对Oracle数据库整体性能的优化，首先要关注的是在有性能问题时数据库排名前几位等待事件是哪些。Oracle等待事件众多，随着版本的升级，数量还在不断增加，可以通过v$event_name查到当前数据库版本包含的等待事件。例如我在Linux平台查11.2.0.4版本的Oracle是有1367个等待事件。SELECT name FROM V$EVENT_NAME ORDER BY name;
如此多的等待事件自然是要分类汇总，并对常见的等待事件有比较深入的认识，才能在Oracle数据库调优这条路上走的更远。本文先将官方文档中的等待事件的相关知识汇总出来，对需要说明的部分会根据个人的理解给予必要的解释。如对哪个等待事件有疑惑，也欢迎回帖参与讨论。
官方文档对等待事件的分类
针对Oracle数据库的等待事件，常见的分类方法是简单的分为2大类，一是非空闲等待，二是空闲等待。 一般来讲，对于空闲等待类的等待事件不需要过多关注。 实际上我们进一步参考Oracle 11g R2 官方文档时，会发现文档对等待事件的分类要更细致，更有利于我们定位排查问题。Classes of Wait Events
Every wait event belongs to a class of wait event. The following list describes each of the wait classes.
Administrative
Waits resulting from DBA commands that cause users to wait (for example, an index rebuild)
Application
Waits resulting from user application code (for example, lock waits caused by row level locking or explicit lock commands)
Cluster
Waits related to Real Application Clusters resources (for example, global cache resources such as 'gc cr block busy')
Commit
This wait class only comprises one wait event - wait for redo log write confirmation after a commit (that is, 'log file sync')
Concurrency
Waits for internal database resources (for example, latches)
Configuration
Waits caused by inadequate configuration of database or instance resources (for example, undersized log file sizes, shared pool size)
Idle
Waits that signify the session is inactive, waiting for work (for example, 'SQL*Net message from client')
Network
Waits related to network messaging (for example, 'SQL*Net more data to dblink')
Other
Waits which should not typically occur on a system (for example, 'wait for EMON to spawn')
Queue
Contains events that signify delays in obtaining additional data in a pipelined environment. The time spent on these wait events indicates inefficiency or other problems in the pipeline. It affects features such as Oracle Streams, parallel queries, or DBMS_PIPE PL/SQL packages.
Scheduler
Resource Manager related waits (for example, 'resmgr: become active')
System I/O
Waits for background process I/O (for example, DBWR wait for 'db file parallel write')
User I/O
Waits for user I/O (for example 'db file sequential read')
Oracle 常见等待事件
+ [log file sync](#2.1) + [log buffer space](#2.2) + [log file switch](#2.3) + [log file parallel write](#2.4) + [buffer busy waits](#2.5) + [free buffer waits](#2.6) + [library cache pin](#2.7) + [library cache lock](#2.8) + [latch events](#2.9) + [direct path read and direct path read temp](#2.10) + [direct path write and direct path write temp](#2.11) + [db file sequential read](#2.12) + [db file scattered read](#2.13) + [read by other session](#2.14) + [cursor: pin S wait on X](#2.15) + [SQL*Net Events](#2.16)log file sync
日志文件同步。用户提交或回滚时，必须等待LGWR将redo信息写入到redo日志文件中，才可以完成。 > When a user session commits (or rolls back), the session's redo information must be flushed to the redo logfile by LGWR. The server process performing the COMMIT or ROLLBACK waits under this event for the write to the redo log to complete. > > **Actions** > > If this event's waits constitute a significant wait on the system or a significant amount of time waited by a user experiencing response time issues or on a system, then examine the average time waited. > > If the average time waited is low, but the number of waits are high, then the application might be committing after every INSERT, rather than batching COMMITs. Applications can reduce the wait by committing after 50 rows, rather than every row. > > If the average time waited is high, then examine the session waits for the log writer and see what it is spending most of its time doing and waiting for. If the waits are because of slow I/O, then try the following: > > Reduce other I/O activity on the disks containing the redo logs, or use dedicated disks. > > Alternate redo logs on different disks to minimize the effect of the archiver on the log writer. > > Move the redo logs to faster disks or a faster I/O subsystem (for example, switch from RAID 5 to RAID 1). > > Consider using raw devices (or simulated raw devices provided by disk vendors) to speed up the writes. > > Depending on the type of application, it might be possible to batch COMMITs by committing every N rows, rather than every row, so that fewer log file syncs are needed.log buffer space
log buffer空间问题。 > This event occurs when server processes are waiting for free space in the log buffer, because all the redo is generated faster than LGWR can write it out. > > **Actions** > > Modify the redo log buffer size. If the size of the log buffer is reasonable, then ensure that the disks on which the online redo logs reside do not suffer from I/O contention. The log buffer space wait event could be indicative of either disk I/O contention on the disks where the redo logs reside, or of a too-small log buffer. Check the I/O profile of the disks containing the redo logs to investigate whether the I/O system is the bottleneck. If the I/O system is not a problem, then the redo log buffer could be too small. Increase the size of the redo log buffer until this event is no longer significant.log file switch
