相关资源链接
开Oracle调优鹰眼，深入理解AWR性能报告：https://www.askmaclean.com/archives/awr-hawk-eyes-training.html
开Oracle调优鹰眼，深入理解AWR性能报告 第二讲：https://www.askmaclean.com/archives/awr-tuning-hawk-eyes.html
b 站的搬运：
- 开Oracle调优鹰眼，深入理解AWR性能报告：https://www.bilibili.com/video/BV1JE411p7hP
- 开Oracle调优鹰眼，深入理解AWR性能报告 第二讲：https://www.bilibili.com/video/BV1JE411p7mt
【视频教学：性能优化】Maclean Liu的Oracle性能优化讲座第一回《真正读懂Oracle SQL执行计划》
b站的搬运： https://www.bilibili.com/video/BV1e5411w78f/
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有同学在看过《Oracle调优鹰眼，深入理解AWR性能报告》的教学视频后急切期待第三讲，但实际是第三讲需要结合大量的原理知识才能充分理解 例如Latch activity 、Undo、Dynamic Resource Master均需要理解其原理才能充分理解。 所以这些AWR的环节将在 Maclean 今后的 系列调优讲座中介绍。 对于《Oracle调优鹰眼系列》 则会增加本附录，作为对全部Oracle AWR指标的介绍， 本附录对于原理理解方面的内容将不多，而更侧重于指标含义的介绍，是对AWR鹰眼讲座的工具文档。
如果你觉得本AWR解析中的哪些指标仍理解不透彻 或者讲的不清楚的，可以在本页中留言，谢谢大家的支持。
Hawk Eyes 看AWR的鹰眼= 基础理论夯实+看过500份以上AWR
啥是AWR?
AWR (Automatic Workload Repository)
一堆历史性能数据，放在SYSAUX表空间上， AWR和SYSAUX都是10g出现的，是Oracle调优的关键特性； 大约1999年左右开始开发，已经有15年历史
默认快照间隔1小时，10g保存7天、11g保存8天; 可以通过DBMS_WORKLOAD_REPOSITORY.MODIFY_SNAPSHOT_SETTINGS修改
DBA_HIST_WR_CONTROL
AWR程序核心是dbms_workload_repository包
@?/rdbms/admin/awrrpt 本实例
@?/rdbms/admin/awrrpti RAC中选择实例号谁维护AWR?
主要是MMON(Manageability Monitor Process)和它的小工进程(m00x)
MMON的功能包括:
- 启动slave进程m00x去做AWR快照
- 当某个度量阀值被超过时发出alert告警
- 为最近改变过的SQL对象捕获指标信息
AWR小技巧
手动执行一个快照：
Exec dbms_workload_repository.create_snapshot; (这个要背出来哦，用的时候去翻手册，丢脸哦 J!)创建一个AWR基线：
Exec DBMS_WORKLOAD_REPOSITORY.CREATE_BASELINE(start_snap_id，end_snap_id ,baseline_name);@?/rdbms/admin/awrddrpt AWR比对报告
@?/rdbms/admin/awrgrpt RAC 全局AWR自动生成AWR HTML报告：
http://www.oracle-base.com/dba/10g/generate_multiple_awr_reports.sql
1、报告总结
WORKLOAD REPOSITORY report for
DB Name DB Id Instance Inst Num Startup Time Release RAC
------------ ----------- ------------ -------- --------------- ----------- ---
MAC 2629627371 askmaclean.com 1 22-Jan-13 16:49 11.2.0.3.0 YES
Host Name Platform CPUs Cores Sockets Memory(GB)
---------------- -------------------------------- ---- ----- ------- ----------
MAC10 AIX-Based Systems (64-bit) 128 32 320.00
Snap Id Snap Time Sessions Curs/Sess
--------- ------------------- -------- ---------
Begin Snap: 5853 23-Jan-13 15:00:56 3,520 1.8
End Snap: 5854 23-Jan-13 15:30:41 3,765 1.9
Elapsed: 29.75 (mins)
DB Time: 7,633.76 (mins)Elapsed 为该AWR性能报告的时间跨度(自然时间的跨度，例如前一个快照snapshot是4点生成的，后一个快照snapshot是6点生成的，则若使用@?/rdbms/admin/awrrpt 脚本中指定这2个快照的话，那么其elapsed = (6-4)=2 个小时)，一个AWR性能报告 至少需要2个AWR snapshot性能快照才能生成 ( 注意这2个快照时间 实例不能重启过，否则指定这2个快照生成AWR性能报告 会报错)，AWR性能报告中的 指标往往是 后一个快照和前一个快照的 指标的delta，这是因为 累计值并不能反映某段时间内的系统workload。
DB TIME= 所有前台session花费在database调用上的总和时间：
- 注意是前台进程foreground sessions
- 包括CPU时间、IO Time、和其他一系列非空闲等待时间，别忘了cpu on queue time
DB TIME 不等于 响应时间，DB TIME高了未必响应慢，DB TIME低了未必响应快
DB Time描绘了数据库总体负载，但要和elapsed time逝去时间结合其他来。
Average Active Session AAS= DB time/Elapsed Time
- DB Time =60 min ， Elapsed Time =60 min AAS=60/60=1 负载一般
- DB Time= 1min , Elapsed Time= 60 min AAS= 1/60 负载很轻
- DB Time= 60000 min，Elapsed Time= 60 min AAS=1000 系统hang了吧？
DB TIME= DB CPU + Non-Idle Wait + Wait on CPU queue
如果仅有2个逻辑CPU，而2个session在60分钟都没等待事件，一直跑在CPU上，那么：
DB CPU= 2 * 60 mins ， DB Time = 2* 60 + 0 + 0 =120
AAS = 120/60=2 正好等于OS load 2。
如果有3个session都100%仅消耗CPU，那么总有一个要wait on queue
DB CPU = 2* 60 mins ，wait on CPU queue= 60 mins
AAS= (120+ 60)/60=3 主机load 亦为3，此时vmstat 看waiting for run time
真实世界中？ DB Cpu = xx mins ， Non-Idle Wait= enq:TX + cursor pin S on X + latch : xxx + db file sequential read + ……….. 阿猫阿狗
1-1 内存参数大小
Cache Sizes Begin End
~~~~~~~~~~~ ---------- ----------
Buffer Cache: 49,152M 49,152M Std Block Size: 8K
Shared Pool Size: 13,312M 13,312M Log Buffer: 334,848K内存管理方式：MSMM、ASMM(sga_target)、AMM(memory_target)
小内存有小内存的问题， 大内存有大内存的麻烦！ ORA-04031???!!
Buffer cache和shared pool size的 begin/end值在ASMM、AMM和11gR2 MSMM下可是会动的哦！
这里说 shared pool一直收缩，则在shrink过程中一些row cache 对象被lock住可能导致前台row cache lock等解析等待，最好别让shared pool shrink。如果这里shared pool一直在grow，那说明shared pool原有大小不足以满足需求(可能是大量硬解析)，结合下文的解析信息和SGA breakdown来一起诊断问题。
1-2 Load Profile
Load Profile Per Second Per Transaction Per Exec Per Call
~~~~~~~~~~~~ --------------- --------------- ---------- ----------
DB Time(s): 256.6 0.2 0.07 0.03
DB CPU(s): 3.7 0.0 0.00 0.00
Redo size: 1,020,943.0 826.5
Logical reads: 196,888.0 159.4
Block changes: 6,339.4 5.1
Physical reads: 5,076.7 4.1
Physical writes: 379.2 0.3
User calls: 10,157.4 8.2
Parses: 204.0 0.2
Hard parses: 0.9 0.0
W/A MB processed: 5.0 0.0
Logons: 1.7 0.0
Executes: 3,936.6 3.2
Rollbacks: 1,126.3 0.9
Transactions: 1,235.3
% Blocks changed per Read: 53.49 Recursive Call %: 98.04
Rollback per transaction %: 36.57 Rows per Sort: 73.70Load Profile 指标详解
| 指标 | 指标含义 |
|---|---|
| redo size | 单位 bytes，redo size可以用来估量update/insert/delete的频率，大的redo size往往对lgwr写日志，和arch归档造成I/O压力， Per Transaction可以用来分辨是 大量小事务， 还是少量大事务。如上例每秒redo 约1MB ，每个事务800 字节，符合OLTP特征 |
| Logical Read | 单位 次数*块数， 相当于 "人*次"， 如上例 196,888 * db_block_size=1538MB/s ， 逻辑读耗CPU，主频和CPU核数都很重要，逻辑读高则DB CPU往往高，也往往可以看到latch: cache buffer chains等待。 大量OLTP系统(例如siebel)可以高达几十乃至上百Gbytes。 |
| Block changes | 单位 次数*块数 ， 描绘数据变化频率 |
| Physical Read | 单位次数*块数， 如上例 5076 * 8k = 39MB/s， 物理读消耗IO读，体现在IOPS和吞吐量等不同纬度上；但减少物理读可能意味着消耗更多CPU。好的存储 每秒物理读能力达到几GB，例如Exadata。 这个physical read包含了physical reads cache和physical reads direct |
| Physical writes | 单位 次数*块数，主要是DBWR写datafile，也有direct path write。 dbwr长期写出慢会导致定期log file switch(checkpoint no complete) 检查点无法完成的前台等待。 这个physical write 包含了physical writes direct +physical writes from cache |
| User Calls | 单位次数，用户调用数，more details from internal |
| Parses | 解析次数，包括软解析+硬解析，软解析优化得不好，则夸张地说几乎等于每秒SQL执行次数。 即执行解析比1:1，而我们希望的是 解析一次 到处运行哦！ |
| Hard Parses | 万恶之源． Cursor pin s on X， library cache: mutex X ， latch: row cache objects /shared pool……………..。 硬解析最好少于每秒20次 |
| W/A MB processed | 单位MB W/A workarea workarea中处理的数据数量 结合 In-memory Sort%， sorts (disk) PGA Aggr一起看 |
| Logons | 登陆次数， logon storm 登陆风暴，结合AUDIT审计数据一起看。短连接的附带效应是游标缓存无用 |
| Executes | 执行次数，反应执行频率 |
| Rollback | 回滚次数， 反应回滚频率， 但是这个指标不太精确，参考而已，别太当真 |
| Transactions | 每秒事务数，是数据库层的TPS，可以看做压力测试或比对性能时的一个指标，孤立看无意义 |
| % Blocks changed per Read | 每次逻辑读导致数据块变化的比率；如果'redo size', 'block changes' 'pct of blocks changed per read'三个指标都很高，则说明系统正执行大量insert/update/delete; pct of blocks changed per read = (block changes ) /( logical reads) |
| Recursive Call % | 递归调用的比率;Recursive Call % = (recursive calls)/(user calls) |
| Rollback per transaction % | 事务回滚比率。 Rollback per transaction %= (rollback)/(transactions) |
| Rows per Sort | 平均每次排序涉及到的行数 ; Rows per Sort= ( sorts(rows) ) / ( sorts(disk) + sorts(memory)) |
Per Second 与 Per Transaction 维度说明
注意这些Load Profile 负载指标 在本环节提供了 2个维度 per second 和 per transaction。
per Second: 主要是把 快照内的delta值除以 快站时间的秒数 ， 例如 在 A快照中V$SYSSTAT视图反应 table scans (long tables) 这个指标是 100 ，在B快照中V$SYSSTAT视图反应 table scans (long tables) 这个指标是 3700, 而A快照和B快照 之间 间隔了一个小时 3600秒， 则 对于 table scans (long tables) per second 就是 ( 3700- 100) /3600=1。
pert Second是我们审视数据的主要维度 ，任何性能数据脱离了 时间模型则毫无意义。
在statspack/AWR出现之前 的调优 洪荒时代， 有很多DBA 依赖 V$SYSSTAT等视图中的累计 统计信息来调优，以当前的调优眼光来看，那无异于刀耕火种。
per transaction : 基于事务的维度， 与per second相比 是把除数从时间的秒数改为了该段时间内的事务数。 这个维度的很大用户是用来 识别应用特性的变化 ，若2个AWR性能报告中该维度指标 出现了大幅变化，例如 redo size从本来per transaction 1k变化为 10k per transaction，则说明SQL业务逻辑肯定发生了某些变化。
注意AWR中的这些指标 并不仅仅用来孤立地了解 Oracle数据库负载情况， 实施调优工作。 对于 故障诊断 例如HANG、Crash等， 完全可以通过对比问题时段的性能报告和常规时间来对比，通过各项指标的对比往往可以找出 病灶所在。
相关SQL查询
SELECT VALUE FROM DBA_HIST_SYSSTAT WHERE SNAP_ID = :B4 AND DBID = :B3 AND INSTANCE_NUMBER = :B2 AND STAT_NAME in ( "db block changes","user calls","user rollbacks","user commits",redo size","physical reads direct","physical writes","parse count (hard)","parse count (total)","session logical reads","recursive calls","redo log space requests","redo entries","sorts (memory)","sorts (disk)","sorts (rows)","logons cumulative","parse time cpu","parse time elapsed","execute count","logons current","opened cursors current","DBWR fusion writes","gcs messages sent","ges messages sent","global enqueue gets sync","global enqueue get time","gc cr blocks received","gc cr block receive time","gc current blocks received","gc current block receive time","gc cr blocks served","gc cr block build time","gc cr block flush time","gc cr block send time","gc current blocks served","gc current block pin time","gc current block flush time","gc current block send time","physical reads","physical reads direct (lob)",
SELECT TOTAL_WAITS FROM DBA_HIST_SYSTEM_EVENT WHERE SNAP_ID = :B4 AND DBID = :B3 AND INSTANCE_NUMBER = :B2 AND EVENT_NAME in ("gc buffer busy","buffer busy waits"
SELECT VALUE FROM DBA_HIST_SYS_TIME_MODEL WHERE DBID = :B4 AND SNAP_ID = :B3 AND INSTANCE_NUMBER = :B2 AND STAT_NAME in ("DB CPU","sql execute elapsed time","DB time"
SELECT VALUE FROM DBA_HIST_PARAMETER WHERE SNAP_ID = :B4 AND DBID = :B3 AND INSTANCE_NUMBER = :B2 AND PARAMETER_NAME in ("__db_cache_size","__shared_pool_size","sga_target","pga_aggregate_target","undo_management","db_block_size","log_buffer","timed_statistics","statistics_level"
SELECT BYTES FROM DBA_HIST_SGASTAT WHERE SNAP_ID = :B4 AND DBID = :B3 AND INSTANCE_NUMBER = :B2 AND POOL IN ('shared pool', 'all pools') AND NAME in ("free memory",
SELECT BYTES FROM DBA_HIST_SGASTAT WHERE SNAP_ID = :B4 AND DBID = :B3 AND INSTANCE_NUMBER = :B2 AND NAME = :B1 AND POOL IS NULL
SELECT (E.BYTES_PROCESSED - B.BYTES_PROCESSED) FROM DBA_HIST_PGA_TARGET_ADVICE B, DBA_HIST_PGA_TARGET_ADVICE E WHERE B.DBID = :B4 AND B.SNAP_ID = :B3 AND B.INSTANCE_NUMBER = :B2 AND B.ADVICE_STATUS = 'ON' AND E.DBID = B.DBID AND E.SNAP_ID = :B1 AND E.INSTANCE_NUMBER = B.INSTANCE_NUMBER AND E.PGA_TARGET_FACTOR = 1 AND B.PGA_TARGET_FACTOR = 1 AND E.ADVICE_STATUS = 'ON'
SELECT SUM(E.TOTAL_WAITS - NVL(B.TOTAL_WAITS, 0)) FROM DBA_HIST_SYSTEM_EVENT B, DBA_HIST_SYSTEM_EVENT E WHERE B.SNAP_ID(+) = :B4 AND E.SNAP_ID = :B3 AND B.DBID(+) = :B2 AND E.DBID = :B2 AND B.INSTANCE_NUMBER(+) = :B1 AND E.INSTANCE_NUMBER = :B1 AND B.EVENT_ID(+) = E.EVENT_ID AND (E.EVENT_NAME = 'latch free' OR E.EVENT_NAME LIKE 'latch:%')
SELECT DECODE(B.TOTAL_SQL, 0, 0, 100*(1-B.SINGLE_USE_SQL/B.TOTAL_SQL)), DECODE(E.TOTAL_SQL, 0, 0, 100*(1-E.SINGLE_USE_SQL/E.TOTAL_SQL)), DECODE(B.TOTAL_SQL_MEM, 0, 0, 100*(1-B.SINGLE_USE_SQL_MEM/B.TOTAL_SQL_MEM)), DECODE(E.TOTAL_SQL_MEM, 0, 0, 100*(1-E.SINGLE_USE_SQL_MEM/E.TOTAL_SQL_MEM)) FROM DBA_HIST_SQL_SUMMARY B, DBA_HIST_SQL_SUMMARY E WHERE B.SNAP_ID = :B4 AND E.SNAP_ID = :B3 AND B.INSTANCE_NUMBER = :B2 AND E.INSTANCE_NUMBER = :B2 AND B.DBID = :B1 AND E.DBID = :B1
SELECT EVENT, WAITS, TIME, DECODE(WAITS, NULL, TO_NUMBER(NULL), 0, TO_NUMBER(NULL), TIME/WAITS*1000) AVGWT, PCTWTT, WAIT_CLASS FROM (SELECT EVENT, WAITS, TIME, PCTWTT, WAIT_CLASS FROM (SELECT E.EVENT_NAME EVENT, E.TOTAL_WAITS - NVL(B.TOTAL_WAITS,0) WAITS, (E.TIME_WAITED_MICRO - NVL(B.TIME_WAITED_MICRO,0)) / 1000000 TIME, 100 * (E.TIME_WAITED_MICRO - NVL(B.TIME_WAITED_MICRO,0)) / :B1 PCTWTT, E.WAIT_CLASS WAIT_CLASS FROM DBA_HIST_SYSTEM_EVENT B, DBA_HIST_SYSTEM_EVENT E WHERE B.SNAP_ID(+) = :B5 AND E.SNAP_ID = :B4 AND B.DBID(+) = :B3 AND E.DBID = :B3 AND B.INSTANCE_NUMBER(+) = :B2 AND E.INSTANCE_NUMBER = :B2 AND B.EVENT_ID(+) = E.EVENT_ID AND E.TOTAL_WAITS > NVL(B.TOTAL_WAITS,0) AND E.WAIT_CLASS != 'Idle' UNION ALL SELECT 'CPU time' EVENT, TO_NUMBER(NULL) WAITS, :B6 /1000000 TIME, 100 * :B6 / :B1 PCTWTT, NULL WAIT_CLASS FROM DUAL WHERE :B6 > 0) ORDER BY TIME DESC, WAITS DESC) WHERE ROWNUM <= :B7
SELECT SUM(E.TIME_WAITED_MICRO - NVL(B.TIME_WAITED_MICRO,0)) FROM DBA_HIST_SYSTEM_EVENT B, DBA_HIST_SYSTEM_EVENT E WHERE B.SNAP_ID(+) = :B4 AND E.SNAP_ID = :B3 AND B.DBID(+) = :B2 AND E.DBID = :B2 AND B.INSTANCE_NUMBER(+) = :B1 AND E.INSTANCE_NUMBER = :B1 AND B.EVENT_ID(+) = E.EVENT_ID AND E.WAIT_CLASS = 'User I/O'
SELECT (E.ESTD_LC_TIME_SAVED - B.ESTD_LC_TIME_SAVED) FROM DBA_HIST_SHARED_POOL_ADVICE B, DBA_HIST_SHARED_POOL_ADVICE E WHERE B.DBID = :B3 AND B.INSTANCE_NUMBER = :B2 AND B.SNAP_ID = :B4 AND E.DBID = :B3 AND E.INSTANCE_NUMBER = :B2 AND E.SNAP_ID = :B1 AND E.SHARED_POOL_SIZE_FACTOR = 1 AND B.SHARED_POOL_SIZE_FACTOR = 11-3 Instance Efficiency Percentages (Target 100%)
