the list, and refer to the Latch sections of the report. Latch Hit % of less than 99 percent is usually a big problem.
Instance Efficiency Percentages (Target 100%)
Buffer Nowait %: 99.99 Redo NoWait %: 100.00
Buffer Hit %: 95.57 In-memory Sort %: 97.55
Library Hit %: 99.89 Soft Parse %: 99.72
Execute to Parse %: 88.75 Latch Hit %: 99.11
Parse CPU to Parse Elapsd %: 52.66 % Non-Parse CPU: 99.99
Interpreting the ratios in this section can be slightly more complex than it may seem at first glance. While high values for the ratios are generally good (indicating high efficiency), such values can be misleading your system may be doing something efficiently that it would be better off not doing at all. Similarly, low values aren't always bad. For example, a low in-memory sort ratio (indicating a low percentage of sorts performed in memory) would not necessarily be a cause for concern in a decision- support system (DSS) environment, where user response time is less critical than in an online transaction processing (OLTP) environment.
Basically, you need to keep in mind the characteristics of your application - whether it is query-intensive or update-intensive, whether it involves lots of sorting, and so on - when you're evaluating the Instance Efficiency Percentages.
The following ratios should be above 90% in a database.
The execute to parse ratio should be very high in a ideal database.
The execute to parse ratio is basically a measure between the number of times a sql is executed versus the number of times it is parsed.
The ratio will move higher as the number of executes go up, while the number of parses either go down or remain the same.
The ratio will be close to zero if the number of executes and parses are almost equal.
The ratio will be negative executes are lower but the parses are higher.
Another Sample Analysis
Instance Efficiency Percentages (Target 100%)
Buffer Nowait %: 98.56 Redo NoWait %: 100.00
Buffer Hit %: 99.96 In-memory Sort %: 99.84
Library Hit %: 99.99 Soft Parse %: 100.00 (A)
Execute to Parse %: 0.10 (A) Latch Hit %: 99.37
Parse CPU to Parse Elapsd %: 58.19 (A) % Non-Parse CPU: 99.84
Shared Pool Statistics Begin End
Memory Usage %: 28.80 29.04 (B)
% SQL with executions>1: 75.91 76.03
% Memory for SQL w/exec>1: 83.65 84.09
• The 100% soft parse ratio (A) indicates the system is not hard-parsing. However the system is soft parsing a lot, rather than only re-binding and re-executing the same cursors, as the Execute to Parse % is very low (A). Also, the CPU time used for parsing (A) is only 58% of the total elapsed parse time (see Parse CPU to Parse Elapsd). This may also imply some resource contention during parsing (possibly related to the latch free event?).
• There seems to be a lot of unused memory in the shared pool (only 29% is used) (B). If there is insufficient memory allocated to other areas of the database (or OS), this memory could be redeployed
***Please see the following NOTES on
shared pool issues
[NOTE:146599.1] Diagnosing and Resolving Error ORA-04031
[NOTE:62143.1] Understanding and Tuning the Shared Pool
[NOTE:105813.1] SCRIPT TO SUGGEST MINIMUM SHARED POOL SIZE
Foreground Events by Total Wait Time
This section provides insight into what events the Oracle database is spending most of it's time on (see wait events). Each wait event is listed, along with the number of waits, the time waited (in seconds), the average wait per event (in microseconds) and the associated wait class.
This is one of the most important sections of the report.
||Total Wait Time (sec)||Wait Avg (ms)||% DB time||Wait Class|
|PX Deq: Slave Session Stats||912,927||3006.7||3
|log file sync||247,094||6,933||28||2.46||Commit|
|db file sequential read||221,301||5,813||26||2.07||User I/O|
|library cache: mutex X||27,915||57.9||2||1.9||Concurrency|
If you turn off the statistic parameter, then the Time(s) wont
appear. Wait analysis should be done with respect to Time(s) as
there could be million of waits but if that happens for a second
or so then who cares. Therefore, time is very important component.
When you are trying to eliminate bottlenecks on your system, your report's Top 10 Timed Events section is the first place to look and you should use the HIGHEST WAIT TIMES to guide the investigation.
of all check for wait class if wait
class is User I/O , System I/O, Others etc this
could be fine but if wait class has value "Concurrency" then
there could be some serious problem.
Next to look at is Total Wait Time (s) which show how many times DB was waiting in this class and then Wait Avg (ms). If Total Wait Time(s) are high but Wait Avg (ms) is low then you can ignore this. If both are high or Wait Avg (ms) is high then this has to further investigate.
check if Wait Class. If their values are in User
I/O , System I/O, Others this could be fine. But if wait
class has value "Concurrency" then there could be some serious
Next to look at is Total Wait Time (s) which show how many times DB was waiting in this class and then Wait Avg (ms).
If Total Wait Time(s) are high but Wait Avg (ms) is low then you can ignore this.
are high or Wait Avg (ms) is high then this
has to further investigate.
In the above screen shot, most of the resource are taken by DB
CPU = 64% DB time. Taking resource by DB CPU is a normal
As you will see, you have several different types of waits, so
let's discuss the most common waits on the next section.