redo日志文件切换。 > There are two wait events commonly encountered: > > log file switch (archiving needed) > > log file switch (checkpoint incomplete) > > In both of the events, the LGWR cannot switch into the next online redo log file. All the commit requests wait for this event. > > **Actions** > > For the log file switch (archiving needed) event, examine why the archiver cannot archive the logs in a timely fashion. It could be due to the following: > > Archive destination is running out of free space. > > Archiver is not able to read redo logs fast enough (contention with the LGWR). > > Archiver is not able to write fast enough (contention on the archive destination, or not enough ARCH processes). If you have ruled out other possibilities (such as slow disks or a full archive destination) consider increasing the number of ARCn processes. The default is 2. > > If you have mandatory remote shipped archive logs, check whether this process is slowing down because of network delays or the write is not completing because of errors. > > Depending on the nature of bottleneck, you might need to redistribute I/O or add more space to the archive destination to alleviate the problem. For the log file switch (checkpoint incomplete) event: > > Check if DBWR is slow, possibly due to an overloaded or slow I/O system. Check the DBWR write times, check the I/O system, and distribute I/O if necessary. See Chapter 8, "I/O Configuration and Design". > > Check if there are too few, or too small redo logs. If you have a few redo logs or small redo logs (for example, 2 x 100k logs), and your system produces enough redo to cycle through all of the logs before DBWR has been able to complete the checkpoint, then increase the size or number of redo logs. See "Sizing Redo Log Files".log file parallel write
redo日志文件并行写。 > This event involves writing redo records to the redo log files from the log buffer.buffer busy waits
SGA中的buffer争用。 > This wait indicates that there are some buffers in the buffer cache that multiple processes are attempting to access concurrently. Query V$WAITSTAT for the wait statistics for each class of buffer. Common buffer classes that have buffer busy waits include data block, segment header, undo header, and undo block. > > Check the following V$SESSION_WAIT parameter columns: > > P1: File ID > > P2: Block ID > > P3: Class ID > > **10.3.1.1 Causes** > > To determine the possible causes, first query V$SESSION to identify the value of ROW_WAIT_OBJ# when the session waits for buffer busy waits. For example: > > SELECT row_wait_obj# > FROM V$SESSION > WHERE EVENT = 'buffer busy waits'; > > To identify the object and object type contended for, query DBA_OBJECTS using the value for ROW_WAIT_OBJ# that is returned from V$SESSION. For example: > > SELECT owner, object_name, subobject_name, object_type > FROM DBA_OBJECTS > WHERE data_object_id = &row_wait_obj; > > **10.3.1.2 Actions** > > The action required depends on the class of block contended for and the actual segment. > > **10.3.1.2.1 segment header** > > If the contention is on the segment header, then this is most likely free list contention. > > Automatic segment-space management in locally managed tablespaces eliminates the need to specify the PCTUSED, FREELISTS, and FREELIST GROUPS parameters. If possible, switch from manual space management to automatic segment-space management (ASSM). > > The following information is relevant if you are unable to use ASSM (for example, because the tablespace uses dictionary space management). > > A free list is a list of free data blocks that usually includes blocks existing in several different extents within the segment. Free lists are composed of blocks in which free space has not yet reached PCTFREE or used space has shrunk below PCTUSED. Specify the number of process free lists with the FREELISTS parameter. The default value of FREELISTS is one. The maximum value depends on the data block size. > > To find the current setting for free lists for that segment, run the following: > > SELECT SEGMENT_NAME, FREELISTS > FROM DBA_SEGMENTS > WHERE SEGMENT_NAME = segment name > AND SEGMENT_TYPE = segment type; > > Set free lists, or increase the number of free lists. If adding more free lists does not alleviate the problem, then use free list groups (even in single instance this can make a difference). If