Resolving common Oracle Wait Events using the Wait Interface

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Wait Event	Possible Causes	Actions	Remarks
db file sequential reads	Use of an unselective index Fragmented Indexes High I/O on a particular disk or mount point Bad application design Index reads performance can be affected by  slow I/O subsystem and/or poor database files layout, which result in a higher average  wait time	Check indexes on the table to ensure that the right index is being used Check the column order of the index with the WHERE clause of the Top SQL statements Rebuild indexes with a high clustering factor Use partitioning to reduce the amount of blocks being visited Make sure optimizer statistics are up to date Relocate ‘hot’ datafiles Consider the usage of multiple buffer pools and cache frequently used indexes/tables in the KEEP pool Inspect the execution plans of the SQL statements that access data through indexes Is it appropriate for the SQL statements to access data through index lookups? Is the application an online transaction  processing (OLTP) or decision support system (DSS)? Would full table scans be more efficient? Do the statements use the right driving  table? The optimization goal is to minimize  both the number of logical and physical I/Os.	The Oracle process wants a block that is currently not in the SGA, and it is waiting for the database block to be read into the SGA from disk. Significant db file sequential read wait time is most likely an application issue. If the DBA_INDEXES.CLUSTERING_FACTOR of the index approaches the number of blocks in the table, then most of the rows in the table are ordered. This is desirable.  However, if the clustering factor approaches the number of rows in the table, it means the rows in the table are randomly ordered and thus it requires more I/Os to complete the operation. You can improve the index’s clustering factor by rebuilding the table so that rows are ordered according to the index key and rebuilding the index thereafter. The OPTIMIZER_INDEX_COST_ADJ and OPTIMIZER_INDEX_CACHING initialization parameters can influence the optimizer to favour the nested loops operation and choose an index access path over a full table scan. Tuning I/O related waits Note# 223117.1 db file sequential read Reference Note# 34559.1
db file scattered reads	The Oracle session has requested and is waiting for multiple contiguous database blocks (up to DB_FILE_MULTIBLOCK_READ_COUNT) to be  read into the SGA from disk. Full Table scans Fast Full Index Scans	Optimize multi-block I/O by setting the parameter DB_FILE_MULTIBLOCK_READ_COUNT Partition pruning to reduce number of blocks visited Consider the usage of multiple buffer pools and cache frequently used indexes/tables in the KEEP pool Optimize the SQL statement that initiated most of the waits. The goal is to minimize the number of physical and logical reads. Should the statement access the data by a full table scan or index FFS? Would an index range or unique scan  be more efficient? Does the query use the right driving table? Are the SQL predicates appropriate for hash or merge join?  If full scans are appropriate, can parallel query improve the response time? The objective is to reduce the demands for both the logical and physical I/Os, and this is best achieved through SQL and application tuning. Make sure all statistics are representative of the actual data. Check the LAST_ANALYZED date	If an application that has been running fine for a while suddenly clocks a lot of time on the db file scattered read event and there hasn’t been a code change, you might want to check to see if one or more indexes has been dropped or become unusable. db file scattered read Reference Note# 34558.1
log file parallel write	LGWR waits while writing contents of the redo log buffer cache to the online log files on disk I/O wait on sub system holding the online  redo log files	Reduce the amount of redo being generated Do not leave tablespaces in hot backup mode for longer than necessary Do not use RAID 5 for redo log files Use faster disks for redo log files Ensure that the disks holding the archived redo log files and the online redo log files are separate so as to avoid contention Consider using NOLOGGING or UNRECOVERABLE options in SQL statements	Reference Note# 34583.1
log file sync	Oracle foreground processes are waiting for a COMMIT or ROLLBACK to complete	Tune LGWR to get good throughput to  disk eg: Do not put redo logs on RAID5 Reduce overall number of commits by batching transactions so that there are fewer distinct COMMIT operations	Reference Note# 34592.1 High Waits on log file sync Note# 125269.1 Tuning the Redolog Buffer Cache and Resolving Redo Latch Contention Note# 147471.1