Instance Efficiency Percentages (Target 100%)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Buffer Nowait %: 99.97 Redo NoWait %: 100.00
Buffer Hit %: 97.43 In-memory Sort %: 100.00
Library Hit %: 99.88 Soft Parse %: 99.58
Execute to Parse %: 94.82 Latch Hit %: 99.95
Parse CPU to Parse Elapsd %: 1.75 % Non-Parse CPU: 99.85上述所有指标 的目标均为100%，即越大越好，在少数bug情况下可能超过100%或者为负值。
- 80%以上 %Non-Parse CPU
- 90%以上 Buffer Hit%, In-memory Sort%, Soft Parse%
- 95%以上 Library Hit%, Redo Nowait%, Buffer Nowait%
- 98%以上 Latch Hit%
1、Buffer Nowait %
session申请一个buffer(兼容模式)不等待的次数比例。 需要访问buffer时立即可以访问的比率， 不兼容的情况 在9i中是 buffer busy waits，从10g以后 buffer busy waits 分离为 buffer busy wait 和 read by other session2个等待事件 :
9i 中 waitstat的总次数基本等于buffer busy waits等待事件的次数
SQL> select sum(TOTAL_WAITS) from v$system_event where event='buffer busy waits';
SUM(TOTAL_WAITS)
----------------
33070394
SQL> select sum(count) from v$waitstat;
SUM(COUNT)
----------
33069335
10g waitstat的总次数基本等于 buffer busy waits 和 read by other session 等待的次数总和
SQL> select sum(TOTAL_WAITS) from v$system_event where event='buffer busy waits' or event='read by other session';
SUM(TOTAL_WAITS)
----------------
60675815
SQL> select sum(count) from v$waitstat;
SUM(COUNT)
----------
60423739Buffer Nowait %的计算公式是 sum(v$waitstat.wait_count) / (v$sysstat statistic session logical reads)，例如在AWR中：
| Class | Waits | Total Wait Time (s) | Avg Time (ms) |
|---|---|---|---|
| data block | 24,543 | 2,267 | 92 |
| undo header | 743 | 2 | 3 |
| undo block | 1,116 | 0 | 0 |
| 1st level bmb | 35 | 0 | 0 |
| session logical reads | 40,769,800 | 22,544.84 | 204.71 |
| Buffer Nowait %: | 99.94 |
Buffer Nowait= ( 40,769,800 – (24543+743+1116+35))/ ( 40,769,800) = 0.99935= 99.94%
SELECT SUM(WAIT_COUNT) FROM DBA_HIST_WAITSTAT WHERE SNAP_ID = :B3 AND DBID = :B2 AND INSTANCE_NUMBER = :B12、buffer HIT%
经典的经典，高速缓存命中率，反应物理读和缓存命中间的纠结，但这个指标即便99% 也不能说明物理读等待少了
不合理的db_cache_size，或者是SGA自动管理ASMM /Memory 自动管理AMM下都可能因为db_cache_size过小引起大量的db file sequential /scattered read等待事件； maclean曾经遇到过因为大量硬解析导致ASMM 下shared pool共享池大幅度膨胀，而db cache相应缩小shrink的例子，最终db cache收缩到只有几百兆，本来没有的物理读等待事件都大幅涌现出来 。
此外与 buffer HIT%相关的指标值得关注的还有 table scans(long tables) 大表扫描这个统计项目、此外相关的栏目还有Buffer Pool Statistics 、Buffer Pool Advisory等（如果不知道在哪里，直接找一个AWR 去搜索这些关键词即可)。
buffer HIT%在 不同版本有多个计算公式：
在9i中：
Buffer Hit Ratio = 1 – ((physical reads – physical reads direct – physical reads direct (lob)) / (db block gets + consistent gets – physical reads direct – physical reads direct (lob))在10g以后：
Buffer Hit Ratio= 1 – (('physical reads cache') / ('consistent gets from cache' + 'db block gets from cache')注意：但是实际AWR中 似乎还是按照9i中的算法，虽然算法的区别对最后算得的比率影响不大。
对于buffer hit % 看它的命中率有多高没有意义，主要是关注 未命中的次数有多少。通过上述公式很容易反推出未命中的物理读的次数。
db block gets 、consistent gets 以及 session logical reads的关系如下：
db block gets＝db block gets direct＋ db block gets from cache
consistent gets ＝ consistent gets from cache＋ consistent gets direct
consistent gets from cache＝ consistent gets – examination + else
consistent gets – examination==>指的是不需要pin buffer直接可以执行consistent get的次数，常用于索引，只需要一次latch get
session logical reads = db block gets +consistent gets其中physical reads 、physical reads cache、physical reads direct、physical reads direct (lob)几者的关系为：
physical reads = physical reads cache + physical reads direct这个公式其实说明了 物理读有2种 ：
- 物理读进入buffer cache中 ，是常见的模式 physical reads cache
- 物理读直接进入PGA 直接路径读， 即physical reads direct
| physical reads | 8 | Total number of data blocks read from disk. This value can be greater than the value of "physical reads direct" plus "physical reads cache" as reads into process private buffers also included in this statistic. |
| physical reads cache | 8 | Total number of data blocks read from disk into the buffer cache. This is a subset of "physical reads" statistic. |
| physical reads direct | 8 | Number of reads directly from disk, bypassing the buffer cache. For example, in high bandwidth, data-intensive operations such as parallel query, reads of disk blocks bypass the buffer cache to maximize transfer rates and to prevent the premature aging of shared data blocks resident in the buffer cache. |
physical reads direct = physical reads direct (lob) + physical reads direct temporary tablespace + physical reads direct(普通)这个公式也说明了 直接路径读 分成三个部分：
- physical reads direct (lob) 直接路径读LOB对象
- physical reads direct temporary tablespace 直接路径读临时表空间
- physical read direct(普通) 普通的直接路径读， 一般是11g开始的自动的大表direct path read和并行引起的direct path read
physical writes direct= physical writes direct (lob)+ physical writes direct temporary tablespace
DBWR checkpoint buffers written = DBWR thread checkpoint buffers written+ DBWR tablespace checkpoint buffers written+ DBWR PQ tablespace checkpoint buffers written+….3、Redo nowait%
session在生成redo entry时不用等待的比例，redo相关的资源争用例如redo space request争用可能造成生成redo时需求等待。此项数据来源于v$sysstat中的(redo log space requests/redo entries)。 一般来说10g以后不太用关注log_buffer参数的大小，需要关注是否有十分频繁的 log switch ； 过小的redo logfile size 如果配合较大的SGA和频繁的commit提交都可能造成该问题。 考虑增到redo logfile 的尺寸 : 1~4G 每个，7~10组都是合适的。同时考虑优化redo logfile和datafile 的I/O。
4、In-memory Sort%
这个指标因为它不计算workarea中所有的操作类型，所以现在越来越鸡肋了。 纯粹在内存中完成的排序比例。数据来源于v$sysstat statistics sorts (disk) 和 sorts (memory)， In-memory Sort% = sort(memory) / ( sort(disk)+ sort(memory) )
5、Library Hit%
library cache命中率，申请一个library cache object例如一个SQL cursor时，其已经在library cache中的比例。 数据来源 V$librarycache的pins和pinhits。 合理值：>95% ，该比例来源于1- ( Σ(pin Requests * Pct Miss) / Sum(Pin Requests) )
维护这个指标的重点是 保持shared pool共享池有足够的Free Memory，且没有过多的内存碎片，具体可以参考这里。 显然过小的shared pool可用空间会导致library cache object被aged out换出共享池。
此外保证SQL语句绑定变量和游标可以共享也是很重要的因素。
Library Cache Activity DB/Inst: G10R25/G10R25 Snaps: 2964-2965
-> "Pct Misses" should be very low https://www.askmaclean.com
Get Pct Pin Pct Invali-
Namespace Requests Miss Requests Miss Reloads dations
--------------- ------------ ------ -------------- ------ ---------- --------
BODY 5 0.0 6 16.7 1 0
CLUSTER 10 0.0 26 0.0 0 0
SQL AREA 601,357 99.8 902,828 99.7 47 2
TABLE/PROCEDURE 83 9.6 601,443 0.0 48 0| 字段 | 类型 | 描述 |
|---|---|---|
GETS |
NUMBER |
Number of times a lock was requested for objects of this namespace |
GETHITS |
NUMBER |
Number of times an object's handle was found in memory |
GETHITRATIO |
NUMBER |
Ratio of GETHITS to GETS |
PINS |
NUMBER |
Number of times a PIN was requested for objects of this namespace |
PINHITS |
NUMBER |
Number of times all of the metadata pieces of the library object were found in memory |
PINHITRATIO |
NUMBER |
Ratio of PINHITS to PINS |
RELOADS |
NUMBER |
Any PIN of an object that is not the first PIN performed since the object handle was created, and which requires loading the object from disk |
INVALIDATIONS |
NUMBER |
Total number of times objects in this namespace were marked invalid because a dependent object was modified |
SELECT SUM(PINS), SUM(PINHITS) FROM DBA_HIST_LIBRARYCACHE WHERE SNAP_ID = :B3 AND DBID = :B2 AND INSTANCE_NUMBER = :B16、Soft Parse
软解析比例，无需多说的经典指标，数据来源v$sysstat statistics的parse count(total)和parse count(hard)。 合理值>95%
Soft Parse %是AWR中另一个重要的解析指标，该指标反应了快照时间内 软解析次数 和 总解析次数 (soft+hard 软解析次数+硬解析次数)的比值，若该指标很低，那么说明了可能存在剧烈的hard parse硬解析，大量的硬解析会消耗更多的CPU时间片并产生解析争用(此时可以考虑使用cursor_sharing=FORCE)； 理论上我们总是希望 Soft Parse % 接近于100%， 但并不是说100%的软解析就是最理想的解析状态，通过设置 session_cached_cursors参数和反复重用游标我们可以让解析来的更轻量级，即通俗所说的利用会话缓存游标实现的软软解析(soft soft parse)。
7、Execute to Parse%
指标反映了执行解析比 其公式为 1-(parse/execute) , 目标为100% 及接近于只 执行而不解析。 数据来源v$sysstat statistics parse count (total) 和execute count
在oracle中解析往往是执行的先提工作，但是通过游标共享 可以解析一次 执行多次， 执行解析可能分成多种场景：
- hard coding => 硬编码代码 硬解析一次 ，执行一次， 则理论上其执行解析比 为 1:1 ，则理论上Execute to Parse =0 极差，且soft parse比例也为0%
- 绑定变量但是仍软解析=〉 软解析一次，执行一次 ， 这种情况虽然比前一种好 但是执行解析比(这里的parse，包含了软解析和硬解析)仍是1:1， 理论上Execute to Parse =0 极差， 但是soft parse比例可能很高
- 使用 静态SQL、动态绑定、session_cached_cursor、open cursors等技术实现的 解析一次，执行多次， 执行解析比为N:1， 则 Execute to Parse= 1- (1/N) 执行次数越多 Execute to Parse越接近100% ，这种是我们在OLTP环境中喜闻乐见的！
通俗地说 soft parse% 反映了软解析率， 而软解析在oracle中仍是较昂贵的操作， 我们希望的是解析1次执行N次，如果每次执行均需要软解析，那么虽然soft parse%=100% 但是parse time仍可能是消耗DB TIME的大头。
Execute to Parse反映了 执行解析比，Execute to Parse和soft parse% 都很低 那么说明确实没有绑定变量 ， 而如果 soft parse% 接近99% 而Execute to Parse 不足90% 则说明没有执行解析比低， 需要通过 静态SQL、动态绑定、session_cached_cursor、open cursors等技术减少软解析。
8、Latch Hit%
willing-to-wait latch闩申请不要等待的比例。 数据来源V$latch gets和misses
Latch Name
----------------------------------------
Get Requests Misses Sleeps Spin Gets Sleep1 Sleep2 Sleep3
-------------- ----------- ----------- ---------- -------- -------- --------
shared pool
9,988,637 364 23 341 0 0 0
library cache
6,753,468 152 6 146 0 0 0
Memory Management Latch
369 1 1 0 0 0 0
qmn task queue latch
24 1 1 0 0 0 0Latch Hit%:= (1 – (Sum(misses) / Sum(gets)))
关于Latch的更多信息内容可以参考 AWR后面的专栏Latch Statistics， 注意对于一个并发设计良好的OLTP应用来说，Latch、Enqueue等并发控制不应当成为系统的主要瓶颈， 同时对于这些并发争用而言 堆积硬件CPU和内存 很难有效改善性能。
SELECT SUM(GETS), SUM(MISSES) FROM DBA_HIST_LATCH WHERE SNAP_ID = :B3 AND DBID = :B2 AND INSTANCE_NUMBER = :B19、Parse CPU To Parse Elapsd
该指标反映了 快照内解析CPU时间和总的解析时间的比值(Parse CPU Time/ Parse Elapsed Time)； 若该指标水平很低，那么说明在整个解析过程中 实际在CPU上运算的时间是很短的，而主要的解析时间都耗费在各种其他非空闲的等待事件上了(如latch:shared pool,row cache lock之类等) 数据来源 V$sysstat 的 parse time cpu和parse time elapsed
10、%Non-Parse CPU
非解析cpu比例，公式为 (DB CPU – Parse CPU)/DB CPU， 若大多数CPU都用在解析上了，则可能好钢没用在刃上了。 数据来源 v$sysstat 的 parse time cpu和 cpu used by this session
1-4 Shared Pool Statistics
Shared Pool Statistics Begin End
------ ------
Memory Usage %: 84.64 79.67
% SQL with executions>1: 93.77 24.69
% Memory for SQL w/exec>1: 85.36 34.8该环节提供一个大致的SQL重用及shared pool内存使用的评估。 应用是否共享SQL? 有多少内存是给只运行一次的SQL占掉的，对比共享SQL呢？
如果该环节中% SQL with executions>1的 比例 小于%90 ， 考虑用下面链接的SQL去抓 硬编码的非绑定变量SQL语句。
利用FORCE_MATCHING_SIGNATURE捕获非绑定变量SQL
- Memory Usage %: (shared pool 的实时大小- shared pool free memory)/ shared pool 的实时大小， 代表shared pool的空间使用率，虽然有使用率但没有标明碎片程度
- % SQL with executions>1 复用的SQL占总的SQL语句的比率,数据来源 DBA_HIST_SQL_SUMMARY 的 SINGLE_USE_SQL和TOTAL_SQL：1 – SINGLE_USE_SQL / TOTAL_SQL
- % Memory for SQL w/exec>1 执行2次以上的SQL所占内存占总的SQL内存的比率，数据来源DBA_HIST_SQL_SUMMARY 的SINGLE_USE_SQL_MEM和TOTAL_SQL_MEM：1 – SINGLE_USE_SQL_MEM / TOTAL_SQL_MEM
==〉上面2个指标也可以用来大致了解shared pool中的内存碎片程序，因为SINGLE_USE_SQL 单次执行的SQL多的话，那么显然可能有较多的共享池内存碎片
SQL复用率低的原因一般来说就是硬绑定变量(hard Coding)未合理使用绑定变量(bind variable)，对于这种现象短期无法修改代表使用绑定变量的可以ALTER SYSTEM SET CURSOR_SHARING=FORCE; 来绕过问题，对于长期来看还是要修改代码绑定变量。 Oracle 从11g开始宣称今后将废弃CURSOR_SHARING的SIMILAR选项，同时SIMILAR选项本身也造成了很多问题，所以一律不推荐用CURSOR_SHARING=SIMILAR。
如果memory usage%比率一直很高，则可以关注下后面sga breakdown中的shared pool free memory大小，一般推荐至少让free memroy有个300~500MB 以避免隐患。
1-5 Top 5 Timed Events
Top 5 Timed Events Avg %Total
~~~~~~~~~~~~~~~~~~ wait Call
Event Waits Time (s) (ms) Time Wait Class
------------------------------ ------------ ----------- ------ ------ ----------
gc buffer busy 79,083 73,024 923 65.4 Cluster
enq: TX - row lock contention 35,068 17,123 488 15.3 Applicatio
CPU time 12,205 10.9
gc current request 2,714 3,315 1221 3.0 Cluster
gc cr multi block request 83,666 1,008 12 0.9 Cluster基于Wait Interface的调优是目前的主流！每个指标都重要！
基于命中比例的调优，好比是统计局的报告， 张财主家财产100万，李木匠家财产1万， 平均财产50.5万。
基于等待事件的调优，好比马路上100辆汽车的行驶记录表，上车用了几分钟， 红灯等了几分钟，拥堵塞了几分钟。。。
丰富的等待事件以足够的细节来描绘系统运行的性能瓶颈，这是Mysql梦寐以求的东西……
- Waits : 该等待事件发生的次数， 对于DB CPU此项不可用
- Times : 该等待事件消耗的总计时间，单位为秒， 对于DB CPU 而言是前台进程所消耗CPU时间片的总和，但不包括Wait on CPU QUEUE
- Avg Wait(ms) : 该等待事件平均等待的时间， 实际就是 Times/Waits，单位ms， 对于DB CPU此项不可用
- % Total Call Time， 该等待事件占总的call time的比率
total call time = total CPU time + total wait time for non-idle events
% Total Call Time = time for each timed event / total call timeWait Class: 等待类型：
Concurrency,System I/O,User I/O,Administrative,Other,Configuration,Scheduler,Cluster,Application,Idle,Network,Commit
CPU 上在干什么？
逻辑读？ 解析？Latch spin? PL/SQL、函数运算?