Wait Classes by Total Wait Time
|Wait Class||Waits||Total Wait Time (sec)||Avg Wait (ms)||% DB time||Avg Active Sessions|
|CPUs||Cores||Sockets||Load Average Begin||Load Average End||%User||%System||%WIO||%Idle|
|%Total CPU||%Busy CPU||%DB time waiting for CPU (Resource Manager)|
|Read+Write Per Second||Read per Second||Write Per Second|
|Optimized Total (MB):||0.0||0.0||0.0|
|Via Buffer Cache (blocks):||71.6||67.3||4.4|
|Host Mem (MB):||32,643.8||32,643.8|
|SGA use (MB):||16,384.0||16,384.0|
|PGA use (MB):||2,500.9||2,505.5|
|% Host Mem used for SGA+PGA:||57.85||57.87|
|Buffer Cache:||7,168M||7,168M||Std Block Size:||8K|
|Shared Pool Size:||8,301M||8,298M||Log Buffer:||21,148K|
Shared Pool Statistics
|Memory Usage %:||87.72||87.54|
|% SQL with executions>1:||83.65||83.33|
|% Memory for SQL w/exec>1:||78.30||80.36|
|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 %: 42.07 43.53
% SQL with executions>1: 73.79 75.08
% Memory for SQL w/exec>1: 76.93 77.64
Memory Usage % = It's 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 tool small and if the
percent is in 50 for example then this could mean that you shared
pool is too large. In general, Memory usage % statistics should be
~70% after the DB has been running a long time. If its quite low,
memory is being wasted.
% SQL with executions>1 = Shows % of SQLs executed more than 1 time. The % should be very near to value 100. If we get a low number here, then the DB is not using shared SQL statements. May be because bind variables are not being used.
% memory for SQL w/exec>1: From the memory space allocated to cursors, shows which % has been used by cursors more than 1.
The values should not be very high (preferably less than 75%).
If you want a quick instance wide wait event status, showing which events are the biggest contributors to total wait time, you can use the following query :
select event, total_waits,time_waited from
where event NOT IN
('pmon timer', 'smon timer', 'rdbms ipc reply', 'parallel deque wait',
'virtual circuit', '%SQL*Net%', 'client message', 'NULL event')
order by time_waited desc;
------------------------ ------------- -------------
db file sequential read 35051309 15965640
latch free 1373973 1913357
db file scattered read 2958367 1840810
enqueue 2837 370871
buffer busy waits 444743 252664
log file parallel write 146221 123435
That generally happens during a full scan of a table or Fast
Full Index Scans. As
full table scans are pulled into memory, they rarely fall
into contiguous buffers but instead are scattered throughout
the buffer cache. A large number here indicates that your
table may have missing indexes, statistics are not updated
or your indexes are not used. Although it may be more
efficient in your situation to perform a full table scan
than an index scan, check to ensure that full table scans
are necessary when you see these waits. Try to cache small
tables to avoid reading them in over and over again, since a
full table scan is put at the cold end of the LRU (Least
Recently Used) list. You
can use the report to help identify the query in question
and fix it.
The init.ora parameter db_file_multiblock_read_count specifies the maximum numbers of blocks read in that way. Typically, this parameter should have values of 4-16 independent of the size of the database but with higher values needed with smaller Oracle block sizes. If you have a high wait time for this event, you either need to reduce the cost of I/O, e.g. by getting faster disks or by distributing your I/O load better, or you need to reduce the amount of full table scans by tuning SQL statements. The appearance of the‘db file scattered read’ and ‘db file sequential read’events may not necessarily indicate a problem, as IO is a normal activity on a healthy instance. However, they can indicate problems if any of the following circumstances are true:
• The data-access method is bad (that is, the SQL statements are poorly tuned), resulting in unnecessary or inefficient IO operations
• The IO system is overloaded and performing poorly
• The IO system is under-configured for the load
• IO operations are taking too long
If this Wait Event is a significant
portion of Wait Time then a number of approaches are possible:
o Find which SQL statements perform Full Table or Fast Full Index scans and tune them to make sure these scans are necessary and not the result of a suboptimal plan.
- The view V$SQL_PLAN view can help:
For Full Table scans:
select sql_text from v$sqltext t, v$sql_plan p
and p.operation='TABLE ACCESS'
order by p.hash_value, t.piece;
For Fast Full Index scans:
select sql_text from v$sqltext t, v$sql_plan p
and p.options='FULL SCAN'
order by p.hash_value, t.piece;
o In cases where such multiblock scans
occur from optimal execution plans it is possible to tune the
size of multiblock I/Os issued by Oracle by setting the instance
parameter DB_FILE_MULTIBLOCK_READ_COUNT so that:
DB_BLOCK_SIZE x DB_FILE_MULTIBLOCK_READ_COUNT = max_io_size of system
Query tuning should be used to optimize online SQL to use indexes.
2. 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.
This could indicate poor joining order of tables or un-selective indexes in your SQL or waiting for writes to TEMP space (direct loads, Parallel DML (PDML) such as parallel updates. It could mean that a lot of index reads/scans are going on. Depending on the problem it may help to tune PGA_AGGREGATE_TARGET and/or DB_CACHE_SIZE.
