I’m observing the process of most awesome SHOW commands being abolished, destroyed and some weird information_schema tables are introduced instead.

Say, even though you can select configuration variables using @@syntax, you can’t do same for much more interesting to DBAs status variables in any more interesting logic.

Apparently instead of doing

SHOW STATUS LIKE "questions"

one has to do this now (I’m being dramatic here, above hasn’t been removed yet, but hasn’t been expanded for better usage either):

SELECT VARIABLE_NAME, VARIABLE_VALUE
FROM INFORMATION_SCHEMA.GLOBAL_STATUS
WHERE VARIABLE_NAME="QUESTIONS"

Do note, those SQL standard followers will get caps-lock button swapped with space bar soon.

Of course, we, DBAs, know that one can simplify stuff by creating stored routines:

CREATE FUNCTION `gstatus`(v varchar(64)) returns varchar(1024)
return
( SELECT …

One of the ways to import data into MySQL is using the LOAD DATA INFILE. It is a faster method than recovering from a dump, as it’s raw data instead of SQL sentences.

The import time depends on the table engine, for example, MyISAM can be 40 times faster than Innodb. Let’s benchmark this:

Preparation

I’m gonna make some benchmarking using MySQL 5.1.36 (64 bits MacOS X). I’ll need a big table, so I’ll take City from the World Database and create a huge table called “city_huge”:

CREATE TABLE city_huge LIKE CITY;

INSERT INTO city_huge 
    SELECT NULL, name, CountryCode, District, Population FROM city;
# Run this sentence 100 times,
# so city_huge table will be 100 times bigger than city.
# Tip: use a script, temporary table, stored procedure...
# or tell your monkey to do so.

SELECT COUNT(*) FROM …

Another year and another successful OSCON has been concluded. While I didn’t attend this year’s conference, let me hereby offer some reflections — basing it on reading blogs and talking to attendees both in person and over Twitter (I’m glad to see both the @MySQL and @MySQL_Community Twitter accounts have a large and quickly growing list of followers).

Let me start by highlighting the 2009 Google O’Reilly Open Source Awards. First on the list is Brian Aker, who is recognised as …

This blog introduces a new application that will provide basic tracking of your running distance and generate charts to monitor progress. There are numerous similar applications that are already available/hosted and this is a very basic application. What's different about this ?

The first version of this application is built using JRuby, Ruby-on-Rails, GlassFish Gem, MySQL, and NetBeans IDE. This combination of technologies is a high quality Rails stack that is used in production deploymnet at various places. Still nothing different ?

A similar version of this application will be built using a variety of Web frameworks such as Java EE, Grails, …

RethinkDB is all the rage today, as its a Y Combinator funded startup, which also launched a developer pre-alpha today. So what is RethinkDB you ask? Yet-another-MySQL-storage-engine, that’s what. But this time, its tuned for solid-state drives (SSDs), which also happen to be all the rage these days.

Anyway, check them out more, and the materials currently tell me that they’re using append-only algorithms, which allow for live schema changes and hot backups, with instantaneous recovery from power failure. Those are just some of the exciting bits.

What didn’t excite me so much was the fact that you were only getting 32-bit or 64-bit Linux binaries, built against MySQL 5.1.31 and you’ll just install it via the INSTALL …

This is the first post in a new series of “Kickfire Basics” blog posts by myself and others here at Kickfire.  This series will review the basics of the Kickfire appliance starting from this post describing how data is stored on disk, to future posts on topics such as loading data into the appliance and writing queries which best leverage the capabilities of the SQL chip.

The Kickfire Equation
Column store + Compression + SQL Chip = performance

The Kickfire Analytic Appliance features the new KFDB storage engine which was built from scratch to handle queries over vast amounts of data.  KFDB is a column store in contrast to most MySQL storage engines which are row stores.  What follows is a description of our column oriented storage engine and how it improves performance over typical row stores.

This post concerns itself with the first part of the equation, the KFDB …

This post was a bit of a test to see if I could write a serious post about a database platform called Hamster.  I think I just made it :)

With all the noise over key/value stores recently, we should keep in mind that this technology isn’t exactly new.  It is being applied to new problems, but many of the foundations have been around for decades.  Probably the oldest of them all, Berkley DB came into existence during the mid ‘80’s and now has over 200 million deployments (according to the Oracle web site).

HamsterDB, while not having the same pedigree of Berkley, has been steadily worked on by Christoph Rupp for the last 5 years.  I spoke to Christoph yesterday about his release of a new edition of …

This blog introduces a new application that will provide basic tracking of your running distance and generate charts to monitor progress. There are numerous similar applications that are already available/hosted and this is a very basic application. What's different about this ?

The first version of this application is built using JRuby, Ruby-on-Rails, GlassFish Gem, MySQL, and NetBeans IDE. This combination of technologies is a high quality Rails stack that is used in production deploymnet at various places. Still nothing different ?

A similar version of this application will be built using a variety of Web frameworks such as Java EE, Grails, …

This blog introduces a new application that will provide basic tracking of your running distance and generate charts to monitor progress. There are numerous similar applications that are already available/hosted and this is a very basic application. What's different about this ?

The first version of this application is built using JRuby, Ruby-on-Rails, GlassFish Gem, MySQL, and NetBeans IDE. This combination of technologies is a high quality Rails stack that is used in production deploymnet at various places. Still nothing different ?

A similar version of this application will be built using a variety of Web frameworks such as Java EE, Grails, …

This is the first post in a new series of "Kickfire Basics" blog posts by myself and others here at Kickfire.  This series will review the basics of the Kickfire appliance starting from this post describing how data is stored on disk, to future posts on topics such as loading data into the appliance and writing queries which best leverage the capabilities of the SQL chip.

The Kickfire Equation Column store + Compression + SQL Chip = performance

The Kickfire Analytic Appliance features the new KFDB storage engine which was built from scratch to handle queries over vast amounts of data.  KFDB is a column store in contrast to most MySQL storage engines which are row stores.  What follows is a description of our column oriented storage engine and how it improves performance over typical row stores.

This post concerns itself with the first part of the equation, the KFDB column store. …