开Oracle调优鹰眼,深入理解AWR性能报告:http://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: http://www.askmaclean.com/archives/awr-tuning-hawk-eyes.html
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:
1.启动slave进程m00x去做AWR快照
2.当某个度量阀值被超过时发出alert告警
3.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: 73.70
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)) |
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。
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_NUM BER = :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_FACT OR = 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, 1 00*(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_SUMM ARY 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.S NAP_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.TO TAL_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 W HERE :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.DB ID(+) = :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 AN D 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 = 1
1-3 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$waitstat; SUM(COUNT) ———- 60423739
Buffer Nowait %的计算公式是 sum(v$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 = :B1
2、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) )
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 http://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
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 = :B1
6、
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$latch gets和misses
Latch Name ---------------------------------------- Get Requests Misses Sleeps Spin Gets Sleep1 Sleep2 Sleep3 -------------- ----------- ----------- ---------- -------- -------- -------- shared pool 9,988,637 364 23 341 0 0 0 library cache 6,753,468 152 6 146 0 0 0 Memory Management Latch 369 1 1 0 0 0 0 qmn task queue latch 24 1 1 0 0 0 0
Latch Hit%:= (1 – (Sum(misses) / Sum(gets)))
关于Latch的更多信息内容可以参考 AWR后面的专栏Latch Statistics, 注意对于一个并发设计良好的OLTP应用来说,Latch、Enqueue等并发控制不应当成为系统的主要瓶颈, 同时对于这些并发争用而言 堆积硬件CPU和内存 很难有效改善性能。
SELECT SUM(GETS), SUM(MISSES) FROM DBA_HIST_LATCH WHERE SNAP_ID = :B3 AND DBID = :B2 AND INSTANCE_NUMBER = :B1
9、
Parse CPU To Parse Elapsd:该指标反映了 快照内解析CPU时间和总的解析时间的比值(Parse CPU Time/ Parse Elapsed Time); 若该指标水平很低,那么说明在整个解析过程中 实际在CPU上运算的时间是很短的,而主要的解析时间都耗费在各种其他非空闲的等待事件上了(如latch:shared pool,row cache lock之类等) 数据来源 V$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 time
Wait 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 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:
http://www.askmaclean.com/archives/db-file-sequential-read-wait-event.html
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,视乎不同的队列锁有不同的情况:
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 time
2-2 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 Commit
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
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 .7
Foreground 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 .4
Background 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$OSSTAT / DBA_HIST_OSSTAT,, TIME相关的指标单位均为百分之一秒
统计项 | 描述 |
NUM_CPU_SOCKETS | 物理CPU的数目 |
NUM_CPU_CORES | CPU的核数 |
NUM_CPUS | 逻辑CPU的数目 |
SYS_TIME | 在内核态被消耗掉的CPU时间片,单位为百分之一秒 |
USER_TIME | 在用户态被消耗掉的CPU时间片,单位为百分之一秒 |
BUSY_TIME | Busy_Time=SYS_TIME+USER_TIME 消耗的CPU时间片,单位为百分之一秒 |
AVG_BUSY_TIME | AVG_BUSY_TIME= BUSY_TIME/NUM_CPUS |
IDLE_TIME | 空闲的CPU时间片,单位为百分之一秒 |
所有CPU所能提供总的时间片 | BUSY_TIME + IDLE_TIME = ELAPSED_TIME * CPU_COUNT |
OS_CPU_WAIT_TIME | 进程等OS调度的时间,cpu queuing |
VM_IN_BYTES | 换入页的字节数 |
VM_OUT_BYTES | 换出页的字节数,部分版本下并不准确,例如Bug 11712010 Abstract: VIRTUAL MEMORY PAGING ON 11.2.0.2 DATABASES,仅供参考 |
IOWAIT_TIME | 所有CPU花费在等待I/O完成上的时间 单位为百分之一秒 |
RSRC_MGR_CPU_WAIT_TIME | 是指当resource manager控制CPU调度时,需要控制对应进程暂时不使用CPU而进程到内部运行队列中,以保证该进程对应的consumer group(消费组)没有消耗比指定resource manager指令更多的CPU。RSRC_MGR_CPU_WAIT_TIME指等在内部运行队列上的时间,在等待时不消耗CPU |
2-6 Service Statistcs
Service Statistics Snaps: 70719-70723 -> ordered by DB Time Physical Logical Service Name DB Time (s) DB CPU (s) Reads (K) Reads (K) ---------------------------- ------------ ------------ ------------ ------------ itms-contentmasterdb-prod 897,099 20,618 35,668 1,958,580 SYS$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