DATABASE TIME: This is the ADDM's measurement of throughput. From the user's
point of view: this is the total amount of time spent by users waiting for
a response from the database after issuing a call (not including
networking). From the database instance point of view: this is the total
time spent by forground processes waiting for a database resource (e.g.,
read I/O), running on the CPU and waiting for a free CPU (run-queue). The
target of ADDM analysis is to reduce this metric as much as possible,
thereby reducing the instance's response time.
AVERAGE DATABASE LOAD: At any given time we can count how many users (also
called 'Active Sessions') are waiting for an answer from the instance. This
is the ADDM's measurement for instance load. The 'Average Database Load' is
the average of the the load measurement taken over the entire analysis
period. We get this number by dividing the 'Database Time' by the analysis
period. For example, if the analysis period is 30 minutes and the 'Database
Time' is 90 minutes, we have an average of 3 users waiting for a response.
IMPACT: Each finding has an 'Impact' associated with it. The impact is the
portion of the 'Database Time' the finding deals with. If we assume that
the problem described by the finding is completely solved, then the
'Database Time' will be reduced by the amount of the 'Impact'.
BENEFIT: Each recommendation has a 'benefit' associated with it. The ADDM
analysis estimates that the 'Database Time' can be reduced by the 'benefit'
amount if all the actions of the recommendation are performed.