using Oracle RAC, then ensure that each instance has its own free list group(s). > > See Also: > Oracle Database Concepts for information about automatic segment-space management, free lists, PCTFREE, and PCTUSED > > **10.3.1.2.2 data block** > > If the contention is on tables or indexes (not the segment header): > > Check for right-hand indexes. These are indexes that are inserted into at the same point by many processes. For example, those that use sequence number generators for the key values. > > Consider using ASSM, global hash partitioned indexes, or increasing free lists to avoid multiple processes attempting to insert into the same block. > > **10.3.1.2.3 undo header** > > For contention on rollback segment header: > > If you are not using automatic undo management, then add more rollback segments. > > **10.3.1.2.4 undo block** > > For contention on rollback segment block: > > If you are not using automatic undo management, then consider making rollback segment sizes larger. >free buffer waits
SGA中free buffer不足时触发的等待。 > This wait event indicates that a server process was unable to find a free buffer and has posted the database writer to make free buffers by writing out dirty buffers. A dirty buffer is a buffer whose contents have been modified. Dirty buffers are freed for reuse when DBWR has written the blocks to disk. > > **10.3.8.1 Causes** > > DBWR may not be keeping up with writing dirty buffers in the following situations: > > The I/O system is slow. > > There are resources it is waiting for, such as latches. > > The buffer cache is so small that DBWR spends most of its time cleaning out buffers for server processes. > > The buffer cache is so big that one DBWR process is not enough to free enough buffers in the cache to satisfy requests. > > **10.3.8.2 Actions** > > If this event occurs frequently, then examine the session waits for DBWR to see whether there is anything delaying DBWR. > > **10.3.8.2.1 Writes** > > If it is waiting for writes, then determine what is delaying the writes and fix it. Check the following: > > Examine V$FILESTAT to see where most of the writes are happening. > > Examine the host operating system statistics for the I/O system. Are the write times acceptable? > > If I/O is slow: > > Consider using faster I/O alternatives to speed up write times. > > Spread the I/O activity across large number of spindles (disks) and controllers. See Chapter 8, "I/O Configuration and Design" for information about balancing I/O. > > **10.3.8.2.2 Cache is Too Small** > > It is possible DBWR is very active because the cache is too small. Investigate whether this is a probable cause by looking to see if the buffer cache hit ratio is low. Also use the V$DB_CACHE_ADVICE view to determine whether a larger cache size would be advantageous. See "Sizing the Buffer Cache". > > **10.3.8.2.3 Cache Is Too Big for One DBWR** > > If the cache size is adequate and the I/O is evenly spread, then you can potentially modify the behavior of DBWR by using asynchronous I/O or by using multiple database writers. > > **10.3.8.3 Consider Multiple Database Writer (DBWR) Processes or I/O Slaves** > > Configuring multiple database writer processes, or using I/O slaves, is useful when the transaction rates are high or when the buffer cache size is so large that a single DBWn process cannot keep up with the load. > > **10.3.8.3.1 DB_WRITER_PROCESSES** > > The DB_WRITER_PROCESSES initialization parameter lets you configure multiple database writer processes (from DBW0 to DBW9 and from DBWa to DBWj). Configuring multiple DBWR processes distributes the work required to identify buffers to be written, and it also distributes the I/O load over these processes. Multiple db writer processes are highly recommended for systems with multiple CPUs (at least one db writer for every 8 CPUs) or multiple processor groups (at least as many db writers as processor groups). > > Based upon the number of CPUs and the number of processor groups, Oracle Database either selects an appropriate default setting for DB_WRITER_PROCESSES or adjusts a user-specified setting. > > **10.3.8.3.2 DBWR_IO_SLAVES** > > If it is not practical to use multiple DBWR processes, then Oracle Database provides a facility whereby the I/O load can be distributed over multiple slave processes. The DBWR process is the only process that scans the buffer cache LRU list for blocks to be written out. However, the I/O for those blocks is performed by the I/O slaves. The number of I/O slaves is determined by the parameter DBWR_IO_SLAVES. > > DBWR_IO_SLAVES is intended for scenarios where you cannot use multiple DB_WRITER_PROCESSES (for example, where you have a single CPU). I/O slaves are also useful when