![
# Plot the 4 graphs
par(mfrow =c(4,1))
plot(sqstat[,'DATEEVT'],sqstat[,'VALUE'],type="l",col="blue"
,xaxt="n",main=stat_name,cex.main=2,xlab="",ylab="VAL
UE")
Axis(side=1,at=sqstat$DATEEVT,round(skip),format="%Y/
%m/%d %H:%M")
plot(sqstat[,'DATEEVT'],sqstat[,'VALUE_PER_SEC'],type="l",c
ol="blue",xaxt="n",xlab="",ylab="VALUE_PER_SEC")
Axis(side=1,at=sqstat$DATEEVT,round(skip),format="%Y/
%m/%d %H:%M")
hist(sqstat[,'VALUE'],xlab="VALUE",main=NULL,border="bl
ue")
hist(sqstat[,'VALUE_PER_SEC'],xlab="VALUE_PER_SEC",main
=NULL,border="blue")](https://image.slidesharecdn.com/examplerusage-131204075904-phpapp02/85/Example-R-usage-for-oracle-DBA-UKOUG-2013-11-320.jpg)
![
# Plot the 4 graphs
par(mfrow =c(4,1))
plot(sqevent[,'DATEEVT'],sqevent[,'TIME_WAITED_MS'],type="l",col="blue"
,xaxt="n",main=event,cex.main=2,xlab="",ylab="TIME_WAITED_MS")
Axis(side=1,at=sqevent$DATEEVT,round(skip),format="%Y/%m/%d
%H:%M")
plot(sqevent[,'DATEEVT'],sqevent[,'TOTAL_WAITS'],type="l",col="blue",xa
xt="n",xlab="",ylab="NB_WAITS")
Axis(side=1,at=sqevent$DATEEVT,round(skip),format="%Y/%m/%d
%H:%M")
plot(sqevent[,'DATEEVT'],sqevent[,'MS_PER_WAIT'],type="l",col="blue",xa
xt="n",xlab="",ylab="MS_PER_WAIT")
Axis(side=1,at=sqevent$DATEEVT,round(skip),format="%Y/%m/%d
%H:%M")
hist(sqevent[,'MS_PER_WAIT'],xlab="MS_PER_WAIT",main=NULL,border="
blue")](https://image.slidesharecdn.com/examplerusage-131204075904-phpapp02/85/Example-R-usage-for-oracle-DBA-UKOUG-2013-14-320.jpg)
![
# compute percentages
pct <- round(dg_space[,'SIZE_GB']/sum(dg_space[,'SIZE_GB'])*100)
# add %
pct <- paste(pct,"%",sep="")
# Add db size to db_name
db_name_size<-paste(dg_space[,'DB_NAME']," (",sep="")
db_name_size<-paste(db_name_size,dg_space[,'SIZE_GB'],sep="")
db_name_size<-paste(db_name_size," GB)",sep="")
# Set the colors
colors<-rainbow(length(dg_space[,'DB_NAME']))
# Plot
pie(dg_space[,'SIZE_GB'], labels = pct, col=colors, main=paste(dg," Disk Group Usage",sep=""))
# Add a legend
legend(x=1.2,y=0.5,legend=db_name_size,fill=colors,cex=0.8)](https://image.slidesharecdn.com/examplerusage-131204075904-phpapp02/85/Example-R-usage-for-oracle-DBA-UKOUG-2013-19-320.jpg)
![myquery<-"
Select to_char(sysdate,'YYYY/MM/DD HH24:MI:SS') as DATEEVT, NAME,VALUE,1 VALUE_PER_SEC
from v$sysstat
where name='"
# Keep them
prev_date<<-qoutput[,'DATEEVT']
prev_value<<-qoutput[,'VALUE']
# Launch the loop for the real-time graph
nb_refresh <- as.integer(nb_refresh)
for(i in seq(nb_refresh)) {
# Get the new data
Sys.sleep(refresh_interval)
qoutput<-dbGetQuery(conn,myquery)
# Keep the current value
current_date<-qoutput[,'DATEEVT']
current_value<-qoutput[,'VALUE']
# compute difference between snap for value and value per sec
#qoutput[,'DATEEVT']<-current_date
qoutput[,'VALUE']<-current_value-prev_value](https://image.slidesharecdn.com/examplerusage-131204075904-phpapp02/85/Example-R-usage-for-oracle-DBA-UKOUG-2013-23-320.jpg)
![myquery<-"
Select to_char(sysdate,'YYYY/MM/DD HH24:MI:SS') as
DATEEVT, EVENT,TOTAL_WAITS,TIME_WAITED_MICRO/1000 as TIME_WAITED_MS,1 MS_PER_WAIT
from v$system_event
where event='"
# Keep them
prev_tw<<-qoutput[,'TIME_WAITED_MS']
prev_twaits<<-qoutput[,'TOTAL_WAITS']
# So we want 4 graphs
par(mfrow =c(4,1))
# Launch the loop for the real-time graph
nb_refresh <- as.integer(nb_refresh)
for(i in seq(nb_refresh)) {
# Get the new data
Sys.sleep(refresh_interval)
qoutput<-dbGetQuery(conn,myquery)
# compute difference between snap for time_waited_ms, total_waits and then compute ms_per_wait
qoutput[,'TIME_WAITED_MS']<-current_tw-prev_tw
qoutput[,'TOTAL_WAITS']<-current_twaits-prev_twaits](https://image.slidesharecdn.com/examplerusage-131204075904-phpapp02/85/Example-R-usage-for-oracle-DBA-UKOUG-2013-26-320.jpg)
Example R usage for oracle DBA UKOUG 2013
- 2. Oracle DBA since 1999 OCP 9i,10g,11g Rac certified Expert Exadata certified implementation specialist Blogger since 2012 @bertranddrouvot BasketBall fan
- 3. Some examples of R usage with the oracle database From a DBA point of view Retrieve system statistics/wait events with some AWR queries Real time Data Dashboard of the database activity
- 4. R installation R programing R studio (powerful and productive user interface for R)
- 5. Because R is a powerful tool for statistical analysis with graphing and plotting packages built in. Furthermore, R can connect to Oracle via a JDBC package which makes importing data very easy.