The sequential read event identifies Oracle reading blocks sequentially, i.e. one after each other. It is normal for this number to be large for a high-transaction, well-tuned system, but it can indicate problems in some circumstances. You should correlate this wait statistic with other known issues within the report, such as inefficient SQL. Check to ensure that index scans are necessary, and check join orders for multiple table joins. The DB_CACHE_SIZE will also be a determining factor in how often these waits show up. Problematic hash-area joins should show up in the PGA memory, but they're also memory hogs that could cause high wait numbers for sequential reads. They can also show up as direct path read/write waits. These circumstances are usually interrelated. When they occur in conjunction with the appearance of the 'db file scattered read' and 'db file sequential read' in the Top 5 Wait Events section, first you should examine the SQL Ordered by Physical Reads section of the report, to see if it might be helpful to tune the statements with the highest resource usage.
It could be because the indexes are fragmented. If that is the case, rebuilding the index will compact it and will produce to visit less blocks.
Then, to determine whether there is a potential I/O bottleneck, examine the OS I/O statistics for corresponding symptoms. Also look at the average time per read in the Tablespace and File I/O sections of the report. If many I/O-related events appear high in the Wait Events list, re-examine the host hardware for disk bottlenecks and check the host-hardware statistics for indications that a disk reconfiguration may be of benefit.
Block reads are fairly inevitable so the aim should be to minimize unnecessary I/O. I/O for sequential reads can be reduced by tuning SQL calls that result in full table scans and using the partitioning option for large tables.
3. Free Buffer Waits.
When a session needs a free buffer and cannot find one, it will post the database writer process asking it to flush dirty blocks (No place to put a new block). Waits in this category may indicate that you need to increase the DB_BUFFER_CACHE, if all your SQL is tuned. Free buffer waits could also indicate that unselective SQL is causing data to flood the buffer cache with index blocks, leaving none for this particular statement that is waiting for the system to process. This normally indicates that there is a substantial amount of DML (insert/update/delete) being done and that the Database Writer (DBWR) is not writing quickly enough; the buffer cache could be full of multiple versions of the same buffer, causing great inefficiency. To address this, you may want to consider accelerating incremental checkpointing, using more DBWR processes, or increasing the number of physical disks. To investigate if this is an I/O problem, look at the report I/O Statistics. Increase the DB_CACHE_SIZE; shorten the checkpoint; tune the code to get less dirty blocks, faster I/O, use multiple DBWR’s.
4. Buffer Busy Waits. A buffer busy wait happens when multiple
processes concurrently want to modify the same block in the
buffer cache. This typically happens during massive parallel
inserts if your tables do not have free lists and it can happen
if you have too few rollback segments. Buffer busy waits should not be greater than 1 percent.
Check the Buffer Wait Statistics section (or V$WAITSTAT) to find
out if the wait is on a segment header. If this is the case,
increase the freelist groups or increase the pctused to pctfree
gap. If the wait is on an undo header, you can address this by
adding rollback segments; if it's on an undo block, you need to
reduce the data density on the table driving this consistent
read or increase the DB_CACHE_SIZE. If the wait is on a data
block, you can move data to another block to avoid this hot
block, increase the freelists on the table, or use Locally
Managed Tablespaces (LMTs). If it's on an index block, you
should rebuild the index, partition the index, or use a reverse
key index. To prevent buffer busy waits related to data blocks,
you can also use a smaller block size: fewer records fall within
a single block in this case, so it's not as "hot." When a DML
(insert/update/ delete) occurs, Oracle writes information into
the block, including all users who are "interested" in the state
of the block (Interested Transaction List, ITL). To decrease
waits in this area, you can increase the initrans, which will
create the space in the block to allow multiple ITL slots. You
can also increase the pctfree on the table where this block
exists (this writes the ITL information up to the number
specified by maxtrans, when there are not enough slots built
with the initrans that is specified). Buffer busy waits can be
reduced by using reverse-key indexes for busy indexes and by
partitioning busy tables.
Buffer Busy Wait on Segment Header – Add freelists (if inserts) or freelist groups (esp. RAC). Use ASSM.
Buffer Busy Wait on Data Block – Separate ‘hot’ data; potentially use reverse key indexes; fix queries to reduce the blocks popularity, use smaller blocks, I/O, Increase initrans and/or maxtrans (this one’s debatable). Reduce records per block
Buffer Busy Wait on Undo Header – Add rollback segments or increase size of segment area (auto undo)
Buffer Busy Wait on Undo block – Commit more (not too much) Larger rollback segments/area. Try to fix the SQL.
5. Latch Free. Latches are low-level queuing mechanisms (they're accurately referred to as mutual exclusion mechanisms) used to protect shared memory structures in the system global area (SGA). Latches are like locks on memory that are very quickly obtained and released. Latches are used to prevent concurrent access to a shared memory structure. If the latch is not available, a latch free miss is recorded. Most latch problems are related to the failure to use bind variables (library cache latch), redo generation issues (redo allocation latch), buffer cache contention issues (cache buffers LRU chain), and hot blocks in the buffer cache (cache buffers chain). There are also latch waits related to bugs; check MetaLink for bug reports if you suspect this is the case. When latch miss ratios are greater than 0.5 percent, you should investigate the issue. If latch free waits are in the Top 5 Wait Events or high in the complete Wait Events list, look at the latch-specific sections of the report to see which latches are contended for.
