My earlier blog posting
shows how to enable, retrieve and interpret BPEL engine performance
statistics to aid performance troubleshooting. The strength of BPEL
engine statistics at EM is its break down per request. But there are
some limitations with the BPEL performance statistics mentioned in that
blog posting:
The statistics were stored in memory instead of being persisted. To
avoid memory overflow, the data are stored to a buffer with limited
size. When the statistic entries exceed the limitation, old data will be
flushed out to give ways to new statistics. Therefore it can only keep
the last X number of entries of data. The statistics 5 hour ago may not
be there anymore.
The BPEL engine performance statistics only includes latencies. It does not provide throughputs.
Fortunately, Oracle SOA Suite runs with the SOA
Infrastructure database and a lot of performance data are naturally
persisted there. It is at a more coarse grain than the in-memory BPEL
Statistics, but it does have its own strengths as it is persisted.
Here
I would like offer examples of some basic SQL queries you can run
against the infrastructure database of Oracle SOA Suite 11G to acquire
the performance statistics for a given period of time. You can run it
immediately after you modify the date range to match your actual system.
1. Asynchronous/one-way messages incoming rates
The
following query will show number of messages sent to one-way/async BPEL
processes during a given time period, organized by process names and
states
select composite_name composite, state, count(*) Count from dlv_message
where receive_date >= to_timestamp('2012-10-24 21:00:00','YYYY-MM-DD HH24:MI:SS')
and receive_date <= to_timestamp('2012-10-24 21:59:59','YYYY-MM-DD HH24:MI:SS')
group by composite_name, state
order by Count;
2. Throughput of BPEL process instances
The
following query shows the number of synchronous and asynchronous
process instances created during a given time period. It list instances
of all states, including the unfinished and faulted ones. The results
will include all composites cross all SOA partitions
select state, count(*) Count, composite_name composite, component_name,componenttype from cube_instance
where creation_date >= to_timestamp('2012-10-24 21:00:00','YYYY-MM-DD HH24:MI:SS')
and creation_date <= to_timestamp('2012-10-24 21:59:59','YYYY-MM-DD HH24:MI:SS')
group by composite_name, component_name, componenttype
order by count(*) desc;
3. Throughput and latencies of BPEL process instances
This
query is augmented on the previous one, providing more comprehensive
information. It gives not only throughput but also the maximum, minimum
and average elapse time BPEL process instances.
select composite_name Composite, component_name Process, componenttype, state,
count(*) Count,
trunc(Max(extract(day from (modify_date-creation_date))*24*60*60 +
extract(hour from (modify_date-creation_date))*60*60 +
extract(minute from (modify_date-creation_date))*60 +
extract(second from (modify_date-creation_date))),4) MaxTime,
trunc(Min(extract(day from (modify_date-creation_date))*24*60*60 +
extract(hour from (modify_date-creation_date))*60*60 +
extract(minute from (modify_date-creation_date))*60 +
extract(second from (modify_date-creation_date))),4) MinTime,
trunc(AVG(extract(day from (modify_date-creation_date))*24*60*60 +
extract(hour from (modify_date-creation_date))*60*60 +
extract(minute from (modify_date-creation_date))*60 +
extract(second from (modify_date-creation_date))),4) AvgTime
from cube_instance
where creation_date >= to_timestamp('2012-10-24 21:00:00','YYYY-MM-DD HH24:MI:SS')
and creation_date <= to_timestamp('2012-10-24 21:59:59','YYYY-MM-DD HH24:MI:SS')
group by composite_name, component_name, componenttype, state
order by count(*) desc;
4. Combine all together
Now let's combine all
of these 3 queries together, and parameterize the start and end time
stamps to make the script a bit more robust. The following script will
prompt for the start and end time before querying against the database:
accept startTime prompt 'Enter start time (YYYY-MM-DD HH24:MI:SS)'
accept endTime prompt 'Enter end time (YYYY-MM-DD HH24:MI:SS)'
Prompt "==== Rejected Messages ====";
REM 2012-10-24 21:00:00
REM 2012-10-24 21:59:59
select count(*), composite_dn from rejected_message
where created_time >= to_timestamp('&&StartTime','YYYY-MM-DD HH24:MI:SS')
and created_time <= to_timestamp('&&EndTime','YYYY-MM-DD HH24:MI:SS')
group by composite_dn;
Prompt " ";
Prompt "==== Throughput of one-way/asynchronous messages ====";
select state, count(*) Count, composite_name composite from dlv_message
where receive_date >= to_timestamp('&StartTime','YYYY-MM-DD HH24:MI:SS')
and receive_date <= to_timestamp('&EndTime','YYYY-MM-DD HH24:MI:SS')
group by composite_name, state
order by Count;
Prompt " ";
Prompt "==== Throughput and latency of BPEL process instances ===="
select state,
count(*) Count,
trunc(Max(extract(day from (modify_date-creation_date))*24*60*60 +
extract(hour from (modify_date-creation_date))*60*60 +
extract(minute from (modify_date-creation_date))*60 +
extract(second from (modify_date-creation_date))),4) MaxTime,
trunc(Min(extract(day from (modify_date-creation_date))*24*60*60 +
extract(hour from (modify_date-creation_date))*60*60 +
extract(minute from (modify_date-creation_date))*60 +
extract(second from (modify_date-creation_date))),4) MinTime,
trunc(AVG(extract(day from (modify_date-creation_date))*24*60*60 +
extract(hour from (modify_date-creation_date))*60*60 +
extract(minute from (modify_date-creation_date))*60 +
extract(second from (modify_date-creation_date))),4) AvgTime,
composite_name Composite, component_name Process, componenttype
from cube_instance
where creation_date >= to_timestamp('&StartTime','YYYY-MM-DD HH24:MI:SS')
and creation_date <= to_timestamp('&EndTime','YYYY-MM-DD HH24:MI:SS')
group by composite_name, component_name, componenttype, state
order by count(*) desc;