buffer busy waits	Buffer busy waits are common in an I/O- bound Oracle system. The two main cases where this can occur are: Another session is reading the block into the buffer Another session holds the buffer in an incompatible mode to our request These waits indicate read/read, read/write,  or write/write contention. The Oracle session is waiting to pin a buffer. A buffer must be pinned before it can be read or modified. Only one process can pin a buffer at any one time. This wait can be intensified by a large block  size as more rows can be contained within the block This wait happens when a session wants to access a database block in the buffer cache but it cannot as the buffer is "busy It is also often due to several processes repeatedly reading the same blocks (eg: if lots of people scan the same index or data  block)	The main way to reduce buffer busy waits is to reduce the total I/O on the system Depending on the block type, the actions will differ Data Blocks Eliminate HOT blocks from the application. Check for repeatedly scanned / unselective indexes. Try rebuilding the object with a higher PCTFREE so that you reduce the number of rows per block.  Check for 'right- hand-indexes' (indexes that get inserted into at the same point by many processes).  Increase INITRANS and MAXTRANS and reduce PCTUSED This will make the table less dense . Reduce the number of rows per block Segment Header Increase of number of FREELISTs   and FREELIST GROUPs Undo Header Increase the number of Rollback Segments	A process that waits on the buffer busy waits event publishes the reason code in the P3 parameter of the wait event. The Oracle Metalink note # 34405.1 provides a table of reference - codes 130 and 220 are the most common. Resolving intense and random buffer busy wait performance problems. Note# 155971.1
free buffer waits	This means we are waiting for a free buffer but there are none available in the cache because there are too many dirty buffers in  the cache Either the buffer cache is too small or the DBWR is slow in writing modified buffers to disk DBWR is unable to keep up to the write requests Checkpoints happening too fast – maybe due  to high database activity and under-sized  online redo log files Large sorts and full table scans are filling the  cache with modified blocks faster than the  DBWR is able to write to disk If the  number of dirty buffers that need to be  written to disk is larger than the number that DBWR can write per batch, then these waits  can be observed	Reduce checkpoint frequency  - increase the size of the online redo log files Examine the size of the buffer cache – consider increasing the size of the buffer cache in the SGA Set disk_asynch_io = true set   If not using asynchronous I/O increase the number of db writer processes or dbwr slaves   Ensure hot spots do not exist by spreading datafiles over disks and disk controllers Pre-sorting or reorganizing data can help	Understanding and Tuning Buffer Cache and DBWR Note# 62172.1 How to Identify a Hot Block within the database Buffer Cache. Note# 163424.1
enqueue waits	This wait event indicates a wait for a lock that is held by another session (or sessions) in an incompatible mode to the requested mode. TX Transaction Lock Generally due to table or application set up issues This indicates contention for row-level lock.  This wait occurs when a transaction tries to update or delete rows that are currently  locked by another transaction. This usually is an application issue. TM DML enqueue lock Generally due to application issues, particularly if foreign key constraints have not been indexed.   ST lock   Database actions that modify the UET$ (used extent) and FET$ (free extent) tables require the ST lock, which includes actions such as drop, truncate, and coalesce.   Contention for the ST lock indicates there are multiple sessions actively performing dynamic disk space allocation or deallocation in dictionary managed tablespaces	Reduce waits and wait times The action to take depends on the lock  type which is causing the most problems Whenever you see an enqueue wait event for the TX enqueue, the first step is to find out who the blocker is and if there are multiple waiters for the same resource Waits for TM enqueue in Mode 3 are primarily due to unindexed foreign key columns. Create indexes on foreign keys  < 10g Following are some of the things you can do to minimize ST lock contention in your database:   Use locally managed tablespaces Recreate all temporary tablespaces using the CREATE TEMPORARY TABLESPACE TEMPFILE… command.	Maximum number of enqueue resources that can be concurrently locked is controlled by the ENQUEUE_RESOURCES parameter. Reference Note# 34566.1 Tracing sessions waiting on an enqueue Note# 102925.1 Details of V$LOCK view and lock modes Note:29787.1