6. Enqueue. An enqueue is a lock that protects a shared
resource. Locks protect shared resources, such as data in a
record, to prevent two people from updating the same data at the
same time application, e.g. when a select for update is
executed.. An enqueue includes a queuing mechanism, which is
FIFO (first in, first out). Note that Oracle's latching
mechanism is not FIFO. Enqueue waits usually point to the ST
enqueue, the HW enqueue, the TX4 enqueue, and the TM enqueue.
The ST enqueue is used for space management and allocation for
dictionary-managed tablespaces. Use LMTs, or try to preallocate
extents or at least make the next extent larger for problematic
dictionary-managed tablespaces. HW enqueues are used with the
high-water mark of a segment; manually allocating the extents
can circumvent this wait. TX4s are the most common enqueue
waits. TX4 enqueue waits are usually the result of one of three
issues. The first issue is duplicates in a unique index; you
need to commit/rollback to free the enqueue. The second is
multiple updates to the same bitmap index fragment. Since a
single bitmap fragment may contain multiple rowids, you need to
issue a commit or rollback to free the enqueue when multiple
users are trying to update the same fragment. The third and most
likely issue is when multiple
users are updating the same block. If there are no free
ITL slots, a block-level lock could occur. You can easily avoid
this scenario by increasing the initrans and/or maxtrans to
allow multiple ITL slots and/or by increasing the pctfree on the
table. Finally, TM enqueues occur during DML to prevent DDL to
the affected object. If you have foreign keys, be sure to index them to avoid
this general locking issue.
Enqueue - ST Use LMT’s or pre-allocate large extents
Enqueue - HW Pre-allocate extents above HW (high water mark.)
Enqueue – TX Increase initrans and/or maxtrans (TX4) on (transaction) the table or index. Fix locking issues if TX6. Bitmap (TX4) & Duplicates in Index (TX4).
Enqueue - TM Index foreign keys; Check application (trans. mgmt.) locking of tables. DML Locks.
7. Log Buffer Space
Look at increasing log buffer size. This wait occurs because you are writing the log buffer faster than LGWR can write it to the redo logs, or because log switches are too slow. To address this problem, increase the size of the redo log files, or increase the size of the log buffer, or get faster disks to write to. You might even consider using solid-state disks, for their high speed.
The session is waiting for space in the log buffer. (Space becomes available only after LGWR has written the current contents of the log buffer to disk.) This typically happens when applications generate redo faster than LGWR can write it to disk.
8. Log File Switch
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
May indicate excessive db files or slow IO subsystem. All commit requests are waiting for "logfile switch (archiving needed)" or "logfile switch (chkpt. Incomplete)." Ensure that the archive disk is not full or slow. DBWR may be too slow because of I/O. You may need to add more or larger redo logs, and you may potentially need to add database writers if the DBWR is the problem.
9. Log File Sync
Could indicate excessive commits. A Log File Sync happens each time a commit (or rollback) takes place. If there are a lot of waits in this area then you may want to examine your application to see if you are committing too frequently (or at least more than you need to). When a user commits or rolls back data, the LGWR flushes the session's redo from the log buffer to the redo logs. The log file sync process must wait for this to successfully complete. To reduce wait events here, try to commit more records (try to commit a batch of 50 instead of one at a time, use BULKS, , for example). Put redo logs on a faster disk, or alternate redo logs on different physical disks (with no other DB Files, ASM, etc) to reduce the archiving effect on LGWR. Don't use RAID 5, since it is very slow for applications that write a lot; potentially consider using file system direct I/O or raw devices, which are very fast at writing information. The associated event, ‘log buffer parallel write’ is used by the redo log writer process, and it will indicate if your actual problem is with the log file I/O. Large wait times for this event can also be caused by having too few CPU resources available for the redolog writer process.
10. Idle Event. There are several idle wait events listed
after the output; you can ignore them. Idle events are generally
listed at the bottom of each section and include such things as
SQL*Net message to/from client and other background-related
timings. Idle events are listed in the stats$idle_event table.
cr request: (OPS) This wait event shows the amount of
time that an instance has waited for a requested data block for
a consistent read and the transferred block has not yet arrived
at the requesting instance. See Note 157766.1 'Sessions Wait
Forever for 'global cache cr request' Wait Event in OPS or RAC'.
In some cases the 'global cache cr request' wait event may be
perfectly normal if large buffer caches are used and the same
data is being accessed concurrently on multiple instances.
In a perfectly tuned, non-OPS/RAC database, I/O wait events
would be the top wait events but since we are avoiding I/O's
with RAC and OPS the 'global cache cr request' wait event often
takes the place of I/O wait events.
library cache pin: Library cache
latch contention may be caused by not using bind variables. It
is due to excessive parsing of SQL statement.
The session wants to pin an object in memory in the library cache for examination, ensuring no other processes can update the object at the same time. This happens when you are compiling or parsing a PL/SQL object or a view.