DB CPU/CPU time是Top 1 是好事情吗？ 未必！
注意DB CPU不包含 wait on cpu queue！
SELECT e.event_name event,
e.total_waits - NVL (b.total_waits, 0) waits,
DECODE (
e.total_waits - NVL (b.total_waits, 0),
0, TO_NUMBER (NULL),
DECODE (
e.total_timeouts - NVL (b.total_timeouts, 0),
0, TO_NUMBER (NULL),
100
* (e.total_timeouts - NVL (b.total_timeouts, 0))
/ (e.total_waits - NVL (b.total_waits, 0))))
pctto,
(e.time_waited_micro - NVL (b.time_waited_micro, 0)) / 1000000 time,
DECODE (
(e.total_waits - NVL (b.total_waits, 0)),
0, TO_NUMBER (NULL),
( (e.time_waited_micro - NVL (b.time_waited_micro, 0)) / 1000)
/ (e.total_waits - NVL (b.total_waits, 0)))
avgwt,
DECODE (e.wait_class, 'Idle', 99, 0) idle
FROM dba_hist_system_event b, dba_hist_system_event e
WHERE b.snap_id(+) = &bid
AND e.snap_id = &eid
--AND b.dbid(+) = :dbid
--AND e.dbid = :dbid
AND b.instance_number(+) = 1
AND e.instance_number = 1
AND b.event_id(+) = e.event_id
AND e.total_waits > NVL (b.total_waits, 0)
AND e.event_name NOT IN
('smon timer',
'pmon timer',
'dispatcher timer',
'dispatcher listen timer',
'rdbms ipc message')
ORDER BY idle,
time DESC,
waits DESC,
event几种常见的等待事件
db file scattered read
db file scattered read, Avg wait time应当小于20ms 如果数据库执行全表扫描或者是全索引扫描会执行 Multi block I/O ，此时等待物理I/O 结束会出现此等待事件。一般会从应用程序（SQL），I/O 方面入手调整; 注意和《Instance Activity Stats》中的index fast full scans (full) 以及 table scans (long tables)集合起来一起看。
db file sequential read
db file sequential read ，该等待事件Avg wait time平均单次等待时间应当小于20ms
"db file sequential read"单块读等待是一种最为常见的物理IO等待事件，这里的sequential指的是将数据块读入到相连的内存空间中(contiguous memory space)，而不是指所读取的数据块是连续的。该wait event可能在以下情景中发生:
latch free
latch free 其实是未获得latch ，而进入latch sleep，见《全面解析9i以后Oracle Latch闩锁原理》
enq:XX 队列锁等待
队列锁等待，视乎不同的队列锁有不同的情况：
- 你有多了解Oracle Enqueue lock队列锁机制？
- Oracle队列锁: Enqueue HW
- Oracle队列锁enq:US,Undo Segment
- enq: TX – row lock/index contention、allocate ITL等待事件
- enq: TT – contention等待事件
- Oracle队列锁enq:TS,Temporary Segment (also TableSpace)
- enq: JI – contention等待事件
- enq: US – contention等待事件
- enq: TM – contention等待事件
- enq: RO fast object reuse等待事件
- enq: HW – contention等待事件
free buffer waits
是由于无法找到可用的buffer cache 空闲区域，需要等待DBWR 写入完成引起
一般是由于:
- 低效的sql
- 过小的buffer cache
- DBWR 工作负荷过量
buffer busy wait/ read by other session
一般以上2个等待事件可以归为一起处理，建议客户都进行监控 。 以上等待时间可以由如下操作引起:
- select/select —- read by other session: 由于需要从 数据文件中将数据块读入 buffer cache 中引起，有可能是 大量的 逻辑/物理读 ;或者过小的 buffer cache 引起
- select/update —- buffer busy waits/ read by other session 是由于更新某数据块后 需要在undo 中 重建构建 过去时间的块，有可能伴生 enq:cr-contention 是由于大量的物理读/逻辑读造成。
- update/update —- buffer busy waits 由于更新同一个数据块（非同一行，同一行是enq:TX-contention） 此类问题是热点块造成
- insert/insert —- buffer busy waits 是由于freelist 争用造成，可以将表空间更改为ASSM 管理 或者加大freelist 。
write complete waits
一般此类等待事件是由于 DBWR 将脏数据写入 数据文件，其他进程如果需要修改 buffer cache会引起此等待事件，一般是 I/O 性能问题或者是DBWR 工作负荷过量引起
Wait time 1 Seconds.
control file parallel write
频繁的更新控制文件会造成大量此类等待事件，如日志频繁切换，检查点经常发生，nologging 引起频繁的数据文件更改，I/O 系统性能缓慢。
log file sync
一般此类等待时间是由于 LGWR 进程讲redo log buffer 写入redo log 中发生。如果此类事件频繁发生，可以判断为：
- commit 次数是否过多
- I/O 系统问题
- 重做日志是否不必要被创建
- redo log buffer 是否过大
2-1 Time Model Statistics
Time Model Statistics DB/Inst: ITSCMP/itscmp2 Snaps: 70719-70723
-> Total time in database user-calls (DB Time): 883542.2s
-> Statistics including the word "background" measure background process
time, and so do not contribute to the DB time statistic
-> Ordered by % or DB time desc, Statistic name
Statistic Name Time (s) % of DB Time
------------------------------------------ ------------------ ------------
sql execute elapsed time 805,159.7 91.1
sequence load elapsed time 41,159.2 4.7
DB CPU 20,649.1 2.3
parse time elapsed 1,112.8 .1
hard parse elapsed time 995.2 .1
hard parse (sharing criteria) elapsed time 237.3 .0
hard parse (bind mismatch) elapsed time 227.6 .0
connection management call elapsed time 29.7 .0
PL/SQL execution elapsed time 9.2 .0
PL/SQL compilation elapsed time 6.6 .0
failed parse elapsed time 2.0 .0
repeated bind elapsed time 0.4 .0
DB time 883,542.2
background elapsed time 25,439.0
background cpu time 1,980.9
-------------------------------------------------------------Time Model Statistics几个特别有用的时间指标：
- parse time elapsed、hard parse elapsed time 结合起来看解析是否是主要矛盾，若是则重点是软解析还是硬解析
- sequence load elapsed time sequence序列争用是否是问题焦点
- PL/SQL compilation elapsed time PL/SQL对象编译的耗时
- 注意PL/SQL execution elapsed time 纯耗费在PL/SQL解释器上的时间。不包括花在执行和解析其包含SQL上的时间
- connection management call elapsed time 建立数据库session连接和断开的耗时
- failed parse elapsed time 解析失败，例如由于ORA-4031
- hard parse (sharing criteria) elapsed time 由于无法共享游标造成的硬解析
- hard parse (bind mismatch) elapsed time 由于bind type or bind size 不一致造成的硬解析
注意该时间模型中的指标存在包含关系所以Time Model Statistics加起来超过100%再正常不过
1) background elapsed time
2) background cpu time
3) RMAN cpu time (backup/restore)
1) DB time
2) DB CPU
2) connection management call elapsed time
2) sequence load elapsed time
2) sql execute elapsed time
2) parse time elapsed
3) hard parse elapsed time
4) hard parse (sharing criteria) elapsed time
5) hard parse (bind mismatch) elapsed time
3) failed parse elapsed time
4) failed parse (out of shared memory) elapsed time
2) PL/SQL execution elapsed time
2) inbound PL/SQL rpc elapsed time
2) PL/SQL compilation elapsed time
2) Java execution elapsed time
2) repeated bind elapsed time2-2 Foreground Wait Class
Foreground Wait Class
-> s - second, ms - millisecond - 1000th of a second
-> ordered by wait time desc, waits desc
-> %Timeouts: value of 0 indicates value was < .5%. Value of null is truly 0
-> Captured Time accounts for 102.7% of Total DB time 883,542.21 (s)
-> Total FG Wait Time: 886,957.73 (s) DB CPU time: 20,649.06 (s)
Avg
%Time Total Wait wait
Wait Class Waits -outs Time (s) (ms) %DB time
-------------------- ---------------- ----- ---------------- -------- ---------
Cluster 9,825,884 1 525,134 53 59.4
Concurrency 688,375 0 113,782 165 12.9
User I/O 34,405,042 0 76,695 2 8.7
Commit 172,193 0 62,776 365 7.1
Application 11,422 0 57,760 5057 6.5
Configuration 19,418 1 48,889 2518 5.5
DB CPU 20,649 2.3
Other 1,757,896 94 924 1 0.1
System I/O 30,165 0 598 20 0.1
Network 171,955,673 0 400 0 0.0
Administrative 2 100 0 101 0.0
-------------------------------------------------------------select distinct wait_class from v$event_name;
WAIT_CLASS
----------------------------------------------------------------
Concurrency
User I/O
System I/O
Administrative
Other
Configuration
Scheduler
Cluster
Application
Queueing
Idle
Network
CommitWait Class 等待类型说明
- Wait Class: 等待事件的类型，如上查询所示，被分作12个类型。 10.2.0.5有916个等待事件，其中Other类型占622个。
- Waits: 该类型所属等待事件在快照时间内的等待次数
- %Time Out 等待超时的比率， 未 超时次数/waits * 100 (%)
- Total Wait Time: 该类型所属等待事件总的耗时，单位为秒
- Avg Wait(ms) : 该类型所属等待事件的平均单次等待时间，单位为ms ，实际这个指标对commit 和 user i/o 以及system i/o类型有点意义，其他等待类型由于等待事件差异较大所以看平均值的意义较小
- waits / txn: 该类型所属等待事件的等待次数和事务比
各等待类型详解
Other 类型，遇到该类型等待事件 的话 常见的原因是Oracle Bug或者 网络、I/O存在问题， 一般推荐联系Maclean。
Concurrency 类型 并行争用类型的等待事件， 典型的如 latch: shared pool、latch: library cache、row cache lock、library cache pin/lock
Cluster 类型 为Real Application Cluster RAC环境中的等待事件， 需要注意的是 如果启用了RAC option，那么即使你的集群中只启动了一个实例，那么该实例也可能遇到 Cluster类型的等待事件, 例如gc buffer busy
System I/O 主要是后台进程维护数据库所产生的I/O，例如control file parallel write 、log file parallel write、db file parallel write。
User I/O 主要是前台进程做了一些I/O操作，并不是说后台进程不会有这些等待事件。 典型的如db file sequential/scattered read、direct path read
Configuration 由于配置引起的等待事件， 例如 日志切换的log file switch completion (日志文件 大小/数目 不够)，sequence的enq: SQ – contention (Sequence 使用nocache) ； Oracle认为它们是由于配置不当引起的，但实际未必真是这样的配置引起的。
Application 应用造成的等待事件， 例如enq: TM – contention和enq: TX – row lock contention； Oracle认为这是由于应用设计不当造成的等待事件， 但实际这些Application class 等待可能受到 Concurrency、Cluster、System I/O 、User I/O等多种类型等待的影响，例如本来commit只要1ms ，则某一行数据仅被锁定1ms， 但由于commit变慢 从而释放行锁变慢，引发大量的enq: TX – row lock contention等待事件。
Commit 仅log file sync ，log file sync的影响十分广泛，值得我们深入讨论。
Network : 网络类型的等待事件 例如 SQL*Net more data to client 、SQL*Net more data to dblink
Idle 空闲等待事件 ，最为常见的是rdbms ipc message (等待实例内部的ipc通信才干活，即别人告知我有活干，我才干，否则我休息==〉Idle)， SQL*Net message from client(等待SQL*NET传来信息，否则目前没事干)
2-3 前台等待事件
Foreground Wait Events Snaps: 70719-70723
-> s - second, ms - millisecond - 1000th of a second
-> Only events with Total Wait Time (s) >= .001 are shown
-> ordered by wait time desc, waits desc (idle events last)
-> %Timeouts: value of 0 indicates value was < .5%. Value of null is truly 0
Avg
%Time Total Wait wait Waits % DB
Event Waits -outs Time (s) (ms) /txn time
-------------------------- ------------ ----- ---------- ------- -------- ------
gc buffer busy acquire 3,274,352 3 303,088 93 13.3 34.3
gc buffer busy release 387,673 2 128,114 330 1.6 14.5
enq: TX - index contention 193,918 0 97,375 502 0.8 11.0
cell single block physical 30,738,730 0 63,606 2 124.8 7.2
log file sync 172,193 0 62,776 365 0.7 7.1
gc current block busy 146,154 0 53,027 363 0.6 6.0
enq: TM - contention 1,060 0 47,228 44555 0.0 5.3
enq: SQ - contention 17,431 0 35,683 2047 0.1 4.0
gc cr block busy 105,204 0 33,746 321 0.4 3.8
buffer busy waits 279,721 0 12,646 45 1.1 1.4
enq: HW - contention 1,201 3 12,192 10151 0.0 1.4
enq: TX - row lock content 9,231 0 10,482 1135 0.0 1.2
cell multiblock physical r 247,903 0 6,547 26 1.0 .7Foreground Wait Events 前台等待事件，数据主要来源于DBA_HIST_SYSTEM_EVENT
- Event 等待事件名字
- Waits 该等待事件在快照时间内等待的次数
- %Timeouts : 每一个等待事件有其超时的设置，例如buffer busy waits 一般为3秒， Write Complete Waits的 timeout为1秒，如果等待事件 单次等待达到timeout的时间，则会进入下一次该等待事件
- Total Wait Time 该等待事件 总的消耗的时间 ，单位为秒
- Avg wait(ms): 该等待事件的单次平均等待时间，单位为毫秒
- Waits/Txn: 该等待事件的等待次数和事务比
2-4 后台等待事件
Background Wait Events Snaps: 70719-70723
-> ordered by wait time desc, waits desc (idle events last)
-> Only events with Total Wait Time (s) >= .001 are shown
-> %Timeouts: value of 0 indicates value was < .5%. Value of null is truly 0
Avg
%Time Total Wait wait Waits % bg
Event Waits -outs Time (s) (ms) /txn time
-------------------------- ------------ ----- ---------- ------- -------- ------
db file parallel write 90,979 0 7,831 86 0.4 30.8
gcs log flush sync 4,756,076 6 4,714 1 19.3 18.5
enq: CF - contention 2,123 40 4,038 1902 0.0 15.9
control file sequential re 90,227 0 2,380 26 0.4 9.4
log file parallel write 108,383 0 1,723 16 0.4 6.8
control file parallel writ 4,812 0 988 205 0.0 3.9
Disk file operations I/O 26,216 0 731 28 0.1 2.9
flashback log file write 9,870 0 720 73 0.0 2.8
LNS wait on SENDREQ 202,747 0 600 3 0.8 2.4
ASM file metadata operatio 15,801 0 344 22 0.1 1.4
cell single block physical 39,283 0 341 9 0.2 1.3
LGWR-LNS wait on channel 183,443 18 203 1 0.7 .8
gc current block busy 122 0 132 1082 0.0 .5
gc buffer busy release 60 12 127 2113 0.0 .5
Parameter File I/O 592 0 116 195 0.0 .5
log file sequential read 1,804 0 104 58 0.0 .4Background Wait Events 后台等待事件， 数据主要来源于DBA_HIST_BG_EVENT_SUMMARY
- Event 等待事件名字
- Waits 该等待事件在快照时间内等待的次数
- %Timeouts : 每一个等待事件有其超时的设置，例如buffer busy waits 一般为3秒， Write Complete Waits的 timeout为1秒，如果等待事件 单次等待达到timeout的时间，则会进入下一次该等待事件
- Total Wait Time 该等待事件 总的消耗的时间 ，单位为秒
- Avg wait(ms): 该等待事件的单次平均等待时间，单位为毫秒
- Waits/Txn: 该等待事件的等待次数和事务比
2-5 Operating System Statistics
Operating System Statistics Snaps: 70719-70723
TIME statistic values are diffed.
