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Previous 30 Newer Entries Showing entries 31 to 60 of 69 Next 9 Older Entries

Displaying posts with tag: blogging (reset)

Settling in for a Winter's Blogfest
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This is the 1st post in my MoFo Futures 2009 blog series. | Post 2 »

Christmas is nearly upon the Christian and Consumerist parts of the globe. Along much of my latitude, snow is piling up in record quantities and weather warnings abound. Even in oft-green Vancouver, there is a foot or so of the white stuff accumulated in my yard and the parking lot outside my window often echos with the sounds of snow-beached cars helplessly spinning their wheels.

Usually all these things taken together would mean a series of harrowing drives to visit family, followed by lovely hours staying warm indoors, eating comfort food, retelling old stories and enjoying the company of loved ones as the year draws to its close.

However, this year our family time is coming after the holidays and I'm left with the unexpected gift of a week or so

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Wordcraft 0.6 available
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I am pleased to announce the release of Wordcraft 0.6.  I have been using it for a month or so now and I am learning some things.

I had been having trouble logging in lately from multiple places.  So, instead of trying to work on the built in session handling I had written, I took my own advice (use stuff that exists) and just switched to PHP sessions.  All the cookie stuff is worked out and I can get a lot done with just a little work.  PHP sessions make me a little nervous.  If you have lots of applications installed on the same site that use them, you can get some odd behavior.  But, why reinvent the wheel right?

I have found myself wanting to save a post while working on it.  To do that before, I would have to uncheck the Published box.  To solve this, I changed the



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iiBench configuration
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A tip of the hat to Mark Callaghan, who suggested I post our my.cnf settings for iiBench. Instead of fiddling around with the configuration file, we adjusted everything on the command line. Here's the relevant script from iiBench/scripts/start_mysql.sh:
#
# Script to start the mysqld process.
# - Documents mysqld parameters used for iiBench testing.
#
DATADIR="/usr/local/mysql/data"
MYSQL_PATH="/usr/local/mysql/bin"

# These parameters were selected for a test machine with:
# - 16GB of memory
# - 2 socket, quad core = 8 cores total.

PARAM=" \
--no-defaults \
--user=mysql  \
--datadir=$DATADIR \
--max_connections=3000 \
--max_connect_errors=10 \
--table_cache=2048 \
--max_allowed_packet=1M \
--binlog_cache_size=1M \
--max_heap_table_size=64M \
--sort_buffer_size=64M \
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iiBench Contest
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I’d like to advertise my previous iiBench posting again (now that we are feeding into PlanetMySQL.)

iiBench Contest - Who Can Insert 1B Rows into MYSQL the Fastest?
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At the recent OpenSQL Camp in Charlottesville, VA, Tokutek offered a challenge to the MySQL community - who can insert a billion rows into MySQL the fastest?  We will post the results on our website and the winner gets a $100 Starbucks card, along with valuable bragging rights.

Tokutek’s technical founders (Michael A. Bender, Martin Farach-Colton, and I), in our academic roles (at Stony Brook, Rutgers, and MIT, respectively) have been investigating how to maintain indexes for large databases.  Part of the challenge for this kind of research is to figure out what to measure.

Some other benchmarks, such as TPC-H and SSB, measure bulk load time rather than insertions.  We are interested in the case where you must insert a small number of rows at a time at a high rate, and keep the index up-to-date.  Indexed insertions are interesting in situations with high incoming

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Wordcraft 0.5 available
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Well, I blogged about Wordcraft the other day.  I have just been running live on the software for 4 days now.  Well, that post had no URI associated with it.  It took me two days to figure this out.  Oops.  Welcome to eating my own dog food.  So, running this live with actual users (and a host of bot spam attempts) I am learning a lot and making a lot of commits.  So, I may very well roll once or twice a week for the first few weeks.

So, with that, I have packaged 0.5.  There are 15 changes in this package.  Some features, but mostly bug fixes.  So, if you could use a simple blog, give it a try and help me debug it.  If you do, please use the Google Code issue tracker.  Maybe I can figure out how to have those things emailed to me.
Wordcraft, a simple PHP blogging application
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So, a while back, not sure when, I was listening to the P3 Podcast and Paul mentioned his dislike for Wordpress.  He said he wished there was a simple blogging application.  I am probably misquoting him horribly.  It was an idea that I had been tinkering with.  So, I started on Wordcraft in my spare time.  Like super spare time.  That time between the kids going to bed and me falling  asleep.  So, it took a while to get it to a usable state.

