“So, I suppose MySQL’s main competitor is Oracle?” is a frequent question I get asked by the press. “Well, we don’t really compete heads-on with other databases. We co-exist! Just as an example: Over a third of respondents in an Oracle User Group survey said they also use MySQL”, I answer.

The reporter then continues “But everyone has a main competitor. Don’t you plan for people to migrate from Oracle to MySQL?”. I continue with “Not really. Migrations do happen, but not all that often. MySQL tends to be used in new applications.”

“But surely you must have some competitive atmosphere, or equivalent feelings towards Oracle.” The reporter never gives up. “Don’t you at least internally joke about your relationship with Oracle?”.

And that’s where I will now have a new answer for whichever reporter nexts goes …

as celia wrote earlier, we are in riga, latvia for a meeting of the mysql developers. she is holed up in the hotel room working on a screenplay (or maybe in the atrium where the wifi is better), and i am in a presentation about blogging.

celia already posted pictures from our excursion day on sunday (the day we didn’t sit around in the meeting rooms at the hotel). i took some video which i will figure out how to deal with once we are back home.

Back in May 2006, I have started my blog using the Blogger platform and one month later moved it to my own website using Wordpress. Couple month later, I joined Pythian and, since then, the vast majority of my blogging activities has been on the Pythian Group Blog.

The Pythian blog has grown significantly since then and many more excellent authors started blogging there. While the Pythian blog was mostly focused on Oracle database just a couple years ago, it’s has got very broad coverage now and is including MySQL, SQL Server and Oracle databases as well as Oracle Application Server, Oracle eBusiness Suite and …

Half a year has gone, most of it with my new company, Sun Microsystems.Kaj has made a detailed digest of his first half.
I took the DBA approach and queried the Planet MySQL database. I know that this qualifies as cheating, but I could not resist.
I published a total of 111 blog posts since January, with peaks in March and April (the users conference was coming).
I wrot about most everything, but some topics are prominent. Users Conference and MySQL proxy …

While this blog is co-authored by the whole MySQL Telecom team, many members in or around the team also write their personal blogs, which you will find very useful. So please follow me on a tour on the absolute top MySQL Cluster blogs in the world:

Johan Andersson is the MySQL Cluster Principal Consultant, and has been with MySQL Cluster since the Ericsson days. He travels around the world to our most demanding customers and shares his guru advice. Rumor has it that recently on a training gig the students made him sign their MySQL t-shirts, can you get closer to living like a rock star than this? Occasionally he also shares some great tips and status info on his blog. Like right now you can find a set of handy scripts to manage all of your MySQL Cluster from one command line, definitively recommended to try!

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While this blog is co-authored by the whole MySQL Telecom team, many members in or around the team also write their personal blogs, which you will find very useful. So please follow me on a tour on the absolute top MySQL Cluster blogs in the world:

Johan Andersson is the MySQL Cluster Principal Consultant, and has been with MySQL Cluster since the Ericsson days. He travels around the world to our most demanding customers and shares his guru advice. Rumor has it that recently on a training gig the students made him sign their MySQL t-shirts, can you get closer to living like a rock star than this? Occasionally he also shares some great tips and status info on his blog. Like right now you can find a set of handy scripts to manage all of your MySQL Cluster from one command line, definitively recommended to try!

…

While this blog is co-authored by the whole MySQL Telecom team, many members in or around the team also write their personal blogs, which you will find very useful. So please follow me on a tour on the absolute top MySQL Cluster blogs in the world:

Johan Andersson is the MySQL Cluster Principal Consultant, and has been with MySQL Cluster since the Ericsson days. He travels around the world to our most demanding customers and shares his guru advice. Rumor has it that recently on a training gig the students made him sign their MySQL t-shirts, can you get closer to living like a rock star than this? Occasionally he also shares some great tips and status info on his blog. Like right now you can find a set of handy scripts to manage all of your MySQL Cluster from one command line, definitively recommended to try!

…

Recall that I’ve claimed that it takes 28 years to fill a disk with random insertions, given a set of reasonable assumptions.  Recall what they are:

We are focusing on the storage engine (a la MySQL) level, and we are looking at a database on a single disk—the one we are using for illustration is the 1TB Hitachi Deskstar 7K1000.  It has a disk seek time 14ms and transfer rate of around 69MB/s. [See tomshardware.com] We insert random pairs, each 8 bytes.  So that’s 62.5 billion pairs to fill the disk, and at 4KB-size blocks, that 2^28 leaves (= 2^40 bytes / 2^12 bytes/leaf).

Now, my analysis requires each insertion to induce a disk seek.  Suppose we do something clever with main memory.  After all, we have this main memory hanging around.  It should be possible to buffer up some insertions, and once we fill up main memory, insert key/value pairs that belong on the same leaf.  Thus, fetching a …

Recall that I’ve claimed that it takes 28 years to fill a disk with random insertions, given a set of reasonable assumptions.  Recall what they are:

We are focusing on the storage engine (a la MySQL) level, and we are looking at a database on a single disk—the one we are using for illustration is the 1TB Hitachi Deskstar 7K1000.  It has a disk seek time 14ms and transfer rate of around 69MB/s. [See tomshardware.com] We insert random pairs, each 8 bytes.  So that’s 62.5 billion pairs to fill the disk, and at 4KB-size blocks, that 2^28 leaves (= 2^40 bytes / 2^12 bytes/leaf).

Now, my analysis requires each insertion to induce a disk seek.  Suppose we do something clever with main memory.  After all, we have this main memory hanging around.  It should be possible to buffer up some insertions, and once we fill up main memory, insert key/value pairs that belong on the same leaf.  Thus, fetching a …