- 8. select s.begin_interval_time,sta.stat_name,sta.VALUE, round(((sta.VALUE)/ ( (extract(day from s.END_INTERVAL_TIME)-extract(day from s.BEGIN_INTERVAL_TIME))*86400 + (extract(hour from s.END_INTERVAL_TIME)-extract(hour from s.BEGIN_INTERVAL_TIME))*3600 + (extract(minute from s.END_INTERVAL_TIME)-extract(minute from s.BEGIN_INTERVAL_TIME))*60 + (extract(second from s.END_INTERVAL_TIME)-extract(second from s.BEGIN_INTERVAL_TIME)) ) ),2) VALUE_PER_SEC from ( select instance_number,snap_id,stat_name, value - first_value(value) over (partition by stat_name order by snap_id rows 1 preceding) "VALUE" from dba_hist_sysstat where stat_name like nvl('&stat_name',stat_name) and instance_number = (select instance_number from v$instance) ) sta, dba_hist_snapshot s where sta.instance_number=s.instance_number and sta.snap_id=s.snap_id and s.BEGIN_INTERVAL_TIME >= trunc(sysdate-&sysdate_nb_day_begin_interval+1) and s.BEGIN_INTERVAL_TIME <= trunc(sysdate-&sysdate_nb_day_end_interval+1) order by s.begin_interval_time asc;
- 11. # Plot the 4 graphs par(mfrow =c(4,1)) plot(sqstat[,'DATEEVT'],sqstat[,'VALUE'],type="l",col="blue" ,xaxt="n",main=stat_name,cex.main=2,xlab="",ylab="VAL UE") Axis(side=1,at=sqstat$DATEEVT,round(skip),format="%Y/ %m/%d %H:%M") plot(sqstat[,'DATEEVT'],sqstat[,'VALUE_PER_SEC'],type="l",c ol="blue",xaxt="n",xlab="",ylab="VALUE_PER_SEC") Axis(side=1,at=sqstat$DATEEVT,round(skip),format="%Y/ %m/%d %H:%M") hist(sqstat[,'VALUE'],xlab="VALUE",main=NULL,border="bl ue") hist(sqstat[,'VALUE_PER_SEC'],xlab="VALUE_PER_SEC",main =NULL,border="blue")
- 12. select e.WAIT_CLASS,e.event_name,s.begin_interval_time,e.TOTAL_WAITS,e.TIME_WAITED_MS,e.TIME_WAITED_MS / TOTAL_WAITS "MS_PER_WAIT" from ( select instance_number,snap_id,WAIT_CLASS,event_name, total_waits - first_value(total_waits) over (partition by event_name order by snap_id rows 1 preceding) "TOTAL_WAITS", (time_waited_micro - first_value(time_waited_micro) over (partition by event_name order by snap_id rows 1 preceding))/1000 "TIME_WAITED_MS" from dba_hist_system_event where WAIT_CLASS like nvl('&WAIT_CLASS',WAIT_CLASS) and event_name like nvl('&event_name',event_name) and instance_number = (select instance_number from v$instance) ) e, dba_hist_snapshot s where e.TIME_WAITED_MS > 0 and e.instance_number=s.instance_number and e.snap_id=s.snap_id and s.BEGIN_INTERVAL_TIME >= trunc(sysdate-&sysdate_nb_day_begin_interval+1) and s.BEGIN_INTERVAL_TIME <= trunc(sysdate-&sysdate_nb_day_end_interval+1) order by 1
- 14. # Plot the 4 graphs par(mfrow =c(4,1)) plot(sqevent[,'DATEEVT'],sqevent[,'TIME_WAITED_MS'],type="l",col="blue" ,xaxt="n",main=event,cex.main=2,xlab="",ylab="TIME_WAITED_MS") Axis(side=1,at=sqevent$DATEEVT,round(skip),format="%Y/%m/%d %H:%M") plot(sqevent[,'DATEEVT'],sqevent[,'TOTAL_WAITS'],type="l",col="blue",xa xt="n",xlab="",ylab="NB_WAITS") Axis(side=1,at=sqevent$DATEEVT,round(skip),format="%Y/%m/%d %H:%M") plot(sqevent[,'DATEEVT'],sqevent[,'MS_PER_WAIT'],type="l",col="blue",xa xt="n",xlab="",ylab="MS_PER_WAIT") Axis(side=1,at=sqevent$DATEEVT,round(skip),format="%Y/%m/%d %H:%M") hist(sqevent[,'MS_PER_WAIT'],xlab="MS_PER_WAIT",main=NULL,border=" blue")