Up until now, I have used Wordpress.com for my blogging.  It works quite well.  You can get started quite quickly and it does what most people need.  My wife uses Blogger for our family blog.  It is, IMO, not as nice as Wordpress.com in some ways.  But, it does allow you to edit your styles (for free) and such which is nice.

So, why would I want

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Wordcraft, a simple PHP blogging application
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So, a while back, not sure when, I was listening to the P3 Podcast and Paul mentioned his dislike for Wordpress.  He said he wished there was a simple blogging application.  I am probably misquoting him horribly.  It was an idea that I had been tinkering with.  So, I started on Wordcraft in my spare time.  Like super spare time.  That time between the kids going to bed and me falling  asleep.  So, it took a while to get it to a usable state.

Up until now, I have used Wordpress.com for my blogging.  It works quite well.  You can get started quite quickly and it does what most people need.  My wife uses Blogger for our family blog.  It is, IMO, not as nice as Wordpress.com in some ways.  But, it does allow you to edit your styles (for free) and such which is nice.

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Picasa Web: Sharing pictures, in particular for blogs
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Yesterday, I started my sporadic series of blog posts where I share my experiences improving my online manners through social networking websites, many of which are powered by MySQL. My first target was the traveller site Dopplr, and this time, it’s Google’s picture sharing site Picasa Web.

My starting point is the same: “Everyone else” among colleagues and friends was there long before me, and I feel like a latecomer. I want to go in, do what seems to be the right thing, and share the observations I had. And everything within the time constraint of not being able to do a full evaluation, as I obviously have other things to do as well.

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Now I’m blogging in Russian, too!
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To understand a bit of Italian, I just need a comparatively small amount of vino bianco. By contrast, to get any information flow going at all in Russian requires larger amounts of … preparation. That doesn’t have to be vodka, it can also be interesting discussions with Russians, or the opportunity to give a speech.

Now, a blog is the scalable way to interact with the rest of humanity, and I’m trying to increase my fluency in all things Web 2.0. So, here goes, may I present my Russian blog:

Like in the case of presenting my Italian blog, let me quote Google Translate’s automatic translation of some of my


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I’m blogging in Italian!
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Given that I don’t speak Italian, it may seem a bit strange that I just started an Italian language blog on http://blogs.arno.fi/dolce_vita/:

But I do have a point with my blog. Let me quote Google Translate’s automatic translation of some of my “writings” — deliberately doing so without making any improvements on the automatic translation:

Why this blog?

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Photography blog in German started
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I just started a new blog on photography, in German. It’s based in http://blogs.arno.fi/foto/ and so far only has just four entries — one on a photo session with fashion photographer Riccardo Desiderio, one on my ensuing autumn portraits of my wife along the Isar here in Munich, one on fun underwater photography (

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On Loyalty, Competition and Underdogs
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“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.”

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boring from another continent
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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.
Alex Gorbachev’s RSS Feeds Aggregated
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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 other

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Kaj's first six months
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Kaj Arnö, MySQL ambassador to Sun, has written a digest of his blogging production this year. It's an intriguing reading, because Kaj has been more on the road than at home this year, mostly performing the duties of communicator, explaining to Sun people what really is this MySQL that had just been acquired, and sharing his findings with fellow (ex) MySQL employees.

Kaj's blogging sometimes has the role of breaking the news to the community. For example, he was the one who first wrote about the Sun acquisition of MySQL (published his post at 8:02 EST, barely one minute after Marten's jaw dropping announcement from the podium.)

Looking back at the first half year
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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



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Family of MySQL Cluster bloggers
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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

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Family of MySQL Cluster bloggers
Employee_Team +0 Vote Up -0Vote Down

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

  [Read more...]
Family of MySQL Cluster bloggers
Employee_Team +0 Vote Up -0Vote Down

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

  [Read more...]
Hacking for Faster Insertions: Is this really how you want to spend your time?
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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

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Hacking for Faster Insertions: Is this really how you want to spend your time?
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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

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Tradeoff: Insertions versus Point Queries
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I’ve been waving my hands about lower bounds.  Well, sometimes I haven’t been waving my hands, because the lower bounds are tight.  But in other cases (lenient insertions, range queries), the lower bounds are very far from what we’re used to.