asynchronous I/O is not available, because the multiple I/O slaves simulate nonblocking, asynchronous requests by freeing DBWR to continue identifying blocks in the cache to be written. Asynchronous I/O at the operating system level, if you have it, is generally preferred. > > DBWR I/O slaves are allocated immediately following database open when the first I/O request is made. The DBWR continues to perform all of the DBWR-related work, apart from performing I/O. I/O slaves simply perform the I/O on behalf of DBWR. The writing of the batch is parallelized between the I/O slaves. > > Note: > Implementing DBWR_IO_SLAVES requires that extra shared memory be allocated for I/O buffers and request queues. Multiple DBWR processes cannot be used with I/O slaves. Configuring I/O slaves forces only one DBWR process to start. > > **10.3.8.3.3 Choosing Between Multiple DBWR Processes and I/O Slaves** > > Configuring multiple DBWR processes benefits performance when a single DBWR process cannot keep up with the required workload. However, before configuring multiple DBWR processes, check whether asynchronous I/O is available and configured on the system. If the system supports asynchronous I/O but it is not currently used, then enable asynchronous I/O to see if this alleviates the problem. If the system does not support asynchronous I/O, or if asynchronous I/O is configured and there is still a DBWR bottleneck, then configure multiple DBWR processes. > > Note: > If asynchronous I/O is not available on your platform, then asynchronous I/O can be disabled by setting the DISK_ASYNCH_IO initialization parameter to FALSE. > > Using multiple DBWRs parallelizes the gathering and writing of buffers. Therefore, multiple DBWn processes should deliver more throughput than one DBWR process with the same number of I/O slaves. For this reason, the use of I/O slaves has been deprecated in favor of multiple DBWR processes. I/O slaves should only be used if multiple DBWR processes cannot be configured.library cache pin
This event manages library cache concurrency. Pinning an object causes the heaps to be loaded into memory. If a client wants to modify or examine the object, the client must acquire a pin after the lock.
library cache lock
This event controls the concurrency between clients of the library cache. It acquires a lock on the object handle so that either:
One client can prevent other clients from accessing the same object The client can maintain a dependency for a long time which does not allow another client to change the objectThis lock is also obtained to locate an object in the library cache.
latch events
latch相关的等待事件。 > A latch is a low-level internal lock used by Oracle Database to protect memory structures. The latch free event is updated when a server process attempts to get a latch, and the latch is unavailable on the first attempt. > > There is a dedicated latch-related wait event for the more popular latches that often generate significant contention. For those events, the name of the latch appears in the name of the wait event, such as latch: library cache or latch: cache buffers chains. This enables you to quickly figure out if a particular type of latch is responsible for most of the latch-related contention. Waits for all other latches are grouped in the generic latch free wait event. > > See Also: > Oracle Database Concepts for more information on latches and internal locks > > **10.3.10.1 Actions** > > This event should only be a concern if latch waits are a significant portion of the wait time on the system as a whole, or for individual users experiencing problems. > > Examine the resource usage for related resources. For example, if the library cache latch is heavily contended for, then examine the hard and soft parse rates. > > Examine the SQL statements for the sessions experiencing latch contention to see if there is any commonality. > > Check the following V$SESSION_WAIT parameter columns: > > P1: Address of the latch > > P2: Latch number > > P3: Number of times process has slept, waiting for the latch > > **10.3.10.2 Example: Find Latches Currently Waited For** > > SELECT EVENT, SUM(P3) SLEEPS, SUM(SECONDS_IN_WAIT) SECONDS_IN_WAIT > FROM V$SESSION_WAIT > WHERE EVENT LIKE 'latch%' > GROUP BY EVENT; > > A problem with the previous query is that it tells more about session tuning or instant instance tuning than instance or long-duration instance tuning. > > The following query provides more information about long duration instance tuning, showing whether the latch waits are significant in the overall database time. > > SELECT EVENT, TIME_WAITED_MICRO, > ROUND(TIME_WAITED_MICRO*100/S.DBTIME,1) PCT_DB_TIME > FROM V$SYSTEM_EVENT, > (SELECT VALUE