This is not really a wait event (hence, the new name), but rather the sum of the CPU used by this session, or the amount of CPU time used during the snapshot window. In a heavily loaded system, if the CPU time event is the biggest event, that could point to some CPU-intensive processing (for example, forcing the use of an index when a full scan should have been used), which could be the cause of the bottleneck. When CPU Other is a significant component of total Response Time the next step is to find the SQL statements that access the most blocks. Block accesses are also known as Buffer Gets and Logical I/Os. The report lists such SQL statements in section SQL ordered by Gets.
14. DB File Parallel
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. This Wait Event is used when Oracle
performs in parallel reads from multiple datafiles to
non-contiguous buffers in memory (PGA or Buffer Cache). This
is done during recovery operations or when buffer prefetching
is being used as an optimization i.e. instead of performing
multiple single-block reads. If this wait is an important
component of Wait Time, follow the same guidelines as 'db file
This may occur during recovery or during regular activity when a session batches many single block I/O requests together and issues them in parallel.
15. PX qref latch
Can often mean that the Producers are producing data quicker
than the Consumers can consume it. Maybe we could increase
parallel_execution_message_size to try to eliminate some of
these waits or we might decrease the degree of parallelism. If
the system workload is high consider to decrease the degree of
parallelism. If you have DEFAULT parallelism on your
object you can decrease the value of
PARALLEL_THREADS_PER_CPU. Have in mind DEFAULT
degree = PARALLEL_THREADS_PER_CPU * #CPU's
16. Log File Parallel
Write. It occurs when waiting for writes of REDO
records to the REDO log files to complete. The wait occurs in
log writer (LGWR) as part of normal activity of copying
records from the REDO log buffer to the current online log.
The actual wait time is the time taken for all the outstanding
I/O requests to complete. Even though the writes may be issued
in parallel, LGWR needs to wait for the last I/O to be on disk
before the parallel write is considered complete. Hence the
wait time depends on the time it takes the OS to complete all
Log file parallel write waits can be reduced by moving log files to the faster disks and/or separate disks where there will be less contention.
17. SQL*Net more data to
This means the instance is sending a lot of data to the client. You can decrease this time by having the client bring back less data. Maybe the application doesn't need to bring back as much data as it is.
18. SQL*Net message to
The “SQL*Net message to client” Oracle metric indicates the server (foreground process) is sending a message to the client, and it can be used to identify throughput issues over a network, especially distributed databases with slow database links. The SQL*Net more data to client event happens when Oracle writes multiple data buffers (sized per SDU) in a single logical network call.
19. enq: TX - row lock
Oracle keeps data consistency with the help of locking mechanism. When a particular row is being modified by the process, either through Update/ Delete or Insert operation, oracle tries to acquire lock on that row. Only when the process has acquired lock the process can modify the row otherwise the process waits for the lock. This wait situation triggers this event. The lock is released whenever a COMMIT is issued by the process which has acquired lock for the row. Once the lock is released, processes waiting on this event can acquire lock on the row and perform DML operation
|Statistic Name||Time (s)||% of DB Time|
|sql execute elapsed time||19,640.87||95.41|
|parse time elapsed||73.75||0.36|
|hard parse elapsed time||38.35||0.19|
|PL/SQL execution elapsed time||32.04||0.16|
|hard parse (sharing criteria) elapsed time||6.98||0.03|
|connection management call elapsed time||4.25||0.02|
|repeated bind elapsed time||3.43||0.02|
|PL/SQL compilation elapsed time||3.04||0.01|
|hard parse (bind mismatch) elapsed time||1.62||0.01|
|sequence load elapsed time||0.74||0.00|
|failed parse elapsed time||0.04||0.00|
|background elapsed time||859.22|
|background cpu time||68.05|
% of DB Time
sql execute elapsed time
parse time elapsed
hard parse elapsed time
failed parse elapsed time
PL/SQL execution elapsed time
hard parse (sharing criteria) elapsed time
connection management call elapsed time
hard parse (bind mismatch) elapsed time
PL/SQL compilation elapsed time
repeated bind elapsed time
sequence load elapsed time
background elapsed time
background cpu time
Then, it’s a good idea to check if most of the statements are identified in the SQL sections:
SQL ordered by Elapsed Time DB/Inst: ORCL/orcl Snaps: 330-336
-> Captured SQL account for 94.6% of Total DB Time (s): 4,589
-> Captured PL/SQL account for 94.5% of Total DB Time (s): 4,589
If only a low percentage has been capture, that usually means that the report cover a period where the database had an heterogeneous activity, either the duration is too long or there is too many unshared SQL (not using bind variables). Here I know I’ll have detail about 94% of the SQL activity, which is good.
This report shows,
system is 97 to 98% idle at time of report taken.