So now, for a bit of math:

Brodal and Fagerberg showed in 2003 that there’s a tradeoff between insertions and queries.  The insertions they consider are lenient.  Well, any lower bound for lenient is a lower bound for strict, but they also gave upper bounds, so it matters.  Also, they don’t know from lenient, but if you look at their upper bound, they are implementing lenient insertions.  The queries they consider are, unfortunately, point queries.  That’s too bad for us, because we’ve already seen that point queries are just too slow to be

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Tradeoff: Insertions versus Point Queries
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I’ve been waving my hands about lower bounds.  Well, sometimes I haven’t been waving my hands, because the lower bounds are tight.  But in other cases (lenient insertions, range queries), the lower bounds are very far from what we’re used to.

So now, for a bit of math:

Brodal and Fagerberg showed in 2003 that there’s a tradeoff between insertions and queries.  The insertions they consider are lenient.  Well, any lower bound for lenient is a lower bound for strict, but they also gave upper bounds, so it matters.  Also, they don’t know from lenient, but if you look at their upper bound, they are implementing lenient insertions.  The queries they consider are, unfortunately, point queries.  That’s too bad for us, because we’ve already seen that point queries are just too slow to be of

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Tradeoffs: Updates versus Range Queries
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Sorry for the delay, now on to range queries and lenient updates.  Let’s call them queries and updates, for short.  So far, I’ve shown that B-trees (and any of a number of other data structures) are very far from the “tight bound.” I’ll say a bound is a tight if it’s a lower bound and you can come up with data structure that matches it.

So how do we match the bandwidth bound for queries and updates?  I already mentioned in passing how to do this, but let’s look more closely.

Fast Updates

The way to get fast updates is to log them.  You can easily saturate disk bandwidth by writing out the insertion, deletion and update requests with no index. 

A query now will typically start by sorting the data.  Even a point query requires looking at all the data, but a range query

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Tradeoffs: Updates versus Range Queries
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Sorry for the delay, now on to range queries and lenient updates.  Let’s call them queries and updates, for short.  So far, I’ve shown that B-trees (and any of a number of other data structures) are very far from the “tight bound.” I’ll say a bound is a tight if it’s a lower bound and you can come up with data structure that matches it.

So how do we match the bandwidth bound for queries and updates?  I already mentioned in passing how to do this, but let’s look more closely.

Fast Updates

The way to get fast updates is to log them.  You can easily saturate disk bandwidth by writing out the insertion, deletion and update requests with no index. 

A query now will typically start by sorting the data.  Even a point query requires looking at all the data, but a range query

  [Read more...]
don’t ask too many questions
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chyrp is a nice looking piece of blog software. individual posts can have different styles, something it borrowed from the hosted tumblr service. i was interested to read about “the sql query massacre of january 19th, 2008” but the numbers gave me pause — 21 queries to generate the index page? that is down from an astounding 116, but that still seems ridiculous to me.

the number of queries to generate the index of this site? two. one of them is SET NAMES utf8. i could see boosting that to three or four if i moved some things like the list of links in the sidebar into the database, or added archive links. call it five if i had user accounts.

but right now, the number of queries used to load the index



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How Fast Can Updates Run?
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Last time, I introduced the notion of strict and lenient updates.  Now it’s time to see what the performance characteristics are of each.

Just to rehash, 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 will insert and delete random pairs, each 8 bytes.  So that’s 62.5 billion pairs to fill the disk.

Strict Updates

These are the easier update types to analyze.  Please review the definition of strict updates from the last blog entry.  Now notice that each insertion or deletion requires a point query.  For example, during an insertion, in order to determine if there’s already a row with a

  [Read more...]
How Fast Can Updates Run?
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Last time, I introduced the notion of strict and lenient updates.  Now it’s time to see what the performance characteristics are of each.

Just to rehash, 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 will insert and delete random pairs, each 8 bytes.  So that’s 62.5 billion pairs to fill the disk.

Strict Updates

These are the easier update types to analyze.  Please review the definition of strict updates from the last blog entry.  Now notice that each insertion or deletion requires a point query.  For example, during an insertion, in order to determine if there’s already a row with a

  [Read more...]
Updates & Discipline
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So far, I’ve analyzed point and range queries.  Now it’s time to talk about insertions and deletions.  We’ll call the combination updates.  Updates come in two flavors, and today we’ll cover both.

Depending on the exact settings of your database, the updates give a varying amount of feedback.  For example, when a key is deleted, all rows with that key are deleted (assuming the database allows duplicate keys).  The normal behavior is to return the number of rows deleted.  The normal behavior when deleting a key that has no corresponding rows in the database is to return an error message.  On insertion, one can allow duplicate or not.  In the latter case, the storage engine returns an error message if a duplication insertion is attempted. 

We’ll see that the details of error messages have a profound

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Previous 30 Newer Entries Showing entries 31 to 60 of 69 Next 9 Older Entries

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