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sytem log audit/reporting and psql

From: Fei Liu <fei(dot)liu(at)aepnetworks(dot)com>
To: pgsql-performance(at)postgresql(dot)org
Subject: sytem log audit/reporting and psql
Date: 2007-04-30 14:13:46
Message-ID: (view raw, whole thread or download thread mbox)
Lists: pgsql-performance
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 
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

My initial testing has not shown any significant difference between a 
partitioning approach and a plain (all entries in master) database 
2005-01-01 | 00:27:55 | firewood | ssh | Login Successful | None | local 
| user9819 |

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 < 

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 < 

Testing database logon_test time based
(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
(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...

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?
5) What are the most glaring limitations and flaws in my design?
6) What are the best approaches to analyze postgresql query performance 
and how to improve postgresql query performance?
Thank you for taking time to review and answer my questions! Let me know 
if I am not clear on any specific detail..



pgsql-performance by date

Next:From: Tom LaneDate: 2007-04-30 15:06:47
Subject: Re: Query performance problems with partitioned tables
Previous:From: Neil Peter BraggioDate: 2007-04-30 14:06:07
Subject: Re: Query performance problems with partitioned tables

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