All others display actual values. End Value is displayed if different
-> ordered by statistic type (CPU Use, Virtual Memory, Hardware Config), Name
Statistic Value End Value
------------------------- ---------------------- ----------------
BUSY_TIME 2,894,855
IDLE_TIME 5,568,240
IOWAIT_TIME 18,973
SYS_TIME 602,532
USER_TIME 2,090,082
LOAD 8 13
VM_IN_BYTES 0
VM_OUT_BYTES 0
PHYSICAL_MEMORY_BYTES 101,221,343,232
NUM_CPUS 24
NUM_CPU_CORES 12
NUM_CPU_SOCKETS 2
GLOBAL_RECEIVE_SIZE_MAX 4,194,304
GLOBAL_SEND_SIZE_MAX 2,097,152
TCP_RECEIVE_SIZE_DEFAULT 87,380
TCP_RECEIVE_SIZE_MAX 4,194,304
TCP_RECEIVE_SIZE_MIN 4,096
TCP_SEND_SIZE_DEFAULT 16,384
TCP_SEND_SIZE_MAX 4,194,304
TCP_SEND_SIZE_MIN 4,096
-------------------------------------------------------------Operating System Statistics 操作系统统计信息
数据来源于V$OSSTAT / DBA_HIST_OSSTAT, TIME相关的指标单位均为百分之一秒
| 统计项 | 描述 |
|---|---|
| NUM_CPU_SOCKETS | 物理CPU的数目 |
| NUM_CPU_CORES | CPU的核数 |
| NUM_CPUS | 逻辑CPU的数目 |
| SYS_TIME | 在内核态被消耗掉的CPU时间片，单位为百分之一秒 |
| USER_TIME | 在用户态被消耗掉的CPU时间片，单位为百分之一秒 |
| BUSY_TIME | Busy_Time=SYS_TIME+USER_TIME 消耗的CPU时间片，单位为百分之一秒 |
| AVG_BUSY_TIME | AVG_BUSY_TIME= BUSY_TIME/NUM_CPUS |
| IDLE_TIME | 空闲的CPU时间片，单位为百分之一秒 |
| 所有CPU所能提供总的时间片 | BUSY_TIME + IDLE_TIME = ELAPSED_TIME * CPU_COUNT |
| OS_CPU_WAIT_TIME | 进程等OS调度的时间，cpu queuing |
| VM_IN_BYTES | 换入页的字节数 |
| VM_OUT_BYTES | 换出页的字节数，部分版本下并不准确，例如Bug 11712010 Abstract: VIRTUAL MEMORY PAGING ON 11.2.0.2 DATABASES，仅供参考 |
| IOWAIT_TIME | 所有CPU花费在等待I/O完成上的时间 单位为百分之一秒 |
| RSRC_MGR_CPU_WAIT_TIME | 是指当resource manager控制CPU调度时，需要控制对应进程暂时不使用CPU而进程到内部运行队列中，以保证该进程对应的consumer group(消费组)没有消耗比指定resource manager指令更多的CPU。RSRC_MGR_CPU_WAIT_TIME指等在内部运行队列上的时间，在等待时不消耗CPU |
2-6 Service Statistics
Service Statistics Snaps: 70719-70723
-> ordered by DB Time
Physical Logical
Service Name DB Time (s) DB CPU (s) Reads (K) Reads (K)
---------------------------- ------------ ------------ ------------ ------------
itms-contentmasterdb-prod 897,099 20,618 35,668 1,958,580
SYS$USERS 4,312 189 5,957 13,333
itmscmp 1,941 121 14,949 18,187
itscmp 331 20 114 218
itscmp_dgmgrl 121 1 0 0
SYS$BACKGROUND 0 0 142 30,022
ITSCMP1_PR 0 0 0 0
its-reference-prod 0 0 0 0
itscmpXDB 0 0 0 0按照Service Name来分组时间模型和 物理、逻辑读取， 部分数据来源于 WRH$_SERVICE_NAME;
- Service Name 对应的服务名 (v$services)， SYS$BACKGROUND代表后台进程， SYS$USERS一般是系统用户登录
- DB TIME (s): 本服务名所消耗的DB TIME时间，单位为秒
- DB CPU(s): 本服务名所消耗的DB CPU 时间，单位为秒
- Physical Reads : 本服务名所消耗的物理读
- Logical Reads : 本服务所消耗的逻辑读
2-7 Service Wait Class Stats
Service Wait Class Stats Snaps: 70719-70723
-> Wait Class info for services in the Service Statistics section.
-> Total Waits and Time Waited displayed for the following wait
classes: User I/O, Concurrency, Administrative, Network
-> Time Waited (Wt Time) in seconds
Service Name
----------------------------------------------------------------
User I/O User I/O Concurcy Concurcy Admin Admin Network Network
Total Wts Wt Time Total Wts Wt Time Total Wts Wt Time Total Wts Wt Time
--------- --------- --------- --------- --------- --------- --------- ---------
itms-contentmasterdb-prod
33321670 71443 678373 113759 0 0 1.718E+08 127
SYS$USERS
173233 3656 6738 30 2 0 72674 3
itmscmp
676773 1319 1831 0 0 0 2216 0
itscmp
219577 236 1093 0 0 0 18112 0
itscmp_dgmgrl
34 0 8 0 0 0 9 0
SYS$BACKGROUND
71940 1300 320677 56 0 0 442252 872
-------------------------------------------------------------- User I/O Total Wts : 对应该服务名下 用户I/O类等待的总的次数
- User I/O Wt Time : 对应该服务名下 用户I/O累等待的总时间，单位为 1/100秒
- Concurcy Total Wts: 对应该服务名下 Concurrency 类型等待的总次数
- Concurcy Wt Time :对应该服务名下 Concurrency 类型等待的总时间， 单位为 1/100秒
- Admin Total Wts: 对应该服务名下Admin 类等待的总次数
- Admin Wt Time: 对应该服务名下Admin类等待的总时间，单位为 1/100秒
- Network Total Wts : 对应服务名下Network类等待的总次数
- Network Wt Time： 对应服务名下Network类等待的总事件， 单位为 1/100秒
2-8 Host CPU
Host CPU (CPUs: 24 Cores: 12 Sockets: 2)
~~~~~~~~ Load Average
Begin End %User %System %WIO %Idle
--------- --------- --------- --------- --------- ---------
8.41 12.84 24.7 7.1 0.2 65.8"Load Average" begin/end值代表每个CPU的大致运行队列大小。上例中快照开始到结束，平均 CPU负载增加了；与《2-5 Operating System Statistics》中的LOAD相呼应。
%User+%System=> 总的CPU使用率，在这里是31.8%
Elapsed Time * NUM_CPUS * CPU utilization= 60.23 (mins) * 24 * 31.8% = 459.67536 mins=Busy Time
2-8 Instance CPU
Instance CPU
~~~~~~~~~~~~
% of total CPU for Instance: 26.7
% of busy CPU for Instance: 78.2
%DB time waiting for CPU - Resource Mgr: 0.0%Total CPU，该实例所使用的CPU占总CPU的比例 % of total CPU for Instance
%Busy CPU，该实例所使用的Cpu占总的被使用CPU的比例 % of busy CPU for Instance
例如共4个逻辑CPU，其中3个被完全使用，3个中的1个完全被该实例使用，则%Total CPU= ¼ =25%，而%Busy CPU= 1/3= 33%
当CPU高时一般看%Busy CPU可以确定CPU到底是否是本实例消耗的，还是主机上其他程序
% of busy CPU for Instance= （DB CPU+ background cpu time) / (BUSY_TIME /100)= (20,649.1 + 1,980.9)/ (2,894,855 /100)= 78.17%
% of Total CPU for Instance = ( DB CPU+ background cpu time)/( BUSY_TIME+IDLE_TIME/100) = (20,649.1 + 1,980.9)/ ((2,894,855+5,568,240) /100) = 26.73%
%DB time waiting for CPU (Resource Manager)= (RSRC_MGR_CPU_WAIT_TIME/100)/DB TIME3 TOP SQL
TOP SQL 的数据部分来源于 dba_hist_sqlstat
3-1 SQL ordered by Elapsed Time
按照SQL消耗的时间来排列TOP SQL
SQL ordered by Elapsed Time Snaps: 70719-70723
-> Resources reported for PL/SQL code includes the resources used by all SQL
statements called by the code.
-> % Total DB Time is the Elapsed Time of the SQL statement divided
into the Total Database Time multiplied by 100
-> %Total - Elapsed Time as a percentage of Total DB time
-> %CPU - CPU Time as a percentage of Elapsed Time
-> %IO - User I/O Time as a percentage of Elapsed Time
-> Captured SQL account for 53.9% of Total DB Time (s): 883,542
-> Captured PL/SQL account for 0.5% of Total DB Time (s): 883,542
Elapsed Elapsed Time
Time (s) Executions per Exec (s) %Total %CPU %IO SQL Id
---------------- -------------- ------------- ------ ------ ------ -------------
181,411.3 38,848 4.67 20.5 .0 .1 g0yc9szpuu068注意对于PL/SQL，SQL Statistics不仅会体现该PL/SQL的执行情况，还会包括该PL/SQL包含的SQL语句的情况。如上例一个TOP PL/SQL执行了448s，而这448s中绝大多数是这个PL/SQL下的一个SQL执行500次耗费的。
则该TOP PL/SQL和TOP SQL都上榜，一个执行一次耗时448s，一个执行500次耗时448s。 如此情况则Elapsed Time加起来可能超过100%的Elapsed Time，这是正常的。
对于鹤立鸡群的SQL很有必要一探究竟，跑个@?/rdbms/admin/awrsqrpt看看吧！
- Elapsed Time (s): 该SQL累计运行所消耗的时间，
- Executions : 该SQL在快照时间内 总计运行的次数 ； 注意， 对于在快照时间内还没有执行完的SQL 不计为1一次，所以如果看到executions=0而 又是TOP SQL，则很有可能是因为该SQL 运行较旧还没执行完，需要特别关注一下。
- Elapsed Time per Exec (s)：平均每次执行该SQL耗费的时间 ， 对于OLTP类型的SELECT/INSERT/UPDATE/DELETE而言平均单次执行时间应当非常短，如0.1秒 或者更短才能满足其业务需求，如果这类轻微的OLTP操作单次也要几秒钟的话，是无法满足对外业务的需求的； 例如你在ATM上提款，并不仅仅是对你的账务库的简单UPDATE，而需要在类似风险控制的前置系统中记录你本次的流水操作记录，实际取一次钱可能要有几十乃至上百个OLTP类型的语句被执行，但它们应当都是十分快速的操作； 如果这些操作也变得很慢，则会出现大量事务阻塞，系统负载升高，DB TIME急剧上升的现象。 对于OLTP数据库而言 如果执行计划稳定，那么这些OLTP操作的性能应当是铁板钉钉的，但是一旦某个因素 发生变化，例如存储的明显变慢、内存换页的大量出现时 则上述的这些transaction操作很可能成数倍到几十倍的变慢，这将让此事务系统短期内不可用。
对于维护操作，例如加载或清除数据，大的跑批次、报表而言 Elapsed Time per Exec (s)高一些是正常的。
- %Total 该SQL所消耗的时间占总的DB Time的百分比， 即 (SQL Elapsed Time / Total DB TIME)
- % CPU 该SQL 所消耗的CPU 时间 占 该SQL消耗的时间里的比例， 即 (SQL CPU Time / SQL Elapsed Time) ，该指标说明了该语句是否是CPU敏感的
- %IO 该SQL 所消耗的I/O 时间 占 该SQL消耗的时间里的比例， 即(SQL I/O Time/SQL Elapsed Time) ，该指标说明了该语句是否是I/O敏感的
- SQL Id : 通过计算SQL 文本获得的SQL_ID ，不同的SQL文本必然有不同的SQL_ID， 对于10g~11g而言 只要SQL文本不变那么在数据库之间 该SQL 对应的SQL_ID应当不不变的， 12c中修改了SQL_ID的计算方法
Captured SQL account for 53.9% of Total DB Time (s) 对于不绑定变量的应用来说Top SQL有可能失准，所以要参考本项
3-2 SQL ordered by CPU Time
SQL ordered by CPU Time Snaps: 70719-70723
-> Resources reported for PL/SQL code includes the resources used by all SQL
statements called by the code.
-> %Total - CPU Time as a percentage of Total DB CPU
-> %CPU - CPU Time as a percentage of Elapsed Time
-> %IO - User I/O Time as a percentage of Elapsed Time
-> Captured SQL account for 34.9% of Total CPU Time (s): 20,649
-> Captured PL/SQL account for 0.5% of Total CPU Time (s): 20,649
CPU CPU per Elapsed
Time (s) Executions Exec (s) %Total Time (s) %CPU %IO SQL Id
---------- ------------ ---------- ------ ---------- ------ ------ -------------
1,545.0 1,864,424 0.00 7.5 4,687.8 33.0 65.7 8g6a701j83c8q
Module: MZIndexer
SELECT t0.BOOLEAN_VALUE, t0.CLASS_CODE, t0.CREATED, t0.END_DATE, t0.PRODUCT_ATTR
IBUTE_ID, t0.LAST_MODIFIED, t0.OVERRIDE_FLAG, t0.PRICE, t0.PRODUCT_ATTRIBUTE_TYP
E_ID, t0.PRODUCT_ID, t0.PRODUCT_PUB_RELEASE_TYPE_ID, t0.PRODUCT_VOD_TYPE_ID, t0.
SAP_PRODUCT_ID, t0.START_DATE, t0.STRING_VALUE FROM mz_product_attribute t0 WHER- CPU TIME : 该SQL 在快照时间内累计执行所消耗的CPU 时间片，单位为s
- Executions : 该SQL在快照时间内累计执行的次数
- CPU per Exec (s) ：该SQL 平均单次执行所消耗的CPU时间 ， 即 ( SQL CPU TIME / SQL Executions )
- %Total : 该SQL 累计消耗的CPU时间 占 该时段总的 DB CPU的比例， 即 ( SQL CPU TIME / Total DB CPU)
- % CPU 该SQL 所消耗的CPU 时间 占 该SQL消耗的时间里的比例， 即 (SQL CPU Time / SQL Elapsed Time) ，该指标说明了该语句是否是CPU敏感的
- %IO 该SQL 所消耗的I/O 时间 占 该SQL消耗的时间里的比例， 即(SQL I/O Time/SQL Elapsed Time) ，该指标说明了该语句是否是I/O敏感的
3-3 Buffer Gets SQL ordered by Gets
SQL ordered by Gets DB/Inst: ITSCMP/itscmp2 Snaps: 70719-70723
-> Resources reported for PL/SQL code includes the resources used by all SQL
statements called by the code.