Cache buffer chain latch	This latch is acquired when searching for data blocks Buffer cache is a chain of blocks and each chain is protected by a child latch when it needs to be scanned Hot blocks are another common cause of cache buffers chains latch contention. This happens when multiple sessions repeatedly access  one or more blocks that are protected by the same child cache buffers chains latch.  SQL statements with high BUFFER_GETS (logical reads) per EXECUTIONS are the main culprits Multiple concurrent sessions are executing the same inefficient SQL that is going after the same data set	Reducing contention for the cache buffer chains latch will usually require reducing logical I/O rates by tuning and minimizing the I/O requirements of  the SQL involved. High I/O rates could be a sign of a hot block (meaning a block highly accessed).   Exporting the table, increasing the PCTFREE significantly, and importing the data. This minimizes the number of  rows per block, spreading them over many blocks. Of course, this is at the expense of storage and full table scans operations will be slower Minimizing the number of records per block in the table For indexes, you can rebuild them with higher PCTFREE values, bearing in mind that this may increase the height of the index. Consider reducing the block size  Starting in Oracle9i Database, Oracle supports multiple block sizes. If the current block size is 16K, you may move the table or recreate the index in a tablespace with an 8K block size. This too will negatively impact full table scans operations. Also, various  block sizes increase management complexity.	The default number of hash latches is usually 1024 The number of hash latches can be adjusted by the parameter _DB_BLOCKS_HASH_LATCHES What are latches and what causes latch contention
Cache buffer LRU chain latch	Processes need to get this latch when they need to move buffers based on the LRU block replacement policy in the buffer cache The cache buffer lru chain latch is acquired in order to introduce a new block into the buffer cache and when writing a buffer back to disk, specifically when trying  to scan the LRU (least recently used) chain containing all the dirty blocks in the buffer cache. Competition for the cache buffers lru chain latch is symptomatic of intense buffer cache  activity caused by inefficient SQL statements. Statements that repeatedly scan  large unselective indexes or perform full table scans are the prime culprits.  Heavy contention for this latch is generally due to heavy buffer cache activity which can be caused, for example, by:  Repeatedly scanning large unselective indexes	Contention in this latch can be avoided implementing multiple buffer pools or increasing the number of LRU latches with the  parameter DB_BLOCK_LRU_LATCHES (The default value is generally  sufficient for most systems). Its possible to reduce contention for the cache buffer lru chain latch by increasing the size of the buffer cache and thereby reducing the rate at which new blocks are introduced into the buffer cache
Direct Path Reads	These waits are associated with direct read operations which read data directly into the sessions PGA bypassing the SGA The "direct path read" and "direct path write" wait events are related to operations that are performed in PGA like sorting, group by operation, hash join In DSS type systems, or during heavy batch periods, waits on "direct path read" are quite normal However, for an OLTP system these waits are significant These wait events can occur during sorting operations which is not surprising as direct path reads and writes usually occur in connection with temporary tsegments SQL statements with functions that require sorts, such as ORDER BY, GROUP BY, UNION, DISTINCT, and ROLLUP, write sort runs to the temporary tablespace when the input size is larger than the work area in the PGA	Ensure the OS asynchronous IO is configured correctly. Check for IO heavy sessions / SQL and see if the amount of IO can be reduced. Ensure no disks are IO bound. Set your PGA_AGGREGATE_TARGET to appropriate value (if the parameter WORKAREA_SIZE_POLICY = AUTO) Or set *_area_size manually (like sort_area_size and then you have to set WORKAREA_SIZE_POLICY = MANUAL Whenever possible use UNION ALL instead of UNION, and where applicable use HASH JOIN instead of SORT MERGE and NESTED LOOPS instead of HASH JOIN.  Make sure the optimizer selects the right driving table. Check to see if the composite index’s columns can be rearranged to match the ORDER BY clause to avoid sort entirely. Also, consider automating the SQL work areas using PGA_AGGREGATE_TARGET in Oracle9i Database. Query V$SESSTAT> to identify sessions with high "physical reads direct"	Default size of HASH_AREA_SIZE  is twice that of SORT_AREA_SIZE Larger HASH_AREA_SIZE will influence optimizer to go for hash joins instead of nested loops Hidden parameter DB_FILE_DIRECT_IO_COUNT can impact the direct path read performance.It sets the maximum I/O buffer size of direct read and write operations. Default is 1M in 9i How to identify resource intensive SQL statements?