DBTIME FROM V$SYS_TIME_MODEL WHERE STAT_NAME = 'DB time') S > WHERE EVENT LIKE 'latch%' > ORDER BY PCT_DB_TIME ASC; > > A more general query that is not specific to latch waits is the following: > > SELECT EVENT, WAIT_CLASS, > TIME_WAITED_MICRO,ROUND(TIME_WAITED_MICRO*100/S.DBTIME,1) PCT_DB_TIME > FROM V$SYSTEM_EVENT E, V$EVENT_NAME N, > (SELECT VALUE DBTIME FROM V$SYS_TIME_MODEL WHERE STAT_NAME = 'DB time') S > WHERE E.EVENT_ID = N.EVENT_ID > AND N.WAIT_CLASS NOT IN ('Idle', 'System I/O') > ORDER BY PCT_DB_TIME ASC; > > Table 10-3 Latch Wait Event > Latch SGA Area Possible Causes Look For: > > Shared pool, library cache > > > Shared pool > > > Lack of statement reuse > > Statements not using bind variables > > Insufficient size of application cursor cache > > Cursors closed explicitly after each execution > > Frequent logins and logoffs > > Underlying object structure being modified (for example truncate) > > Shared pool too small > > > Sessions (in V$SESSTAT) with high: > > parse time CPU > > parse time elapsed > > Ratio of parse count (hard) / execute count > > Ratio of parse count (total) / execute count > > Cursors (in V$SQLAREA/V$SQLSTATS) with: > > High ratio of PARSE_CALLS / EXECUTIONS > > EXECUTIONS = 1 differing only in literals in the WHERE clause (that is, no bind variables used) > > High RELOADS > > High INVALIDATIONS > > Large (1mb) SHARABLE_MEM > > cache buffers lru chain > > > Buffer cache LRU lists > > > Excessive buffer cache throughput. For example, inefficient SQL that accesses incorrect indexes iteratively (large index range scans) or many full table scans > > DBWR not keeping up with the dirty workload; hence, foreground process spends longer holding the latch looking for a free buffer > > Cache may be too small > > > Statements with very high logical I/O or physical I/O, using unselective indexes > > cache buffers chains > > > Buffer cache buffers > > > Repeated access to a block (or small number of blocks), known as a hot block > > > Sequence number generation code that updates a row in a table to generate the number, rather than using a sequence number generator > > Index leaf chasing from very many processes scanning the same unselective index with very similar predicate > > Identify the segment the hot block belongs to > > row cache objects > > > **10.3.10.3 Shared Pool and Library Cache Latch Contention** > > A main cause of shared pool or library cache latch contention is parsing. There are several techniques that you can use to identify unnecessary parsing and several types of unnecessary parsing: > > Unshared SQL > > Reparsed Sharable SQL > > By Session > > cache buffers lru chain > > cache buffers chains > > row cache objects > > **10.3.10.3.1 Unshared SQL** > > This method identifies similar SQL statements that could be shared if literals were replaced with bind variables. The idea is to either: > > Manually inspect SQL statements that have only one execution to see whether they are similar: > > SELECT SQL_TEXT > FROM V$SQLSTATS > WHERE EXECUTIONS < 4 > ORDER BY SQL_TEXT; > > Or, automate this process by grouping what may be similar statements. Estimate the number of bytes of a SQL statement that are likely the same, and group the SQL statements by this number of bytes. For example, the following example groups statements that differ only after the first 60 bytes. > > SELECT SUBSTR(SQL_TEXT, 1, 60), COUNT(*) > FROM V$SQLSTATS > WHERE EXECUTIONS < 4 > GROUP BY SUBSTR(SQL_TEXT, 1, 60) > HAVING COUNT(*) > 1; > > Or report distinct SQL statements that have the same execution plan. The following query selects distinct SQL statements that share the same execution plan at least four times. These SQL statements are likely to be using literals instead of bind variables. > > SELECT SQL_TEXT FROM V$SQLSTATS WHERE PLAN_HASH_VALUE IN > (SELECT PLAN_HASH_VALUE > FROM V$SQLSTATS > GROUP BY PLAN_HASH_VALUE HAVING COUNT(*) > 4) > ORDER BY PLAN_HASH_VALUE; > > **10.3.10.3.2 Reparsed Sharable SQL** > > Check the V$SQLSTATS view. Enter the following query: > > SELECT SQL_TEXT, PARSE_CALLS, EXECUTIONS > FROM V$SQLSTATS > ORDER BY PARSE_CALLS; > > When the PARSE_CALLS value is close to the EXECUTIONS value for a given statement, you might be continually reparsing that statement. Tune the statements with the higher numbers of parse calls. > > **10.3.10.3.3 By Session** > > Identify unnecessary parse calls by identifying the session in which they occur. It might be that particular batch programs or certain types of applications do most of the reparsing. To achieve this goal, run the following query: > > SELECT pa.SID, pa.VALUE "Hard Parses", ex.VALUE "Execute Count" > FROM V$SESSTAT pa, V$SESSTAT ex > WHERE pa.SID = ex.SID > AND