If you found very high %busy, %user or sys % and indeed this will led to low idle %. Investigate what is causing this. OS Watcher is the tool which can help in this direction.
|Wait Class||Waits||%Time -outs||Total Wait Time (s)||Avg wait (ms)||%DB time|
|Event||Waits||%Time -outs||Total Wait Time (s)||Avg wait (ms)||Waits /txn||% DB time|
|direct path write temp||1,837,854||0||2,267||1||10.53||11.01|
|direct path read||2,838,190||0||930||0||16.26||4.52|
|log file sync||169,635||0||666||4||0.97||3.24|
|db file sequential read||13,222||0||143||11||0.08||0.69|
|direct path read temp||1,837,007||0||131||0||10.53||0.64|
|PX Deq: Slave Session Stats||131,555||0||107||1||0.75||0.52|
|db file scattered read||8,448||0||26||3||0.05||0.13|
|kksfbc child completion||441||100||22||51||0.00||0.11|
|latch: shared pool||4,849||0||16||3||0.03||0.08|
|library cache: mutex X||67,703||0||14||0||0.39||0.07|
|library cache lock||346||0||12||35||0.00||0.06|
|cursor: pin S wait on X||582||0||9||16||0.00||0.05|
|os thread startup||116||0||4||32||0.00||0.02|
|SQL*Net message to client||1,739,132||0||2||0||9.97||0.01|
|cursor: mutex S||1,666||0||2||1||0.01||0.01|
|latch: row cache objects||1,658||0||1||1||0.01||0.01|
|read by other session||92||0||1||12||0.00||0.01|
|db file parallel read||344||0||1||3||0.00||0.00|
|PX Deq: Signal ACK EXT||65,787||0||1||0||0.38||0.00|
|PX Deq: Signal ACK RSG||65,787||0||1||0||0.38||0.00|
|enq: PS - contention||758||0||1||1||0.00||0.00|
|enq: RO - fast object reuse||40||0||1||13||0.00||0.00|
|Disk file operations I/O||1,386||0||0||0||0.01||0.00|
|enq: KO - fast object checkpoint||539||0||0||1||0.00||0.00|
|PX qref latch||964||100||0||0||0.01||0.00|
|latch: parallel query alloc buffer||836||0||0||0||0.00||0.00|
|latch: cache buffers chains||174||0||0||1||0.00||0.00|
|SQL*Net more data to client||16,415||0||0||0||0.09||0.00|
|enq: TX - index contention||5||0||0||37||0.00||0.00|
|library cache load lock||1||0||0||139||0.00||0.00|
|asynch descriptor resize||71,974||100||0||0||0.41||0.00|
|PX Deq: Table Q Get Keys||486||0||0||0||0.00||0.00|
|buffer busy waits||676||0||0||0||0.00||0.00|
|cursor: pin S||189||0||0||0||0.00||0.00|
|row cache lock||17||0||0||2||0.00||0.00|
|direct path sync||15||0||0||2||0.00||0.00|
|latch: cache buffer handles||1||0||0||29||0.00||0.00|
|PX Deq: Table Q qref||1,160||0||0||0||0.01||0.00|
|wait list latch free||13||0||0||1||0.00||0.00|
|latch: object queue header operation||32||0||0||0||0.00||0.00|
|control file sequential read||584||0||0||0||0.00||0.00|
|SQL*Net message from client||1,739,106||0||260,904||150||9.97|
|jobq slave wait||41,892||100||20,964||500||0.24|
|PX Deq: Execution Msg||746,687||0||1,612||2||4.28|
|PX Deq: Table Q Normal||1,057,627||0||387||0||6.06|
|PX Deq Credit: send blkd||128,373||0||266||2||0.74|
|PX Deq: Execute Reply||710,735||0||51||0||4.07|
|PX Deq: Parse Reply||65,790||0||13||0||0.38|
|PX Deq: Join ACK||65,790||0||4||0||0.38|
|PX Deq Credit: need buffer||1,783||0||3||1||0.01|
|PX Deq: Table Q Sample||1,275||0||1||0||0.01|
Foreground Wait Events
Avg %Time Total Wait wait Waits
Event Waits -outs Time (s) (ms) /txn ---------------------------- -------------- ------ ----------- ------- --------- control file parallel write 1,220 .0 18 15 1.6 control file sequential read 6,508 .0 6 1 8.7 CGS wait for IPC msg 422,253 100.0 1 0 566.0 change tracking file synchro 60 .0 1 13 0.1 db file parallel write 291 .0 0 1 0.4 db file sequential read 90 .0 0 4 0.1 reliable message 136 .0 0 1 0.2 log file parallel write 106 .0 0 2 0.1 lms flush message acks 1 .0 0 60 0.0 gc current block 2-way 200 .0 0 0 0.3 change tracking file synchro 59 .0 0 1 0.1
example our control file parallel write waits (which occurs during
writes to the control file) are taking up 18 seconds total, with
an average wait of 15 milliseconds per wait.
Additionally we can see that we have 1.6 waits per transaction (or 15ms * 1.6 per transaction = 24ms).