Direct Path  Writes	These are waits that are associated with direct write operations that write data from users’ PGAs to data files or temporary tablespaces Direct load operations (eg: Create Table as  Select (CTAS) may use this) Parallel DML operations Sort IO (when a sort does not fit in memory	If the file indicates a temporary tablespace check for unexpected disk sort operations. Ensure <Parameter:DISK_ASYNCH_IO> is TRUE . This is unlikely to reduce wait times from the wait event timings but may reduce sessions elapsed times (as synchronous direct IO is not accounted for in wait event timings). Ensure the OS asynchronous IO is configured correctly. Ensure no disks are IO bound
Latch Free Waits	This wait indicates that the process is waiting for a latch that is currently busy (held by another process). When you see a latch free wait event in the V$SESSION_WAIT view, it means the process failed to obtain the latch in the willing-to-wait mode after spinning _SPIN_COUNT times and went to sleep. When processes compete heavily for latches, they will also consume more CPU resources because of spinning. The result is a higher response time	If the TIME spent waiting for latches is significant then it is best to determine which latches are suffering from contention.	A latch is a kind of low level lock. Latches apply only to memory structures in the SGA. They do not apply to database objects. An Oracle SGA has many latches, and they exist to protect various memory structures from potential corruption  by concurrent access. The time spent on latch waits is an effect, not a cause; the cause is that you are doing too many block gets, and block gets require cache buffer chain latching What are Latches and what causes Latch contention Database Lock and Latch Information Knowledge Browser Product Page
Library cache latch	The library cache latches protect the cached SQL statements and objects definitions held in the library cache within the shared pool. The library cache latch must be acquired in order to add a new statement to the library cache Application is making heavy use of literal SQL- use of bind variables will reduce this latch considerably	Latch is to ensure that the application is reusing as much as possible SQL statement representation. Use bind variables whenever possible in the application You can reduce the library cache latch hold time by properly setting the SESSION_CACHED_CURSORS parameter Consider increasing shared pool	Larger shared pools tend to have long free lists and processes that need to allocate space in them must  spend extra time scanning the long free lists while holding the shared pool latch if your database is not yet on Oracle9i Database, an oversized shared pool can increase the contention for the shared pool latch.
Shared pool latch	The shared pool latch is used to protect critical operations when allocating and freeing memory in the shared pool Contentions for the shared pool and library cache latches are mainly due to intense hard  parsing. A hard parse applies to new cursors and cursors that are aged out and must be re-executed The cost of parsing a new SQL statement is expensive both in terms of CPU requirements and the number of times the library cache and shared pool latches may need to be acquired and released.	Ways to reduce the shared pool latch are, avoid hard parses when possible, parse once, execute many. Eliminating literal SQL is also useful to avoid the shared pool latch. The size  of the shared_pool and use of MTS (shared server option) also greatly influences the shared pool latch. The workaround is to set the initialization parameter CURSOR_SHARING to FORCE. This allows statements that differ in literal  values but are otherwise identical to share a cursor and therefore reduce latch contention, memory usage, and  hard parse.	<Note 62143.1> explains how to identify and correct problems with the shared pool, and shared pool latch.
Row cache objects latch	This latch comes into play when user processes are attempting to  access the cached data dictionary values.	It is not common to have contention in this latch and the only way to reduce contention for this latch is by increasing the size of the shared pool (SHARED_POOL_SIZE). Use Locally Managed tablespaces for your application objects especially indexes Review and amend your database logical design , a good example is to merge or decrease the number of indexes on tables with heavy inserts	Configuring the library cache to an acceptable size usually ensures that the data  dictionary cache is also properly sized. So tuning Library Cache will tune Row Cache indirectly

