I have finally managed to watch and slightly edit the September 2007 Boston MySQL User Group presentation I did on the MySQL Proxy.

It's geared towards beginners, and has lots of examples, including explaining some of the examples that come bundled with the MySQL Proxy.

Direct Play

Download video (.wmv file, 612 Mb)
Download video (.wmv file, 76.10 Mb)

Enjoy!

Some resources:
Presentation Slides PowerPoint (ppt) or PDF or …

Welcome to the 72nd edition of Log Buffer, the weekly review of database blogs.
Oracle OpenWorld (OOW) is over, and Lucas Jellema of the AMIS Technology blog notes the OOW Content Catalog has been updated with most of the presentations available for download.
On his way home from OOW, Chris Muir of the appropriately titled One Size Doesn’t Fit All blog notes how OOW and the Australian Oracle User Group Conference and OOW compare with regards to 99% fewer attendees in AUSOUG Perth conference - from 45k down to 350. …

I started this as a response to Keith Murphy’s post at http://www.paragon-cs.com/wordpress/?p=54, but it got long, so it deserves its own post. The basic context is figuring out how not to cause duplicate information if a large INSERT statement fails before finishing.
Firstly, the surefire way to make sure there are no duplicates if you have a unique (or primary) key is to use INSERT IGNORE INTO.
Secondly, I just experimented with adding an index to an InnoDB table that had 1 million rows, and here’s what I got (please note, this is one experience only, the plural of “anecdote” is *not* “data”; also I did this in this particular order, so there may have been caching taking place):  More »

So, O’Reilly’s ONLamp.com has published the “Top 10 MySQL Best Practices” at http://www.onlamp.com/pub/a/onlamp/2002/07/11/MySQLtips.html. Sadly, I find most “best practice” list do not thoroughly explain the “why” enough so that people can make their own decisions.
For instance, #3 is “Protect the MySQL installation directory from access by other users.” I was intrigued at what they would consider the “installation” directory. By reading the tip, they actually mean the data directory. They say nothing of the log directory, nor that innodb data files may be in different places than the standard myisam data directories.  More »

I?ve recently done a quick scalability test with MySQL 5.0 and 5.1 to check the new auto-inc patch with InnoDB and to see how MySQL 5.1 scales with InnoDB:

New in MySQL 5.1: innodb_autoinc_lock_mode = 1 (?consecutive? lock mode)
With this lock mode, ?simple inserts? (only) use a new locking model where a light-weight mutex is used during the allocation of auto-increment values, and no AUTO-INC table-level lock is used, unless an AUTO-INC lock is held by another transaction. If another transaction does hold an AUTO-INC lock, a ?simple insert? waits for the AUTO-INC lock, as if it too were a ?bulk insert.?
http://dev.mysql.com/doc/refman/5.1/en/innodb-auto-increment-handling.html#innodb-auto-increment-configurable
This fixes the …

aka…..”when good queries go bad!”
So, today the developers were debugging why a script was running much longer than expected. They were doing text database inserts, and got to the point where they realized that double the amount of text meant the queries took double the amount of time.
You see, they were doing similar text inserts over and over, instead of using connection pooling and/or batching them. Apparently the other DBA explained that it was a limitation of MySQL, but either the developers didn’t convey what they were doing well, or the DBA didn’t think to mention batching.  More »

I'm working on alternative strategies to make the use and maintenance of a multi-terabyte data warehouse implementation tolerably fast. For example, it's clear that a reporting query on a 275-million row table is not going to be fun by anyone's definition, but that for most purposes, it can be pre-processed to various aggregated tables of significantly smaller sizes.

However, what is not obvious is what would be the best strategy for creating those tables. I'm working with MySQL 5.0 and Business Objects' Data Integrator XI, so I have a couple of options.

I can just CREATE TABLE ... SELECT ... to see how things work out. This approach is simple to try, but essentially unmaintanable; no good.

I can define the process as a BODI data flow. This is good in many respects, as it creates a documented flow of how the aggregates are updated, is fairly easy to hook up to the workflows which pull in new data from source systems, and …

If you're using MySQL with the MyISAM engine for a high-writes application, delay_key_write is usually very good.

Let me explain further: delay_key_write is a table option which causes the database NOT to flush the MyISAM key file after every write. This is a really good thing, as if you're doing another write very soon anyway, this is likely to just waste I/O time.

This doesn't sound like a good idea right, because it means that if the power fails (or mysql crashes, or something), then you'll be left with a broken index file? No, it's still a very good idea:

• delay_key_write does NOT appear to affect the MyISAM data file - that will still be flushed according to the normal policy.
• If you had delay_key_write off, then a power failure or crash during the index write would cause the same level of corruption.
• Broken MyISAM index files need to be rebuilt, regardless of how little …

As a quick follow up to the last post on malloc() speed comparisons, I decided to extend the size out a bit to cover malloc()ing up to 500M.

Time for        128k:   0.035259
Time for        256k:   0.009718
Time for        1M:     0.478129
Time for        5M:     0.968945
Time for        10M:    0.965172
Time for        50M:    0.674316
Time for        500M:   1.018901

As you can see, once you make the jump up to mmap() (>256k), the cost is fairly well constant (give or take fluctuations). So it’s not that huge memory buffers are terrible, just that there is a cost difference between the smaller and larger buffer sizes that may or may not matter in your case.

…

The recent read_buffer issue got me curious, so I hacked up a quick (ugly) test program to see if I could show the different speeds of malloc()ing different buffer sizes. Here’s the test code:

[C]
#include
#include
#include

#define LOOP 10000

int main(void) {

int x[4] = { 128,256,1024,5*1024 } ;

int f = 0;
for(f=0;f<4;f++) {
int val = x[f];

timeval before;
timeval after;

gettimeofday(&before,0);

int loop=0;
for(loop=0;loop after.tv_usec) {
udiff = (float)(after.tv_usec + 10000000 - before.tv_usec)/10000000.0;
diff = after.tv_sec - 1 - before.tv_sec;
} else {
udiff = (float)(after.tv_usec - before.tv_usec)/1000000.0;
diff = after.tv_sec - before.tv_sec;
}
printf(”Time for %dk:\t%f\n”,val,((float)diff+udiff));

}

return 0; …