pa.STATISTIC#=(SELECT STATISTIC# > FROM V$STATNAME WHERE NAME = 'parse count (hard)') > AND ex.STATISTIC#=(SELECT STATISTIC# > FROM V$STATNAME WHERE NAME = 'execute count') > AND pa.VALUE > 0; > > The result is a list of all sessions and the amount of reparsing they do. For each session identifier (SID), go to V$SESSION to find the name of the program that causes the reparsing. > > Note: > Because this query counts all parse calls since instance startup, it is best to look for sessions with high rates of parse. For example, a connection which has been up for 50 days might show a high parse figure, but a second connection might have been up for 10 minutes and be parsing at a much faster rate. > > The output is similar to the following: > > SID Hard Parses Execute Count > ------ ----------- ------------- > 7 1 20 > 8 3 12690 > 6 26 325 > 11 84 1619 > > **10.3.10.3.4 cache buffers lru chain** > > The cache buffers lru chain latches protect the lists of buffers in the cache. When adding, moving, or removing a buffer from a list, a latch must be obtained. > > For symmetric multiprocessor (SMP) systems, Oracle Database automatically sets the number of LRU latches to a value equal to one half the number of CPUs on the system. For non-SMP systems, one LRU latch is sufficient. > > Contention for the LRU latch can impede performance on SMP computers with a large number of CPUs. LRU latch contention is detected by querying V$LATCH, V$SESSION_EVENT, and V$SYSTEM_EVENT. To avoid contention, consider tuning the application, bypassing the buffer cache for DSS jobs, or redesigning the application. > > **10.3.10.3.5 cache buffers chains** > > The cache buffers chains latches are used to protect a buffer list in the buffer cache. These latches are used when searching for, adding, or removing a buffer from the buffer cache. Contention on this latch usually means that there is a block that is greatly contended for (known as a hot block). > > To identify the heavily accessed buffer chain, and hence the contended for block, look at latch statistics for the cache buffers chains latches using the view V$LATCH_CHILDREN. If there is a specific cache buffers chains child latch that has many more GETS, MISSES, and SLEEPS when compared with the other child latches, then this is the contended for child latch. > > This latch has a memory address, identified by the ADDR column. Use the value in the ADDR column joined with the X$BH table to identify the blocks protected by this latch. For example, given the address (V$LATCH_CHILDREN.ADDR) of a heavily contended latch, this queries the file and block numbers: > > SELECT OBJ data_object_id, FILE#, DBABLK,CLASS, STATE, TCH > FROM X$BH > WHERE HLADDR = 'address of latch' > ORDER BY TCH; > > X$BH.TCH is a touch count for the buffer. A high value for X$BH.TCH indicates a hot block. > > Many blocks are protected by each latch. One of these buffers will probably be the hot block. Any block with a high TCH value is a potential hot block. Perform this query several times, and identify the block that consistently appears in the output. After you have identified the hot block, query DBA_EXTENTS using the file number and block number, to identify the segment. > > After you have identified the hot block, you can identify the segment it belongs to with the following query: > > SELECT OBJECT_NAME, SUBOBJECT_NAME > FROM DBA_OBJECTS > WHERE DATA_OBJECT_ID = &obj; > > In the query, &obj is the value of the OBJ column in the previous query on X$BH. > > **10.3.10.3.6 row cache objects** > > The row cache objects latches protect the data dictionary.direct path read and direct path read temp
直接路径读。 > When a session is reading buffers from disk directly into the PGA (opposed to the buffer cache in SGA), it waits on this event. If the I/O subsystem does not support asynchronous I/Os, then each wait corresponds to a physical read request. > > If the I/O subsystem supports asynchronous I/O, then the process is able to overlap issuing read requests with processing the blocks existing in the PGA. When the process attempts to access a block in the PGA that has not yet been read from disk, it then issues a wait call and updates the statistics for this event. Hence, the number of waits is not necessarily the same as the number of read requests (unlike db file scattered read and db file sequential read). > > Check the following V$SESSION_WAIT parameter columns: > > P1: File_id for the read call > > P2: Start block_id for the read call > > P3: Number of blocks in the read call > > **10.3.4.1 Causes** > > This situation occurs in the following situations: > > The sorts are too large to fit in memory and some of the sort data is written out directly to disk. This data