1.直接查询视图 select * from DBA_HIST_SYSMETRIC_SUMMARY where metric_unit = Transactions Per Second METRIC_NAME 监控指标 METRIC_UNIT 监控单位 INTSIZE 采样长度(厘秒),一般是360000 NUM_INTERVAL 观察间隔(分钟),一般是60 AVERAGE 监控时间段内的
1.直接查询视图 select * from DBA_HIST_SYSMETRIC_SUMMARY where metric_unit = 'Transactions Per Second'
METRIC_NAME 监控指标 METRIC_UNIT 监控单位 INTSIZE 采样长度(厘秒),一般是360000 NUM_INTERVAL 观察间隔(分钟),一般是60 AVERAGE 监控时间段内的平均值
--查询一段时间内的平均每秒事务数 select instance_number, metric_unit, trunc(begin_time) time, round(avg(average), 2) average from DBA_HIST_SYSMETRIC_SUMMARY where metric_unit = 'Transactions Per Second' and begin_time >= to_date('2014-08-04 08:00:00', 'yyyy-mm-dd hh24:mi:ss') and end_time
查询一段时间内的平均每天事务数,只要将平均每秒事务数*60^60*24即可。
2.查询AWR报告也可以。
3.--下面脚本,分别在两个时间点执行,相减可以得到某个用户commit的次数,即事务数 select s.USERNAME,sum(se.VALUE) "session transaction number",sum(sy.VALUE) " database transaction number" from v$session s,v$sesstat se,v$sysstat sy where s.sid=se.SID and se.STATISTIC#=sy.STATISTIC# and sy.NAME='user commits' and s.USERNAME=upper('&username') group by s.USERNAME;

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Best practices for handling string data types and indexes in MySQL include: 1) Selecting the appropriate string type, such as CHAR for fixed length, VARCHAR for variable length, and TEXT for large text; 2) Be cautious in indexing, avoid over-indexing, and create indexes for common queries; 3) Use prefix indexes and full-text indexes to optimize long string searches; 4) Regularly monitor and optimize indexes to keep indexes small and efficient. Through these methods, we can balance read and write performance and improve database efficiency.


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