Buffer busy wait event

http://adminoracle10g.blogspot.tw/2012/12/buffer-busy-wait-event_28.html

Buffer busy wait event

Buffer busy wait 

Oracle has several types of buffer classes, such as data block, segment header, undo header, and undo block. How you fix a buffer busy wait situation will depend on the types of buffer classes that are causing

the problem.

 Buffer Busy Waits usually happen on Oracle 10 and 11 mainly because of insert contention into tables or Indexes.  There are a few other rare cases of contention on old style RBS segments, file headers blocks and freelists

Before Oracle 10 and 11 there was one other major reason which was readers waiting for readers, ie one user does a phyiscal IO of a block into memory and a second user want to read that block. The second user waits until the IO is finished by the first user. Starting in 10g this wait has been given the name "read by other session".  Before Oracle 10g this was also a "buffer busy wait".

How can we find the block contention? 

àTo find the block on which currently  suffering from read by other session wait event.

SELECT sw.sql_id ,sw.p1 "file#", sw.p2 "block#", sw.p3 "class#" ,event

FROM v$session sw WHERE event= 'buffer busy wait';

àUsing blok_id or file_id we can find the segment

SELECT relative_fno, owner, segment_name, segment_type

FROM dba_extents

WHERE file_id = &FILE

AND &BLOCK BETWEEN block_id AND block_id + blocks - 1;

àFind the sql causing issue using the sql_id we find in first query

SELECT

      s.p1 file_id, s.p2 block_id,o.object_name obj,

       o.object_type otype,

       s.SQL_ID,

       w.CLASS,event

FROM v$session s,

     ( SELECT ROWNUM CLASS#, CLASS FROM v$waitstat ) w,

      all_objects o

WHERE

 event IN ('buffer busy wait')

and

    w.CLASS#(+)=s.p3

   AND o.object_id (+)= s.row_wait_OBJ#

ORDER BY 1;

SELECT sql_text FROM   v$sqltext WHERE sql_id=&sq_id ORDER BY piece

àWe can also find block and sql on which causing “read by other session” using v$active_session_history

SELECT

     p1 file_id ,  p2  block_id ,o.object_name obj,

       o.object_type otype,

       ash.SQL_ID,

       w.CLASS

FROM v$active_session_history ash,

     ( SELECT ROWNUM CLASS#, CLASS FROM v$waitstat ) w,

      all_objects o

WHERE event='buffer busy wait'

   AND w.CLASS#(+)=ash.p3

   AND o.object_id (+)= ash.CURRENT_OBJ#

      AND ash.sample_time > SYSDATE - &MIN/(60*24)

ORDER BY 1;

 
 &Min  : time you want to see history

SELECT sql_text FROM   v$sqltext WHERE sql_id=&sq_id ORDER BY piece

SELECT

       bbw.cnt,

       bbw.obj,

       bbw.otype,

       bbw.sql_id,

       bbw.block_type,

       NVL(tbs.NAME,TO_CHAR(bbw.p1)) TBS,

       tbs_defs.assm ASSM

FROM (

    SELECT

       COUNT(*) cnt,

       NVL(object_name,CURRENT_OBJ#) obj,

       o.object_type otype,

       ash.SQL_ID sql_id,

       NVL(w.CLASS,'usn '||TO_CHAR(CEIL((ash.p3-18)/2))||' '||

                    DECODE(MOD(ash.p3,2),

                         1,'header',

                         0,'block')) block_type,

       --nvl(w.class,to_char(ash.p3)) block_type,

       ash.p1 p1

    FROM v$active_session_history ash,

        ( SELECT ROWNUM CLASS#, CLASS FROM v$waitstat ) w,

        all_objects o

    WHERE event='buffer busy wait'

      AND w.CLASS#(+)=ash.p3

      AND o.object_id (+)= ash.CURRENT_OBJ#

      AND ash.session_state='WAITING'

     AND ash.sample_time > SYSDATE - &min/(60*24)

      --and w.class# > 18

   GROUP BY o.object_name, ash.current_obj#, o.object_type,

         ash.sql_id, w.CLASS, ash.p3, ash.p1

  ) bbw,

    (SELECT   file_id,

       tablespace_name NAME

  FROM dba_data_files

   ) tbs,

    (SELECT

 tablespace_name    NAME,

        extent_management  LOCAL,

        allocation_type    EXTENTS,

        segment_space_management ASSM,

        initial_extent

     FROM dba_tablespaces

   ) tbs_defs

  WHERE tbs.file_id(+) = bbw.p1

    AND tbs.NAME=tbs_defs.NAME

ORDER BY bbw.cnt

The preceding queries will reveal the specific type of buffer causing the high buffer waits. Your fix

will depend on which buffer class causes the buffer waits, as summarized in the following subsections.

