使用python进行Oracle数据库性能趋势分析
一、 概述
随着信息系统业务需求快速增长,业务系统关联日益复杂,信息系统性能问题逐渐突显,一旦出现信息系统性能问题及不可用问题,将严重影响信息系统的稳定运行及用户体验。
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结合运维实践,数据库性能问题是造成信息系统性能下降和非停的重要原因之一,如何进行常态化的数据库性能趋势分析,及时发现数据库性能衰减“病灶”,常态化提升信息系统性能,避免救火式性能优化,成为衡量信息系统管理部门运营能力的重要指标之一。
二、研究目标
使用python语言进行Oracle数据库性能趋势分析。
三、工具介绍
开发语言:python
2.7
数据库:Oracle
11.2.0.4
Web框架:Django
图形展示工具:echart
四、算法介绍
核心算法由运行可靠率、资源竞争率、进程等待率和SQL稳定率四部分组成,如下图所示,本文主要以SQL稳定率为例:
Trend =100-100*sum(( c_time-h_time) /h_time)
说明:
Trend:表示信息系统性能趋势(%)
c_time:前一小时SQL平均执行时间(秒)
h_time: 3个月内SQL平均执行时间(秒)
五、效果展示
(1)、系统性能趋势:
(2)、TOPSQL性能趋势分析
(3)、TOPSQL日性能趋势分析
(4)、TOPSQL月性能趋势分析
六、核心代码
核心代码分为数据采集层、数据转换层、web展示层。
(1)、数据采集层:
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def get_topsql_info(username,password,ip,port,dbname,c_type,param=0,b_param=0):
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s_top10 = ''
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#s_snap_id = 0
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print oracle_link_target
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if c_type == 'sql_topsql':
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sql_topsql="
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select round(Elapsed_Time, 2) Elapsed_Time,
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round(cpu_time, 2) cpu_time,
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Executions,
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round(elap_per_exec, 2) elap_per_exec,
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round(total_db_time, 2) total_db_time,
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sql_id,
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substr(nvl(sql_module, ' ** SQL module Not Available ** '), 1, 30) sql_module,
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sql_text
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from (select nvl((sqt.elap / 1000000), to_number(null)) Elapsed_Time,
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nvl((sqt.cput / 1000000), to_number(null)) CPU_Time,
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sqt.exec Executions,
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decode(sqt.exec,
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0,
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to_number(null),
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(sqt.elap / sqt.exec / 1000000)) Elap_per_Exec,
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(100 *
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(sqt.elap /
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(SELECT sum(e.VALUE) - sum(b.value)
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FROM DBA_HIST_SYS_TIME_MODEL e, DBA_HIST_SYS_TIME_MODEL b
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WHERE B.SNAP_ID = "+str(b_param)+"
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AND E.SNAP_ID = "+str(param)+"
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AND B.DBID = (select dbid from v$database)
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AND E.DBID = (select dbid from v$database)
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AND B.INSTANCE_NUMBER =
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(select instance_number from v$instance)
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AND E.INSTANCE_NUMBER =
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(select instance_number from v$instance)
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and e.STAT_NAME = 'DB time'
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and b.stat_name = 'DB time'))) Total_DB_Time,
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sqt.sql_id,
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to_char(decode(sqt.module,
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null,
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null,
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'Module: ' || sqt.module)) SQL_Module,
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nvl(to_char(substr(st.sql_text, 1, 30)),
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' ** SQL Text Not Available ** ') SQL_Text
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from (select sql_id,
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max(module) module,
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sum(elapsed_time_delta) elap,
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sum(cpu_time_delta) cput,
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sum(executions_delta) exec
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from dba_hist_sqlstat
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dba_hist_sqltext st
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where st.sql_id(+) = sqt.sql_id
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order by nvl(sqt.elap, -1) desc, sqt.sql_id)
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where rownum < 100
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"
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elif c_type == 'top10':
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#a list of top10: m_top10
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m_top10=get_hsql_info(t,'top10')
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#after get top10
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#end get top10
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for h_sql_id in m_top10:
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l_sql_id = h_sql_id[0]
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s_top10 = s_top10+",'"+l_sql_id+"'"
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s_top10 = s_top10.strip(',')
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sql_hsql_top10="select sql_id,to_char(substr(sql_text,1,2000)) sql_text,length(sql_text) sql_length,command_type from dba_hist_sqltext t where t.sql_id in ("+s_top10+')'
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else:
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cmd=sql_tablespace
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#print s_top10
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#print log_cmd_i
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cmd =""
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if c_type == 'sql_topsql':
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cmd=sql_topsql
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elif c_type == 'top10':
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cmd=sql_hsql_top10
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else:
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cmd=sql_tablespace
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#print len(m_top10)
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print 'before get topsql exe sql: '
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print cmd
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print 'get db shell: '
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conn = cx_Oracle.connect(oracle_link_target)
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cursor = conn.cursor()
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cur = cursor.execute(cmd)
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db_list = cur.fetchall()
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#print 'before return db_list'
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#print db_list
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return db_list
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cursor.close()
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conn.close()
(2)、数据转换层
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select row_number() over(partition by ip order by to_number(total_db_time) desc) rn,
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ip,
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db_name,
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sql_id,
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decode(elap_per_exec, '0', 0.01, elap_per_exec) elap_per_exec,
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decode(elap_avg_exec, '0', 0.01, elap_avg_exec) elap_avg_exec,
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decode(sign(decode(elap_avg_exec, '0', 0.01, elap_avg_exec) - decode(elap_per_exec, '0', 0.01, elap_per_exec)),
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1,
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'up',
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-1,
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'down',
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'equ') sql_status,
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round((decode(elap_avg_exec, '0', 0.01, elap_avg_exec) -
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decode(elap_per_exec, '0', 0.01, elap_per_exec)) /
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decode(elap_avg_exec, '0', 0.01, elap_avg_exec),
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2) sql_cont,
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executions,
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total_db_time,
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substr(sql_module, 1, 12) sql_module,
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substr(sql_text, 1, 12) sql_text,
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ch_date
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from (select rownum rn,
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d.ip,
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d.db_name,
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d.sql_id,
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replace(d.elap_per_exec, 'None', 0) elap_per_exec,
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e.elap_avg_exec,
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d.executions,
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d.sql_module,
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d.sql_text,
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d.ch_date,
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d.total_db_time
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from hsql.h_topsql d,
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(select b.ip,
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b.sql_id,
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round(avg(replace(b.elap_per_exec, 'None', 0)),
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2) elap_avg_exec
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from hsql.h_topsql_bak b
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group by b.ip, b.sql_id) e
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where d.sql_id = e.sql_id
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and d.ip = e.ip)));
(3)、web展示层
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def topsql_line_servlet(request):
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cursor = conn.cursor()
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query = "select ip,
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(select service_name
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from hsql.h_instance h
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where h.ip = b.ip
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and rownum = 1) service_name,
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sql_id,
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executions,
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elap_per_exec,
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to_char(ch_date, 'hh34:mi') sj,
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to_char(ch_date, 'yyyy-mm-dd') rq
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from hsql.h_topsql b
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where ch_date > trunc(sysdate)
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order by sj"
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print query
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cursor.execute(query)
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resultset = cursor.fetchall()
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cursor.close()
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conn.close()
七、总结
通过Oracle性能趋势分析工具的应用可以进行细粒度的数据库性能管理,及时发现潜在的信息系统性能衰减隐患,通过持续性、常态化的信息系统性能优化,优化信息系统提升,提升用户体验。
文章题目:使用python进行Oracle数据库性能趋势分析
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