Background Wait Events
|Event||Waits||%Time -outs||Total Wait Time (s)||Avg wait (ms)||Waits /txn||% bg time|
|log file parallel write||189,549||0||615||3||1.09||71.53|
|db file async I/O submit||28,334||0||37||1||0.16||4.27|
|os thread startup||711||0||25||35||0.00||2.88|
|control file parallel write||32,030||0||16||1||0.18||1.88|
|db file sequential read||899||0||14||15||0.01||1.57|
|latch: shared pool||7,111||0||9||1||0.04||1.09|
|latch: call allocation||15,537||0||9||1||0.09||1.07|
|ARCH wait on ATTACH||259||0||3||11||0.00||0.34|
|Log archive I/O||726||0||2||3||0.00||0.24|
|row cache lock||2||0||1||746||0.00||0.17|
|control file sequential read||83,303||0||1||0||0.48||0.12|
|db file parallel read||30||0||1||17||0.00||0.06|
|Disk file operations I/O||1,214||0||0||0||0.01||0.05|
|log file sequential read||838||0||0||0||0.00||0.05|
|direct path sync||5||0||0||31||0.00||0.02|
|ADR block file read||80||0||0||2||0.00||0.02|
|log file sync||11||0||0||11||0.00||0.01|
|enq: PR - contention||3||0||0||23||0.00||0.01|
|latch: cache buffers chains||1||0||0||46||0.00||0.01|
|db file single write||264||0||0||0||0.00||0.00|
|latch: parallel query alloc buffer||63||0||0||1||0.00||0.00|
|LGWR wait for redo copy||521||0||0||0||0.00||0.00|
|Data file init write||20||0||0||1||0.00||0.00|
|latch: row cache objects||1||0||0||15||0.00||0.00|
|latch: session allocation||27||0||0||1||0.00||0.00|
|db file scattered read||3||0||0||4||0.00||0.00|
|direct path write||14||0||0||1||0.00||0.00|
|asynch descriptor resize||1,442||100||0||0||0.01||0.00|
|wait list latch free||10||0||0||1||0.00||0.00|
|rdbms ipc reply||57||0||0||0||0.00||0.00|
|log file single write||40||0||0||0||0.00||0.00|
|ADR block file write||25||0||0||0||0.00||0.00|
|ADR file lock||30||0||0||0||0.00||0.00|
|library cache: mutex X||2||0||0||1||0.00||0.00|
|SQL*Net message to client||587||0||0||0||0.00||0.00|
|rdbms ipc message||323,540||38||356,760||1103||1.85|
|PX Idle Wait||65,885||0||115,165||1748||0.38|
|DIAG idle wait||35,863||100||35,900||1001||0.21|
|Space Manager: slave idle wait||5,626||97||27,710||4925||0.03|
|Streams AQ: qmn slave idle wait||642||0||17,953||27964||0.00|
|Streams AQ: qmn coordinator idle wait||1,282||50||17,953||14004||0.01|
|shared server idle wait||598||100||17,946||30010||0.00|
|SQL*Net message from client||795||0||1||1||0.00|
|class slave wait||83||0||0||0||0.00|
|Service Name||DB Time (s)||DB CPU (s)||Physical Reads (K)||Logical Reads (K)|
Next in the report we find several different reports that
present SQL statements that might be improved by tuning. Any SQL
statement appears in the top 5 statements in two or more areas
below, then it is a prime candidate for tuning. The sections are:
It's interesting to mention that the SUM of columns %CPU + %IO
should be close to 100. If this is far from 100, this can indicate
SQL Ordered by Elapsed Time
Total Elapsed Time = CPU Time + Wait Time.
Shows which SQL statement runs for a longer time. If a SQL statement appears in the total elapsed time area of the report this means its CPU time plus any other wait times made it pop to the top of the pile. Excessive Elapsed Time could be due to excessive CPU usage or excessive wait times.
This is the area that you need to examine and probably the one that will be reported by the users or application support. From a consumer perspective, the finer details don’t matter. The application is slow. Full stop.
In conjunction with excessive Elapsed time check to see if this piece of SQL is also a high consumer under Total CPU Time. It is normally the case. Otherwise check the wait times and Total Disk Reads. They can either indicate issues with wait times (slow disks, latch gets etc) or too much Physical IO associated with tables scans or sub-optimal indexes. This section is a gate opener and often you will need to examine other sections.
SQL Ordered by CPU Time
When a statement appears in the Total CPU Time area this indicates it used excessive CPU cycles during its processing. Excessive CPU processing time can be caused by sorting, excessive function usage or long parse times. Indicators that you should be looking at this section for SQL tuning candidates include high CPU percentages in the service section for the service associated with this SQL (a hint, if the SQL is uppercase it probably comes from a user or application; if it is lowercase it usually comes from the internal or background processes). To reduce total CPU time, reduce sorting by using composite indexes that can cover sorting and use bind variables to reduce parse times.
SQL Ordered by Buffer Gets
Total buffer gets mean a SQL statement is reading a lot of data from the db block buffers. Generally speaking buffer gets (AKA logical IO or LIO) are OK, except when they become excessive. The old saying that you reduce the logical IO, because then the physical IO (disk read) will take care of itself holds true. LIO may have incurred a PIO in order to get the block into the buffer in the first place. Reducing buffer gets is very important and should not be underestimated. To get a block from db block buffers, we have to latch it (i.e. in order to prevent someone from modifying the data structures we are currently reading from the buffer). Although latches are less persistent than locks, a latch is still a serialization device. Serialization devices inhibit scalability, the more you use them, the less concurrency you get. Therefore in most cases optimal buffer gets can result in improved performance. Also note that by lowering buffer gets you will require less CPU usage and less latching. |Thus to reduce excessive buffer gets, optimize SQL to use appropriate indexes and reduce full table scans. You can also look at improving the indexing strategy and consider deploying partitioning (licensed).