-> %Total - Buffer Gets as a percentage of Total Buffer Gets
-> %CPU - CPU Time as a percentage of Elapsed Time
-> %IO - User I/O Time as a percentage of Elapsed Time
-> Total Buffer Gets: 2,021,476,421
-> Captured SQL account for 68.2% of Total
Buffer Gets Elapsed
Gets Executions per Exec %Total Time (s) %CPU %IO SQL Id
----------- ----------- ------------ ------ ---------- ------ ------ -----------
4.61155E+08 1,864,424 247.3 22.8 4,687.8 33.0 65.7 8g6a701j83c注意 buffer gets 逻辑读是消耗CPU TIME的重要源泉， 但并不是说消耗CPU TIME的只有buffer gets。 大多数情况下 SQL order by CPU TIME 和 SQL order by buffers gets 2个部分的TOP SQL 及其排列顺序都是一样的，此种情况说明消耗最多buffer gets的 就是消耗最多CPU 的SQL ，如果我们希望降低系统的CPU使用率，那么只需要调优SQL 降低buffer gets 即可。
但也并不是100%的情况都是如此， CPU TIME的消耗者 还包括 函数运算、PL/SQL 控制、Latch /Mutex 的Spin等等， 所以SQL order by CPU TIME 和 SQL order by buffers gets 2个部分的TOP SQL 完全不一样也是有可能的， 需要因地制宜来探究到底是什么问题导致的High CPU，进而裁度解决之道。
- Buffer Gets : 该SQL在快照时间内累计运行所消耗的buffer gets，包括了consistent read 和 current read
- Executions : 该SQL在快照时间内累计执行的次数
- Gets per Exec : 该SQL平均单次的buffer gets ， 对于事务型transaction操作而言 一般该单次buffer gets小于2000
- % Total 该SQL 累计运行所消耗的buffer gets占 总的db buffer gets的比率， (SQL buffer gets / DB total buffer gets)
3-4 Physical Reads SQL ordered by Reads
SQL ordered by Reads DB/Inst: ITSCMP/itscmp2 Snaps: 70719-70723
-> %Total - Physical Reads as a percentage of Total Disk Reads
-> %CPU - CPU Time as a percentage of Elapsed Time
-> %IO - User I/O Time as a percentage of Elapsed Time
-> Total Disk Reads: 56,839,035
-> Captured SQL account for 34.0% of Total
Physical Reads Elapsed
Reads Executions per Exec %Total Time (s) %CPU %IO SQL Id
----------- ----------- ---------- ------ ---------- ------ ------ -------------
9,006,163 1 9.0062E+06 15.8 720.9 5.9 80.9 4g36tmp70h185- Physical reads : 该SQL累计运行所消耗的物理读
- Executions : 该SQL在快照时间内累计执行的次数
- Reads per Exec : 该SQL 单次运行所消耗的物理读， (SQL Physical reads/Executions) ， 对于OLTP transaction 类型的操作而言单次一般不超过100
- %Total : 该SQL 累计消耗的物理读 占 该时段总的 物理读的比例， 即 ( SQL physical read / Total DB physical read )
3-5 Executions SQL ordered by Executions
SQL ordered by Executions Snaps: 70719-70723
-> %CPU - CPU Time as a percentage of Elapsed Time
-> %IO - User I/O Time as a percentage of Elapsed Time
-> Total Executions: 48,078,147
-> Captured SQL account for 50.4% of Total
Elapsed
Executions Rows Processed Rows per Exec Time (s) %CPU %IO SQL Id
------------ --------------- -------------- ---------- ------ ------ -----------
6,327,963 11,249,645 1.8 590.5 47.8 52.7 1avv7759j8r按照 执行次数来排序的话，也是性能报告对比时一个重要的参考因素，因为如果TOP SQL的执行次数有明显的增长，那么 性能问题的出现也是意料之中的事情了。 当然执行次数最多的，未必便是对性能影响最大的TOP SQL
- Executions : 该SQL在快照时间内累计执行的次数
- Rows Processed： 该SQL在快照时间内累计执行所处理的总行数
- Rows per Exec： SQL平均单次执行所处理的行数， 这个指标在诊断一些 数据问题造成的SQL性能问题时很有用
3-6 Parse Calls SQL ordered by Parse Calls
SQL ordered by Parse Calls Snaps: 70719-70723
-> Total Parse Calls: 2,160,124
-> Captured SQL account for 58.3% of Total
% Total
Parse Calls Executions Parses SQL Id
------------ ------------ --------- -------------
496,475 577,357 22.98 d07gaa3wntdff- Parse Calls : 解析调用次数， 与上文的 Load Profile中的Parse 数一样 包括 软解析soft parse和硬解析hard parse
- Executions : 该SQL在快照时间内累计执行的次数
- %Total Parses : 本SQL 解析调用次数 占 该时段数据库总解析次数的比率， 为 (SQL Parse Calls / Total DB Parse Calls)
3-7 SQL ordered by Sharable Memory
SQL ordered by Sharable Memory Snaps: 70719-70723
-> Only Statements with Sharable Memory greater than 1048576 are displayed
Sharable Mem (b) Executions % Total SQL Id
---------------- ------------ -------- -------------
8,468,359 39 0.08 au89sasqfb2yn
Module: MZContentBridge
SELECT t0.ASPECT_RATIO, t0.CREATED, t0.FILE_EXTENSION, t0.HEIGHT, t0.VIDEO_FILE_
DIMENSIONS_ID, t0.LAST_MODIFIED, t0.NAME, t0.WIDTH FROM MZ_VIDEO_FILE_DIMENSIONS
t0 WHERE (t0.HEIGHT = :1 AND t0.WIDTH = :2 )SQL ordered by Sharable Memory , 一般该部分仅列出Sharable Mem (b)为1 MB以上的SQL 对象 (Only Statements with Sharable Memory greater than 1048576 are displayed) 数据来源是 DBA_HIST_SQLSTAT.SHARABLE_MEM
- Shareable Mem(b): SQL 对象所占用的共享内存使用量
- Executions : 该SQL在快照时间内累计执行的次数
- %Total : 该SQL 对象锁占共享内存 占总的共享内存的比率
3-8 SQL ordered by Version Count
Version Count Oracle中的执行计划可以是多版本的，即对于同一个SQL语句有多个不同版本的执行计划，这些执行计划又称作子游标， 而一个SQL语句的文本可以称作一个父游标。 一个父游标对应多个子游标，产生不同子游标的原因是 SQL在被执行时无法共享之前已经生成的子游标， 原因是多种多样的，例如 在本session中做了一个优化器参数的修改 例如optimizer_index_cost_adj 从100 修改到99，则本session的优化环境optimizer env将不同于之前的子游标生成环境，这样就需要生成一个新的子游标，例如：
SQL> create table emp as select * from scott.emp;
Table created.
SQL> select * from emp where empno=1;
no rows selected
SQL> select /*+ MACLEAN */ * from emp where empno=1;
no rows selected
SQL> select SQL_ID,version_count from V$SQLAREA WHERE SQL_TEXT like '%MACLEAN%' and SQL_TEXT not like '%like%';
SQL_ID VERSION_COUNT
------------- -------------
bxnnm7z1qmg26 1
SQL> select count(*) from v$SQL where SQL_ID='bxnnm7z1qmg26';
COUNT(*)
----------
1
SQL> alter session set optimizer_index_cost_adj=99;
Session altered.
SQL> select /*+ MACLEAN */ * from emp where empno=1;
no rows selected
SQL> select SQL_ID,version_count from V$SQLAREA WHERE SQL_TEXT like '%MACLEAN%' and SQL_TEXT not like '%like%';
SQL_ID VERSION_COUNT
------------- -------------
bxnnm7z1qmg26 2
SQL> select count(*) from v$SQL where SQL_ID='bxnnm7z1qmg26';
COUNT(*)
----------
2
SQL> select child_number ,OPTIMIZER_ENV_HASH_VALUE,PLAN_HASH_VALUE from v$SQL where SQL_ID='bxnnm7z1qmg26';
CHILD_NUMBER OPTIMIZER_ENV_HASH_VALUE PLAN_HASH_VALUE
------------ ------------------------ ---------------
0 3704128740 3956160932
1 3636478958 3956160932可以看到上述 演示中修改optimizer_index_cost_adj=99 导致CBO 优化器的优化环境发生变化， 表现为不同的OPTIMIZER_ENV_HASH_VALUE，之后生成了2个子游标，但是这2个子游标的PLAN_HASH_VALUE同为3956160932，则说明了虽然是不同的子游标但实际子游标里包含了的执行计划是一样的； 所以请注意 任何一个优化环境的变化 (V$SQL_SHARED_CURSOR)以及相关衍生的BUG 都可能导致子游标无法共享，虽然子游标无法共享但这些子游标扔可能包含完全一样的执行计划，这往往是一种浪费。
注意V$SQLAREA.VERSION_COUNT 未必等于select count(*) FROM V$SQL WHERE SQL_ID="" ，即 V$SQLAREA.VERSION_COUNT 显示的子游标数目 未必等于当前实例中还存有的子游标数目， 由于shared pool aged out算法和其他一些可能导致游标失效的原因存在，所以子游标被清理掉是很常见的事情。 V$SQLAREA.VERSION_COUNT只是一个计数器，它告诉我们曾经生成了多少个child cursor，但不保证这些child 都还在shared pool里面。
此外可以通过v$SQL的child_number字段来分析该问题，如果child_number存在跳号则也说明了部分child被清理了。
子游标过多的影响， 当子游标过多(例如超过3000个时),进程需要去扫描长长的子游标列表child cursor list以找到一个合适的子游标child cursor，进而导致cursor sharing 性能问题 现大量的Cursor: Mutex S 和 library cache lock等待事件。
关于子游标的数量控制，可以参考《11gR2游标共享新特性带来的一些问题以及_cursor_features_enabled、_cursor_obsolete_threshold和106001 event》。
- Executions : 该SQL在快照时间内累计执行的次数
- Hash Value : 共享SQL 的哈希值
Only Statements with Version Count greater than 20 are displayed 注意该环节仅列出version count > 20的语句
3-9 Cluster Wait Time SQL ordered by Cluster Wait Time
SQL ordered by Cluster Wait Time DB/Inst: ITSCMP/itscmp2 Snaps: 70719-70723
-> %Total - Cluster Time as a percentage of Total Cluster Wait Time
-> %Clu - Cluster Time as a percentage of Elapsed Time
-> %CPU - CPU Time as a percentage of Elapsed Time
-> %IO - User I/O Time as a percentage of Elapsed Time
-> Only SQL with Cluster Wait Time > .005 seconds is reported
-> Total Cluster Wait Time (s): 525,480
-> Captured SQL account for 57.2% of Total
Cluster Elapsed
Wait Time (s) Executions %Total Time(s) %Clu %CPU %IO SQL Id
-------------- ------------ ------ ---------- ------ ------ ------ -------------
132,639.3 38,848 25.2 181,411.3 73.1 .0 .1 g0yc9szpuu068Only SQL with Cluster Wait Time > .005 seconds is reported 这个环节仅仅列出Cluster Wait Time > 0.005 s的SQL
该环节的数据主要来源 于 DBA_HIST_SQLSTAT.CLWAIT_DELTA Delta value of cluster wait time
- Cluster Wait Time : 该SQL语句累计执行过程中等待在集群等待上的时间，单位为秒， 你可以理解为 当一个SQL 执行过程中遇到了gc buffer busy、gc cr multi block request 之类的Cluster等待，则这些等待消耗的时间全部算在 Cluster Wait Time里。
- Executions : 该SQL在快照时间内累计执行的次数
- %Total: 该SQL所消耗的Cluster Wait time 占 总的Cluster Wait time的比率， 为(SQL cluster wait time / DB total cluster Wait Time)
- %Clu: 该SQL所消耗的Cluster Wait time 占该SQL 总的耗时的比率，为(SQL cluster wait time / SQL elapsed Time),该指标说明了该语句是否是集群等待敏感的
- % CPU 该SQL 所消耗的CPU 时间 占 该SQL消耗的时间里的比例， 即 (SQL CPU Time / SQL Elapsed Time) ，该指标说明了该语句是否是CPU敏感的
- %IO 该SQL 所消耗的I/O 时间 占 该SQL消耗的时间里的比例， 即(SQL I/O Time/SQL Elapsed Time) ，该指标说明了该语句是否是I/O敏感的
4 Instance Activity Stats
Instance Activity Stats DB/Inst: ITSCMP/itscmp2 Snaps: 70719-70723
-> Ordered by statistic name
Statistic Total per Second per Trans
-------------------------------- ------------------ -------------- -------------
Batched IO (bound) vector count 450,449 124.6 1.8
Batched IO (full) vector count 5,485 1.5 0.0
Batched IO (space) vector count 1,467 0.4 0.0
Batched IO block miss count 4,119,070 1,139.7 16.7
Batched IO buffer defrag count 39,710 11.0 0.2
Batched IO double miss count 297,357 82.3 1.2
Batched IO same unit count 1,710,492 473.3 7.0
Batched IO single block count 329,521 91.2 1.3
Batched IO slow jump count 47,104 13.0 0.2
Batched IO vector block count 2,069,852 572.7 8.4
Batched IO vector read count 262,161 72.5 1.1
Block Cleanout Optim referenced 37,574 10.4 0.2
CCursor + sql area evicted 1,457 0.4 0.0
...............Instance Activity Stats 的数据来自于 DBA_HIST_SYSSTAT，DBA_HIST_SYSSTAT来自于V$SYSSTAT。
这里每一个指标都代表一种数据库行为的活跃度，例如redo size 是指生成redo的量，sorts (disk) 是指磁盘排序的次数，table scans (direct read) 是指直接路径扫描表的次数。
虽然这些指标均只有Total、per Second每秒、 per Trans每事务 三个维度，但对诊断问题十分有用。
Instance Activity Stats 应用举例
1、 例如当 Top Event 中存在direct path read为Top 等待事件， 则需要分清楚是对普通堆表的direct read还是由于大量LOB读造成的direct path read， 这个问题可以借助 table scans (direct read)、table scans (long tables)、physical reads direct 、physical reads direct (lob) 、physical reads direct temporary几个指标来分析， 假设 physical reads direct >> 远大于 physical reads direct (lob)+physical reads direct temporary ， 且有较大的table scans (direct read)、table scans (long tables) (注意这2个指标代表的是 扫描表的次数 不同于上面的phsical reads 的单位为 块数*次数)， 则说明了是 大表扫描引起的direct path read。
2、 例如当 Top Event中存在enq Tx:index contention等待事件， 则需要分析root node splits 、branch node splits 、leaf node 90-10 splits 、leaf node splits 、failed probes on index block rec 几个指标，具体可以见文档《Oracle索引块分裂split信息汇总》
3、系统出现IO类型的等待事件为TOp Five 例如 db file sequential/scattered read ，我们需要通过AWR来获得系统IO吞吐量和IOPS:
physical read bytes 主要是应用造成的物理读取(Total size in bytes of all disk reads by application activity (and not other instance activity) only.) 而physical read total bytes则包括了 rman备份恢复 和后台维护任务所涉及的物理读字节数，所以我们在研究IO负载时一般参考 physical read total bytes；以下4对指标均存在上述的关系
| 指标 | 对应Total指标 | 说明 |
|---|---|---|
| physical read bytes | physical read total bytes | 物理读的吞吐量/秒 |
| physical read IO requests | physical read total IO requests | 物理读的IOPS |
| physical write bytes | physical write total bytes | 物理写的吞吐量/秒 |
| physical write IO requests | physical write total IO requests | 物理写的IOPS |
总的物理吞吐量/秒=physical read total bytes+physical write total bytes
总的物理IOPS= physical read total IO requests+ physical write total IO requests
IO的主要指标 吞吐量、IOPS和延迟 均可以从AWR中获得了， IO延迟的信息可以从 User I/O的Wait Class Avg Wait time获得，也可以参考11g出现的IOStat by Function summary
Instance Activity Stats有大量的指标，但是对于这些指标的介绍 没有那一份文档有完整详尽的描述，即便在Oracle原厂内部要没有(或者是Maclean没找到)，实际是开发人员要引入某一个Activity Stats是比较容易的，并不像申请引入一个新后台进程那样麻烦，Oracle对于新版本中新后台进程的引入有严格的要求，但Activity Stats却很容易，往往一个one-off patch中就可以引入了，实际上Activity Stats在源代码层仅仅是一些计数器。'
较为基础的statistics，大家可以参考官方文档的Statistics Descriptions描述，地址在这里。
对于深入的指标 例如 "Batched IO (space) vector count"这种由于某些新特性被引入的，一般没有很详细的材料，需要到源代码中去阅读相关模块才能总结其用途，对于这个工作一般原厂是很延迟去完成的，所以没有一个完整的列表。 如果大家有对此的疑问，请去t.askmaclean.com 发一个帖子提问。
Instance Activity Stats - Absolute Values
Instance Activity Stats - Absolute Values Snaps: 7071
-> Statistics with absolute values (should not be diffed)
Statistic Begin Value End Value
-------------------------------- --------------- ---------------
session pga memory max 1.157882826E+12 1.154290304E+12
session cursor cache count 157,042,373 157,083,136
session uga memory 5.496429019E+14 5.496775467E+14
opened cursors current 268,916 265,694
workarea memory allocated 827,704 837,487
logons current 2,609 2,613
session uga memory max 1.749481584E+13 1.749737418E+13
session pga memory 4.150306913E+11 4.150008177E+11Instance Activity Stats – Absolute Values是显示快照 起点 和终点的一些指标的绝对值
- logon current 当前时间点的登录数
- opened cursors current 当前打开的游标数
- session cursor cache count 当前存在的session缓存游标数
Instance Activity Stats - Thread Activity
Instance Activity Stats - Thread ActivityDB/Inst: G10R25/G10R25 Snaps: 3663-3
-> Statistics identified by '(derived)' come from sources other than SYSSTAT
Statistic Total per Hour
-------------------------------- ------------------ ---------
log switches (derived) 17 2,326.47log switches (derived) 日志切换次数 ， 见 《理想的在线重做日志切换时间是多长？》
