Tiger,

I didn't mean that I am opening closing the database connection for each 
record.  I only have 1 database connection which is opened at the beginning of 
the perl script and closed when (if ever) the script is killed.

The open and close I was referring to was for the actual log messages.  The log 
messages themselves indicate when a firewall connection was opened or closed, 
so whenever I get a log message indicated that a connection was closed, I need 
to SELECT the proper record ID from the database first, in order to know which 
record to UPDATE with the new information.

Sorry for the confusion.

On 09/21/2011 10:41 AM, tiger peng wrote:
The first bottleneck is open/close for each record, which are time consuming 
operation. Why don't you just use one connection?
The second one is do the condition check in script instead of doing all within 
database server.

Try to fix this two issues. If it is still too slow, we can do more tuning.



________________________________
From: Brandon Phelps<bphe...@gls.com>
To: tiger peng<tigerpeng2...@yahoo.com>
Cc: Tim Bunce<tim.bu...@pobox.com>; John Scoles<byter...@hotmail.com>; 
"dbi-users@perl.org"<dbi-users@perl.org>
Sent: Wednesday, September 21, 2011 9:04 AM
Subject: Re: Tail Module + DBI Module, can\'t keep up!

Thanks for the responses.

I would love to be able to output the data to a flat file or something and do 
bulk inserts daily, however this isn't really feasible for my situation because 
the database operations depend on other records.

Basically the system I am creating works like this currently:

1. Syslog-ng accepts syslog messages from a sonicwall firewall on the network, 
and logs to a file in /var/log
3. Perl script runs as a daemon, extracting open_dt, close_dt, protocol, 
source_ip, destination_ip, source_port, destination_port, bytes_sent, and 
bytes_received
2. Database table called sonicwall_connections, partitioned by the open_dt 
column, 1 partition per day
4. Perl script from (3) constantly reads in the /var/log files using the Tail 
module.  It does the following:
     a. When a connection is opened, it INSERTs into the sonicwall_connections 
table
     b. When a connection is closed, it SELECTs from the sonicwall_connection 
table the last record id that matches the protocol, source_ip, source_port, and 
destination_port
     c. If a record exists matching this criteria, it UPDATEs that record's 
close_dt column with the time the connection was closed
     d. If a record does not exist, then in our case this means that the 
connection was denied due to firewall rules, and we instead INSERT into a 
different table, sonicwall_denied


Previously I was doing the entire SELECT prepare and execute in my never ending 
while loop that parsed the log.  I have since changed this per the 
recommendations here.  Now, I open my connection to the database using 
Perl::DBI, then I create my prepared statement (outside the while loop), and I 
simply call execute() within the while loop, so the prepared statement only has 
to be created once.  This seems to have helped a small bit in that the script 
does not fall behind quite as fast now, however it does still fall behind.  
Currently with these modifications it is 10AM EST and the script is still 
working on log entries from yesterday night.  Around 12 hours behind, compared 
to the 22-23 hours behind it was previously, so this change obviously did help 
a bit... still not perfect yet though.

As you can see from the procedure above I cannot really work with a flat file 
or non-indexed hot table because I constantly need to read back in records that 
were previously inserted.  Sometimes the record I need was inserted just 
seconds before, while other times I need to retrieve records that have been 
there for days.  For example, when a user loads a website in their browser, I 
get my initial open connection log entry and I do the initial INSERT, but only 
a second later (depending on the size of the site obviously) I am ready to 
UPDATE the record with the close_dt time (when the web request connection was 
closed).  On the other hand if a user downloads a 10GB file via FTP, or starts 
a remote desktop session, the connection could potentially be opened for hours 
if not days, so I also need to be able to read records from that long ago, in 
order to get the record ID to UPDATE when the connection is closed.

Another reason I cannot store records to a temporary table or flat file is because the 
actual web gui users most often need the data right away.  For example if a manager sees 
a huge spike in bandwidth on our fiber connection, they need to be able to instantly open 
the web page and get the exact details of that connection, such as the source ip 
address... this way they can determine what employee has initiated the download or 
whatever, and to what destination they are connected.  This way they can say, "Oh 
this employee has a VNC connection opened to their house, that doesn't really seem work 
related", etc.

On 09/16/2011 12:42 PM, tiger peng wrote:
I just finished tuning an application almost exactly reverse Brandon's, not 
parsing text file and then loading, but extracting, parsing/transforming the 
data, and then generate XML file.

Original application did fetch and parsing record one-by-one, as it the code 
for parsing/transforming is no implemented efficient. It took around 40 minutes 
to pull data from a highly active transaction database, the DBAs complained 
about it.I split the extracting, let it quickly dump the data to a flat file ( 
~8 minutes) and then parsing/transforming the data and generate the XML file 
(~3 minutes). It is possible to speed it up even more by (parallel) pulling the 
data into memory (not very big, ~2GB) or dumping the flat data file on DB 
server side.


  
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
----------------------
--
*From:* tiger peng<tigerpeng2...@yahoo.com>
*To:* Tim Bunce<tim.bu...@pobox.com>; John Scoles<byter...@hotmail.com>
*Cc:* "bphe...@gls.com"<bphe...@gls.com>; 
"dbi-users@perl.org"<dbi-users@perl.org>
*Sent:* Friday, September 16, 2011 10:13 AM
*Subject:* Re: Tail Module + DBI Module, can\'t keep up!

Tim,

I bet at that time your MySQL did not support partition.

Now, with partition management available, the jobs should be easy.
Using a small partition, called hot partition in DW/ETL field, to receive the 
new data, as there is only small chunk of data the insert/update DML should be 
executed fast (keep index locally).When it reach a threshold, split the hot 
partition, use the fresh hot partition to receive newer data, and merge the 
older data to the main partition.

Using of text file has its advantage: When ever the DB is down, the parsing of 
log files can still run by itself; and when the DB is back, bulk-load tool can 
be used to catch up the load quickly.

Tiger


  
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
----------------------
--
*From:* Tim Bunce<tim.bu...@pobox.com>
*To:* John Scoles<byter...@hotmail.com>
*Cc:* tigerpeng2...@yahoo.com; bphe...@gls.com; dbi-users@perl.org
*Sent:* Friday, September 16, 2011 4:20 AM
*Subject:* Re: Tail Module + DBI Module, can\'t keep up!

This is all fine advice for performance tuning DBI app and worth doing.
But there's always a limit to what can be achieved on the client.
So it's worth exploring what can be done on the server side, beyond
standard tuning practices.

I wrote a high volume log insertion app using mysql many years ago.
I can't remember the performance figures, but it was fast.

A major cost of insertion is maintaining indices. So for maximum
insertion speed you want to have no indices. But to query the log
records you'll probably want indices.

The way I approached this was to have per-hour tables: log_YYYYMMDDHH
The loader streams the records into the table as fast as it can.
(From memory I used a multi-row INSERT statement, no placeholders.)
Every time it switches to a new hour it triggered the building of
indices on the previous hour's table. It also triggered recreating
per-day views (log_YYYYMMDD) that abstracted the hourly tables.

The same technique could be applied at whatever time granularity meets
your needs, from table-per day to table-per minute.

If you can delay the loading of the log data by whatever period you're
using (eg hourly) then you have the option of preparing the log data as
hourly text files and then, when each is complete, using a proprietary
bulk-loading tool to load it.

Tim.




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