Re: database design question, new to postgresql
Fei Liu wrote:
Fei Liu wrote:
Hello group, I need to design and develop a web reporting system to
let users query/view syslog files on a unix host. For now, I am
concentrating on the authentication file that has user logon
(success/failure) and logoff records. The log file is logrotated
every week or so. My reporting system parses the log entries and put
the result into a postgresql database (I am proposing to use
postgresql as the backend). Since this deals with multi-year archive
and I believe 'partitioing' is an ideal feature to handle this
problem. So here is the design scheme:
CREATE TABLE logon_success(
name varchar(32) not null,
srcip inet not null,
date date not null,
time time not null,
...
);
CREATE TABLE logon_success_yy${year}mm${month}(
CHECK (date >= DATE '$year-$month-01' AND date < DATE
'$next_year-$next_month-1')
)
INHERITS ($tname)
;
As you can see from the sample code, I am using perl to dynamically
generate children tables as I parse log files in a daily cron job
script. Once the log file is analyzed and archived in the database, I
have a simple web UI that sysadmin can select and view user logon
events. I have built a sample framework and it works so far. Keep in
mind, this reporting system is not limited to just user logon, it
should also work with system events such as services
failures/startup, hardware failures, etc
Now here are my questions:
1) Should I use database to implement such a reporting system? Are
there any alternatives, architects, designs?
2) Is partitioning a good approach to speed up log query/view? The
user comment in partitioning in pgsql manual seems to indicate
partitioning may be slower than non-partitioned table under certain
circumstances.
3) How to avoid repetitive log entry scanning since my cron job
script is run daily but logrotate runs weekly? This means everytime
my script will be parsing duplicate entries.
4) When parsing log files, it's quite possible that there are
identical entries, for example a user logins really fast, resulting 2
or more identical entries..In this case can I still use primary
key/index at all? If I can, how do I design primary key or index to
speed up query?
3) What are the most glaring limitations and flaws in my design?
Thank you for taking time to review and answer my questions! Let me
know if I am not clear on any specific detail..
Fei
---------------------------(end of broadcast)---------------------------
TIP 6: explain analyze is your friend
Let me add one more question, what are the best approaches to analyze
postgresql query performance and how to improve postgresql query
performance?
Fei
My initial testing has not shown any significant difference between a
partitioning approach and a plain (all entries in master) database
approach...
2005-01-01 | 00:27:55 | firewood | ssh | Login Successful | None |
local | user9819 | 192.168.1.31
My test was based on two artificial tables that has 1700 records per day
from 2004-02-01 to 2007-04-27, around 2 million entries that are
identical in both tables.
My test script:
echo Testing database $t1 time based
time psql -p 5583 netilla postgres << EOF
select count(date) from $t1 where date > '2005-03-01' and date <
'2006-12-11';
\q
EOF
echo Testing database $t2 time based
time psql -p 5583 netilla postgres << EOF
select count(date) from $t2 where date > '2005-03-01' and date <
'2006-12-11';
\q
EOF
Result:
./timing_test.sh
Testing database logon_test time based
count
---------
1121472
(1 row)
0.00user 0.00system 0:02.92elapsed 0%CPU (0avgtext+0avgdata 0maxresident)k
0inputs+0outputs (0major+456minor)pagefaults 0swaps
Testing database logon_test2 time based
count
---------
1121472
(1 row)
0.00user 0.00system 0:02.52elapsed 0%CPU (0avgtext+0avgdata 0maxresident)k
0inputs+0outputs (0major+456minor)pagefaults 0swaps
But the numbers are really not static and logon_test2 (with
partitioning) sometimes behave worse than logon_test...
Fei
Home |
Main Index |
Thread Index