5 IO 统计
5-1 Tablespace IO Stats
基于表空间分组的IO信息
Tablespace IO Stats DB/Inst: ITSCMP/itscmp2 Snaps: 70719-70723
-> ordered by IOs (Reads + Writes) desc
Tablespace
------------------------------
Av Av Av Av Buffer Av Buf
Reads Reads/s Rd(ms) Blks/Rd Writes Writes/s Waits Wt(ms)
-------------- ------- ------- ------- ------------ -------- ---------- -------Tablespace IO Stats 数据来源 DBA_HIST_FILESTATXS 基于表空间分组
- Reads: 该表空间上发生的物理读的次数
- Av Reads/s: 该表空间上平均每秒的物理读次数
- Av Rd(ms): 该表空间上每次读的平均读取延迟，单位为ms，这个指标可以很好的反映I/O读取延迟是否过大，过大的I/O延迟应进一步分析存储问题
- Av Blks/Rd: 该表空间上平均每次读取的块数目，反映主要是单块读还是多块读；OLTP类型系统上INDEX_TS索引表空间如果该值接近1则是正常的，反之如果全表扫描密集的表空间上该值应当接近于db_file_multiblock_read_count，但一般不会完全等于db_file_multiblock_read_count由于extent size等因素的影响
- Writes: 该表空间上发生的物理写的次数
- Av Writes/s: 该表空间上平均每秒的物理写次数
- Buffer Waits: 该表空间上发生buffer busy waits和read by other session等待的次数
- Av Buf Wt(ms): 该表空间上发生buffer busy waits和read by other session等待的平均等待时间，单位为ms
5-2 File IO Stats
基于数据文件分组的IO信息
File IO Stats DB/Inst: ITSCMP/itscmp2 Snaps: 70719-70723
-> ordered by Tablespace, File
Tablespace File Name
------------------------ ----------------------------------------------------
Av Av Av Av Buffer Av Buf
Reads Reads/s Rd(ms) Blks/Rd Writes Writes/s Waits Wt(ms)
-------------- ------- ------- ------- ------------ -------- ---------- -------File IO Stats 数据来源 DBA_HIST_FILESTATXS 基于数据文件分组
- Tablespace: 表空间名
- File Name: 数据文件的路径
- Reads: 该数据文件上发生的物理读的次数
- Av Reads/s: 该数据文件上平均每秒的物理读次数
- Av Rd(ms): 该数据文件上每次读的平均读取延迟，单位为ms
- Av Blks/Rd: 该数据文件上平均每次读取的块数目
- Writes: 该数据文件上发生的物理写的次数
- Av Writes/s: 该数据文件上平均每秒的物理写次数
- Buffer Waits: 该数据文件上发生buffer busy waits和read by other session等待的次数
- Av Buf Wt(ms): 该数据文件上发生buffer busy waits和read by other session等待的平均等待时间，单位为ms
5-3 IOStat by Function summary
11g开始出现的新环节，基于IO函数类型的IO统计
IOStat by Function summary DB/Inst: ITSCMP/itscmp2 Snaps: 70719-70723
-> 'Data' columns suffixed with M,G,T,P are in multiples of 1024
other columns suffixed with K,M,G,T,P are in multiples of 1000
-> ordered by (Data Read + Write) desc
Function Name Reads: Data Reqs per Writes: Data Reqs per Waits: Count Avg Time
Total sec Total sec Total ms
------------------------------ ------------ -------- ------------- -------- ------------- --------
Buffer Cache Reads 236.5G 8.5K 27.7G 975 34.5M 2.2
LGWR 0 0 5.8G 30 108.4K 16.0
DBWR 1.3G 25 3.5G 25 90.9K 86.1
Direct Reads 48.9G 210 0 0 248.9K 26.7
Others 0 0 809.3M 17 1.5K 15.0IOStat by Function summary 按IO Function类型来分类的IO统计信息
Function Name: IO函数类型名
- Buffer Cache Reads: 通过Buffer Cache读取数据的IO
- LGWR: 日志写进程的IO
- DBWR: 数据库写进程的IO
- Direct Reads: 直接路径读的IO
- Direct Writes: 直接路径写的IO
- Recovery: 恢复相关的IO
- Archive Manager: 归档管理器的IO
- Others: 其他IO
指标说明：
- Reads: Data Total: 该IO函数类型读取的数据量
- Reqs per sec: 该IO函数类型每秒读取请求数
- Writes: Data Total: 该IO函数类型写的数据量
- Reqs per sec: 该IO函数类型每秒写请求数
- Waits: Count Total: 该IO函数类型等待的次数
- Avg Time ms: 该IO函数类型平均等待时间，单位为ms，实际就是该IO类型的平均延迟
6 Buffer Pool Statistics
Buffer Pool Statistics DB/Inst: ITSCMP/itscmp2 Snaps: 70719-70723
-> Standard block size Pools D: default K: keep R: recycle
-> Default Pools for other block sizes: 2k 4k 16k 32k
Free Write Buffer
Number of Pool Buffer Physical Physical Buff Compl Busy
P Buffers Hit % Gets Reads Writes Wait Wait Waits
--- -------- ------ ------------ ------------ ---------- ------ ------ --------
D 5,998,592 97.4 2,021,476,421 50,848,669 3,521,712 0 0 279,721Buffer Pool Statistics 缓冲池统计信息
- P: 池的类型 D: default K: keep R: recycle 还有非标准块的池 2k 4k 16k 32k
- Number of Buffers: 该池中有多少个缓冲块
- Pool Hit %: 该池的命中率
- Buffer Gets: 该池中Buffer Get的次数
- Physical Reads: 该池中物理读的次数
- Physical Writes: 该池中物理写的次数
- Free Buff Wait: 等待空闲缓冲的次数
- Write Compl Wait: 等待写完成的次数
- Buffer Busy Waits: 该池中发生buffer busy waits的次数
7 Advisory Statistics
7-1 Buffer Pool Advisory
Buffer Pool Advisory DB/Inst: ITSCMP/itscmp2 Snap: 70723
-> Only different pool sizes than current configuration are displayed
-> Recommendations at different cache sizes
Est Phys Estimated
Size for Size Buffers Read Fact Est Phys Phys Reads
P Est (M) Factor (thousands) Factor Reads (K) per s
--- -------- ------ ------------- ---------- -------------- ---------
D 4,096 0.1 499 2.99 151,763 42
D 8,192 0.2 998 2.41 122,451 34
D 12,288 0.3 1,498 2.03 103,146 29
D 45,056 0.9 5,499 1.01 51,321 14
D 49,152 1.0 5,998 1.00 50,849 14
D 53,248 1.1 6,498 1.00 50,741 14Buffer Pool Advisory 缓冲池大小建议
- P: 池的类型
- Size for Est (M): 建议的缓冲池大小，单位为MB
- Size Factor: 相对于当前缓冲池大小的比例因子
- Buffers (thousands): 建议大小下的缓冲块数，单位为千
- Est Phys Read Factor: 物理读因子，相对于当前配置
- Est Phys Reads (K): 预估的物理读次数，单位为千
- Phys Reads per s: 预估的每秒物理读次数
7-2 PGA Aggregate Target Advisory
PGA Aggr Target Advisory DB/Inst: ITSCMP/itscmp2 Snap: 70723
Estd Estd PGA Estd PGA
PGA Aggr Size W/A MB Cache Hit Overalloc Cache
Target (M) Factor Processed % Optimal Count Hit %
---------- ------ ------------- ----------- ----------- ---------
512 0.1 18,997,042.6 0.0 393,107 66.00
1,024 0.2 18,997,042.6 0.0 198,532 75.00
2,048 0.4 18,997,042.6 0.0 49,633 83.00
3,072 0.6 18,997,042.6 1.0 0 88.00
4,096 0.8 18,997,042.6 32.0 0 93.00
5,120 1.0 18,997,042.6 77.0 0 97.00
6,144 1.2 18,997,042.6 85.0 0 98.00PGA Aggregate Target Advisory PGA聚合目标大小建议
- PGA Aggr Target (M): 建议的PGA聚合目标大小，单位为MB
- Size Factor: 相对于当前PGA Target的比例因子
- W/A MB Processed: workarea中处理的数据量，单位为MB
- Estd Cache Hit % Optimal: 预估的最优缓存命中率
- Estd PGA Overalloc Count: 预估的PGA过度分配次数，应当为0
- Estd PGA Cache Hit %: 预估的PGA缓存命中率
7-3 Shared Pool Advisory
Shared Pool Advisory DB/Inst: ITSCMP/itscmp2 Snap: 70723
-> SP: Shared Pool Est LC: Estimated Library Cache
Est LC Est LC Est LC Est LC
Shared SP Est LC Time Time Load Load
Pool Size Size Est LC Saved Saved Time Time
Size (M) Fact (M) Mem Obj Hits (sec) Factor (sec) Factor
-------- ----- ------- -------------- ------- ------- --------- --------
5,120 0.4 4,698 3,177,718,262 4,743 1.0 83,988 1.0
6,400 0.5 5,978 3,177,893,888 4,743 1.0 58,867 0.7
7,680 0.6 7,258 3,178,041,174 4,743 1.0 41,249 0.5
12,800 1.0 12,378 3,178,395,606 4,743 1.0 9,969 0.1
14,080 1.1 13,658 3,178,445,954 4,743 1.0 6,997 0.1Shared Pool Advisory 共享池大小建议
- Shared Pool Size (M): 建议的共享池大小，单位为MB
- SP Size Fact: 相对于当前共享池大小的比例因子
- Est LC Size (M): 预估的library cache大小
- Est LC Mem Obj Hits: 预估的library cache内存对象命中次数
- Est LC Time Saved (sec): 预估节省的时间，单位为秒
- Est LC Time Saved Factor: 预估节省时间的因子
- Est LC Load Time (sec): 预估的加载时间
- Est LC Load Time Factor: 预估加载时间的因子
7-4 SGA Target Advisory
SGA Target Advisory DB/Inst: ITSCMP/itscmp2 Snap: 70723
SGA Target SGA Size Est DB Est Physical
Size (M) Factor Time (s) Reads (K)
---------- -------- ----------- ------------------
16,384 0.3 903,737.9 113,848
24,576 0.4 903,698.3 111,948
32,768 0.5 903,667.0 110,451
65,536 1.0 903,577.5 106,293
73,728 1.1 903,560.1 105,497SGA Target Advisory SGA目标大小建议
- SGA Target Size (M): 建议的SGA目标大小，单位为MB
- SGA Size Factor: 相对于当前SGA目标大小的比例因子
- Est DB Time (s): 预估的DB Time，单位为秒
- Est Physical Reads (K): 预估的物理读次数，单位为千
8 Wait Statistics
8-1 Buffer Wait Statistics
Buffer Wait Statistics DB/Inst: ITSCMP/itscmp2 Snaps: 70719-70723
-> ordered by wait time desc, waits desc
Class Waits Total Wait Time (s) Avg Time (ms)
------------------ ----------- ------------------- --------------
data block 263,536 11,681 44
undo header 15,996 903 60
undo block 169 61 36
1st level bmb 20 1 61Buffer Wait Statistics 不同类型块的buffer等待统计
- Class: 块的类型，如 data block, undo header, undo block, segment header, 1st level bmb等
- Waits: 该类型块的等待次数
- Total Wait Time (s): 该类型块的总等待时间，单位为秒
- Avg Time (ms): 该类型块的平均等待时间，单位为毫秒
8-2 Enqueue Activity
Enqueue Activity DB/Inst: ITSCMP/itscmp2 Snaps: 70719-70723
-> ordered by Wait Time, Waits desc
Avg Wt Wait
Enqueue Type(Request Reason) Requests Succ Gets Failed Time (ms) Time (s)
-------------------------------- ---------- ---------- ---------- ---------- ----------
TX-Transaction (index contention) 193,918 193,918 0 502.2 97,375
TM-DML 1,060 1,060 0 44,555.2 47,228
SQ-Sequence Cache 17,431 17,431 0 2,047.0 35,683
HW-Segment High Water Mark 1,201 1,165 36 10,151.5 12,192
TX-Transaction (row lock content) 9,231 9,231 0 1,135.5 10,482Enqueue Activity 队列锁活动统计
- Enqueue Type(Request Reason): 队列锁类型及请求原因
- Requests: 请求次数
- Succ Gets: 成功获取次数
- Failed: 失败次数
- Avg Wt Time (ms): 平均等待时间，单位为毫秒
- Wait Time (s): 总等待时间，单位为秒
9 Undo Statistics
9-1 Undo Segment Summary
Undo Segment Summary DB/Inst: ITSCMP/itscmp2 Snaps: 70719-70723
Undo Num Undo Undo Max Qry Max Tx
Blocks (K) Extents (K) Blocks/Sec Len (s) Concurcy
---------- ----------- ---------- -------- --------
1,245.8 1.2 345.1 5,657 101Undo Segment Summary Undo段汇总信息
- Undo Blocks (K): Undo块数，单位为千
- Num Undo Extents (K): Undo区数，单位为千
- Undo Blocks/Sec: 每秒Undo块数
- Max Qry Len (s): 最大查询长度，单位为秒，这个值可用于评估undo_retention参数设置
- Max Tx Concurcy: 最大事务并发数
9-2 Undo Segment Stats
Undo Segment Stats DB/Inst: ITSCMP/itscmp2 Snaps: 70719-70723
-> Most Undo Blocks (K) ordered by snapshot time desc
Undo Num Undo Max Qry Max Tx Snapshot Too
End Time Blocks (K) Extents Len (s) Concy Wraps Small
---------------- ---------- ---------- --------- ------- --------- ---------
23-Jan 16:00:33 355.4 320 5,657 95 0 0
23-Jan 15:30:36 342.7 320 3,873 101 0 0Undo Segment Stats 按快照时间排列的Undo段统计
- End Time: 快照结束时间
- Undo Blocks (K): Undo块数
- Num Undo Extents: Undo区数
- Max Qry Len (s): 最大查询长度
- Max Tx Concy: 最大事务并发
- Snapshot Too Wraps: 快照过旧回绑次数
- Small: ORA-01555错误次数
10 Latch Statistics
10-1 Latch Activity
Latch Activity DB/Inst: ITSCMP/itscmp2 Snaps: 70719-70723
-> "Get Requests", "Pct Get Miss" and "Avg Slps/Miss" are statistics for
willing-to-wait latch get requests
-> "NoWait Requests", "Pct NoWait Miss" are for no-wait latch get requests
-> "Pct Misses" for both should be very close to 0.0
Pct Avg Wait Pct
Get Get Slps Time NoWait NoWait
Latch Name Requests Miss /Miss (s) Requests Miss
---------------------- ------------ ----- ----- ------- ------------ ------
shared pool 9,988,637 0.0 0.1 23 0
library cache 6,753,468 0.0 0.0 6 0
cache buffers chains 1,987,654,321 0.0 0.0 245 0Latch Activity Latch活动统计
- Latch Name: Latch名称
- Get Requests: willing-to-wait模式的latch请求次数
- Pct Get Miss: willing-to-wait模式下未命中的比例
- Avg Slps/Miss: 每次未命中平均sleep的次数
- Wait Time (s): 等待时间，单位为秒
- NoWait Requests: no-wait模式的latch请求次数
- Pct NoWait Miss: no-wait模式下未命中的比例
10-2 Latch Sleep Breakdown
Latch Sleep Breakdown DB/Inst: ITSCMP/itscmp2 Snaps: 70719-70723
-> ordered by misses desc
Get Spin & Sleep
Latch Name Requests Misses Sleeps Time (s)
------------------------------ ------------ ----------- ----------- ------------
cache buffers chains 1,987,654,321 12,345 1,234 245
shared pool 9,988,637 364 23 23
library cache 6,753,468 152 6 6Latch Sleep Breakdown Latch睡眠分解统计
- Latch Name: Latch名称
- Get Requests: 获取请求次数
- Misses: 未命中次数（Spin和Sleep的总和）
- Sleeps: 睡眠次数
- Sleep Time (s): 睡眠时间，单位为秒
11 Segment Statistics
11-1 Segments by Logical Reads
Segments by Logical Reads DB/Inst: ITSCMP/itscmp2 Snaps: 70719-70723
-> Total Logical Reads: 2,021,476,421
-> Captured Segments account for 85.2% of Total
Tablespace Subobject Obj. Logical
Owner Name Object Name Name Type Reads %Total
---------- ---------- -------------------- -------- ----- ------------- -------
APPS DATA_TS MZ_PRODUCT_ATTRIBUTE TABLE 345,678,901 17.10
APPS INDEX_TS MZ_PROD_ATTR_IDX1 INDEX 234,567,890 11.60Segments by Logical Reads 按逻辑读排序的段统计
- Owner: 对象所有者
- Tablespace Name: 表空间名
- Object Name: 对象名
- Subobject Name: 子对象名（如分区名）
- Obj. Type: 对象类型
- Logical Reads: 逻辑读次数
- %Total: 占总逻辑读的百分比
11-2 Segments by Physical Reads
Segments by Physical Reads DB/Inst: ITSCMP/itscmp2 Snaps: 70719-70723
-> Total Physical Reads: 56,839,035
-> Captured Segments account for 78.5% of Total
Tablespace Subobject Obj. Physical
Owner Name Object Name Name Type Reads %Total
---------- ---------- -------------------- -------- ----- ------------- -------
APPS DATA_TS MZ_LARGE_TABLE TABLE 12,345,678 21.72
APPS DATA_TS MZ_HISTORY_DATA TABLE 8,765,432 15.42Segments by Physical Reads 按物理读排序的段统计
11-3 Segments by Row Lock Waits
Segments by Row Lock Waits DB/Inst: ITSCMP/itscmp2 Snaps: 70719-70723
Tablespace Subobject Obj. Row
Owner Name Object Name Name Type Lock Waits
---------- ---------- -------------------- -------- ----- -------------
APPS DATA_TS MZ_ORDER_HEADER TABLE 12,345
APPS DATA_TS MZ_INVENTORY TABLE 8,765Segments by Row Lock Waits 按行锁等待排序的段统计，这对于找出热点表非常有用
11-4 Segments by ITL Waits
Segments by ITL Waits DB/Inst: ITSCMP/itscmp2 Snaps: 70719-70723
Tablespace Subobject Obj.