SQL Ordered by Disk Reads
High total disk reads mean a SQL statement is reading a lot of data from disks rather than being able to access that data from the db block buffers. High physical reads after a server reboot are expected as the cache is cold and data is fetched from the disk. However, disk reads (or physical reads) are undesirable in an OLTP system, especially when they become excessive. Excessive disk reads do cause performance issues. The usual norm is to increase the db buffer cache to allow more buffers and reduce ageing . Total disk reads are typified by high physical reads, a low buffer cache hit ratio, with high IO wait times. Higher wait times for Disk IO can be associated with a variety of reasons (busy or over saturated SAN, slower underlying storage, low capacity in HBC and other hardware causes). Statistics on IO section in AWR, plus the Operating System diagnostic tools as simple as iostatcan help in identifying these issues. To reduce excessive disk reads, consider partitioning, use indexes and look at optimizing SQL to avoid excessive full table scans.
SQL Ordered by Executions
High total executions need to be reviewed to see if they are genuine executions or loops in SQL code. I have also seen situations where autosys jobs fire duplicate codes erroneously. In general statements with high numbers of executions usually are being properly reused. However, there is always a chance of unnecessary loop in PL/SQL, Java or C#. Statements with high number of executions, high number of logical and or physical reads are candidates for review to be sure they are not being executed multiple times when a single execution would serve. If the database has excessive physical and logical reads or excessive IO wait times, then look at the SQL statements that show excessive executions and show high physical and logical reads.
Whenever a statement is issued by a user or process, regardless of whether it is in the SQL pool it undergoes a parse. As explained under Parsing, the parse can be a hard parse or a soft parse. Excessive parse calls usually go with excessive executions. If the statement is using what are known as unsafe bind variables then the statement will be reparsed each time. If the header parse ratios are low look here and in the version count areas.
SQL Ordered by Memory
Sharable Memory refers to Shared Pool memory area in SGA , hence this particular section in AWR Report states about the SQL STATEMENT CURSORS which consumed the maximum amount of the Shared Pool for their execution.
In general high values for Sharable Memory doesn’t necessary imply there is an issue It simply means that:
- These SQL statements are big or complex and Oracle has to keep lots of information about these statements OR
- big number of child cursors exist for those parent cursors
- combination of 1 & 2
In case of point 2, it may be due to poor coding such as bind variables mismatch, security mismatch or overly large SQL statements that join many tables. In a DSS or DW environment large complex statements are normal. In an OLTP database large or complex statements are usually the result of over-normalization of the database design, attempts to use an OLTP system as a DW or simply poor coding techniques. Usually large statements will result in excessive parsing, recursion, and large CPU usage.
SQL Ordered by Version Count
High version counts are usually due to multiple identical-schema databases, unsafe bind variables, or Oracle bugs.
The SQL that is stored in the shared pool SQL area (Library cache) is reported in this section in different ways:
. SQL ordered by Buffer Gets
. SQL ordered by Physical Reads
. SQL ordered by Executions
. SQL ordered by Parse Calls
- SQL ordered by Gets:
This section reports the contents of the SQL area ordered by the number of buffer gets and can be used to identify the most CPU Heavy SQL.
- Many DBAs feel that if the data is already contained within the buffer cache the query should be efficient. This could not be further from the truth. Retrieving more data than needed, even from the buffer cache, requires CPU cycles and interprocess IO. Generally speaking, the cost of physical I/O is not 10,000 times more expensive. It actually is in the neighborhood of 67 times and actually almost zero if the data is stored in the UNIX buffer cache.
- The statements of interest are those with a large number of gets per execution especially if the number of executions is high.
- High buffer gets generally correlates with heavy CPU usage
- SQL ordered by Reads:
This section reports the contents of the SQL area ordered by the number of reads from the data files and can be used to identify SQL causing IO bottlenecks which consume the following resources.
- CPU time needed to fetch unnecessary data.
- File IO resources to fetch unnecessary data.
- Buffer resources to hold unnecessary data.
- Additional CPU time to process the query once the data is retrieved into the buffer.
- SQL ordered by Executions:
This section reports the contents of the SQL area ordered by the number of query executions. It is primarily useful in identifying the most frequently used SQL within the database so that they can be monitored for efficiency. Generally speaking, a small performance increase on a frequently used query provides greater gains than a moderate performance increase on an infrequently used query. Possible reasons for high Reads per Exec are use of unselective indexes require large numbers of blocks to be fetched where such blocks are not cached well in the buffer cache, index fragmentation, large Clustering Factor in index etc.
- SQL ordered by Parse Calls:
This section shows the number of times a statement was parsed as compared to the number of times it was executed. One to one parse/executions may indicate that:
- Bind variables are not being used.
The shared pool may be too small and the parse is not being retained long enough for multiple executions.
- cursor_sharing is set to exact (this should NOT be changed without considerable testing on the part of the client).