- 15. Query is a little bit complicated (comes from OEM)
- 19. # compute percentages pct <- round(dg_space[,'SIZE_GB']/sum(dg_space[,'SIZE_GB'])*100) # add % pct <- paste(pct,"%",sep="") # Add db size to db_name db_name_size<-paste(dg_space[,'DB_NAME']," (",sep="") db_name_size<-paste(db_name_size,dg_space[,'SIZE_GB'],sep="") db_name_size<-paste(db_name_size," GB)",sep="") # Set the colors colors<-rainbow(length(dg_space[,'DB_NAME'])) # Plot pie(dg_space[,'SIZE_GB'], labels = pct, col=colors, main=paste(dg," Disk Group Usage",sep="")) # Add a legend legend(x=1.2,y=0.5,legend=db_name_size,fill=colors,cex=0.8)
- 20. Basically the script takes a snapshot based on the v$sysstat view then computes and graphs the delta with the previous snapshot.
- 23. myquery<-" Select to_char(sysdate,'YYYY/MM/DD HH24:MI:SS') as DATEEVT, NAME,VALUE,1 VALUE_PER_SEC from v$sysstat where name='" # Keep them prev_date<<-qoutput[,'DATEEVT'] prev_value<<-qoutput[,'VALUE'] # Launch the loop for the real-time graph nb_refresh <- as.integer(nb_refresh) for(i in seq(nb_refresh)) { # Get the new data Sys.sleep(refresh_interval) qoutput<-dbGetQuery(conn,myquery) # Keep the current value current_date<-qoutput[,'DATEEVT'] current_value<-qoutput[,'VALUE'] # compute difference between snap for value and value per sec #qoutput[,'DATEEVT']<-current_date qoutput[,'VALUE']<-current_value-prev_value
- 24. Basically the script takes a snapshot based on the v$system_event view then computes and graphs the delta with the previous snapshot.
- 26. myquery<-" Select to_char(sysdate,'YYYY/MM/DD HH24:MI:SS') as DATEEVT, EVENT,TOTAL_WAITS,TIME_WAITED_MICRO/1000 as TIME_WAITED_MS,1 MS_PER_WAIT from v$system_event where event='" # Keep them prev_tw<<-qoutput[,'TIME_WAITED_MS'] prev_twaits<<-qoutput[,'TOTAL_WAITS'] # So we want 4 graphs par(mfrow =c(4,1)) # Launch the loop for the real-time graph nb_refresh <- as.integer(nb_refresh) for(i in seq(nb_refresh)) { # Get the new data Sys.sleep(refresh_interval) qoutput<-dbGetQuery(conn,myquery) # compute difference between snap for time_waited_ms, total_waits and then compute ms_per_wait qoutput[,'TIME_WAITED_MS']<-current_tw-prev_tw qoutput[,'TOTAL_WAITS']<-current_twaits-prev_twaits
- 27. myquery<-" select to_char(sysdate,'YYYY/MM/DD HH24:MI:SS') as DATEEVT, wait_class as WAIT_CLASS, time_waited_micro/1000 as TIME_WAITED_MS, EVENT as EVENT from v$system_event where WAIT_CLASS != 'Idle' union select to_char(sysdate,'YYYY/MM/DD HH24:MI:SS') as DATEEVT, 'CPU' as WAIT_CLASS ,sum(value/1000) as TIME_WAITED_MS ,'CPU' as EVENT from v$sys_time_model where stat_name IN ('background cpu time', 'DB CPU') "
- 29. Sub-graph for the time waited (in ms) per wait class:
- 30. Sub-graph for the wait events distribution of the wait class having the max time waited during the last snap:
- 31. Sub-graph for the wait class distribution since the script has been launched:
- 33. # Split the screen no_display<-split.screen( figs = c( 2, 1 ) ) # Split the second screen no_display<-split.screen( figs = c( 1, 2 ), screen=2 ) Much more complicated: source code is available through my blog.
- 34. Retrieve and visualize in real time the output of my asm_metrics.pl utility. What is yours ?