Contention for Segment Header

Every table and index segment has a header block. This block contains the following metadata:

information about the high watermark of the segment, a list of the extents making up the segment,

and information about the free space. To manage the free space, the header block contains

(depending on the type of segment space management that is in use) either freelists or a list of

blocks containing automatic segment space management information. Typically, contention

for a segment header block is experienced when its content is modified by several processes

concurrently. Note that the header block is modified in the following situations:

• If INSERT statements make it necessary to increase the high watermark

• If INSERT statements make it necessary to allocate new extents

• If DELETE, INSERT, and UPDATE statements make it necessary to modify a freelist

A possible solution for these situations is to partition the segment in order to spread the

load over several segment header blocks. Most of the time, this might be achieved with hash

partitioning, although, depending on the load and the partition key, other partitioning methods

might work as well. However, if the problem is because of the second or third situation, other

solutions exist. For the second, you should use bigger extents. In this way, new extents would

seldom be allocated. For the third, which does not apply to tablespaces using automatic segment

space management, freelists can be moved into other blocks by means of freelist groups. In

fact, when several freelist groups are used, the freelists are no longer located in the segment

header block (they are spread on a number of blocks equal to the value specified with the

parameter FREELIST GROUPS, so you will have less contention on them—you are not simply

moving the contention to another place!). Another possibility is to use a tablespace with automaticsegment space management instead of freelist segment space management

In short

If your queries show that the buffer waits are being caused by contention on the segment header, there’s free list contention in the database, due to several processes attempting to insert into the same data block—each of these processes needs to obtain a free list before it can insert data into that block. If you aren’t already using it, you must switch from manual space management to automatic segment space management (ASSM)—under ASSM, the database doesn’t use free lists. However, note that moving to ASSM may not be easily feasible in most cases. In cases where you can’t implement ASSM, you must increase the free lists for the segment in question. You can also try increasing the free list groups as well.

Contention for  Undo Header and Undo Block

Contention for these types of blocks occurs in two situations. The first, and only for undo

header blocks, is when few undo segments are available and lots of transactions are concurrently

committed (or rolled back). This should be a problem only if you are using manual undo

management. In other words, it usually happens if the database administrator has manually

created the rollback segments. To solve this problem, you should use automatic undo management.

The second situation is when several sessions modify and query the same blocks at the

same time. As a result, lots of consistent read blocks have to be created, and this requires you

to access both the block and its associated undo blocks. There is little that can be done about

this situation, other than reducing the concurrency for the data blocks, thereby reducing the

ones for the undo blocks at the same time.

Contention for Extent Map Blocks

As discussed in the section “Contention for Segment Header Blocks,” the segment header

blocks contain a list of the extents that make up the segment. If the list does not fit in the

segment header, it is distributed over several blocks: the segment header block and one or

more extent map blocks. Contention for a segment header block is experienced when concurrent

INSERT statements have to constantly allocate new extents. To solve this problem, you

should use bigger extents.

Contention for Freelist Blocks

As discussed in the section “Contention for Segment Header Blocks,” freelists can be moved

into other blocks, called freelist blocks, by means of freelist groups. Contention for a freelist

block is experienced when concurrent DELETE, INSERT, or UPDATE statements have to modify the freelists. To solve this problem, you should increase the number of freelist groups. Another possibility is to use a tablespace with automatic segment space management instead of freelist segment space management.

Contention for Data Blocks

All the blocks that make up a table or index which stor actual data are called data blocks. Contention for them has two main causes.

The first is when the frequency of table or index scans on a given segment is very high. This problem is because of inefficient execution plans causing frequent table or index scans over the same blocks. Usually it is because of inefficient related-combine operations (for example, nested loop joins). Here, even two or three SQL statements executed concurrently might be enough to cause contention.

The second is when the frequency of executions is very high. This problem is the execution of several SQL statements accessing the same block at the same time. In other words, it is the number of SQL statements executed concurrently against (few) blocks that is the problem. It might be that both happen at the same time. If this is the case, take care of solving the first problem before facing the second one. In fact, the second problem might disappear when the first is gone.