is later read back in, using direct reads. > > Parallel slaves are used for scanning data. > > The server process is processing buffers faster than the I/O system can return the buffers. This can indicate an overloaded I/O system. > > **10.3.4.2 Actions** > > The file_id shows if the reads are for an object in TEMP tablespace (sorts to disk) or full table scans by parallel slaves. This wait is the largest wait for large data warehouse sites. However, if the workload is not a Decision Support Systems (DSS) workload, then examine why this situation is happening. > 10.3.4.2.1 Sorts to Disk > > Examine the SQL statement currently being run by the session experiencing waits to see what is causing the sorts. Query V$TEMPSEG_USAGE to find the SQL statement that is generating the sort. Also query the statistics from V$SESSTAT for the session to determine the size of the sort. See if it is possible to reduce the sorting by tuning the SQL statement. If WORKAREA_SIZE_POLICY is MANUAL, then consider increasing the SORT_AREA_SIZE for the system (if the sorts are not too big) or for individual processes. If WORKAREA_SIZE_POLICY is AUTO, then investigate whether to increase PGA_AGGREGATE_TARGET. See "PGA Memory Management". > > **10.3.4.2.2 Full Table Scans** > > If tables are defined with a high degree of parallelism, then this setting could skew the optimizer to use full table scans with parallel slaves. Check the object being read into using the direct path reads. If the full table scans are a valid part of the workload, then ensure that the I/O subsystem is adequate for the degree of parallelism. Consider using disk striping if you are not already using it or Oracle Automatic Storage Management (Oracle ASM). > > **10.3.4.2.3 Hash Area Size** > > For query plans that call for a hash join, excessive I/O could result from having HASH_AREA_SIZE too small. If WORKAREA_SIZE_POLICY is MANUAL, then consider increasing the HASH_AREA_SIZE for the system or for individual processes. If WORKAREA_SIZE_POLICY is AUTO, then investigate whether to increase PGA_AGGREGATE_TARGET. > > See Also: > > "Managing Excessive I/O" > > "PGA Memory Management"10.3.5 direct path write and direct path write temp
直接路径写。 > When a process is writing buffers directly from PGA (as opposed to the DBWR writing them from the buffer cache), the process waits on this event for the write call to complete. Operations that could perform direct path writes include sorts on disk, parallel DML operations, direct-path INSERTs, parallel create table as select, and some LOB operations. > > Like direct path reads, the number of waits is not the same as number of write calls issued if the I/O subsystem supports asynchronous writes. The session waits if it has processed all buffers in the PGA and cannot continue work until an I/O request completes. > > See Also: > Oracle Database Administrator's Guide for information about direct-path inserts > > Check the following V$SESSION_WAIT parameter columns: > > P1: File_id for the write call > > P2: Start block_id for the write call > > P3: Number of blocks in the write call > > **10.3.5.1 Causes** > > This happens in the following situations: > > Sorts are too large to fit in memory and are written to disk > > Parallel DML are issued to create/populate objects > > Direct path loads > > **10.3.5.2 Actions** > > For large sorts see "Sorts to Disk". > > For parallel DML, check the I/O distribution across disks and ensure that the I/O subsystem is adequately configured for the degree of parallelism. >db file sequential read
单块读。 > This event signifies that the user process is reading a buffer into the SGA buffer cache and is waiting for a physical I/O call to return. A sequential read is a single-block read. > > Single block I/Os are usually the result of using indexes. Rarely, full table scan calls could get truncated to a single block call because of extent boundaries, or buffers present in the buffer cache. These waits would also show up as db file sequential read. > > Check the following V$SESSION_WAIT parameter columns: > > P1: The absolute file number > > P2: The block being read > > P3: The number of blocks (should be 1) > > See Also: > "db file scattered read" for information about managing excessive I/O, inadequate I/O distribution, and finding the SQL causing the I/O and the segment the I/O is performed on > > **10.3.3.1 Actions** > > On a healthy system, physical read waits should be the biggest waits after the idle waits. However, also consider whether there are db file sequential reads on a large data warehouse that should be seeing mostly full table scans with parallel query.db file scattered read