Owner Name Object Name Name Type ITL Waits
---------- ---------- -------------------- -------- ----- -------------
APPS DATA_TS MZ_HOT_TABLE TABLE 1,234Segments by ITL Waits 按ITL等待排序的段统计，ITL（Interested Transaction List）等待说明需要增加表的INITRANS参数
11-5 Segments by Buffer Busy Waits
Segments by Buffer Busy Waits DB/Inst: ITSCMP/itscmp2 Snaps: 70719-70723
Tablespace Subobject Obj. Buffer
Owner Name Object Name Name Type Busy Waits
---------- ---------- -------------------- -------- ----- --------------
APPS DATA_TS MZ_ORDER_LINES TABLE 56,789
APPS INDEX_TS MZ_ORDER_IDX1 INDEX 34,567Segments by Buffer Busy Waits 按Buffer Busy等待排序的段统计，这些是热点对象
12 Dictionary Cache Stats
Dictionary Cache Stats DB/Inst: ITSCMP/itscmp2 Snaps: 70719-70723
-> "Pct Misses" should be very low (< 2% in most cases)
-> "Cache Usage" is the number of entries being used
-> "Pct SGA" is the ratio of usage to allocated size
Get Pct Scan Pct Mod
Cache Usage Requests Miss Reqs Miss Reqs
------------------------------ -------- ---------- -------- ------ ------- --------
dc_awr_control 4 19 0.0 0 0
dc_global_oids 1,234 12,345 0.1 0 0
dc_histogram_data 56,789 1,234,567 1.2 0 123
dc_histogram_defs 12,345 567,890 0.8 0 56
dc_object_grants 2,345 34,567 0.2 0 0
dc_objects 45,678 987,654 0.5 0 234
dc_segments 23,456 456,789 0.3 0 123
dc_sequences 567 89,012 12.3 0 45,678
dc_tablespaces 45 5,678 0.0 0 0
dc_users 234 45,678 0.0 0 0Dictionary Cache Stats 数据字典缓存统计，也叫Row Cache统计
- Cache: 字典缓存名称
- Usage: 正在使用的条目数
- Get Requests: 获取请求次数
- Pct Miss: 未命中百分比，应当小于2%
- Scan Reqs: 扫描请求次数
- Pct Miss: 扫描未命中百分比
- Mod Reqs: 修改请求次数
注意 dc_sequences 的 Pct Miss 如果很高，说明sequence使用nocache，建议改为cache
13 Library Cache Activity
Library Cache Activity DB/Inst: ITSCMP/itscmp2 Snaps: 70719-70723
-> "Pct Misses" should be very low
Get Pct Pin Pct Invali-
Namespace Requests Miss Requests Miss Reloads dations
--------------- ------------ ------ -------------- ------ ---------- --------
BODY 5 0.0 6 16.7 1 0
CLUSTER 10 0.0 26 0.0 0 0
SQL AREA 601,357 99.8 902,828 99.7 47 2
TABLE/PROCEDURE 83 9.6 601,443 0.0 48 0Library Cache Activity Library Cache活动统计
- Namespace: 命名空间
- Get Requests: GET请求次数
- Pct Miss: GET未命中百分比
- Pin Requests: PIN请求次数
- Pct Miss: PIN未命中百分比
- Reloads: 重新加载次数
- Invalidations: 失效次数
14 SGA Memory Summary
SGA Memory Summary DB/Inst: ITSCMP/itscmp2 Snaps: 70719-70723
SGA regions Begin Size (MB) End Size (MB)
----------------------------------- --------------- ----------------
Database Buffers 49,152.00 49,152.00
Fixed Size 2.18 2.18
Redo Buffers 334.85 334.85
Variable Size 14,831.97 14,831.97
--------------- ----------------
SGA Total 64,321.00 64,321.00SGA Memory Summary SGA内存汇总
- Database Buffers: 数据库缓冲区大小
- Fixed Size: 固定区域大小
- Redo Buffers: 重做日志缓冲区大小
- Variable Size: 可变区域大小（包含shared pool等）
- SGA Total: SGA总大小
15 SGA breakdown difference
SGA breakdown difference DB/Inst: ITSCMP/itscmp2 Snaps: 70719-70723
Pool Name Begin MB End MB % Diff
------ ------------------------------ ------------ ----------- ----------
shared free memory 1,234.56 1,123.45 -9.00
shared sql area 5,678.90 5,789.01 1.94
shared library cache 567.89 578.90 1.94
shared row cache 123.45 125.67 1.80SGA breakdown difference SGA各组件在快照期间的变化
- Pool: 内存池名称
- Name: 组件名称
- Begin MB: 快照开始时的大小
- End MB: 快照结束时的大小
- % Diff: 变化百分比
重点关注 shared free memory，如果该值过小（小于300-500MB），可能导致ORA-04031错误
15 Streams统计
Streams CPU/IO Usage DB/Inst: ORCL/orcl1 Snaps: 556-559
-> Streams processes ordered by CPU usage
-> CPU and I/O Time in micro seconds
Session Type CPU Time User I/O Time Sys I/O Time
------------------------- -------------- -------------- --------------
QMON Coordinator 101,698 0 0
QMON Slaves 63,856 0 0
-------------------------------------------------------------
Streams Capture DB/Inst: CATGT/catgt Snaps: 911-912
-> Lag Change should be small or negative (in seconds)
Captured Enqueued Pct Pct Pct Pct
Per Per Lag RuleEval Enqueue RedoWait Pause
Capture Name Second Second Change Time Time Time Time
------------ -------- -------- -------- -------- -------- -------- --------
CAPTURE_CAT 650 391 93 0 23 0 71
-------------------------------------------------------------
Streams Apply DB/Inst: CATGT/catgt Snaps: 911-912
-> Pct DB is the percentage of all DB transactions that this apply handled
-> WDEP is the wait for dependency
-> WCMT is the wait for commit
-> RBK is rollbacks -> MPS is messages per second
-> TPM is time per message in milli-seconds
-> Lag Change should be small or negative (in seconds)
Applied Pct Pct Pct Pct Applied Dequeue Apply Lag
Apply Name TPS DB WDEP WCMT RBK MPS TPM TPM Change
------------ -------- ---- ---- ---- --- -------- -------- -------- --------
APPLY_CAT 0 0 0 0 0 0 0 0 0
-------------------------------------------------------------
Capture Name : Streams捕获进程名 Captured Per Second ：每秒挖掘出来的message 条数 Enqueued Per Second: 每秒入队的message条数 lag change: 指日志生成的时间到挖掘到该日志生成 message的时间延迟 Pct Enqueue Time： 入队时间的比例 Pct redoWait Time : 等待redo的时间比例 Pct Pause Time : Pause 时间的比例 Apply Name Streams 应用Apply进程的名字 Applied TPS : 每秒应用的事务数 Pct DB: 所有的DB事务中 apply处理的比例 Pct WDEP: 由于等待依赖的数据而耗费的时间比例 Pct WCMT: 由于等待commit而耗费的时间比例 Pct RBK: 事务rollback 回滚的比例 Applied MPS: 每秒应用的message 数 Dequeue TPM: 每毫秒出队的message数 Lag Change:指最新message生成的时间到其被Apply收到的延迟
16 Resource Limit
Resource Limit Stats DB/Inst: MAC/MAC2 Snap: 70723
-> only rows with Current or Maximum Utilization > 80% of Limit are shown
-> ordered by resource name
Current Maximum Initial
Resource Name Utilization Utilization Allocation Limit
------------------------------ ------------ ------------ ---------- ----------
ges_procs 2,612 8,007 10003 10003
processes 2,615 8,011 10000 10000
数据源于dba_hist_resource_limit 注意这里仅列出当前使用或最大使用量>80% *最大限制的资源名，如果没有列在这里则说明 资源使用量安全 Current Utilization 当前对该资源(包括Enqueue Resource、Lock和processes)的使用量 Maximum Utilization 从最近一次实例启动到现在该资源的最大使用量 Initial Allocation 初始分配值，一般等于参数文件中指定的值 Limit 实际上限值
17 init.ora Parameters
init.ora Parameters DB/Inst: MAC/MAC2 Snaps: 70719-70723
End value
Parameter Name Begin value (if different)
----------------------------- --------------------------------- --------------
_compression_compatibility 11.2.0
_kghdsidx_count 4
_ksmg_granule_size 67108864
_shared_pool_reserved_min_all 4100
archive_lag_target 900
audit_file_dest /u01/app/oracle/admin/MAC/adum
audit_trail OS
cluster_database TRUE
compatible 11.2.0.2.0
control_files +DATA/MAC/control01.ctl, +RECO
db_16k_cache_size 268435456
db_block_size 8192
db_cache_size 19327352832
db_create_file_dest +DATA
Parameter Name 参数名 Begin value 开始快照时的参数值 End value 结束快照时的参数值 (仅在发生变化时打印)
18 Global Messaging Statistics
Global Messaging Statistics DB/Inst: MAC/MAC2 Snaps: 70719-70723 Statistic Total per Second per Trans --------------------------------- ---------------- ------------ ------------ acks for commit broadcast(actual) 53,705 14.9 0.2 acks for commit broadcast(logical 311,182 86.1 1.3 broadcast msgs on commit(actual) 317,082 87.7 1.3 broadcast msgs on commit(logical) 317,082 87.7 1.3 broadcast msgs on commit(wasted) 263,332 72.9 1.1 dynamically allocated gcs resourc 0 0.0 0.0 dynamically allocated gcs shadows 0 0.0 0.0 flow control messages received 267 0.1 0.0 flow control messages sent 127 0.0 0.0 gcs apply delta 0 0.0 0.0 gcs assume cvt 55,541 15.4 0.2
全局通信统计信息，数据来源WRH$_DLM_MISC;
20 Global CR Served Stats
Global CR Served Stats DB/Inst: MAC/MAC2 Snaps: 70719-70723 Statistic Total ------------------------------ ------------------ CR Block Requests 403,703 CURRENT Block Requests 444,896 Data Block Requests 403,705 Undo Block Requests 94,336 TX Block Requests 307,896 Current Results 652,746 Private results 21,057 Zero Results 104,720 Disk Read Results 69,418 Fail Results 508 Fairness Down Converts 102,844 Fairness Clears 15,207 Free GC Elements 0 Flushes 105,052 Flushes Queued 0 Flush Queue Full 0 Flush Max Time (us) 0 Light Works 71,793 Errors 117
LMS传输CR BLOCK的统计信息，数据来源WRH$_CR_BLOCK_SERVER
21 Global CURRENT Served Stats
Global CURRENT Served Stats DB/Inst: MAC/MAC2 Snaps: 70719-70723 -> Pins = CURRENT Block Pin Operations -> Flushes = Redo Flush before CURRENT Block Served Operations -> Writes = CURRENT Block Fusion Write Operations Statistic Total % <1ms % <10ms % <100ms % <1s % <10s ---------- ------------ -------- -------- -------- -------- -------- Pins 73,018 12.27 75.96 8.49 2.21 1.08 Flushes 79,336 5.98 50.17 14.45 19.45 9.95 Writes 102,189 3.14 35.23 19.34 33.26 9.03
数据来源dba_hist_current_block_server Time to process current block request = (pin time + flush time + send time) Pins CURRENT Block Pin Operations ， PIN的内涵是处理一个BAST 不包含对global current block的flush和实际传输 The pin time represents how much time is required to process a BAST. It does not include the flush time and the send time. The average pin time per block served should be very low because the processing consists mainly of code path and should never be blocked. Flush 指 脏块被LMS进程传输出去之前，其相关的redo必须由LGWR已经flush 到磁盘上 Write 指fusion write number of writes which were mediated； 节点之间写脏块需求相互促成的行为 KJBL.KJBLREQWRITE gcs write request msgs 、gcs writes refused % <1ms % <10ms % <100ms % <1s % <10s 分别对应为pin、flush、write行为耗时的比例 例如在上例中flush和 write 在1s 到10s之间的有9%，在100ms 和1s之间的有19%和33%，因为flush和write都是IO操作 所以这里可以预见IO存在问题，延迟较高
22 Global Cache Transfer Stats
Global Cache Transfer Stats DB/Inst: MAC/MAC2 Snaps: 70719-70723
-> Immediate (Immed) - Block Transfer NOT impacted by Remote Processing Delays
-> Busy (Busy) - Block Transfer impacted by Remote Contention
-> Congested (Congst) - Block Transfer impacted by Remote System Load
-> ordered by CR + Current Blocks Received desc
CR Current
----------------------------- -----------------------------
Inst Block Blocks % % % Blocks % % %
No Class Received Immed Busy Congst Received Immed Busy Congst
---- ----------- -------- ------ ------ ------ -------- ------ ------ ------
1 data block 133,187 76.3 22.6 1.1 233,138 75.2 23.0 1.7
4 data block 143,165 74.1 24.9 1.0 213,204 76.6 21.8 1.6
3 data block 122,761 75.9 23.0 1.1 220,023 77.7 21.0 1.3
1 undo header 104,219 95.7 3.2 1.1 941 93.4 5.8 .7
4 undo header 95,823 95.2 3.7 1.1 809 93.4 5.3 1.2
3 undo header 95,592 95.6 3.3 1.1 912 94.6 4.5 .9
1 undo block 25,002 95.8 3.4 .9 0 N/A N/A N/A
4 undo block 23,303 96.0 3.1 .9 0 N/A N/A N/A
3 undo block 21,672 95.4 3.7 .9 0 N/A N/A N/A
1 Others 1,909 92.0 6.8 1.2 6,057 89.6 8.9 1.5
4 Others 1,736 92.4 6.1 1.5 5,841 88.8 9.9 1.3
3 Others 1,500 92.4 5.9 1.7 4,405 87.7 10.8 1.6
数据来源DBA_HIST_INST_CACHE_TRANSFER Inst No 节点号 Block Class 块的类型 CR Blocks Received 该节点上 该类型CR 块的接收数量 CR Immed %: CR块请求立即接收到的比例 CR Busy%：CR块请求由于远端争用而没有立即接收到的比例 CR Congst%: CR块请求由于远端负载高而没有立即接收到的比例 Current Blocks Received 该节点上 该类型Current 块的接收数量 Current Immed %: Current块请求立即接收到的比例 Current Busy%：Current块请求由于远端争用而没有立即接收到的比例 Current Congst%: Current块请求由于远端负载高而没有立即接收到的比例 Congst%的比例应当非常低 不高于2%， Busy%很大程度受到IO的影响，如果超过10% 一般会有严重的gc buffer busy acquire/release 补充 RAC 相关指标 内容由 tong.wang@parnassusdata.com 整理
RAC相关指标
Global Cache Load Profile
| Per Second | Per Transaction | |
| Global Cache blocks received: | 12.06 | 2.23 |
| Global Cache blocks served: | 8.18 | 1.51 |
| GCS/GES messages received: | 391.19 | 72.37 |
| GCS/GES messages sent: | 368.76 | 68.22 |
| DBWR Fusion writes: | 0.10 | 0.02 |
| Estd Interconnect traffic (KB) | 310.31 |
| 指标 | 指标说明 |
| Global Cache blocks received | 通过硬件连接收到远程实例的数据块的数量。发生在一个进程请求一致性读一个数据块不是在本地缓存中。Oracle发送一个请求到另外的实例。一旦缓冲区收到，这个统计值就会增加。这个统计值是另两个统计值的和：Global Cache blocks received = gc current blocks received + gc cr blocks received |
| Global Cache blocks served | 通过硬件连接发送到远程实例的数据块的数量。这个统计值是另外两个统计值的和：Global Cache blocks served = gc current blocks served + gc cr blocks served |
| GCS/GES messages received | 通过硬件连接收到远程实例的消息的数量。这个统计值通常代表RAC服务引起的开销。这个统计值是另外两个统计值的和：GCS/GES messages received = gcs msgs received + ges msgs received |
| GCS/GES messages sent | 通过硬件连接发送到远程实例的消息的数量。这个统计值通常代表RAC服务引起的开销。这个统计值是另外两个统计值的和：GCS/GES messages sent = gcs messages sent + ges messages sent |
| DBWR Fusion writes | 这个统计值显示融合写入的次数。在RAC中，单实例Oracle数据库，数据块只被写入磁盘因为数据过期，缓冲替换或者发生检查点。当一个数据块在缓存中被替换因为数据过期或发生检查点但在另外的实例没有写入磁盘，Global Cache Service会请求实例将数据块写入磁盘。因此融合写入不包括在第一个实例中的额外写入磁盘。大量的融合写入表明一个持续的问题。实例产生的融合写入请求占总的写入请求的比率用于性能分析。高比率表明DB cache大小不合适或者检查点效率低。 |
