MySQL
MySQL clustering strategies and comparisions
Compare: 1. MySQL Clustering(ndb-cluster stogare) 2. MySQL / GFS-GNBD/ HA 3. MySQL / DRBD /HA 4. MySQL Write Master / Multiple MySQL Read Slaves 5. Standalone MySQL Servers(Functionally seperated)
MySQL
Compare: 1. MySQL Clustering(ndb-cluster stogare) 2. MySQL / GFS-GNBD/ HA 3. MySQL / DRBD /HA 4. MySQL Write Master / Multiple MySQL Read Slaves 5. Standalone MySQL Servers(Functionally seperated)
Product
The new version of a8cjdbc finished some limitations. Now Clobs and Blobs are supported, and some fixes using binary data. The version was also fully tested with Postgres and mySQL. Since Version 1.3 there is also a free trail version for download available. Check it out and test yourself.
Product
The new version of a8cjdbc finished some limitations. Now Clobs and Blobs are supported, and some fixes using binary data. The version was also fully tested with Postgres and mySQL. Since Version 1.3 there is also a free trail version for download available. Check it out and test yourself.
Example
Ever feel like the blogosphere is 500 million channels with nothing on? Tailrank finds the internet's hottest channels by indexing over 24M weblogs and feeds per hour. That's 52TB of raw blog content (no, not sewage) a month and requires continuously processing 160Mbits of IO. How
Product
From the website: The lbpool project provides a load balancing JDBC driver for use with DB connection pools. It wraps a normal JDBC driver providing reconnect semantics in the event of additional hardware availability, partial system failure, or uneven load distribution. It also evenly distributes all new connections among slave
Example
Update: Flickr hits 2 Billion photos served. That's a lot of hamburgers. Flickr is both my favorite bird and the web's leading photo sharing site. Flickr has an amazing challenge, they must handle a vast sea of ever expanding new content, ever increasing legions of users,
Product
Practically any software project nowadays could not survive without a database (DBMS) backend storing all the business data that is vital to you and/or your customers. When projects grow larger, the amount of data usually grows larger exponentially. So you start moving the DBMS to a separate server to
Example
Slashdot effect: overwhelming unprepared sites with an avalanche of reader's clicks after being mentioned on Slashdot. Sure, we now have the "Digg effect" and other hot new stars, but Slashdot was the original. And like many stars from generations past, Slashdot plays the elder statesman'
MySQL
the authors of drupal have paid considerable attention to performance and scalability. consequently even a default install running on modest hardware can easily handle the demands a small website. if you are lucky, eventually the time comes when you need to service more users than your system can handle. at
Example
A man had a dream. His dream was to blend a bunch of RSS/Atom/RDF feeds into a single feed. The man is Beau Lebens of Feedville and like most dreamers he was a little short on coin. So he took refuge in the home of a cheap hosting
Example
Fotolog, a social blogging site centered around photos, grew from about 300 thousand users in 2004 to over 11 million users in 2007. Though they initially experienced the inevitable pains of rapid growth, they overcame their problems and now manage over 300 million photos and 800,000 new photos are
General Discussion
Hi, I'm interested in peoples thoughts on the best choice for a database clustering solution. I have a database that is mostly varchars and numbers that doesn't store any binary data at all. It's used at about 70% read and 30% writes - though