To solve the first problem, SQL tuning is necessary. An efficient execution plan must be

executed in place of the inefficient one.

To solve the second problem, several approaches are available. Which one you have to use

depends on the type of the SQL statement (that is, DELETE, INSERT, SELECT,1 and UPDATE) and on the type of the segment (that is, table or index).

However, before starting, you should always ask one question when the frequency of execution is high: is it really necessary to execute those SQL statements against the same data so often? Actually, it is not unusual to see applications that unnecessarily execute the same SQL

statement too often

. If the frequency of execution cannot be reduced, there are the following

possibilities.

• If there is contention for a table’s blocks because of DELETE, SELECT, and UPDATE statements, you should reduce the number of rows per block. Note that this is the opposite of

the common best practice to fit the maximum number of rows per block. To store fewer

rows per block, either a higher PCTFREE or a smaller block size can be used.

• If there is contention for a table’s blocks because of INSERT statements and freelist segment

space management is in use, the number of freelists can be increased. In fact, the goal of

having several freelists is precisely to spread concurrent INSERT statements over several

blocks. Another possibility is to move the segment into a tablespace with automatic

segment storage management.

• If there is contention for an index’s blocks, there are two possible solutions. First, the

index can be created with the option REVERSE. Note, however, that this method does not

help if the contention is on the root block of the index. Second, the index can be hash

partitioned, based on the leading column of the index key (this creates multiple root

blocks and so helps with root block contention if a single partition is accessed). Because

global hash-partitioned indexes are available as of Oracle Database 10g only, this is not

an option with Oracle9i.

The important thing to note about reverse indexes is that range scans on them cannot

apply restrictions based on range conditions (for example, BETWEEN, >, or <=). Of course, equality predicates are supported

A buffer busy wait indicates that more than one process is simultaneously accessing the same data block. One of the reasons for a high number of buffer busy waits is that an inefficient query is reading too many data blocks into the buffer cache, thus potentially keeping in wait other sessions that want to access one or more of those same blocks. Not only that, a query that reads too much data into the buffer cache may lead to the aging out of necessary blocks from the cache. You must investigate queries that involve the segment causing the buffer busy waits with a view to reducing the number of data blocks they’re reading into the buffer cache.

If your investigation of buffer busy waits reveals that the same block or set of blocks is involved most of the time, a good strategy would be to delete some of these rows and insert them back into the table, thus forcing them onto different data blocks.

Check your current memory allocation to the buffer cache, and, if necessary, increase it. A larger

buffer cache can reduce the waiting by sessions to read data from disk, since more of the data will

already be in the buffer cache. You can also place the offending table in memory by using the KEEP POOL in the buffer cache By making the hot block always available in memory, you’ll

avoid the high buffer busy waits.

Indexes that have a very low number of unique values are called low cardinality indexes. Low

cardinality indexes generally result in too many block reads. Thus, if several DML operations are

occurring concurrently, some of the index blocks could become “hot” and lead to high buffer busy waits.

As a long-term solution, you can try to reduce the number of the low cardinality indexes in your

database. Each Oracle data segment such as a table or an index contains a header block that records information such as free blocks available. When multiple sessions are trying to insert or delete rows from the same segment, you could end up with contention for the data segment’s header block.

Summary

data block

IF OTYPE =

INDEX , then the insert index leaf block is probably hot, solutions are

Hash partition the index

Use reverse key index

TABLE, then insert block is hot,solutions

Use free lists

Put Object in ASSM tablespace

Segment header - If "segment header" occurs at the same time as CLASS= "data block" on the same object and the object is of OTYPE= "TABLE"  then this is just a confirmation that the TABLE needs to use free lists or  ASSM.

File Header Block - Most likely extent allocation problems, look at extent size on tablespace and increase the extent size to there are few extent allocations and less contention on the File Header Block.

free lists - Add free list groups to the object

undo header - Not enough UNDO segments, if using old RBS then switch to AUM

undo block - Hot spot in UNDO, application issue 

Reference:-
Apress.Troubleshooting.Oracle.Performance.Jun.2008
oracle_database_11g_performance_tuning_recipes