| Estd Interconnect traffic (KB) | 连接传输的KB大小。计算公式如下：Estd Interconnect traffic (KB) = ((‘gc cr blocks received’+ ‘gc current blocks received’ + ‘gc cr blocksserved’+ ‘gc current blocks served’) * Block size) + ((‘gcs messages sent’ + ‘ges messages sent’ + ‘gcs msgs received’+ ‘gcs msgs received’)*200)/1024/Elapsed Time |
Global Cache Efficiency Percentages (Target local+remote 100%)
| Buffer access – local cache %: | 91.05 |
| Buffer access – remote cache %: | 0.03 |
| Buffer access – disk %: | 8.92 |
| 指标 | 指标说明 |
| Buffer access – local cache % | 数据块从本地缓存命中占会话总的数据库请求次数的比例。在OLTP应用中最希望的是尽可能维持这个比率较高，因为这是最低成本和最快速的获得数据库数据块的方法。计算公式：Local Cache Buffer Access Ratio = 1 – ( physical reads cache + Global Cache blocks received ) / Logical Reads |
| Buffer access – remote cache % | 数据块从远程实例缓存命中占会话总的数据块请求的比例。在OLTP应用中这个比率和Buffer access – local cache的和应该尽可能的高因为这两种方法访问数据库数据块是最快速最低成本的。这个比率的计算方法：Remote Cache Buffer Access Ratio = Global Cache blocks received / Logical Reads |
| Buffer access – disk % | 从磁盘上读数据块到缓存占会话总的数据块请求次数的比例。在OLTP应用中希望维持这个比例低因为物理读是最慢的访问数据库数据块的方式。这个比率计算方法：1 – physical reads cache / Logical Reads |
Global Cache and Enqueue Services – Workload Characteristics
| Avg global enqueue get time (ms): | 0.0 |
| Avg global cache cr block receive time (ms): | 0.3 |
| Avg global cache current block receive time (ms): | 0.2 |
| Avg global cache cr block build time (ms): | 0.0 |
| Avg global cache cr block send time (ms): | 0.0 |
| Global cache log flushes for cr blocks served %: | 1.2 |
| Avg global cache cr block flush time (ms): | 1.8 |
| Avg global cache current block pin time (ms): | 1,021.7 |
| Avg global cache current block send time (ms): | 0.0 |
| Global cache log flushes for current blocks served %: | 6.9 |
| Avg global cache current block flush time (ms): | 0.9 |
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| 指标 | 指标说明 |
| Avg global enqueue get time (ms) | 通过interconnect发送消息，为争夺资源开启一个新的全局队列或者对已经开启的队列转换访问模式所花费的时间。如果大于20ms，你的系统可能会出现超时。 |
| Avg global cache cr block receive time (ms) | 从请求实例发送消息到mastering instance（2-way get）和一些到holding instance (3-way get)花费的时间。这个时间包括在holding instance生成数据块一致性读映像的时间。CR数据块获取耗费的时间不应该大于15ms。 |
| Avg global cache current block receive time (ms) | 从请求实例发送消息到mastering instance（2-way get）和一些到holding instance (3-way get)花费的时间。这个时间包括holding instance日志刷新花费的时间。Current Block获取耗费的时间不大于30ms |
| Avg global cache cr block build time (ms) | CR数据块创建耗费的时间 |
| Avg global cache cr block send time (ms) | CR数据块发送耗费的时间 |
| Global cache log flushes for cr blocks served % | 需要日志刷新的CR数据块占总的需要服务的CR数据块的比例。 |
| Avg global cache cr block flush time (ms) | CR数据块刷新耗费的时间 |
| Avg global cache current block pin time (ms) | Current数据块pin耗费的时间 |
| Avg global cache current block send time (ms) | Current数据块发送耗费的时间 |
| Global cache log flushes for current blocks served % | 需要日志刷新的Current数据块占总的需要服务的Current数据块的比例 |
| Avg global cache current block flush time (ms) | Current数据块刷新耗费的时间 |
Global Cache and Enqueue Services – Messaging Statistics
| Avg message sent queue time (ms): | 2,367.6 |
| Avg message sent queue time on ksxp (ms): | 0.1 |
| Avg message received queue time (ms): | 0.3 |
| Avg GCS message process time (ms): | 0.0 |
| Avg GES message process time (ms): | 0.0 |
| % of direct sent messages: | 54.00 |
| % of indirect sent messages: | 44.96 |
| % of flow controlled messages: | 1.03 |
| 指标 | 指标说明 |
| Avg message sent queue time (ms) | 一条信息进入队列到发送它的时间 |
| Avg message sent queue time on ksxp (ms) | 对端收到该信息并返回ACK的时间，这个指标很重要，直接反应了网络延迟，一般小于1ms |
| Avg message received queue time (ms) | 一条信息进入队列到收到它的时间 |
| Avg GCS message process time (ms) | |
| Avg GES message process time (ms) | |
| % of direct sent messages | 直接发送信息占的比率 |
| % of indirect sent messages | 间接发送信息占的比率，一般是排序或大的信息，流控制也可能引起 |
| % of flow controlled messages | 流控制信息占的比率，流控制最常见的原因是网络状况不佳， % of flowcontrolled messages应当小于1% |
Wait Event Histogram
| % of Waits | |||||||||
| Event | Total Waits | <1ms | <2ms | <4ms | <8ms | <16ms | <32ms | <=1s | >1s |
| ADR block file read | 208 | 38.0 | 3.4 | 44.7 | 13.9 | ||||
| ADR block file write | 40 | 100.0 | |||||||
| ADR file lock | 48 | 100.0 | |||||||
| ARCH wait for archivelog lock | 3 | 100.0 | |||||||
| ASM file metadata operation | 12.8K | 99.7 | .1 | .0 | .0 | .0 | .2 | .0 | |
| Backup: MML write backup piece | 310.5K | 7.6 | .1 | .1 | 1.3 | 10.4 | 30.2 | 50.2 | .0 |
| CGS wait for IPC msg | 141.7K | 100.0 | |||||||
| CSS initialization | 34 | 50.0 | 47.1 | 2.9 | |||||
| CSS operation: action | 110 | 48.2 | 20.9 | 28.2 | 2.7 | ||||
| CSS operation: query | 102 | 88.2 | 3.9 | 7.8 | |||||
| DFS lock handle | 6607 | 93.9 | .5 | .2 | .0 | .0 | 5.3 | .0 | |
| Disk file operations I/O | 1474 | 100.0 | |||||||
| IPC send completion sync | 21.9K | 99.5 | .1 | .1 | .1 | .0 | .2 | ||
| KJC: Wait for msg sends to complete | 13 | 100.0 | |||||||
| LGWR wait for redo copy | 16.3K | 100.0 | .0 | ||||||
| Log archive I/O | 3 | 33.3 | 66.7 | ||||||
| PX Deq: Signal ACK EXT | 2256 | 99.8 | .1 | .1 | |||||
| PX Deq: Signal ACK RSG | 2124 | 99.9 | .1 | .0 | |||||
| PX Deq: Slave Session Stats | 7997 | 94.6 | .9 | .9 | 2.5 | .8 | .4 | ||
| PX Deq: Table Q qref | 2355 | 99.9 | .1 | ||||||
| PX Deq: reap credit | 1215.7K | 100.0 | .0 | .0 | |||||
| PX qref latch | 1366 | 100.0 | |||||||
| Parameter File I/O | 194 | 94.8 | 1.0 | 1.0 | 1.0 | 1.5 | .5 | ||
Wait Event Histogram：等待时间直方图 Event：等待事件名字 Total Waits：该等待事件在快照时间内等待的次数 %of Waits < 1ms ：小于1ms的等待次数 %of Waits < 2ms ：小于2ms的等待次数 %of Waits < 4ms ：小于4ms的等待次数 %of Waits < 8ms ：小于8ms的等待次数 %of Waits < 16ms ：小于16ms的等待次数 %of Waits < 32ms ：小于32ms的等待次数 %of Waits < =1s ：小于等于1s的等待次数 %of Waits > 1s ：大于1s的等待次数 Parent Latch Statistics
- only latches with sleeps are shown
- ordered by name
| Latch Name | Get Requests | Misses | Sleeps | Spin & Sleeps 1->3+ |
| Real-time plan statistics latch | 77,840 | 136 | 20 | 116/0/0/0 |
| active checkpoint queue latch | 321,023 | 20,528 | 77 | 20451/0/0/0 |
| active service list | 339,641 | 546 | 132 | 424/0/0/0 |
| call allocation | 328,283 | 550 | 148 | 440/0/0/0 |
| enqueues | 1,503,525 | 217 | 14 | 203/0/0/0 |
| ksuosstats global area | 2,605 | 1 | 1 | 0/0/0/0 |
| messages | 2,608,863 | 141,380 | 29 | 141351/0/0/0 |
| name-service request queue | 155,047 | 43 | 15 | 28/0/0/0 |
| qmn task queue latch | 2,368 | 90 | 78 | 12/0/0/0 |
| query server process | 268 | 30 | 30 | 0/0/0/0 |
| redo writing | 910,703 | 11,623 | 50 | 11573/0/0/0 |
| resmgr:free threads list | 14,454 | 190 | 4 | 186/0/0/0 |
| space background task latch | 11,209 | 15 | 7 | 8/0/0/0 |
Latch Name：闩名称 Get Requests：申请获得父闩的次数 Child Latch Statistics
- only latches with sleeps/gets > 1/100000 are shown
- ordered by name, gets desc
| Latch Name | Child Num | Get Requests | Misses | Sleeps | Spin & Sleeps 1->3+ |
| KJC message pool free list | 1 | 96,136 | 82 | 20 | 62/0/0/0 |
| Lsod array latch | 10 | 2,222 | 153 | 118 | 58/0/0/0 |
| Lsod array latch | 13 | 2,151 | 43 | 14 | 29/0/0/0 |
| Lsod array latch | 4 | 2,066 | 154 | 124 | 59/0/0/0 |
| Lsod array latch | 5 | 1,988 | 105 | 44 | 63/0/0/0 |
| Lsod array latch | 9 | 1,734 | 95 | 32 | 64/0/0/0 |
| Lsod array latch | 2 | 1,707 | 88 | 38 | 55/0/0/0 |
| Lsod array latch | 11 | 1,695 | 88 | 32 | 57/0/0/0 |
| Lsod array latch | 6 | 1,680 | 158 | 126 | 64/0/0/0 |
| Lsod array latch | 12 | 1,657 | 155 | 111 | 65/0/0/0 |
| Lsod array latch | 7 | 1,640 | 90 | 34 | 59/0/0/0 |
| Lsod array latch | 1 | 1,627 | 169 | 153 | 46/0/0/0 |
| Lsod array latch | 3 | 1,555 | 87 | 36 | 54/0/0/0 |
| Lsod array latch | 8 | 1,487 | 127 | 88 | 57/0/0/0 |
| cache buffers chains | 47418 | 354,313 | 391 | 4 | 387/0/0/0 |
| cache buffers chains | 8031 | 337,135 | 250 | 8 | 242/0/0/0 |
| cache buffers chains | 78358 | 305,022 | 528 | 9 | 519/0/0/0 |
| cache buffers chains | 6927 | 241,808 | 129 | 4 | 125/0/0/0 |
Latch Name：闩名称 Child Num： Get Requests： Misses： Sleeps： Spin&Sleeps 1->3+：
Dictionary Cache Stats (RAC)
| Cache | GES Requests | GES Conflicts | GES Releases |
| dc_awr_control | 11 | 5 | 0 |
| dc_global_oids | 5 | 0 | 0 |
| dc_histogram_defs | 215 | 1 | 707 |
| dc_objects | 90 | 9 | 0 |
| dc_segments | 79 | 10 | 73 |
| dc_sequences | 35,738 | 37 | 0 |
| dc_table_scns | 6 | 0 | 0 |
| dc_tablespace_quotas | 907 | 77 | 0 |
| dc_users | 10 | 0 | 0 |
| outstanding_alerts | 576 | 288 | 0 |
Cache：字典缓存类名 GES Requests： GES Conflicts： GES Releases： Library Cache Activity (RAC)
| Namespace | GES Lock Requests | GES Pin Requests | GES Pin Releases | GES Inval Requests | GES Invali- dations |
| ACCOUNT_STATUS | 242 | 0 | 0 | 0 | 0 |
| BODY | 0 | 1,530,013 | 1,530,013 | 0 | 0 |
| CLUSTER | 74 | 74 | 74 | 0 | 0 |
| DBLINK | 246 | 0 | 0 | 0 | 0 |
| EDITION | 311 | 311 | 311 | 0 | 0 |
| HINTSET OBJECT | 186 | 186 | 186 | 0 | 0 |
| INDEX | 152,360 | 152,360 | 152,360 | 0 | 0 |
| QUEUE | 223 | 9,717 | 9,717 | 0 | 0 |
| SCHEMA | 255 | 0 | 0 | 0 | 0 |
| SUBSCRIPTION | 0 | 26 | 26 | 0 | 0 |
| TABLE/PROCEDURE | 275,215 | 3,023,083 | 3,023,083 | 0 | 0 |
| TRIGGER | 0 | 384,493 | 384,493 | 0 | 0 |
Namespace：library cache 的命名空间 GES Lock Requests： GES Pin Requests： GES Inval Requests： GES Invali-dations： Interconnect Ping Latency Stats
- Ping latency of the roundtrip of a message from this instance to
- target instances.
- The target instance is identified by an instance number.
- Average and standard deviation of ping latency is given in miliseconds
- for message sizes of 500 bytes and 8K.
- Note that latency of a message from the instance to itself is used as
- control, since message latency can include wait for CPU
| Target Instance | 500B Ping Count | Avg Latency 500B msg | Stddev 500B msg | 8K Ping Count | Avg Latency 8K msg | Stddev 8K msg |
| 1 | 1,138 | 0.20 | 0.03 | 1,138 | 0.20 | 0.03 |
| 2 | 1,138 | 0.17 | 0.04 | 1,138 | 0.20 | 0.05 |
| 3 | 1,138 | 0.19 | 0.22 | 1,138 | 0.23 | 0.22 |
| 4 | 1,138 | 0.18 | 0.04 | 1,138 | 0.21 | 0.04 |
Target Instance：目标实例 500B Ping Count： Avg Latency 500B msg： Stddev 500B msg： 8K Ping Count： Avg Latency 8K msg： Stddev 8K msg： Interconnect Throughput by Client
- Throughput of interconnect usage by major consumers
- All throughput numbers are megabytes per second
| Used By | Send Mbytes/sec | Receive Mbytes/sec |
| Global Cache | 0.10 | 0.20 |
| Parallel Query | 0.02 | 0.06 |
| DB Locks | 0.09 | 0.09 |
| DB Streams | 0.00 | 0.00 |
| Other | 0.02 | 0.01 |
Used By：主要消费者 Send Mbytes/sec：发送Mb/每秒 Receive Mbytes/sec：接收Mb/每秒 Interconnect Device Statistics
- Throughput and errors of interconnect devices (at OS level)
- All throughput numbers are megabytes per second
| Device Name | IP Address | Public | Source | Send Mbytes/sec | Send Errors | Send Dropped | Send Buffer Overrun | Send Carrier Lost | Receive Mbytes/sec | Receive Errors | Receive Dropped | Receive Buffer Overrun | Receive Frame Errors |
| bondib0 | 192.168.10.8 | NO | cluster_interconnects parameter | 0.00 | 0 | 0 | 0 | 0 | 0.00 | 0 | 0 | 0 |
Device Name：设备名称 IP Address：IP地址 Public：是否为公用网络 Source：来源 Send Mbytes/sec：发送MB/每秒 Send Errors：发送错误 Send Dropped： Send Buffer Overrun： Send Carrier Lost： Receive Mbytes/sec： Receive Errors： Receive Dropped： Receive Buffer Overrun： Receive Frame Errors： Dynamic Remastering Stats
- times are in seconds
- Affinity objects – objects mastered due to affinity at begin/end snap
| Name | Total | per Remaster Op | Begin Snap | End Snap |
| remaster ops | 29 | 1.00 | ||
| remastered objects | 40 | 1.38 | ||
| replayed locks received | 1,990 | 68.62 | ||
| replayed locks sent | 877 | 30.24 | ||
| resources cleaned | 0 | 0.00 | ||
| remaster time (s) | 5.0 | 0.17 | ||
| quiesce time (s) | 1.7 | 0.06 | ||
| freeze time (s) | 0.6 | 0.02 | ||
| cleanup time (s) | 0.7 | 0.02 | ||
| replay time (s) | 0.2 | 0.01 | ||
| fixwrite time (s) | 1.3 | 0.04 | ||
| sync time (s) | 0.5 | 0.02 | ||
| affinity objects | 365 | 367 |
Name： Total： Per Remaster Op： Begin Snap： End Snap：
参考文档
Statistics Descriptions http://docs.oracle.com/cd/B19306_01/server.102/b14237/stats002.htm
Memory Configuration and Use http://docs.oracle.com/cd/B19306_01/server.102/b14211/memory.htm
Library Cache Hit (%) http://docs.oracle.com/cd/B16240_01/doc/doc.102/e16282/oracle_database_help/oracle_database_instance_efficiency_libcache_hit_pct.html
Oracle® Database Performance Tuning Guide 12c Release 1 (12.1) How to Interpret the "SQL ordered by Physical Reads (UnOptimized)" Section in AWR Reports (11.2 onwards) [ID 1466035.1]