I wrote this script years ago using more common views and extrapolated the necessary information.

rem Script: get_redo_apply_metrics.sql
rem Reports standby apply rate with lag
rem
set lines 120 pages 60
column SizeM format 9,999.9
column “MB/sec” format 9,999.99

select TIMESTAMP,
completion_time “ArchTime”,
SEQUENCE#,
round((blocks*block_size)/(1024*1024),1) “SizeM”,
round((TIMESTAMP-lag(TIMESTAMP,1,TIMESTAMP) OVER (order by TIMESTAMP))*24*60*60,1) “Diff(sec)
“,
round((blocks*block_size)/1024/ decode(((TIMESTAMP-lag(TIMESTAMP,1,TIMESTAMP)
OVER (order by TIMESTAMP))*24*60*60),0,1,
(TIMESTAMP-lag(TIMESTAMP,1,TIMESTAMP) OVER (order by TIMESTAMP))*24*60*60),1) “KB/sec”,
round((blocks*block_size)/(1024*1024)/ decode(((TIMESTAMP-lag(TIMESTAMP,1,TIMESTAMP)
OVER (order by TIMESTAMP))*24*60*60),0,1,
(TIMESTAMP-lag(TIMESTAMP,1,TIMESTAMP) OVER (order by TIMESTAMP))*24*60*60),3) “MB/sec”,
round(((lead(TIMESTAMP,1,TIMESTAMP) over (order by TIMESTAMP))-completion_time)*24*60*60,1) ”
Lag(sec)”
from v$archived_log a, v$dataguard_status dgs
where a.name = replace(dgs.MESSAGE,’Media Recovery Log ‘,”)
and dgs.FACILITY = ‘Log Apply Services’
order by TIMESTAMP;

Note that I’m not clear on what you would like to know about the archivers on the primary database.

but is it x$kcclh.lhtsm can be used to monitor primary instnace local archiver?

Output may be of less use in real time apply.

jason.

Good findings!!!

This will definitely be helpful when monitoring your standby databases. I will save these queries in my favorite one’s.

I have recently been advocating using Analytic Functions in SQL to make life easier, so thought of re-writing your queries using AF.

Query 1:
========
select sequence#, fromtime, totime,
((to_date(totime,’MM/DD/RR HH24:MI:SS’)
– to_date(fromtime,’MM/DD/RR HH24:MI:SS’)) * 1440) minutes_to_apply
from (select lhseq sequence#, lhtsm fromtime,
lead(lhtsm) over (order by lhseq) totime
from sys.x$kcclh)
order by sequence#

Query 2:
========
select min(lhlot) min_log_date, max(lhlot) max_log_date,
numtodsinterval(max(to_number(a_lhtsm – b_lhtsm)),’DAY’) max_log_apply_duration,
numtodsinterval(min(to_number(a_lhtsm – b_lhtsm)),’DAY’) min_log_apply_duration,
numtodsinterval(avg(to_number(a_lhtsm – b_lhtsm)),’DAY’) avg_log_apply_duration
from ( select lhlot lhlot,
to_date(lhtsm,’MM/DD/RR HH24:MI:SS’) a_lhtsm,
to_date(lag(lhtsm) over (order by lhrid),’MM/DD/RR HH24:MI:SS’) b_lhtsm,
lhrid
from sys.x$kcclh )
order by lhrid;

Query 3:
========
select sequence#, fromtime, totime,
((to_date(totime,’MM/DD/RR HH24:MI:SS’)
– to_date(fromtime,’MM/DD/RR HH24:MI:SS’)) * 1440) minutes_to_apply
from (select lhseq sequence#, lhtsm totime,
lag(lhtsm) over (order by lhseq) fromtime
from sys.x$kcclh)
where to_date(fromtime,’MM/DD/RR HH24:MI:SS’) > sysdate – 60
and ((to_date(totime,’MM/DD/RR HH24:MI:SS’) -
to_date(fromtime,’MM/DD/RR HH24:MI:SS’)) * 1440) > 30
order by sequence#;

Thanks again Marc for the findings.

Regards

Asif Momen
http://momendba.blogspot.com