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Showing entries 1 to 7

Displaying posts with tag: column store (reset)

Log Buffer #182, a Carnival of the Vanities for DBAs
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This is the 182nd edition of Log Buffer, the weekly review of database blogs. Make sure to read the whole edition so you do not miss where to submit your SQL limerick!

This week started out with me posting about International Women’s Day, and has me personally attending Confoo (Montreal) which is an excellent conference I hope to return to next year. I learned a lot from confoo, especially the blending nosql and sql session I attended.

This week was also the Hotsos Symposium. Doug’s

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Liveblogging at Confoo: Blending NoSQL and SQL
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Persistence Smoothie: Blending NoSQL and SQL – see user feedback and comments at http://joind.in/talk/view/1332.

Michael Bleigh from Intridea, high-end Ruby and Ruby on Rails consultants, build apps from start to finish, making it scalable. He’s written a lot of stuff, available at http://github.com/intridea. @mbleigh on twitter

NoSQL is a new way to think about persistence. Most NoSQL systems are not ACID compliant (Atomicity, Consistency, Isolation, Durability).

Generally, most NoSQL systems have:

  • Denormalization
  • Eventual Consistency
  • Schema-Free
  • Horizontal Scale

NoSQL tries to scale (more) simply, it is starting to go







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Kickfire Basics – The KFDB columnar storage engine
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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



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Kickfire Launches On-Demand Trials
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Join the Sun and Kickfire team tomorrow to see the unveiling of the Kickfire’s On-Demand Trial. You can sign up for the live webinar and trial review here: http://tinyurl.com/kickfiretrial.

At Kickfire we’re very excited about this launch. We’ve had many customers who have asked for a quick way to trial the system to get a sense of the performance. In order to speed up setup time we are providing users with access to US Bureau of Transportation’s database. This database contains flight data from the last twenty years. The trial consists of four parts:

1) An overview of Kickfire and its technology (includes a short Flash movie)
2) An interactive tutorial of a couple of sample queries. The tutorial explains the DB schema, the SQL and the Kickfire features that get performance
3) A pick list of sample queries and

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Data Warehouse/Analytic Appliances – What to Consider
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Why was Teradata able to become the leader of data warehousing at the super high-end (e.g. greater than 25 TB’s)?  Why was Netezza only the second pure-play data warehousing company to go public by focusing on the 10 – 25 TB range of opportunities?  Why did Oracle after so many years of denial finally announce a joint hardware / software product for data warehousing with HP, the Exadata data warehouse server?  Why did Microsoft acquire DATAllegro, one of the earlier data warehousing appliances? Why are there now dozens of data warehouse appliances available on the market today, and – more importantly – how should a customer choose which one to purchase? 

In all these cases, the vendors have listened to the market and concluded that the most optimal way to serve the customer is through a true data warehouse appliance.  Given that there are so many flavors of appliances, though,

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Kickfire: relational algebra in a chip
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I spent the day Thursday with some of Kickfire’s engineers at their headquarters. In this article, I’d like to go over a little of the system’s architecture and some other details.

Everything in quotation marks in this article is a quote. (I don’t use quotes when I’m glossing over a technical point — at least, not in this article.)

Even though I saw one of Kickfire’s engineers running queries on the system, they didn’t let me actually take the keyboard and type into it myself. So everything I’m writing here is still second-hand knowledge. It’s an unreleased product that’s in very rapid development, so this is understandable.

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Kickfire: stream-processing SQL queries
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Some of you have noticed Kickfire, a new sponsor at this year’s MySQL Conference and Expo. Like Keith Murphy, I have been involved with them for a while now. This article explains the basics of how their technology is different from the current state of the art in complex queries on large amounts of data.

Kickfire is developing a MySQL appliance that combines a pluggable storage engine (for MySQL 5.1) with a new kind of chip. On the surface, the storage engine is not that revolutionary: it is a column-store engine with data compression and some other techniques to reduce disk I/O, which is kind of par for the course in data warehousing today. The chip is the really exciting part of the technology.

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Showing entries 1 to 7

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