多块读。 > This event signifies that the user process is reading buffers into the SGA buffer cache and is waiting for a physical I/O call to return. A db file scattered read issues a scattered read to read the data into multiple discontinuous memory locations. A scattered read is usually a multiblock read. It can occur for a fast full scan (of an index) in addition to a full table scan. > > The db file scattered read wait event identifies that a full scan is occurring. When performing a full scan into the buffer cache, the blocks read are read into memory locations that are not physically adjacent to each other. Such reads are called scattered read calls, because the blocks are scattered throughout memory. This is why the corresponding wait event is called 'db file scattered read'. multiblock (up to DB_FILE_MULTIBLOCK_READ_COUNT blocks) reads due to full scans into the buffer cache show up as waits for 'db file scattered read'. > > Check the following V$SESSION_WAIT parameter columns: > > P1: The absolute file number > > P2: The block being read > > P3: The number of blocks (should be greater than 1) > > **10.3.2.1 Actions** > > On a healthy system, physical read waits should be the biggest waits after the idle waits. However, also consider whether there are direct read waits (signifying full table scans with parallel query) or db file scattered read waits on an operational (OLTP) system that should be doing small indexed accesses. > > Other things that could indicate excessive I/O load on the system include the following: > > Poor buffer cache hit ratio > > These wait events accruing most of the wait time for a user experiencing poor response time > > **10.3.2.2 Managing Excessive I/O** > > There are several ways to handle excessive I/O waits. In the order of effectiveness, these are as follows: > > Reduce the I/O activity by SQL tuning. > > Reduce the need to do I/O by managing the workload. > > Gather system statistics with DBMS_STATS package, allowing the query optimizer to accurately cost possible access paths that use full scans. > > Use Automatic Storage Management. > > Add more disks to reduce the number of I/Os for each disk. > > Alleviate I/O hot spots by redistributing I/O across existing disks. > > See Also: > Chapter 8, "I/O Configuration and Design" > > The first course of action should be to find opportunities to reduce I/O. Examine the SQL statements being run by sessions waiting for these events and statements causing high physical I/Os from V$SQLAREA. Factors that can adversely affect the execution plans causing excessive I/O include the following: > > Improperly optimized SQL > > Missing indexes > > High degree of parallelism for the table (skewing the optimizer toward scans) > > Lack of accurate statistics for the optimizer > > Setting the value for DB_FILE_MULTIBLOCK_READ_COUNT initialization parameter too high which favors full scans > > **10.3.2.3 Inadequate I/O Distribution** > > Besides reducing I/O, also examine the I/O distribution of files across the disks. Is I/O distributed uniformly across the disks, or are there hot spots on some disks? Are the number of disks sufficient to meet the I/O needs of the database? > > See the total I/O operations (reads and writes) by the database, and compare those with the number of disks used. Remember to include the I/O activity of LGWR and ARCH processes. > > **10.3.2.4 Finding the SQL Statement executed by Sessions Waiting for I/O** > > Use the following query to determine, at a point in time, which sessions are waiting for I/O: > > SELECT SQL_ADDRESS, SQL_HASH_VALUE > FROM V$SESSION > WHERE EVENT LIKE 'db file%read'; > > **10.3.2.5 Finding the Object Requiring I/O** > > To determine the possible causes, first query V$SESSION to identify the value of ROW_WAIT_OBJ# when the session waits for db file scattered read. For example: > > SELECT row_wait_obj# > FROM V$SESSION > WHERE EVENT = 'db file scattered read'; > > To identify the object and object type contended for, query DBA_OBJECTS using the value for ROW_WAIT_OBJ# that is returned from V$SESSION. For example: > > SELECT owner, object_name, subobject_name, object_type > FROM DBA_OBJECTS > WHERE data_object_id = &row_wait_obj;read by other session
This event occurs when a session requests a buffer that is currently being read into the buffer cache by another session. Prior to release 10.1, waits for this event were grouped with the other reasons for waiting for buffers under the 'buffer busy wait' event
Wait Time: Time waited for the buffer to be read by the other session (in microseconds)
cursor: pin S wait on X
A session waits for this event when it is requesting a shared mutex pin and another session is holding an exclusive mutex pin on the same cursor object.
Wait Time: Microseconds